FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Authors: Yangyang Yu
Zhiyuan Yao
Haohang Li
Zhiyang Deng
Yuechen Jiang
Yupeng Cao
Zhi Chen
Jordan W. Suchow
Zhenyu Cui
Rong Liu
Zhaozhuo Xu
Denghui Zhang
Koduvayur Subbalakshmi
GUOJUN XIONG
Yueru He
Jimin Huang
Dong Li
Qianqian Xie
Venue: NeurIPS 2024 poster (2024)
Keywords: Multi-agent system
Financial Large Language Models
Portfolio Management
Primary area: machine_learning_for_other_sciences_and_fields

Abstract: Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging. These tasks require multiple interactions with a volatile environment for every decision, demanding sufficient intelligence to maximize returns and manage risks. Although LLMs have been used to develop agent systems that surpass human teams and yield impressive investment returns, opportunities to enhance multi-source information synthesis and optimize decision-making outcomes through timely experience refinement remain unexplored. Here, we introduce FinCon, an LLM-based multi-agent framework tailored for diverse financial tasks. Inspired by effective real-world investment firm organizational structures, FinCon utilizes a manager-analyst communication hierarchy. This structure allows for synchronized cross-functional agent collaboration towards unified goals through natural language interactions and equips each agent with greater memory capacity than humans. Additionally, a risk-control component in FinCon enhances decision quality by episodically initiating a self-critiquing mechanism to update systematic investment beliefs. The conceptualized beliefs serve as verbal reinforcement for the future agent’s behavior and can be selectively propagated to the appropriate node that requires knowledge updates. This feature significantly improves performance while reducing unnecessary peer-to-peer communication costs. Moreover, FinCon demonstrates strong generalization capabilities in various financial tasks, including stock trading and portfolio management.

FINCON: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
Yangyang Yu².* Zhiyuan Yao".*, Haohang Li Zhiyang Deng" Yuechen Jiang"., Yupeng Cao Zhi Chen Jordan W. Suchow', Zhenyu Cui', Rong Liu', Zhaozhuo Xu', Denghui Zhang' Koduvayur Subbalakshmi', Guojun Xiong", Yueru He Jimin Huang 3, Dong Li³, Qianqian Xie
'Stevens Institute of Technology Harvard University Thhe Fin AI
*These authors contributed equally
Corresponding author: qianqi xieexaleeeeeee
Abstract
Large language models (LLMs) have shown potential in complex financial tasks, but sequential financial decision-making remains challenging due the volatile environment and the need for intelligent risk management. While ILM-based agent systems have achieved impressive returns, optimizing multi-source infor- mation synthesis and decision-making through timely experience refinement is underexplored. We introduce FINCON, an LLM-based multi-agent framework with CONceptual verbal reinforcement for diverse FINancial tasks. Inspired by real-world investment firm structures, FINCON employs manager-analyst hier- archy, enabling synchronized frossffuntional agent collaboration towards unified goals via natural language interactions. Its dual-level risk-control component enhances decision-maker by monitoring daily market risk and updating systematic investment beliefs through self-critique. These conceptualized beliefs provide verbal reinforcement for future decisions, selectively propagated to relevant agents, improving performance while reducing unnecessary peer-to-peer communication costs. FINCON generalizes well across tasks, including single stock trading and portfolio management.
Introduction
The intricacies and fluctuations inherent in financial markets pose significant challenges for making high-quality, sequential investment decisions. In tasks such single stock trading and portfolio management, each intelligent decision is driven by multiple market interactions and the integration of diverse information streams, characterized by varying levels of timeliness and modalities 2 The primary objective of these tasks is to maximize profit while managing present market risks in an open-ended environment.
In practice, trading firms often depend on synthesized teamwork, structured hicrarchically with functional roles such data analysts, risk analysts, and portfolio managers communicating across levels [3 4 These roles are responsible for the careful integration of diverse resources. However, the cognitive limitations of human team members hinder their capacity rapidly process market signals and achieve optimal investment outcomes
To enhance investment returns and address the limitations of human decision-making, various studies have explored methods such as deep reinforcement learning (DRL) to develop agent systems that simulate market environments and automate investment strategies 6 7 8). Concurrently,
We will release the code and demo in the
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

---

advancements in large language models (LLMs) have shown great potential in performing complex tasks, including reasoning [0]. tool-using
planning [2]. decision-maken [ [4, and even
in various financial applications 15
16
17 R 19, suggesting they may surpass existing agent
architectures. Language agents, in particular, are distinguished by their human-like communication and flexible, prompt-based structures, making them well-suited to diverse decision-making settings 22 21
23].
To achieve optimal decision-making performance, two critical factors must be considered: (I)
Organizing agents to facilitate effective teamwork and efficient communication, and (2) Enabling
agents to continuously learn and refine their actions. Studies have shown that mimicking human organizational structures can successfully coordinate language agents for specific tasks [24 22 26].
Additionally, recent advances in textual gradient-based prompt optimization [27] [2]] and verbal
reinforcement [29 30) have proven effective iteratively improving the reasoning and decision-
making capabilities of language agents.
Language agent systems designed for financial pociiion-making, such as FINGPT [31], FINMEM [32), and FINAGENT [33]. have shown strong performance. However, they face several limitations. First, their reliance on agents' risk preferences based on short-term market fluctuations fails control long- term risk exposure, potentially overlooking fundamental factors driving investment returns. A more effective approach is to quantify investment risks using established measures of risk from quantitative finance [34 (35). Second, these systems are often limited to single-asset trading tasks, making them less adaptable to multi-asset financial applications like portfolio management. Third, they place significant pressure on a single agent to understand and process information within constrained context window, which can degrade decision quality. Although approaches like STOCK AGENT [36 use multi-agent systems for stock trading, their reliance on extensive discussions between numerous LLM agents leads to high communication costs and slow decision-making Moreover, the absence of a clear optimization objective can compromise outcome effectiveness. Additional related work in the literature is discussed in the Appendix A..
To address these issues, we propose FINCON, an LLM-based multi-agent framework for critical financial tasks, such as single-stock trading and portfolio management, shown in Figure [[ Our main contributions are: 1) Inspired by real-world investment roles, we introduce a novel Synthesized Manager- Anlyst hierarchical communication structure with a risk-control component. This structure allocates financial data from different sources to corresponding functional analyst agents, allowing them focus on specific insights, while the manager consolidates these inputs to make informed trading decisions. The streamlined communication reduces redundant peer-to-peer interac- tion, lowering costs and improving efficiency. 2) Our framework generalizes beyond stock trading to handle portfolio management, an area not previously addressed by other financial language agent systems. 3) We developed a dual-level risk control component to update risk assessments both within and across episodes. Within episodes, risk is supervised using the Conditional Value Risk (CVaR), a quantile- based risk measure [37]. Across episodes, we introduced a verbal reinforcement mechanism, where investment beliefs are updated based on reasoning trajectories and profit-ande and- (PnL)) trends, distilled into conceptual perspectives. These insights are selectively back- pack-propagated from the manager to relevant analyst agents. Our ablation studies demonstrate the effectiveness of this risk control design in managing market risk and enhancing trading performance.
Mutti Mioi---ddail Market Environment Information
Mylt-Type LLMG
LLM Functions
Single- Agent Modules
FinCon Muft--Aeent System
Daily Fiancial Dall:: Marker
Noura
Teatual Date
Sentmment
Foundation
Uamres
Code Generator
(Caneration Coffgiuration
Agant Synthee load Component Apamss
Osartely Anvurtty
Celsuibtur
Profic
Approx Report
Gemin
Witrral
Thno Party AM
Perception
Nove Anaiyut
Iset Analyal
Iepport Analyy
Daily Anast
Tabular Data
Fissnctal
Financiall Pino- luned LLMo
Dasataan ane
Memoory
Agent Group Interaction
Bloorbergtoo
Docoment Looder
samning Conterence Cas Audio Transonpt
PrrOPt
Action
Wiim Trapectory Continl
Flask Contral Companent
Owen Trieesory Central
Figure I: The general framework of FINCON.
2

---

2
Preliminaries
Here, we outline the mathematical notations for the two major financial decision-making tasks that will be explicitly discussed our work. We also formally present the generalized modeling formulation using Partially Observable Markov Decision Process (POMDP) 38 for financial decision-male making tasks.
2.1 Financial Decision-making Tasks Formulation
Single Stock Trading Tasks. FINCON uses analyst agents group {Mrr to process multi-modal market information sources. The processed information is then used by a manager agent M, to make trading decisions (buy, sell, hold), and to provide relevant reasoning texts. Note that the "sell" signal means the system makes "short-sellingg decision, that is, negative trading position is allowed. Additionally, FINCON evaluates the daily investment risk, followed by manager agent from risk-control component M,.
for the
Portfolio Trading Tasks. In addition processing multi-mode market information, the analyst agents also construct stock pool for portfolio management by considering the statistical correlations between stock returns. The manager agent then makes trading decisions for each stock in the pool. Finally, the manager agent determines the portfolio weights for all stocks using external optimization solver that applies the mean-variance optimization described below [39
[0, 1],
"buy"
max(w, (c) - (w, Σw) s.t. Wp
E 1-100 0).
"sell"
Vn E (1,",, NN
(I)
0,
"hold"
where (w1-.. WN) E RN is portfolio weights vector, μ and E the shrinkage estimators of N-dimensionna sample expected return and N x N sample covariance matrix of chosen stocks' daily return sequences respectively [35]. We note that portfolio weights rebalanced daily basis. In our implementation, we begin by calculating the portfolio weights through solving the aforementioned optimization problem. Next, the target positions determined by linearly scaling these portfolio weights from the previous step.
2.2 Modeling Quantitative Trading as POMDP
with time index = 1,, ..... and discount factor (0, 1]. The components of this model are as follows: (1) state space X' x y where is the observable component and y unobservable component of the financial market; (2) the action space of analyst agents group is A = I A', where A' represents the collection of processed market information in textual format done by agent i (total analyst agents), and for manager agent, its action space is A. which is modeled as "buy", " "sell", "hold"] for portfolio management task among N-stocks; (3) the reward function R(o, b, a) X x ) y/x A + R uses daily profit & loss (PnL.) as the output; (4) the observation process {Other S X' is an T-dimensional process, with the 7th entry {Oi representing one type of uni-modal information flow solely processed by the analyst agent i; (5) the reflection process (BtT C ) represents the manager agent's self-reflection, which is updated from B, to B1+1 on daily basis 42) (7) the processed information flow Ô, = (O)... ... ,O!) E A T, which represents the information processing outputs from analyst agents group.
Formally, we model quantitative trading task as an infinite horizon POMDP 40
for single stock trading task and as (( (("byy" "sell", "hold") x 1))
Then, our multi-agent system is supposed to learn the policies of all agents: the policies of analyst agents Tye X + i {1...., .I3 (the ways to process information. i.c. Ōj The (1O))). and the policy of manager agent Tg- A x y + A (the ways to make trading decisions, i.c. A, Tgu (-),,, B,)) such that the system maximizes cumulative trading reward while controlling risk [43] All policies IIg = ( TT") are parameterized by textual prompts = [(0')/=+ 0*). By updating prompts via the risk-control component M,, the whole system optimizes policies II, in verbal reinforcement manner. By denoting daily profit & loss (PnL) by Rille R(O,, B,, Ar), the

---

optimization objective for the whole system can be written as:
max E
tET
a risk-sensitive optimization problem that leverages textual gradient descent, fundamentally dif- fering from DRL algorithms designed for POMDPs. Further details the textual gradient descent approach are provided in the Appendix A.2 A.2
3 Architecture of FINCON
In this section, we present the architecture of FINCON using a two-level hierarchy. First, we describe the hierarchical framework for coordinating the agents' synchronous work and communication. Then, we elaborate on the functionalities of each module that constitutes each agent in FINCON. Finally, aim elaborate how FINCON solves the objective function expressed Equation ? through a verbal reinforcement approach.
3.1 Synthesized Multi-agent Hierarchical Structure Design
The agent system of FINCON consists two main components: the Manager-Analys Agent Group component and the Risk-Contur Control component.
Working Loop of Manager-Analyst Agent Group
Task Environment (Financlal market)
Selected symbol: TSLA,
Q1
TSLA.
MSFT,
YE++
MSFT, PFE
NFLXX GE
Stock Prices
Company
10-0
Company
Research Reports
ECC Audio Recordinge
Observations
Stock Selection Agent
News Agent
10-0/ 10-0
Analyat Reporta
EDC Agent
Agent
Filing Agent
Agent
Agent Group
+
to Conceptuelized teedback
A M: investmert-relatte ght
upon significant investment insight
Manager Agent (M)
Reward: Trading profits and losses (Pri.s)
Actions: Trading direction
PnLss Series
Conditional Valus Risk (GVaR)
Episode
Cpisode k=1
Control Component
Within-Episode Risk Control: high occurring value drop CVaR).
Update Manager's investment belief conceptual verbal reinforcement textunl-based gradient descent
Over-Episode Risk Control:
Figure 2: The detailed architecture of FINCON contains two key components: Manager-Analyee agent group and Risk Control. It also presents the between-ccoponent interaction of FIN CON and decision-making flow.
3.1.1 Manager-Analyst Agent Group
Analogous to human investment firm, FINCON establishes a unique hierarchical structure to organize its multi-agent system, synthesizing their efforts to achieve superior decision-making outcomes.

---

3.1 Synthesized Multi-agent Hierarchical Structure Design
The primary goal is to enhance information presentation and comprehension while minimizing unnecessary communication costs. The working mechanism of each agent is illustrated in Figure 2
Analyst Agents. In FINCON, analyst agents distill concise investment insights from large volumes of multi-source market data, each focused specific trading target. To ensure high-quality reasoning by reducing task load and sharpening focus, each agent processes information from a single source in a uni-modal fashion, providing pre-specified outputs based on prompts. This setup mimics an efficient human team, where each analyst specializes in a specific fuucction, filtering out market noise and extracting key insights. These agents assist the manager agent by consolidating denoised investment information from multiple perspectives. We implement seven distinct types of analyst agents using LLMs, each producing unique investment insights, as shown in the upper section of Figure 2 Based on input modalities, three textual data processing agents extract insights and sentiments from daily news and financial reports. An audio agent uses the Whisper API to interpret investment signals from earnings call recordings. Additionally, a data analysis agent and a stock selection agent compute critical financial metrics, such as momentum and CVaR, using tabular time series data. The stock selection agent also oversees portfolio selection by applying the classic risk diversification method in quantitative finance N.
Manager Agent. In FINCON, the manager agent acts as the sole decision-maker, responsible for generating trading actions for sequential financial tasks. For portfolio management, calculates portfolio weights using convex optimization techniques constrained by directional trading decisions (see optimization problem as presented in Formula (T). Four key mechanisms support each decision: 1) Consolidating distilled insights from multiple analyst agents. 2) Receiving timely risk alerts and conceptual investment updates from the risk control component. 3) Refining its investment beliefs about the influence of different information sources on trading decisions for specific targets. 4) Conducting self-reflection by reviewing reasoning outcomes from previous trading actions.
3.1.2 Risk-Control Component
We have innovatively designed a dual-level risk-control mechanism consisting of within-episode and over-episode risk management. The within-episode mechanism detects market risk within a single training episode, allowing the manager agent to promptly adjust trading actions to mitigate potential losses by accounting for short-term trading performance and market fluctuations. This mechanism also operates during the testing phase. In contrast, the over-episode mechanism functions exclusively during the training stage, providing prompt optimization guidance by comparing the trading performance of the current episode with the previous one. This reflection enables the manager agent to update its investment beliefs based on performance differences. By drawing on prior observations of market risk and profitability patterns, these two mechanisms help avoid repeated investment errors, thereby enhancing future returns.
The within-episode risk alert is triggered by a sudden drop in the CVaR value. Conditional Value at Risk (CVaR) represents the average of the perft-performing I 1% of daily trading Profits and Losses (PnLs). A decrease in CVaR typically indicates that recent trading decisions have led to PnLs within this bottom percentile, signaling a potentially high-risk market condition. When this occurs, the manager agent adopts a risk-averse stance for that day's trading actions, regardless of the prior risk status.
Within-Episode Risk Control:
The over-episode investment belief updates facilitate adjustments in the emphasis placed on analysts' information distillation and the manager's 's action generation. Through the Actor-Critic mechanism, FINCON episodically optimizes its investment strategy for given trading target, as defined by objective (Equation 2 ). by reflecting on a series of winning and losing actions. This episodic reflection is powered by a unique Conceptual Verbal Reinforcement (CVRF). CVRF assesses the performance of consecutive training episodes by analyzing the information per- spectives provided by analysts and reflected in the manager's ' decision-makee and attributes the evaluation outcomes to these specific aspects. By comparing the conceptualized insights from more profitable versus less profitable episodes, the system informs both the manager and analyst agents about necessary belief adjustments, helping prioritize the most relevant market information for increased profitability, as detailed in Algorithm T CVRF leverages text-based gradi- ent descent to offer optimal conceptual investment guidance for the manager agent, refining prompts with the latest investment beliefs. The guidance is organized according to perspectives provided by
Over-Episode Risk Control:
It then conceptualizes
5

---

3.2 Modular Design of FINCON Agents
the respective analyst agents, key financial indicators (such as historical momentum), or other crucial viewpoints.
Factor
Gradient-basee based model optimizer
LLM-based prompt optimizer
Upgrade direction
Model value gradient momentum
Prompt reflection trajectory
Update method
Learning rate descent
Overlapping percentage of trading decisions
Table 1: Analogy between glossaries model optimizer and prompt optimizer.
These belief updates are first received by the manager agent and then selectively propagated to relevant agents, minimizing over-commumication. Unlike the text-based gradient descent proposed by Tang et 28), which uses prompt editing distance as a learning rate, we derive investment belief updates by measuring the overlapping percentage of trading actions between two consecutive training trajectories at each helief update, as presented in Table [ This approach has proven effective in improving the performance of synthesized agent system, where each worker has clearly defined and specialized role. The above describes the workflow of FINCON during the training stage, while the workflow during the testing stage is detailed in the Appendix A.3
Algorithm Training Stage Algorithm of FINCON: Conceptual Verbal Reinforcement using Textual-based Gradient Descent
Initialize manager-analysts component { (Mir M and risk-control component M,.
Initialize trading start date s, stock pool of portfolio and portfolio weights we 0.
Initialize Prompts 0. policy TTg.
while episode < Max do
for 0 S S 1' do
Run policy 11g (collecting daily PnL re. portfolio weights we and daily CVaR value pt).
if P: < Pt-1 or 7: < O then
Trigger Ma self-reflcction and generate self-reflection text Be-
end if
Get the investment trajectory H4 and calculate the objective function value (Function P
end for
Compare the objective function values of episodes & 1 & k, and decide which episode has higher
performance;
Pass sustained profitable and losing trades from two episodes Ha-1 & Hy into risk-control component
Mr:
Guide M, to summarize conceptualized investment insights [ct- ................................ 1 & {ch.....,ch ,""";
Compare two sets of conceptualized insights and give the reasoning for higher performance (providing textual optimization direction, i.e. meta prompt);
Calculate overlapping percentage between trading decision sequences from two episodes (providing the learning rate T); Update the prompts by textual dradient-descent:
0 t- M, r, meta prompt).
end while
3.2
Modular Design of FINCON Agents
Here, we explain the modular design of FINCON agents. Inspired by the recent works of Park et al. 44 and Sumers et al. (45) on developing the cognitive structure of language agents for human-like behavior, agents in FINCON integrate four modules to support their necessary functionalities, along with a shared general configuration, as detailed in Appendix AA.4
General Configuration and Profiling Module. This module defines task types (c.g., stock trading, portfolio management) and specifies trading targets, including sector and performance details. The profllng module outlines each agent's 'ss roles and responsibilities. The concatenated textual content from these parts is used to query investment-relatee Perception Module. This module defines how each agent interacts with the market, specifying the information they perceive, receive, and communicate, with interactions tailored to each agent's role. In detail, it converts raw market data, feedback from other agents, and information retrieved from the memory module into formats compatible with large language models, enabling them to process these inputs effecttvelyy. Memory Module. The memory module comprises three key components:
events from the agents' memory databases.
6

---

working memory. procedural memory, and episodic memory. Much like how humans process events in their working memory [46]. FINCON agents leverage their working memory to perform range of tasks, including observation, distillation, and refinement of available memory events, all tailored to the specific roles of the agents. Procedural memory and episodic memory are critical for recording historical actions, outcomes, and reflections during sequential decision decision-making. Procedural memory is generated after each decision step within an episode, storing data as memory events. For trading inquiries, top events are retrieved from procedural memory and ranked based on recency, relevance, and importance, following a simplified version of the method proposed by Yu et al. [32], with further details provided in Appendix A.13 Each functional analyst agent has distinct procedural memory decay rates, reflecting the timeliness of various financial data sources, which crucial for aligning multi-type data influencing specific time points and supporting informed decision- manager agent enhances the procedural memory of analyst agents by providing feedback through an access counter. Both analyst and manager agents maintain procedural memory, but they keep different records, as illustrated in Appendix AA.4] Episodic memory, exclusive to the manager agent, stores actions, PnL series from previous episodes, and updated conceptual investment beliefs from the risk control component.
The
Experiments
Does FINCON demonstrate robust- ness across multiple financial decision-making tasks, especially single-asset trading and portfolio management? RQ2: Is the within-episode risk control mechanism in FINCON effective in maintain- ing superior decision-making performance? RQ3: Is the over-episode risk control mechanism in FinCON effective in timely updating the manager agent's beliefs to enhance trading performance?
Our experiment answers the key research questions (RQs): RQ1:
4.1 Experimental Setup
(i) Multi-Modal Datasets. We construct a market environment representation using real-world financial data, including stock prices, daily news, company filings (Form 10-Q. Form 10-K, etc.). and ECC audio from January 3, 2022, to June 10, 2023, as detailed Appendix, [A.8] Each data source is assigned to specific analyst agents based on its timeliness. (ii) Evaluation Metries. We evaluate FINCON and other state-of-the-ar (SOTA) agents using metrics such as Cumulative Return (CR%), Sharpe Ratio (SR), and Max Drawdown (MDD%). CR and SR are prioritized because they provide comprehensive insights into overall performance and risk-adjusted returns, essential for informed investment decisions. In contrast, MDD focuses evaluating the potential for significant losses, making it a secondary considcration in this context. Details are provided in Appendix AA.10 (iii) Comparative Methods. For single-stock trading, compare FINCON with DRL agents (A2C, PPO, DQN) and LL.M-based agents (GENERATIVE AGENT (GA), FINGPT, FINMEM, FINAGENT) as well as the Buy-and-Hold (B & H) strategy. For portfolio management, Markowitz MV, FinRL-A2C, and Eeigh-Weighted ETF strategy, with further details provided in Appendix 1. The detailed experiment parameter configurations of the above agent systems are articulated in Appendix. A.14 (iv) Implementation Details. All LLM-based based agents use GPT-4-Turbo, with temperature set 0.3. FINCON is trained from January 3, 2022, to October 4, 2022, and tested from October 5, 2022, to June 10, 2023. DRL agents are trained over the period from January 1, 2018. to October 4, 2022. to ensure that there sufficient data available for model convergence. Performance is based on the median CR and SR from five repeated epochs. For more detailed explanation of the experimental setup, please refer the Appendix A.5
compare FINCON with
4.2 Main Results
In response to RQ1, we analyze FInCON's performance on two types of financial decision- decision-making making tasks: single-asset trading and portfolio management. The system's ability manage these sequen- tially complex decisions is thoroughly evaluated in the following sections.
4.2.1 Single Asset Trading Task
In this task, we evaluate FINCON's performance against other leading algorithmic trading models by trading eight different stocks. As presented in the tables above, FINCON significantly outperforms both based and baL-based approaches in terms of CRs and SRs. Additionally, FINCON

---

4.2 Main Results
achieves one of the lowest MDD values across most trading assets, demonstrating effective risk man- agement while still delivering the highest investment returns. For detailed performance comparisons across all models and metrics, refer to Table 1.
Overall, even with extended training periods, DRL-based models tend underperform, with the A2C algorithm lagging significantly behind other agents in general. Notably, the training periods for Nio Inc. (NIO) and Coinbase Global Inc. (COIN) require clarification. NIO, which completed its IPO in September 2018, has a slightly shorter training period than other tickers, yet the DRL algorithms for NIO still achieved convergence. contrast, Coinbase Global Inc. (COIN), which completed its IPO in April 2021, presented more significant challenge due to the limited available trading data, causing DRL algorithms to struggle with convergence. This limitation underscores a major drawback for DRL agents when trading recently listed IPOs. Consequently, our analysis of COIN focuses on comparisons between FINCON, LL---baeed agents, and the buy-and-hold (B & H) strategy. this context, FINCON demonstrates clear advantage, achieving cumulative return of over 57% and Sharpe ratio of 0.825. Furthermore, -.seeddddd agents, which can leverage diverse data types and require minimal training, effectively mitigate the challenges faced by DRL algorithms.
TSLA
AMZN
NIO
MSFT
Categories
Models
CR 1
SRt
MDD%
CR 56 1
SRT
MDD%
CR%
1
SRT
MDD%
CR% f
SKr
MDDS J
Market
6,425
0.145
58.150
2,030
0.072
34.241
-77.210
-1.499
1,200
15.010
Our Model
FINCON
82,871
1.972
29.727
24.848
0.904
25,889
17.461
0.335
40.647
31.625
1,538
15,010
GA
16.535
0.391
54.131
-5,631
-0.199
37.213
-3.176
-1.574
3.155
-31821
-1.414
39,808
M..M-
FINGPT
1,549
0,044
42,400
-29.811
-1.810
29.671
-4,999
-0,121
37.344
21.535
1.315
16.503
FINMEM
34.624
1.552
15.674
-18.011
-0.773
36,825
-48.437
-1,180
64.
-22.036
-1.247
29.435
FINAGENT
11,960
0.271
55.734
-24.588
-1.493
33.074
0.933
0.051
19.181
-27.534
-1.247
39.544
A2C
35.644
-0.805
61.502
-12.560
-0.444
37,106
91.910
-1.728
68.911
21.397
0.962
21.458
DRL- BRL-bbsed DQN
PPO
1,409
0.032
49.740
3,863
0.138
28.085
-72.119
-1.352
62.093
-4.611
-0.214
30,950
-1.296
+0.029
58.150
11.171
0.398
31.174
-35.419
-0.662
27.021
1.216
21.458
AAPL
GOOG
NFLX
COIN
Categories Models
CR% f
SRt
MDD%
CR % 1
SRT
MDD%
CR%
SRT
MDD%
CR% f
SRT
MDD%
Market
B&IT
22,315
1.107
20.659
22.420
0.891
21.191
$7.338
1.794
20.926
-21.56
-0.311
60.187
Our Model
FINCON
27.352
1.597
15.266
25.077
1.052
17.530
69.239
2.370
20.792
$7.045
0.825
42.679
GA
5.694
0.372
-1.5155
-0.192
8.210
41.770
1.485
20.926
19.271
0.277
LLM-based
FINGPT
20.321
1.161
16.759
0,242
0.011
26.984
11.925
0.472
20.201
.99.553
-1.807
74.967
FINMEM
12.397
0.994
0.311
0.018
21.503
-10.306
-0.478
27.692
0.811
0.017
FINAGENT
20.757
1.041
19,896
-7.440
-1.024
10.360
61.303
1.960
20.926
5.971
-0.106
56.882
A2C
13.781
0.683
14,226
8.562
0.340
21.191
-8.176
-0.258
49.579
DRL-based
PPO
14,041
0.704
22.785
2.434
0.097
25,202
-33.144
-1.049
33.377
DQN
21.125
1.048
16.131
20.690
0.822
21.191
21.753
0.687
39.733
Table 2: Comparison of key performance metrics during the testing period for the single-asset trading tasks involving eight stocks, between FINCON and other algorithmic agents. Note that the highest and second highest CRs and SRs have been lested and found statistically significant using the Wilcoxon signed-rank test. The highest CRs and SRs are highlighted in red, while the second highest are marked in blue.
In alignment with market trends, FINCON consistently exhibits superior recision-making quality compared to other LLM-based agents, regardless of market aanditinss-wheheee bullish (c.g., GOOG, MSFT), bearish (e.g., NIO), or mixed (e.g.. TSLA). We attribute this performance to its high-quality distillation of information through synthesized multi-agent collaboration mechanism, combined with its dual-level risk control design, positioning FINCON as a leader in the space. By contrast, FINGPT primarily relies on sentiment analysis of financial information, failing to fully exploit the potential of LLMs to integrate nuanced textual insights with numerical financial indicators. Similarly, GA and FINMEM use single-agent frameworks without sophisticated information distillation processes or diverse toolset, placing heavy cognitive demand on the agent to process multi-source information, especially when dealing with large and varied data modalities. Moreover, their static or minimal investment belief systems result in weak filtering of market noise. As illustrated in Figure (a) & (b) of Appendix A.7.2 this limitation leads these models to consistently hold lower positions and hesitate between "buy" or 'sell' decisions, ultimately resulting in suboptimal performance.
FINCON overcomes these challenges through its innovative multi- synthesis, enabling it to deliver superior outcomes. Although FINAGENT performs well when integrating images and tabular data, it struggles to remain competitive when incorporating audio data, such as ECC recordings, which are critical in real-worle trading. Additionally, FINAGENT relies on similarity- retrieval, which can lead to decisions based outdated information, often resulting in errors. In
memory
8

---

4.3
Ablation Studies
contrast, FINCON's 's memory structure accounts for the varying timeliness of multi multi-source financial data, significantly enhancing decision quality and overall performance.
4.2.2 Portfolio Management Task
In this task, we compare FINCON's performance with the Markowitz Mean-Variane Variance (MV) portfolio
47 and FINRL [48] in managing two small portfolios: Portfolio (TSLA, MSFT, and PFE) and Portfolio 2 (AMZN, GM, and LLYY.. These assets were selected by the stock selection agent from pool of 42 stocks, each with sufficient news data (over 800 news articles during the combined training and testing periods), as illustrated in Figure 9 Appendix A The training and testing periods, the backbone model and the parameter settings are consistent with those used in the single-asset trading task. For the Markowitz MV portfolio, we estimate the covariance matrix and expected returns using the same training data. the case of FINRL, we use five years of training data prior to the test period. As detailed in Table 3 and Figure 3] our results show that FINCON outperforms both the Markowitz MV portfolio and FINRL as well as the market baseline Seina--Wiggted ETF, achieving significantly higher CRs and SRs, as well as MDDs.
However, managing multi-asset portfolios introduces more complexity, leading to a higher likeli- hood of hallucination compared to single-asset trading. This is due to the increased input length and complexity involved in multi-assed sicision-mmking While FINCON mitigates this issue by distributing tasks across specialized agents that focus on critical investment insights, it occasionally generates incorrect information, such as non-existent indices of memory events. Handling multi-asset decision-making requires sophisticated logic and substantial market information, which poses significant challenge for LLMs when processing extended contexts. This complexity has left port- folio management relatively unexplored in previous language agent studies. Nonetheless, FINCON demonstrates considerable potential by constructing agent systems that can tackle complex financial tasks through effective resource optimization, even when managing relatively compact portfolios.
Models
CR % +
SR
MDD %
Models
CR % 1
SR*
MDD %.
FINCON
113.836
3.269
16.163
FINCON
32.922
1.371
21.502
Markowitz MV
12.636
0.614
17.842
Markowitz MV
10.289
0.540
25.099
FINRL-A2C
19.461
0.831
26.917
FINRL-A2C
11.589
0.649
15.787
ETF
9.344
0.492
21.223
Weighh-e ETF
15.061
0.867
14.662
Table Key performance metries comparison among portfolio management strategies Portfolio & FINCON leads all performance metrics.
nomma
Eiiel ET?
Weighted
2.0
Pertroie Sasuu
300330
203037
2020447
2026-7
203601
2025-06
2010-19
20100
Date
Figure 3: Portfolio values of Portfolio 1 & changes over time for all the strategies. The computation of portfolio value refers to Equation in Appendix A.10
4.3 Ablation Studies
In response to RQ2 and RQ3, we conduct a comprehensive evaluation of our unique risk control component through two ablation studies. Both studies maintain consistency with the training and testing periods used the main experiments. The first study examines the effectiveness of the within- episode risk control mechanism, which leverages Conditional Value at Risk (CVaR) to manage risk in real-time, as detailed Table Comparisons on primary metrics illustrate that the success of utilizing CVaR for within-episode risk control is evident in both bullish and bearish market environments in the
9

---

single asset trading case. Moreover, in portfolio trading with mixed price trends, our within- aithin-episode risk control mechanism performs robustly by monitoring the entire portfolio's value fluctuations. The second study focuses on the over-episode risk control mechanism, demonstrating its critical role updating the trading manager agent's 's beliefs to provide more comprehensive understanding of current trading conditions, both decision-making scenarios underscore the effectiveness of using CVRF to update investment beliefss episodically, guiding the agent towards more profitable investment strategies. Additionally, FINCON demonstrates significant learning gains, achieving these results after only four training episodes episodes-suustantiaaee fewer than what is typically required by traditional RL algorithmic trading agents. More visualizations and analysis are provided the Appendix A.6 A.6
articulated in Table 5 The markedly improved CRs and SRs in
Task
Assets
Market Trend
Models
CR %
SR1
MDD %
w/ CVaR
25.077
1.052
17.530
GOOG
General Bullish
w/o CVaR
-1.461
-0.006
27.079
Single Stock
w/ CVaR
17.461
0.335
40.647
NIO
General Bearish
w/o CVaR
-52.887
-1.002
70.243
Portfolio
(TSLA,
w/ CVaR
113.836
3.269
16.163
Mixed
Management
MSFT, PFE)
w/o CVaR
14.699
1.142
17.511
Table 4: Key metries FINCON with without implementing CVaR for within-episode risk control. The performance FINCON with the implementation of CVaR won leading performance both single-asset trading and portfolio management tasks.
Task
Assets
Market Trend
Models
CR %6
SR
MDD %.
w/ belief
25.077
1.052
17.530
GOOG
General Bullish
w/o belief
-11.944
-0.496
29.309
Single Stock
w/ belicf 17.461
0.335
40.647
NIO
General Bearish
w/o belief
8.197
0.156
55.688
Portfolio
(TSLA,
w/ belief 113.836
3.269
16.163
Mixed
Management
MSFT, PFE)
w/o belief
28.432
1.181
27.535
Table 5: Key metrics FINCON with without implementing belief updates for over-episode risk control. The performance of FINCON with the implementation CVRF won a leading performance both single-asset trading and portfolio management tasks.
Conclusion
In this paper, present FINCON, novel LLM-based multi-agent framework for financial decision- making tasks, including single stock trading and portfolio management. Central to FINCON the Synthesized Manager-Analyst hierarchical communication strueture and dual-level risk control component. This communication method channels financial data from multiple sources to specialized analyst agents, who distill into key investment insights. The manager agent then synthesizes these insights for decision-making Our experimental evaluations demonstrate the efficacy of our risk control mechanism in mitigating investment risks and enhancing trading performance. Additionally, the streamlined communication structure reduces overhead. The dual-level risk control component introduces a novel approach to defining agent personas, enabling dynamic updates of risk and market beliefs within agent communication, A valuable future research direction would be to scale FINCON's framework manage large-sized portfolios comprising tens of assets, while maintaining the impressive decision-making quality demonstrated with smaller portfolios. Given the LLM's input length constraint, a critical challenge lies in striking an optimal balance between information conciseness through agent distillation and potential performance deterioration when extending the current context window. Addressing this will be essential for ensuring quality-assured outcomes.
10

---

REFERENCES
References
[1] Harry Markowitz. Portfolio selection. The Journal of Finance, 7(1):77-91, 1952.
[2] Xuan-Hong Dang, Syed Yousaf Shah, and Petros Zerfos. "hee squawk bot": Joint learning of time series and text data modalities for automated financial information filtering. arXiv preprint arXiv:1912.
2019.
[3] John I. Maginn, Donald I. Tuttle, Dennis W Mcleavey, and Jerald E Pinto. Managing investment portfolios: a dynamic process, volume 3. John Wiley & Sons, 2007.
[4] Roy Radner. The organization of decentralized information processing. Econometrica: Journal of the Econometric Society, pages 1109- 146, 1993.
[5] George A Miller. The magical number seven, plus minus two: Some limits our capacity for processing information. Psychological review, 63(2):81, 1956.
[6] Rundong Wang, Hongxin Wei, Bo An, Zhouyan Feng, and Jun Yao. Commission fee is not enough: A hierarchical reinforced framework for portfolio management. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 626-633, 2021.
Weiguang Han, Boyi Zhang, Qianqian Xie, Min Peng, Yanzhao Lai, and Jimin Huang. Select and trade: Towards unified pair trading with hierarchical reinforcement learning. In Proceed- ings of the 29th ACM SIGKDD Conference Knowledge Discovery and Data Mining, pages
1123-4134, 2023.
[8] Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, and Bo An. Earnhft: Efficient hierarchical reinforcement learning for high frequency trading. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 14669-14676, 2024.
[9] Jie Huang and Kevin Chen-Chuan Chang. Towards reasoning in large language models: A survey. arXiv preprint arXiv:2212.104003 2022.
[10] Mingyu Jin, Qinkai Yu, Haiyan Zhao, Wenyue Hua, Yanda Meng. Yongfeng Zhang, Mengnan Du, et al. The impact of reasoning step length on large language models. arXiv preprint SKKiv:2401.04925: 2024.
[11] Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou. Large language models as tool makers. arXiv preprint arXiv:2305. 2023.
[12] Jingqing Ruan, Yihong Chen, Bin Zhang, Zhiwei Xu, Tianpeng Bao, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, et al. Tptu: Task planning and tool usage of large language model-based ai agents. In NeurIPS 2023 Foundation Models for Decision Making Workshop, 2023.
[13] Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. Lim-planner: Few-shot grounded planning for embodied agents with large language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2998-3009, 2023.
[14] Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannanch Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. Art: Automatic multi-step reasoning and tool-use for large language models. arXiv preprint arXiv:2303.0900 2023.
[15] Qianqian Xie, Weiguang Han, Xiao Zhang. Yanzhao Lai, Min Peng, Alejandro Lopez- Lira, and Jimin Huang. Pixiu: comprehensive benchmark, instruction dataset and large language model for finance. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, editors, Advances Neural Information Processing Systems, volume 36, pages -34444 Curran Associates, Inc., 2023.
[16] Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng, Weiguang Han, Alejandro Lopez- Lira, Xiao- Yang Liu, Meikang Qiu, Sophia Ananiadou, Min Peng, Jimin Huang, and Qianqian Xie. Dólares or dollars? unraveling the bilingual prowess of financial lims between spanish and english. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '24, page 6236-6246, New York, NY, USA, 2024. Association for Computing Machinery.
[17] Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, et al. The finben: An holistic financial benchmark for large language models. arXiv preprint 2rXiv:2402.12659 2024.

---

REFERENCES
[18] Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Wanlong Yu, Jimin Huang, and Qianqian Xie. No language is an island: Unifying chinese and english in financial large language models, instruction data, and benchmarks. arXiv preprint arXiv:2403. 06249, 2024.
[19] Yuzhe Yang. Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, et al. Ucfe: A user-centric financial expertise benchmark for large language models. arXiv preprint arXiv:2410. 14059, 2024.
[20] Joon Sung Park, Joseph C O'Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304 03442, 2023.
[21] Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. Autogen: Enabling next-gen Ilm applications via multi-agent conversation framework. arXiv preprint (rXii:23008081555 2023.
[22] Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, and Huajun Chen. Autoact: Automatic agent learning from scratch via self-planning. arXiv preprint arXiv:2401 005268, 2024.
[23] Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang. Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2338..00::
2023.
[24] Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L Griffiths, and Mengdi Wang. Embodied Ilm agents learn to cooperate in organized teams. arXiv preprint arXiv:2403. 12482, 2024.
[25] Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, Yunghwei Lai, Xinhui Kang, Weizhi Ma, and Yang Liu. Agent hospital: A simulacrum of hospital with evolvable medical agents. arXiv preprint mmXiv:2405.02955 2024.
[26] Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang. Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors. In The Twelfth International Conference on Learning Representations, 2023.
[27] Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. Au- tomatic prompt optimization with" gradient descent" and beam search. arXiv preprins arXiv:2305.0344 2023.
[28) Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Siyuan Lu, Yaliang Li, and Ji-Rong Wen. Unleashing the potential of large language models as prompt optimizers: An analogical analysis with gradient-based model optimizers. arXiv preprint arXiv:2402.1 17564, 2024.
[29] Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, Jianguo Zhang, Devansh Arpit, et al. Retroformer: Retrospective large language agents with policy gradient optimization. arXiv preprint arXiv:2.38..22/51 2023.
[30] Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Re- flexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36, 2024.
[31] Hongyang Yang. Xiao-Yare Yang Liu, and Christina Dan Wang. Fingpt: Open-source financial
large language models. arXiv preprint arXiv:2306. 1.X0022306.0031,, 2023.
[32] Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Denghui Zhang, Rong Liu, Jordan W Suchow, and Khaldoun Khashanah. Finmem: A performance-enhaneed Ilm trading agent with layered memory and character design. arXiv preprint arXiv:2311.13743 2023.
[33] Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, et al. Finagent: A multimodal foundation agent for finan- cial trading: 'Tool-augmented, diversified, and generalist. arXiv preprint 2402 2024.
[34] Freddy Delbaen and Sara Biagini. Coherent risk measures. Springer, 2000.
[35] Frank J Fabozzi, Sergio M Focardi, and Petter N Kolm. Quantitative equity investing: Tech- niques and strategies. John Wiley & Sons, 2010.
12

---

REFERENCES
[36] Chong Zhang, Xinyi Liu, Mingyu Jin, Zhongmou Zhang, Lingyao Li, Zhengting Wang, Wenyue Hua, Dong Shu, Suiyuan Zhu, Xiaobo Jin, et al. When ai meets finance (stockagent): Large language model-based stock trading in simulated real-world environments. arXiv preprint arXiv:2407. 18957, 2024.
[37] Keith Kuester, Stefan Miunik, and Mare Paolella. Value-at-risk prediction: A comparison of alternative strategies. Journal of Financial Econometrics, 4(1):53-89, 2006.
[38] Matthijs TJ Spaan. Partially observable markov decision processes. In Reinforcement learning: State-of-the-art, pages 387-414. Springer, 2012.
[39] Frank J Fabozzi, Harry M Markowitz, and Francis Gupta. Portfolio selection. Handbook of finance, 2, 2008.
[40] Yang Liu, Qi Liu, Hongke Zhao, Zhen Pan, and Chuanren Liu. Adaptive quantitative trading: An imitative deep reinforcement learning approach. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 2128-2212 21352115,41 2020.
[41] Taylan Kabbani and Ekrem Duman. Deep reinforcement learning approach for trading automation in the stock market. IEEE Access, 10:93564-93574, 2022.
[42] Thomas L. Griffiths, Jian---as Zhu, Erin Grant, and R Thomas McCoy. Bayes in the age of intelligent machines. arXiv preprint MxXiv:2311.10200 2023.
[43] Ashwin Rao and Tikhon Jelvis. Foundations of reinforcement learning with applications in finance. Chapman and Hall/CRC, 2022.
[44] Theodore Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L. Griffiths. Cognitive architectures for language agents, 2023.
[45] Jintian Zhang, Xin Xu, and Shumin Deng. Exploring collaboration mechanisms for Ilm agents:
A social psychology view. arXiv preprint arXiv:2310.02124, 2023.
[46] Anthony D Wagner. Working memory contributions to human learning and remembering. Neuron, 22(1):19-22 1999.
[47] Harry Markowitz and G Peter Todd. Mean-variance analysis in portfolio choice and capital markets, volume 66. John Wiley & Sons, 2000.
[48] Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang. Bowen Xiao, and Christina Dan Wang. Finrl: A deep reinforcement learning library for automated stock trading in quantitative finance. arXiv preprint arXiv:2011.0900
2020.
[49] Noah Shinn, Beck Labash, and Ashwin Gopinath. Reflexion: autonomous agent with dynamic memory and self reffereflect.nn. arXiv preprint 2rXiv:2303.11333 2023.
[50] Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Lim agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19632-19642, 2024.
[51] Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. Competition-level code generation with alphacode. Science, (6664)))): 1092- 2022.
[52] Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. Codet: Code generation with generated tests. arXiv preprint arXiv:2207. 10397, 2022.
[53] Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. Communicative agents for software development. arXiv preprint arXiv:2307.07924 2023.
[54] Zelai Xu, Chao Yu, Fei Fang, Yu Wang, and Yi Wu. Language agents with reinforcement learning for strategic play in the werewolf game. arXiv preprint arXiv:2310. 18940, 2023.
[55] Weiyu Ma, Qirui Mi, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, and Jun Wang. Large language models play starcraft ii: Benchmarks and chain of summarization approach. arXiv preprint ar3iv:2312 1165, 2023.
[56] J de Curtò, I de Zarzà, Gemma Roig, Juan Carlos Cano, Pietro Manzoni, and Carlos T Calafate. Llm-informed multi-armed bandit strategies for non-stationary environments. Electronics, 12(13):2814, 2023.
13

---

REFERENCES
[57] Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Gengchen Mai, et al. Revolutionizing finance with llms: An overview of applications and insights. arXiv preprint arXiv:2401. /1641, 2024.
[58] Zhiwei Liu, Xin Zhang, Kailai Yang, Qianqian Xie, Jimin Huang, and Sophia Ananiadou. Fmdllama: Financial misinformation detection based on large language models. arXiv preprini arXiv:2409. 14X4: 2024.
[59] Yupeng Cao, Zhi Chen, Qingyun Pei, Fabrizio Dimino, Lorenzo Ausicllo, Prashant Kumar, KP Subbalakshmi, and Papa Momar Ndiaye. Risklabs: Predicting financial risk using large language model based on multi-sources data. arXiv preprint arXiv:2404.07452. 2024.
[60] Qianqian Xie, Dong Li, Mengxi Xiao, Zihao Jiang, Ruoyu Xiang, Xiao Zhang, Zhengyu Chen, Yueru He, Weiguang Han, Yuzhe Yang, et al. Open-finllms: Open multimodal large language models for financial applications. arXiv preprint arXiv:2408. /178, 2024.
[61] Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Daniele Giardino, Marco Re, and Sergio Spanò. Multi-agent reinforcement learning: A review of challenges and applications. Applied Sciences, 11(44): 2021.
[62] Kaiqing Zhang, Zhuoran Yang, and Tamer Başar. Multi-agent reinforcement learning: A selective overview of theories and algorithms. Handbook of reinforcement learning and control, pages 321-384, 2021.
[63] Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson. Learning to communicate with deep multi multi-agent reinforcement learning. Advances neural information processing systems, 29, 2016.
[64] Toru Lin, Jacob Huh, Christopher Stauffer, Ser Nam Lim, and Phillip Isola. Learning to ground multi-agent communication with autoencoders. Advances in Neural Information Processing 2021.
Systems,
[65] Woojun Kim, Jongeui Park, and Youngchul Sung. Communication in multi-agent reinforce-
ment learning: Intention sharing. In International Conference on Learning Representations, 2020.
[66] Changxi Zhu, Mehdi Dastani, and Shihan Wang. A survey of multi-agent reinforcement learning with communication. arXiv preprint arXiv:2203.088) 2022.
[67] Zhiyuan Yao, Zheng Li, Matthew Thomas, and Ionut Florescu. Reinforcement learning in agent-based market simulation: Unveiling realistic stylized facts and behavior. arXiv preprint 0rXiv:2403.1970 2024.
[68] HaoHang Li and Steve Yang. Impact of false information from spoofing strategies: An abm model of market dynamics. In 2022 IEEE Symposium Computational Intelligence for Financial Engineering and Economics (CIFEr), pages 1-10. IEEE, 2022.
[69] Kuan Wang, Yadong Lu, Michael Santacroce, Yeyun Gong, Chao Zhang, and Yelong Shen. Adapting Ilm agents through communication. arXiv preprini arXiv:2310.01444 2023.
[70] Zhao Mandi, Shreeya Jain, and Shuran Song. Roco: Dialectic multi-robot collaboration with large language models. arXiv preprint arXiv:2307.00 04738, 2023.
[71] Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B Tenenbaum, Tianmin Shu, and Chuang Gan. Building cooperative embodied agents modularly with large language models. arXiv preprint arXiv:2307..024488 2023.
[72] Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305. 14325, 2023.
[73] Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. Chateval: Towards better Ilmebased evaluators through multi-agent debate. arXiv preprint arXiv:2308.
2023.
[74] Frank Xing. Designing heterogeneous 1lm agents for financial sentiment analysis. arXiv preprint arXiv:2401.00 05090,0599991 2024.
[75] Irene de Zarzà i Cubero, Joaquim de Curtò i Díaz, Gemma Roig, and Carlos T Calafate. Optimized financial planning: Integrating individual and cooperative budgeting models with Il recommendations, AI, 5(1:911114, 2024.
14

---

REFERENCES
[76] Xiangpeng Wan, Haicheng Deng, Kai Zou, and Shiqi Xu. Enhancing the efficiency and accuracy of underlying asset reviews in structured finance: The application of multi-agent 2024.
framework. arXiv preprint arXiv:2405.04420
[77] Chong Zhang, Xinyi Liu, Mingyu Jin, Zhongmou Zhang, Lingyao Li, Zhengting Wang, Wenyue Hua, Dong Shu, Suiyuan Zhu, Xiaobo Jin, et When ai meets finance (stockagent): Large language model-based stock trading in simulated real-world environments. arXiv preprint arXiv:2407.1892 2024.
[78] Patrick Bolton and Mathias Dewatripont. The firm as a communication network. The Quarterly Journal of Economics, 109(4):809-839, 1994.
[79] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022.
[80] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of thouughh prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824 24837, 2022.
[81] Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36, 2024.
[82] Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305. 14992, 2023.
[83] Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, et al. Ghost in the minecraft: Generally capable agents for open-world enviroments via large language models with text-based knowledge and memory. 2023.
arXiv preprint 2rXiv:2305.
[84] Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, al. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6):1-26, 2024.
[85] Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. Reinforcement learning: A survey. Journal of artificial intelligence research, 4:237-285, 1996.
[86] Guojun Xiong, Shufan Wang, Daniel Jiang, and Jian Li. Personalized federated reinforce- ment learning with shared representations. In Deployable RL: From Research to Practice@ Reinforcement Learning Conference 2024, 2024.
[87] Guojun Xiong, Ujwal Dinesha, Debajoy Mukherjee, Jian Li. and Srinivas Shakkottai. Dopl: Direct online preference learning for restless bandits with preference feedback. arXiv preprint arXiv:2410.0533 055277, 2024.
[88] Zhiyuan Yao, Ionut Florescu, and Chihoon Lee. Control in stochastic environment with delays: A model-based reinforcement learning approach. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 34, pages 663-670, 2024.
[89] Bin Zhang, Hangyu Mao, Jingqing Ruan, Ying Wen, Yang Li, Shao Zhang, Zhiwei Xu, Dapeng Li, Ziyue Li, Rui Zhao, et al. Controlling large language model- agents for large-scale decision- decision-making: makinng An actor-critic approach. arXiv preprint arXiv:2311. 138884 2023.
[90] John Hull. Risk Management and Financial Institutions. John Wiley Sons, 2007.
[91] William F. Sharpe. The sharpe ratio. The Journal of Portfolio Management, 21(1):49-58, 1994.
[92] Andrew Ang and Joseph Chen. Downside risk. Journal of Portfolio Management, 293):1033- 122, 2003.
[93] Kiao-Yang Liu, Guoxuan Wang, and Daochen Zha. Fingpt: Democratizing internet-scale data for financial large language models. arXiv preprint arXiv:2307. 10485, 2023.
[94] Yu Qin and Yi Yang. What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 390-401, 2019.
15

---

REFERENCES
[95] Linyi Yang, Tin Lok James Ng, Barry Smyth. and Riuhai Dong. Html: Hierarchical transformer-bast multi-task learning for volatility prediction. In Proceedings of The Web Conference 2020, pages 441-451, 2020.
[96] Yupeng Cao, Zhi Chen, Qingyun Pei, Prashant Kumar, KP Subbalakshmi, and Papa Momar Ndiaye. Eec analyzer: Extract trading signal from earnings conference calls using large language model for stock performance prediction. arXiv preprint arXiv:2404. 18470, 2024.
[97] John C Hull. Options, Futures, and Other Derivatives. Pearson Education, 2017.
[98] Xiao- Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, and Christina Dan Wang. FinRL:: Deep rein- forcement learning framework to automate trading in quantitative finance. ACM International Conference on Al Finance (ICAIF), 2021.
[99] Xiao- Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Wang, Zhaoran Wang, and Jian Guo. Finrl-meta: Market environments and benchmarks for data-driven financial reinforcement learning. Advances in Neural Information Processing 2022.
Systems,
[100] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforce- ment learning. International conference on machine learning, pages 1928-1937. PMLR, 2016.
[101] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv: 100.:0/777.0647, 2017.
[102] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, loannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning, arXiv preprint arXiv:1312.5602, 2013.
[103] Yuliya Plyakha, Raman Uppal, and Grigory Vilkov. Why does equal-weighted portfolio outperform value-and prige-weighted portfolios? Available at SSRN 2724535, 2012.
[104] Jaap MJ Murre and Joeri Dros. Replication and analysis of ebbinghaus' forgetting curve. PloS one, 10(7):e01 20644, 2015.
[105] Guardrails ai. attpss//dook cwainraiaarddrrrrr com Large Language Models.
Open source library for interacting with
16

---

A Appendix
A.1 Related Work
LLM Agents for Financial Decision Making, There are considerable efforts towards developing general-pune purerae LLM agent for sequential decision-makinn making 449 [0] and such type of tasks often involve episodic interactions with environment and verbal reflections for action refinement, such as coding competition [51 52], software development [3 23 game- paaniig [54] 55]. Furthermore,
researchers have started to exploit how LLM agents can perform better in harder decision-makie
tasks from finance [56 57 58 59 60), in which there are more volatile environments, leading to that the numerous unpredictable elements can obscure agent's ability to reflect accurately on the reasons for poor decision outcomes. FinMem 32 enhances single stock trading performance by embedding memory modules with LLM agent for reflection-refinement and FinAgent [3 improved trading profits via using external quantitative tool to fight against volatile environment.
Multi-Agent System and Communication Structures. In traditional multi-agent systems 61 the way for agents' communication is pre-determined, like sharing data or state observations
62
63
65
66
67 68
The emergence of large language model brings flexibility for human- sumaa--undersandable
communications
669
20 23 770,, so some work tries to elevate recision-makinge ability of L.M--
based multi-agent system by letting agents engage in discussions [11 or debates [72] (73). The
similar peer-communication strategy was as well utilized by the multi-agent system for financial tasks
74
75
76
However, such approach are not optimal for unified-goal financial tasks that prioritize
profits
77 because they suffer from potentially ambiguous optimization objectives and are unable
to control the unnecessary communication costs [78]
Prompt Optimization and Verbal Reinforcement. To enhance the reasoning or decision-making of LLM agents, many prompt optimization techniques have been proposed, like ReAct [79], Chain of Thought (CoT) 80) Tree of Thoughts (ToT) [8], ART [4], intended for that LLM agents can automatically generate intermediate reasoning steps as an iterative program. In addition, to make LLM agents make decisions like humans and generate more understandable reasoning texts, some researchers recommend incorporating cognitive structures S2 44 84) Inspired by these previous work and DRL algorithms
86 87 67 88), verbal reinforcement 29
89 24 was developed
for L.M agents such that they can update actions based on iterative self-reflection while integrating additional L.M as a prompt optimizer (7
A.2 Textual Cradient-Descent
In an LLM-based prompt optimizer, a meta- peetapp 27 28 is used to refine the task prompt for better performance. For example, for a mathematical reasoning task, the task prompt might be "Let's solve the problem," while the meta-prompt could be "Improve the prompt to help a model better perform mathematical reasoning."
Although prompt optimization lacks explicit gradients to control the update direction, we can simulate "textual gradient" by using LLMs' reflection capabilities. By generating feedback from past successes and failures on trading decisions, LLMs can produce "semantic" gradient signals that guide the optimization process.
Adjusting the optimization process's 's direction is crucial, similar to tuning the learning rate in traditional parameter optimization. An inappropriate learning rate cause the process oscillate or converge too slowly. Similarly, without proper control, the LLM-based optimizer might overshoot oscillate during prompt optimization.
To mimic learning rate effects, we measure the overlapping percentage between trading decision sequences from consecutive iterations. We then directly edit the previous task prompt to enhance performance. The meta-prompt instructs the LLM to modify the current prompt based on feedback, ensuring stable and incremental improvement process. This method allows for effective exploitation of existing prompts, leading to gradual performance enhancement.
17

---

A.3
FINCON Testing Stage Workflow
A.3 FINCON Testing Stage Workflow
During the testing stage, FiNCON will utilize the investment beliefs learned from the training stage, and the over-episode risk control mechanism will no longer operate. However, the within-episode risk control mechanism will still function, allowing the manager agent to adjust trading actions in real-time based on short-term trading performance and market fluctuations. This ensures that even during testing, FINCON can promptly respond to market risks and potentially prevent losses while leveraging the knowledge gained during training.
Algorithm/ 2 Testing Stage Algorithm of FINCON
Initialize trading start s, stock pool portfolio portfolio weights wo Inherit manager-analystt component ) & Inherit the reflections B, learned prompts 6, the trained policy Ile.
for I + < t S S do
Run policy 1L (collecting daily PnL rı. portfolio weights wa daily CVaR value pc).
if P: < Pt-1 or тs < 0 then Trigger M, self-reflection.
end if
Get one investment trajectory H.
end for
Output performance metrics calculation results based on H.
18

---

A.4 Figure of Modular Design of Agents in FINCON
A.4 Figure of Modular Design of Agents in FINCON
General Configuration
1. Investment task introduction
2. Trading target background
3. Trading sectors
4. Historical financial performance overview
Profiling Module
Manager Agent:
1. Role assignment: You experienced trading manager in the investment firm
2. Role description: Your responsibilities are to consolidate investment insights from analysts and make trading actions on (asset symbols}...
Analyst Agents:
1. Role assignment: You the investment analysts for news/ market data/ Form 10-K (Q)
ECC audio recording...
2. Role duty description: Your responsibilities distill investment insights and other
indicators like financial sentiment for (asset symbols)
Perception Module
Manager Agent:
1. Perceive: Investment insights from amalyst agents; daily risk alert and episode-level investment belief updates from the risk control component.
2. Send: Feedback analyst agents about their contribution to significant investment
earnings & losses.
Analyst Agents:
1. Perceive: Market information from certain information sources.
2. Send: Relevant market insights manager agent.
3. Receive: Feedback from the manager agent.
Memory Module
Manager Agent:
1. Working:
Consolidation
RRefinement
-Reflection
2. Procedural:
-Trading action records
Reflection records
3. Episodic:
Trajectory history
Analyst Agents:
1. Working:
Retrieval Distilled Investment-relater insights
Observation
Distillation Financial sentiment
2. Procedural:
Investment report recommended actions
Action Module
Manager Agent:
1. Conduct: Trading actions.
2. Reflect: Trading reasons and analyst agents' contribution assessment.
Figure 4: The detailed modular design of the manager and analyst agents. The general configuration and profiling modules generate text-based queries to retrieve investment-related information from the agents' memory databases. The perceptual and memory modules interact with LLMs via prompts to extract key investment insights. The action module of the manager agent consolidates these insights facilitate informed trading decisions.
19

---

A.5 Experimental Setup
A.5 Experimental Setup
Multi-Modal Datasets. We collect comprehensive multi-modal dataset simulate realistic market environment. This dataset includes stock price data, daily financial news, company filing reports (10K and 10Q), and ECC (Earnings Call Conference) audio recordings spanning from January 2022, to June 10, 2023. Each data source is assigned to specific analyst agents based on the timeliness of the information. For example, annual filings (10K) exhibit longer-term persistence, quarterly filings (10Q) and ECC data have medium-term relevance, and daily financial news provides the most immediate information.
Evaluation Metrics. We evaluate FINCON and benchmark it against other state-of-the-art LLM-based and DRL-based agent systems using three key financial performance metrics: Cumulative Return (CR%) 190], Sharpe Ratio (SR) [91], and Max Drawdown (MDD%) [92]. These metrics help quantify each model's profitability, risk- iis-ssdjstted returns, and risk management performance, respectively.
Comparative Methods. the single-stock trading task, we compare FINCON against seven algo- rithmic agents and the widely accepted Buy-and-Hold (B & H) baseline. 'he three DRI-based agents-A2C, PPO, and DQN-an are from the FinRL framework
[88] while the four state-of-the-an LLM-based agents include GENERATIVE AGENT [20]. FINGPT [93 FINMEM 32 and FINA-
GENT [33] For portfolio management, we benchmark FINCON against the classical Markowitz MV portfolio selection strategy [1. the RL-based FinRL-A2C agent [48), and the B & H strategy, which holds an squal-weighted position across all assets (equal-weighted ETF). Our focus on classical, RL-based, and B & methods is due to the current lack of mature LLM-based agents for portfolio management tasks.
Implementation Details. In our experiments, all LLM-based agent systems, including FINCON, GPT-4-Turbo as the backbone model, with the temperature parameter set at 0.3 to balance response consistency with creative reasoning. FINCON is trained financial data from January 2022, to October 4, 2022, and tested on data from October 5, 2022, to June 10, 2023. Since deep reinforcement learning (DRL) agents require extensive data for convergence, their training period is extended to nearly five years (January 1, 2018, to October 4, 2022) to ensure fair comparison. The testing period remains the same across all models. The final performance metrics based the test trajectory with the median CR and SR values from five repeated epochs. If the median CR and SR occur in different epochs, performance is assessed based on the trajectory with the median CR value.
20

---

A.6 Single Stock Trading Result Graphs
A.6 Single Stock Trading Result Graphs
1.20
EJH
400
Fragen FF
62
403.00
Cumvant Beam
202013
ANCA
AAPL
GOOG
Oureative
Preger
PEO
RIGHT
-02
Dane
NFLX
COI
PrCan
Figure 5: CRs over time for single-ase asset trading tasks. FINCON outperformed other comparative strategics, achieving the highest CRs across all six stocks by the end the testing period, regardless of market conditions.

---

A.7 Detailed Ablation Study
A.7 Detailed Ablation Study
A.7.1 The Effectiveness of -iisone Risk Control mechanism via CVaR
To answer the RQ2, we conduct the first ablation study. We assess the efficacy of FINCON's within- episode risk control mechanisms by monitoring system risk changes through CVaR. To demonstrate the robustness of FINCON, implementation across two task types: single-asset trading and portfolio management. Furthermore, in single-asset trading tasks, we consider assets in both general bullish and bearish market conditions in the testing phase for comprehensive consideration.
compare the performance of FINCON with versus without CVaR
Our results demonstrate that implementing CVaR in FINCON is highly effecttve across all finan- cial metrics for both task types, as shown in Table 4 and Fig 6 For single-asset trading tasks, FInCON without within-episode risk control yields negative CRs and significantly higher MDDs, underperforming compared to the Buy-and- and-Holld strategy (CR GOOG: 22. 22.42%, CR of NIO: -77.210%), highlightngg the severe consequences of ignoring environmental risks. In portfolio management, the CR increases dramatically from 14.699% to 113.836% with within-episode risk control, demonstrating its effectiveness in risk supervision even amid non-uniform market trends.
GOOG
NIO
B 6-
FinCon w/ CvaR FinCoo wes Cralt
0.4
0.2
Oimutettee
Comslettr
-02
0.4
BGG
FinCon wro CVbR
-n.
Annon w/ CloR
202-10
2002-11
2022-12
2023-01
2003-02
2003
2023-44
2023-05
2013
2022
2022-33
2022-12
2523-01
2223-
2023-09
2023
202000
2021-06
A
[1] Single Stock: General Bullish
(b) [2] Single Stock: General Bearlish
Partfolio
RrCan Chiit
2.0
RrCon we CVaR
1.5
3.8
3.4-
2022-19
2022-11
202-42
Jessar
201ber
2073 cz
2023-00
(c) Multi-Assets
Figure 6: CRs FINCON with vs. without implementing CVaR for within-episode risk control show that the CVaR mechanism significanlly improves FINCON's performance. This is evident from two metries: (a) cumulative returns over time single stocks in both bullish and bearish market conditions, and (b) portfolio value over time for multi-asset portfolio. In both cases, FINCON with CVaR demonstrates substantially higher gains.
Specifically, the success of utilizing CVaR for within-episode risk control evident in both bullish and bearish market environments, as shown in the single asset trading case. In bullish markets, CVaR sharply captures immediate market shocks and timely informs FINCON to exereise caution, even amidst general optimism. Conversely, in bearish markets, CVaR consistently alerts FINCON to significant price drops, ensuring awareness market risks. Moreover, portfolio trading with mixed price trends, our within-episode risk control mechanism performs robustly by monitoring the entire portfolio's 's value fluctuations, enabling the trading manager agent to adjust potentially aggressive operations for each asset promptly.
22

---

A.7 Detailed Ablation Study
A.7.2 The Efficacy of Over-Episode Belief Updates Using CVRF
In the second ablation study, to answer RQ3, we use the same assets to examine the effectiveness of FINCON's over-episode risk control mechanisms. This is achieved by consistently improving FINCON's beliefs about market conditions for the targeted assets. To ensure consistent belief output for each training episode, we set the temperature parameter to specifically for belief generation.
We collect third-time belief updates over four training episodes using our innovative CVRF mecha- nism. The overlap of trading actions between the last two adjacent episodes increases over 80%. and the updated investment beliefs are mostly aligned. To illustrate FINCON's 's evolving investment beliefs through iterative training, we use the GOOG investment belief update as an example, as shown in Figure 8 Compared to the initial and final belief updates, each conceptual aspect, such as historical momentum and news insights, is enriched with executable information through our CRVF mechanism, leading to more profitalle actions.
The results in Table and Figure 7 indicate that the over-episode belief update mechanism is more critical than within-episode risk control in enhancing FINCON's decision-making Without this funetionality, key metrics like CR, SR, and MDD are lower than without the within-episode risk control in single asset trading, Although the CR of 28, 43.4% outperforms the -eighhte ETF strategy's 9.344% for portfolio management, the SR of 1.181 is higher than strategy's 's 0.492, with the belief update feature, performance significantly further improves. It can achieve a CR of 113.836% and an SR of 3.269. These results demonstrate that using CVRF to update investment beliefs over episodes efficiently steers the agent's 's investment beliefs towards more profitable directions. FINCON achieves superior performance on multiple tasks, with learning gains evident after just four training episodes, requiring far fewer episodes than traditional RL algorithmic trading agents.
ETF
GOOG
NI
B H
Frntan Baiie FinCon Beteer
0.4
0.1
0.0
-02
-02
-0.3
-08
8&H
Ficcon Balkef
RnCon nio Bullf of
202.10
2012-11
2022-12
2023-1
201k-27
2023-07
202344
2023-55
10:3.00
202-10
2022-11
2022-12
2123-01
2013-42
2023-6
2823 d4
202-55
2023-6
4g
4
(a)
[1] Single Stock: General Bullish
(b) [2] Single Stock: General Bearlish
Porttolio
TinCon
2.0
TinCon wis Bellet
3.5
3.6-
3.4-
1.2
1.5
8025-0
2022.12
202233
202501
1023
2023-05
2022.00
(c) Multi-Assets
Figure 7: CRs of FINCON with vs. without belief updates for over-episode risk control. (a) The CRs over time for single stocks. The performance of FINCON with belief updates consistently leads in both bullish and bearish market conditions. (b) The portfolio values over time for multi-asset portfolio. FINCON's performance with belief updates also won substantially higher gains.
23

---

A.7 Detailed Ablation Sudy
Updated Investment Belief Contents
First Update:
('historical momentum': 'Enhance the use of momentum indicators by establishing systematic rules for entry and exit based momentum values to improve consistency and predictability in trading actions.", sentiment analysis better understand immediate market reactions and adjust trading strategies accordingly....
'news insights': 'Integrate advanced real-time
"Form 10-Q': 'Incorporating annual report insights (10-K) could provide comprehensive view of the company's 's long-term performance and strategic direction, aiding in more informed decision-making." "other aspects': ('sector trends", 'technological advancements")
Last Update:
'The more profitable strategy effectively utilizes negative momentum values to make timely sell decisions, avoiding potential losses during downward trends, and effectively utilizes positive momentum values to make timely buy decisions, facilitating potential gains during upward trends. It suggested refine the of momentum indicators, possibly by adjusting thresholds incorporating additional short-term momentum metrics to enhance the timing accuracy buy/sell decisions.",
("historical momentum':
"news insights": 'It is recommended to further develop systematic approach to quantify the impact of especially the sentiment scores and regulatory changes, refine trading decisions." FForm 10-Q': suggested that even if there is strong financial performance present 10-Q reports, is better to prioritize immediate market signals. For future strategies, is suggested to balance these aspects more evenly, especially in stable or bullish market conditions, to avoid premature exits from potentially profitable positions.". ..... "other aspects': : ('sector trends", 'technological advancements', 'macroeconomic factors'. 'regulatory risks') J. .....
Tradng Action Overlapping Percentages
1. Second vs. First Training Episode: 46.939%
2. Third vs. Second Training Episode: 71.429%
3.
Fourth vs. Third Training Episode: 81.633%
Figure 8: The first time and last time LLM generated investment belief updates CVRF for GOOG.
24

---

A.8
Raw Data Sources
A.8 Raw Data Sources
We assessed the performance of FINCON using multi-modal financial data from January 3, 2022, to June 10, 2022, sourced from reputable databases and APIs including Yahoo Finance (via yfinance), Alpaca News API, and Capital IQ, detailed explained in Table. These data, initially stored in the Raw Financial Data Warchouse as available observations of the financial market environment, are diverged into the corresponding FINCON's Analysts' Procedural Memory Databases based timeliness through working memory's s summarization operation.
Data Sources
News data associated with ticker: News data is sourced from REFINITIV REAL-TIME NEWS mainly contains news from Reuters.
Form 10-Q, Part Item 2 (Management's Discussion and Analysis of Financial Condition and Results of Operations): Quarterly reports (Form 10-Q) are required by the U.S. Securities and Exchange Commission (SEC).
Form 10-K, Section 7 (Management's Discussion and Analysis of Financial Condition and Results of Operations): Annual reports (Form 10-K) required by the U.S. Securities and Exchange Commission (SEC), sourced from EDGAR, and downloaded via SEC API.
Historical stock price: Daily open price, high price, close price, adjusted close price, and volume data from Yahoo Finance.
Zacks Equity Research:
Zacks Rank:
The Zacks Rank is a short-term rating system that is most effective over the one- to three-month holding horizon. The underlying driver for the quantitatively determined Zacks Rank is the same the Zacks Recommendation and reflects trends in carnings estimate revisions.
Zacks Analyst: Reason Sell, Reason to Buy, and potential risks.
Earning Conference Calls (ECC): ECC is type of unstructured financial data (audio) that is crucial for understanding market dynamics and investor sentiment. The company executive board delivers ECC about recent financial outcomes, future projections, and strategic directions. Recent studies have underscored the importance of not only the textual content of these calls but also the audio feature. Analyses have revealed that the audio elements -sccenttssschhh as tone, pace, and inflections- offer significant predictive value regarding company performance and stock movements [94] 96].
Table 6: Raw data and memory warehouses of FINCON
A.9
Distribution of Data
Number of News Items per Ticker
2303
Nurbbo Nows 0019
2000
1003
MESA
WW
AA
DNC
AMIT
AMON
DEL
WYC
5X
C01
BNG
DNY
Figure 9: The distribution of news from REFINITIV REAL-TIME NEWS for the 42 stocks in the experiments
25

---

A.10 Formulas of Classic Financial Metrics for Risk Estimator and Decision-making Task Performance Evaluation
Number of 10K & 10Q Filings Ticker
Nurbut of ninp
VEN Wil HaY HSS
AR
NETA
UN
(M
XON
caoo
CX
BLK
COM
WYC
Figure 10: The distribution of 10k10q from Securities and Exchange Commission (SEC) for the 42 stocks in the experiments
Number Zeck Reports per Ticker
50
Numbo cack hopott
US
PA
NO
ON
INC
NETA Lol
Figure 11: The distribution of Analyst Report from Zacks Equity Research for the 42 stocks in the experiments
A.10 Formulas of Classic Financial Metrics for Risk Estimator and Decision-making Task Performance Evaluation
The risk estimator uses the following metrics:
Profit and Loss (PnL)197] PnL quantifies the net outcome of trading activities over specified period by accounting for the realized gains and losses from financial instruments like stocks and derivatives.
Value Risk (VaR) of 99L VaR is statistical tool used to estimate the potential loss in portfolio, within a defined confidence interval. Mathematically, it is defined as Equation 3
VaR. (PnL) = inf {1 R P(PPL & () 2 a}
where is the confidence level.
CVaR is a statistical tool used to estimate the expected potential loss worse than the VaR value in portfolio, within a defined confidence interval. Mathematically, it is defined as Equation
Conditional Value at Risk (CVaR) PnL 97|:
CVaR = B PuL|PuL & (aRa()nL)
26

---

A.11 Baseline and Comparative Models on Single Stock Trading Task
where a is the confidence level.
The performance evaluation of algorithmic trading agents incorporates the following metrics:
Cumulative Return PnI. [90]: Cumulative Return is a key trading performance metric because it provides comprehensive insight into investment performance, especially for strategies that emphasize long-term growth and reinvestment. The effectiveness of different investment strategies is evaluated based their Cumulative Returns, which reflect the total change in value over time. In this study, we compute Cumulative Returns over the specified period by summing daily logarithmic returns, as outlined in Equation [S] This method is widely accepted in the finance area due to its ability to precisely capture minor price fluctuations and symmetrically address gains and losses. In essence, higher Cumulative Return typically indicates a more effective strategy.
Cumulative Return
Ti
P++1
In
action;
(5)
Pt
where r; represents the PnL for day /+ 1, Pt is the closing price on day i, P++1 is the closing price on day + 1, and action, denotes the trading decision made by the model for that day.
Portfolio Value: Portfolio value represents the total worth of all the investments held in a portfolio at given point in time. It is metric used only in the portfolio management task.
Cumulative Simple Return,
II (1 Daily Simple Return,) - k=1
(6)
Portfolio Value, = Initial Investment Amount x (1 + Cumulative Simple Return,)
where the initial amount set as $1, 000, 000.
Sharpe Ratio of PnL [91]: Sharpe Ratio is another core metric for evaluating investment performance and adjusting returns for risk. It calculated by dividing the portfolio's average PnL (R,) over the risk-free rate (Ry) by its volatility (op). as shown in Equation 8 This metric adjusts returns for risk, with a higher ratio indicating better risk-adjusted performance. Essential in comparing different portfolios or strategies, it contextualizes performance against similar investments. Although a Sharpe Ratio above is typically considered favorable and above 2 as excellent, these benchmarks can vary depending on the context of comparison.
R Op
R
Sharpe Ratio
(8)
Max Drawdown of PnI. [92]: Max Drawdown is metric for assessing risk. It represents the most significant decrease in portfolio's ss value, from its highest (P peak) to its lowest point (Proouh) until a new peak emerges, detailed in Equation 9 Indicative of investment strategy robustness, smaller Max Drawdown suggests reduced risk.
P Ppak
P'rough
Max Drawdown .max(
Ppeak
A.11 Baseline and Comparative Models Single Stock Trading Task
Buy-and-Hon strategy (B&H):
A passive investment approach, where investor purchases stocks and holds onto them for an extended period regardless of market fluctuations, is commonly used as a baseline comparison of stock trading strategies.
27

---

A.12 Portfolio Management
LLM trading agents:
We evaluate FINCON against four L.M agents in the context of stock trading.
GENERAL- GENERATIVE AGENTS - GA: generative agent by Park et al. 20]. originally intended to simulate realistic human behavior and make everyday decisions, has been adapted here for specific stock trading tasks. This agent's ' architecture includes memory module that employs recency, relevance, and importance metrics to extract pivotal memory events for informed decision-making However, does not provide layered memory module to effectively differentiate the time sensitivities unique to various types of financial data. Additionally, although features a profiling module to define agent attributes like professional background, the model does not specify the agent's persona. In our experiments, we modified the original prompt template created by Park et al., which intended for general daily tasks, to suit financial investment tasks.
FINGPT:: A novel open-source LLM framework specialized for converting incoming textual and numeric information into informed financial decision-making, introduced by Yang et al 31. It claims superiority over the traditional buy-and- strategy.
FINMEM:FINMEM employs specialized profiling module and self-adaptive risk settings for enhanced market robustness. Its memory module integrates working memory and layered long-term memory, enabling effective data processing. This allows FINMEM to leverage market insights and improve trading decisions (32].
FINAGENT:FIN AGENT developed upon FINMEM, which leverages the use of tool-using capabilities of LLMs to incorporate multi-modal financial data [33. It claims an further improved trading performance on single asset trading (stocks and cryptocurrencies).
DRL trading agents:
As the FINMEM is practiced and examined on the basis of single stock trading and discrete trading actions, we choose three advanced DRL algorithms fitting into the same scenarios according to the previous and shown expressive performance in the work of Liu al 98 99
The DRL training
agents only take numeric features as inputs.
Advantage Actor-Critic (A2C): A2C ((100) is applied to optimize trading actions in the financial environment. It operates by simultaneously updating both the policy (actor) and the value (critic) functions, providing balance between exploration and exploitation.
Proximal Policy Optimization (PPO): PPO (101) is employed in stock trading due to its stability and efficiency. One salient advantage of PPO is that it maintains balance between exploration and exploitation by bounding the policy update, preventing drastic policy changes.
Deep Q-Network (DQN): DQN (1102) ) is an adaptation of Q-learning, that can be used to optimize investment strategies. Unlike traditional Q-Jearnin that relies on tabular approach for storing Q-values, DQN generalizes Q-value estimation across states using deep learning, making more scalable for complex trading environments.
A.12 Portfolio Management
Selection MI introduced by Harry Markowitz 1952, is a framework for constructing portfolios that optimize expected return for given level of risk minimize risk for a given level of expected return. This method uses expected returns, variances, and covariances of asset returns to determine the optimal asset allocation, thereby balancing risk and return through diversification.
Markowitz Portfolio
FinRL-A2C 48 is an RL algorithm proposed to address single stock trading and portfolio opti- mization problems in Liu al.. The RL models make trading decisions (i.e., portfolio weights) based on the observation of previous market conditions and the brokerage information of the RL agents. The implementation of this algorithm provided and is used as baselines in our study.
Equal-Weighter ETF [03] : is portfolio giving equal allocation to all stocks, similar to a buy-and-
hold strategy in single-stock trading, can provide a benchmark on market trends.
28

---

A.13
Ranking Metrics for Procedural Memory in FINCON
A.13 Ranking Metrics for Procedural Memory in FINCON
Upon receiving an investment inquiry, each agent in FINCON retrieves the top-K K pivotal memory events from its procedural memory, where K is a hyperparameter. These events are selected based on their information retrieval score. For any given memory event E, its information retrieval score y is
defined by
S Relevancy + Iimportance
(10)
which is adpated from Park et al (20) but with modified relevancy and importance computations, and is scaled to [0, 1] before summing up. Upon the arrival of trade inguiry I' in processing memory SE event E via LLM prompts, the agent computes the relevancy score Relevancy that measures the cosine
similarity between the embedding vectors of the memory event textual content me and the LLM prompt query mp, which is defined as follows:
E Relevancy
mE mp
(11)
me x |mpl2
Note that the LLM prompt query inputs trading inquiry and trader characteristics. On the other hand, the importance score Simporance is inversely correlates with the time gap between the inquiry and the event's memory timestamp 87 = 4p L5. mirroring Ebbinghaus's forgetting curve [104]. More precisely, if we denote the initial score value of memory event o and degrading ratio 0 (0, 1), then the importance score is computed via
S Importance
B x 6hc
(12)
Note that the ratio 0 measures the diminishing importance of an event over time, which inspired by design of [20]. But in our design, the factors of recency and importance are handled by one equation. Different agents in FINCON admit different choices of {uF, 0} for memory event F.
Additionally, an access counter function facilitates memory event augmentation, so that critical events impacting trading decisions can be augmented by FINCON, while trivial events are gradually faded, This is achieved by using the LLM validation tool Guardrails Al [105] to track critical memory ID. A memory ID deemed critical to investment gains receives +5 its importance score S access counter implementation enables FINCON to capture and prioritize crucial events based on type and retrieval frequency.
Importance This
A.14 Detailed Configurations in Experiments
The training period was chosen to account for the seasonal nature of corporate financial reporting and the duration of data retention in FINCON's memory module. The selected training duration ensures the inclusion of at least one publication cycle of either Form 10-Q, ECC, or Form 10-K. This strategy ensures that the learned conceptualized investment guidance considers a more comprehensive scope of factors. Additionally, the training duration allowed FINCON sufficient time to establish inferential links between financial news, market indicators. and stock market trends, thereby accumulating substantial experience. Furthermore, we set the number of top memory events retrieved for each agent We ran FINCON.
To maintain consistency in the comparison, the training and testing phases for the other three LLM- based agents were aligned with those of FINMEM. For parameters of other LLM-based agents that are not encompassed by FINMEM's configuration, they were kept in accordance with their original settings specified in their respective source codes.
FINCON's performance was benchmarked against that of the most effective comparative model, using Cumulative Return and Sharpe Ratio as the primary evaluation metrics. The statistical significance of FINCON's s superior performance was ascertained through the non parameeettiic Wilcoxon signed-rank test, which is particularly apt for the non-Gaussian distributed data.
A.15 FINCON performance on extreme market conditions
To further illustrate the robustness of FINCON's performance, we assess its effectiveness in two distinct scenarios: (1) single-asset trading task using TSLA and (2) portfolio management task involving a combination of TSLA, MSFT, and PFE. Our evaluation focuses on key financial metrics, including Cumulative Returns (CRs), Sharpe Ratios (SRs), and Maximum Drawdown (MDD). The
29

---

A.15 FinCON performance on extreme market conditions
training period spanned from January 17, 2022, to March 31, 2022, while the testing phase covered April 2022, October 15, 2022. This specific timeframe was chosen due the elevated levels of the CBOE Volatility Index (VIX), which averaged above 20, signaling greater market volatility during these months.
As demonstrated in Table 7 and Figure 12 Cumulative Returns (CRs) and Sharpe Ratios (SRs) in single stock trading tasks. Regarding portfolio management tasks, the results of all baselines (four benchmarks) are detailed in Table and Figure 13 In these comparisons, FINCON consistently attained the highest scores the primary performance metrics.
FINCON is the sole agent system to achieve positive
Models
CR %
SR1
MDD %
TSLA
B&H
-56.388
-1.625
52.077
LMW
ROOF
ADC
700
FINCON
22.460
0.695
45.215
GA
-51.251
-1.547
48.763
Comieennn becum
FINGPT
-20.035
-0.805
32.199
FINMEM
-47,809
-1.549
49.560
FINAGENT
-31.119
-1.933
33.224
A2C
-73.251
-2.142
56,998
PPO
-78.007
-2.284
59.003
DQN
-8,452
-1.328
8.463
Table 7: Key performance comparison for single trading under the high volatility condition using TSLA an example. FINCON leads all performance metrics.
Figure 12: CR changes time all the strategies under the high volatility condition using TSLA as an ex- ample of the single asset trading task.
Porttoli Value
1.0
Models
CR % 1
SR1
MDD %1
0.a
FINCON
-8.429
.0,294
26.176
Markowitz MV
-28. 99.999
-1.055
31,831
FINRL-A2C
-15.932
-1.195
21.569
RiConenormal Markowitz
Equael-Veighhte FiniLL-CC
Equal- Weighted ETF
-28.008
-1.731
30,070
Table 8: Key performance comparison among all portfolio management strategies Portfoliol under the high volatility condition. FiNCON leads all performance metries.
2012
2012
250
2012
Date
Figure 13: Portfoliol value changes over time for all the strategies under the high volatility condition.
30

---

A.15 FinCON performance on extreme market conditions
NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
See in section 5
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
31

---

A.15
FinCON performance on extreme market conditions
Justification:
See in Algorithm
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
We provide detailed experiment results.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
32

---

A.15
FinCON performance on extreme market conditions
Answer:
[Yes]
Justification: Yes
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.cm
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ /pipps.ccppulic/guuidd wwwiii/gguie///eeeeeeeeic CoodeSubeiiieeee ssi wswonnolli onnolicy)))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental Setting/Details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
See in experiment section.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment Statistical Significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification:
See in ablation study.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
33

---

A.15 FinCON performance on extreme market conditions
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of computing workers, memory, time of cxecution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: See in Section 4 and Appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g.. preliminary or failed experiments that didn't make into the paper).
9. Code Of Ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics htttps:/ http:/::///uiip.p./e cc https////ppp..ttttcccceteeeeeeee
Answer:
[Yes]
Justification:
We complied with Code Of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is a special consid- eration due to laws or regulations their jurisdiction).
10. Broader Impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: See in section 5
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to
34

---

A.15 FinCON performance on extreme market conditions
generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
there are negative socictal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
Justification: Described.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: Described.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, wwppppswpptseeee www.reeewthhhe....c.. com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's is creators.
13. New Assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
35

---

A.15 FinCON performance on extreme market conditions
Answer: [Yes]
Justification:
Described.
Guidelines:
The answer NA means that the paper does release new assets.
Researchers should communicate the details the dataset/codeemoodll as part their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and Research with Human Subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [Yes]
Justification: Described.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [Yes]
Justification: Described.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
36


Reviews:
[{"review_id": "RHRW4F13aT", "reviewer": "Reviewer_rfBX", "summary": "The study introduces FINCON, a large language model (LLM)-based multi-agent framework designed to improve financial decision-making,  where it utilizes a manager-analyst communication hierarchy to enhance the synthesis of multi-source information and optimize decision-making outcomes through a risk-control component and conceptual verbal reinforcement.", "questions": "1. Does FINCON demonstrate robustness across multiple financial decision-making tasks?\n\n2. Does the risk-control component optimize analyst outcomes and manager information allocation?\n\n3. How does FINCON perform under extreme market conditions, such as financial crises or significant market volatility?", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Integrating a manager-analyst hierarchical structure with LLMs to simulate the decision-making processes in financial settings is useful.\n\n2. The study provides a strong empirical performance.", "weaknesses": "The implications of applying such systems in real-world financial markets may need to be discussed, including potential ethical concerns and the impact on market dynamics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing the novelty and soundness of our approach. To address your mentioned weakness and questions:\n\n**[W: Ethical concerns and market impact] Clarification about Ethical Concerns & Impact on Market Dynamics**\n\n\n1. **Regarding Data:**\n\nWe fully respect the copyright of data sources and adhere to all relevant guidelines. **For public datasets,** there are no concerns regarding copyright or other restrictions. The datasets we used, such as Form 10-K, Form 10-Q, and earnings conference calls, are required filings with the SEC and are archived in publicly accessible databases. **For proprietary datasets,** we respect their copyrights and have retrieved them from valid sources with proper consent. Although we did not disclose the data to comply with copyright requirements, we have clearly specified their sources and provided the necessary code for future researchers to reproduce our work. In addition, we plan to include copyright information of our data source in Appendix B in future revisions.\n\n2. **Regarding Market Impact**\n\nFinCon is primarily an academic exchange product and is not intended for commercial use. We will emphasize this point on our homepage, in articles, and in our code. However, if the industry is inspired by our work to design similar trading systems, we still have reason to believe that this would be beneficial for financial markets. Such a system is an automated trading system that accelerates the reflection of public information in the market, helping stock prices to converge to their true value and benefiting investors [1]. This ensures that they can trade the underlying asset at a fair price and reduce their losses due to delayed price updates. It aligns with the classical Efficient-Market Hypothesis in finance, which states that prices should immediately reflect all available information [2]. Moreover, as an assistant, the system can reduce human errors, thereby enhancing accuracy and reliability in financial operations [3]. This, in turn, decreases systemic risks in financial markets and improves the overall efficiency of financial services.\n\nBy incorporating the mentioned improvements, we believe we can **fully address ethical concerns raised by reviewers**, and we think the development of such an automated trading system can **benefit the financial market.**\n\n[1] Dubey, R. K., Babu, A. S., Jha, R. R., & Varma, U. (2022). Algorithmic trading efficiency and its impact on market-quality. Asia-Pacific Financial Markets, 29(3), 381-409.\n\n[2] Miller, C. N., Roll, R., & Taylor, W. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.\n\n[3] Todorović, V., Pešterac, A., & Tomić, N. (2019). The impact of automated trading systems on financial market stability. Facta Universitatis, Series: Economics and Organization, 255-268.\n\n**[Q1: Robustness] Generalization to Multiple Financial-decision Marking Tasks**\n\nWe have conducted extensive experiments and ablation studies to demonstrate FINCON's robustness in decision-making tasks, particularly in single-stock trading and portfolio management. For more details, please check our latest results in **Table A, B, and C** comment box below and the **Table 1, 2, 3 and 4 in the pdf file attached with author rebuttal**. Our evaluation spans multiple assets under diverse market conditions. Notably, FINCON exhibits strong generalization capabilities in making automated investment decisions for both individual and combined financial products. This versatility indicates that FINCON's trading objectives could potentially extend to other financial decision-making tasks, including investments in cryptocurrencies, ETFs, or combinations thereof, provided relevant data is available. We will further add a discussion about FINCON's other potentials in our future revisions.\n\n**[Q2: Risk-control component working mechanism] FINCON's risk-control component directly impacts the manager's reasoning and influences analyst outcomes through the manager agent's feedback, which is informed by the present risk scenario.** \n\nFINCON provides a two-fold risk control mechanism to dynamically update part of the manager's decision-making contexts: (1) in-trajectory risk control alerts the manager agent to emerging market risks during active episodes, enabling immediate strategy adjustments; and (2) across-trajectory risk control adjusts the manager agent's investment beliefs for better weight allocation of information perspectives provided by each analyst. The belief updates focus on certain trading targets and is refined over episodes. In summary, our risk-control mechanism directly and timely shapes the manager agent's critical investment-related contexts in the decision-making process. The manager agent, considering these contexts, identifies critical memory insights that facilitate its decisions and informs analyst agents accordingly. The analyst agents then reinforce these memories in their memory system by increasing their ranking scores, ensuring that information considered useful for decisions has a higher chance of being selected for future decision-making and a slower decay rate from the system. Through this process, the risk-control component optimizes the workflows for both manager and analyst agents as a synthesized system.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oZ5bHPTyIu", "reviewer": "Reviewer_bNfR", "summary": "The research introduces FINCON, an LLM-based multi-agent framework designed for a variety of financial tasks. FINCON is inspired by effective organizational structures in real-world investment firms and employs a manager-analyst communication hierarchy. Experimental evaluations show that FINCON’s risk control mechanism effectively mitigates investment risks and enhances trading performance. The hierarchical communication structure and risk control components improve decision quality, streamline information flow, and reduce overheads.", "questions": "N/A", "limitations": "While the model shows strong generalization capabilities in various tasks, the scalability of the model to even larger datasets, more diverse financial instruments, or real-time applications is not fully explored. The computational demands and potential bottlenecks in real-time processing need to be addressed.", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The introduction of FINCON presents a novel approach by integrating Large Language Models (LLMs) into a multi-agent framework specifically designed for financial decision-making tasks. \n- The dual-level risk control component that includes a self-critiquing mechanism to update systematic investment beliefs is a unique addition.\n- The paper is written clearly, with well-structured sections that guide the reader through the problem, methodology, experiments, and conclusions. Each section logically follows from the previous one, making the overall argument easy to follow.\n- The paper’s findings contribute significantly to the literature, offering new insights into the applications of LLMs in financial decision-making and advancing the state-of-the-art in this interdisciplinary field.", "weaknesses": "The inner workings of the multi-agent interactions and risk control mechanisms may not be fully transparent, which can raise trust issues among users and stakeholders who rely on the model for high-stakes financial decisions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for your appreciation of our work, particularly regarding the innovative nature and efficacy of the FINCON framework. The reviewer's concerns about FINCON's weaknesses and limitations are carefully explained and addressed below.\n\n**[Weakness: Transparency] The multi-agent interactions are transparent, and the risk-control mechanism is fully accessible.**\n\n1. The multi-agent interactions are conveyed and recorded by natural language, which is completely accessible to users. This fosters trust compared to other autonomous trading systems. This makes them suitable for monitoring and intervention if necessary. Additionally, the memories stored in the memory module can be queried in a structured form for review when needed.\n\n2. The FINCON system features a dual-level risk control mechanism designed to enhance decision-making quality in volatile market environments while ensuring full transparency: (1) In-trajectory risk control, which monitors for sudden market risks during an ongoing episode. This mechanism is activated when there is a decrease in Conditional Value at Risk (CVaR), signaling that recent trading decisions might have pushed Profit and Loss (PnL) values into the worst 1% of outcomes, thus indicating high-risk conditions; and (2) Across-trajectory risk control, which refines the manager agent's investment strategies over multiple episodes. This is done by optimizing the integration of analyst-provided information for specific trading objectives. Both levels of risk control are operationalized through natural language prompt templates directed at the FINCON manager agent. The in-trajectory risk alerts are conveyed through real-time LLM prompts, while the across-trajectory adjustments are implemented by periodically updating the manager's profile prompts with new investment beliefs every two episodes. This structure ensures transparency of its internal mechanism and allows users to understand and potentially tailor the risk management strategies employed.\n\nHowever, the intrinsic mechanisms of LLM are still under active research [1]. We will add a discussion about the limitation in the future revision.\n\n[1] Luo, H., & Specia, L. (2024). From understanding to utilization: A survey on explainability for large language models. arXiv preprint arXiv:2401.12874.\n\n**[Limitation: Scalability and computational demand] Scalability and computational demand will not be a bottleneck in our case.**\n\nWe appreciate the reviewer's inquiry. Our work is the first to use LLM agents for portfolio management, representing a significant innovation in AI-driven financial decision-making. While real-time processing may seem time-consuming, we operate at a daily trading frequency, a common lower-frequency approach in real markets. In our case, each decision-making step around 20 seconds to 1 minutes using an agent backed by LLM, such as GPT-4 Turbo, which is well-suited for daily trading. Given the comparison between the required trading frequency and our current average response time, FINCON demonstrates significant potential to manage larger-scale data streams and portfolios effectively, as long as the data is accessible. In our future research, we plan to incrementally test the model on larger and more complex datasets, and we also intend to incorporate a wider variety of financial instruments, such as options, futures and swaptions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aajvWwkl76", "reviewer": "Reviewer_pGXR", "summary": "The paper proposes a specialized multi-LLM-agent composition for (1) stock trading and (2) portfolio management. The authors collect historical multimodal data up to 10 years to evaluate their setup. Inspired by the real world financial institution structure, the authors instruct the 8 specialized agents to perform stock and financial report analysis as well as final decision making. The authors introduce conceptual verbal reinforcement (CVR) to improve the initial prompts of the agents.", "questions": "[1] What is the difference between CVR and OPRO introduced by Yang 2024 (Large Language Models as Optimizers)?\n\n[2] Is RL optimization performed simultaneously with CVR or one after another?\n\n[3] On lines 708-709 you state “To mimic learning rate effects, we measure the overlapping percentage between conceptualized investment insights from consecutive iterations”. Where can I find the details of the implementation of this algorithm?\n\n[4] On lines 222-223 the Action module in the trading scenario selects between long, short and neutral, however the volume is not specified. How is it supposed to work?\n\n[5] In Table 1 why are there no numbers for AMZN? Also why are the best results not highlighted for stocks other than TSLA?\n\n[6] How would you address the problem of manual labor to create specialized initial prompts for the large variety of stocks listed in Appendix J?", "limitations": "The limitations are well discussed.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "[1] The paper tries to apply LLM agents to the financial problems which is a very novel and promising field of study.\n\n[2] The study is well structured and encloses the comparison with 6 state of the art competitors and the B&H baseline for single stock trading and 3 competitors for portfolio management. 3 different metrics are taken into account: CR%, SR and MDD% that helps to obtain a reliable signal about a method's performance. Specifically Figure 3 shows impressive results visible by a naked eye.\n\n[3] Use of textual gradient feedback is intriguing. Furthermore, in 3.1.2 the authors introduce the idea of “distance between concepts” which looks novel.\n\nUpdate after the rebuttal.\nThe authors thoroughly addressed all the weaknesses and questions I raised. I update the overall score to Accept. The change of heart comes from the updated body of measurable results from the experiments the authors have conducted during the rebuttal period. It could have been Strong Accept had the authors shown the statistical significance of the results in Table 1. Nevertheless, at its core, the paper is the first one that publicly introduces a trading bot that performs trades based on the news feed analysis by an LLM-based agent, which is very valuable for both financial and AI communities.", "weaknesses": "[1] In Table 2 the proposed method performs better than B&H only for TSLA and worse - for 4 other stocks: MSFT, NFLX and COIN. This result is inconclusive with respect to the question of whether the proposed method generalizes at all.\n\n[2] Even though Figure 3 shows a clear advantage of the proposed method relative to the competitors, it is not convincing that this advantage does not come from the cherry-picking of the stocks in the “example” portfolio: TSLA, MSFT, and PFE. A more rigorous study is needed, for example to measure the average score of all possible picks of 3 stocks out of 5 available.\n\nOn line 304 the authors claim to ablate CVR, however in Table 3 for the single stock there are no numbers for w/o CVR.\n\n[3] Combining a multi-agent setup, reinforcement Learning, CVR and convex optimization (at Manager) looks like an overly sophisticated setup. It is not clear which parts of the bundle produce how much of the impact if any.\n\nSpecifically, the Memory module consists of working memory, procedural memory, and episodic memory. The ablations for these three types of memory are not provided.\n\n[4] The reproducibility is limited since the authors do not provide the code of their solution.\n“Justification: We provide detailed experiment results.” is not on point since this clause is intended to address the reproducibility of the results by reviewer and potential future readers.\n\n[5] Yang 2024 “Large Language Models as Optimizers” could have been cited as the first paper that proposes textual optimization by LLMs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer's feedback. In response to the identified weaknesses and limitations:\n\n**[W1: Experimental results] We have updated the experimental results to include other three stocks.**\n\nWe have updated the experimental results to include other three stocks including AAPL, NIO and AMZN. And the new results still suggest that our model still significantly outperforms the comparative models regarding to key metrics, Cumulative Return (CR) and Sharpe Ratio (SR), as depicted in **Table A**.\n\n| Categories | Models | TSLA  | | AMZN |  | NIO  |  | AAPL |  | GOOG |  | COIN | | NFLX | |\n|------------|----------|---------------|-----------|---------------|-----------|---------------|-----------|---------------|-----------|---------------|-----------|---------------|-----------|---------------|-----------|\n| | | CR% | SR | CR% | SR | CR% | SR | CR% | SR | CR% | SR | CR% | SR | CR% | SR |\n| Market | B&H | 6.425 | 0.145 | 1.914 | 0.067|-77.210| -1.449 | 22.315 | 1.107 | 22.420 | 0.891 | -21.756 | -0.311 | 0.621 | 1.925 |\n| Our Model  | **FIN-CON** |**82.871**|**1.972**|**24.964**|**0.906**|**17.461**|**0.335**|**27.352**|**1.597**|**25.077**|**1.052**|**57.045**|**0.825**|**0.741**| **2.368**|\n| LM-Based   |GA|16.535 | 0.391| -5.515|-0.195| -3.176|-1.574| 5.694|0.372| -0.0151| -0.0192|19.271| 0.277| 0.466| 1.638|\n| | FIN-GPT  | 1.549| 0.044| -29.811| -1.805| -4.959| -0.121| 20.321| 1.161| 0.207| 0.822| -99.553|-1.807| 0.168| 0.655 |\n| | FIN-MEM  | 34.624| 1.552| -18.126| -0.776| -48.437| -1.180| 12.396| 0.994| 0.311| 0.018| 0.811| 0.017| -0.091| -0.420|\n| | FIN-AGENT| 11.960 | 0.271| -24.704| -1.496| 0.933| 0.051| 20.757| 1.041| -7.440 | -1.024| -5.971| -0.106 |0.661| 2.092|\n| DRL-based  | A2C| -35.644 | -0.805| -12.676| -0.447| -91.190| -1.728|13.781| 0.683| 8.562| 0.340|NA |NA |-0.107| -0.333|\n| | PPO | 1.409| 0.032| 3.863| 0.137| -72.119| -1.352| 14.041| 0.704 | 2.434| -0.097|NA| NA| -0.380| -1.188|\n| | DQN | -1.296| 0.029| 11.171| 0.398| -35.419| -0.662| 21.125| 1.048 | 20.690| 0.822 | NA| NA | 0.169 | 0.528|\n\nTable A: Comparative analysis of trading agent systems on single asset task: FINCON outperforms on key metrics (Cumulative Returns (CRs) and Sharpe Ratios (SRs)) across multiple stocks. _**As the COIN first IPOed in 2021, the RL algorithms failed to converge to a stable result with limited data. Thus, We marked the metrics as 'NA'. Note: Due to the space limit, we only include the values of primary metrics.**_\n\n**[W2: Stock selection and portfolio construction/ Ablation about CVR] We have clarified the criteria to select the stock and added experimental results for another portfolio. An ablation study for the CVR mechanism has also been added.]** \n\n1. Other than simply enumerating all combinations of 3 tickers out of 5 available, our stock selection agent within FINCON constructs the portfolio based on the classic financial principle of diversification from a pool of 42 tickers (number determined by data APIs accessible to us), which achieved by constraining the statistical correlation between the candidates in this pool. Fixing a stock as the target, we found the tickers maintain the most diverse historical return patterns compared to it to form the portfolio.At the same time, we eliminated the combination with tickers has less than 1000 news records in the running period.\n   Additionally, we had the stock selection agent execute another round of portfolio construction with AMZN as the target, resulting in a new portfolio: AMZN, GM, and LLY. FINCON again exhibited significantly better performance over comparative models in terms of CR and SR, further illustrating its robustness in portfolio management tasks (**Table B** below). \n   While we currently use FINCON to trade compact portfolios (three symbols) due to time and budget constraints, its promising performance suggests a potential for managing larger portfolios.\n\n| Model | SR | CR |\n|-------|----|----|\n| FINCON | 1.501 | 37.4 |\n| Equal-weighted | 1.048 | 18.797 |\n| FinRL - A2C | 0.846 | 15.710 |\n| Markowitz MV | 0.654 | 12.983 |\n\nTable B: Key performance metric comparison among all management strategies for a portfolio consisting of (AMZN, GM, LLY). All baselines are kept the same as the ones in the paper.\n\n2. In response to the ablation study about CVR, we have updated the results in **Table C**, as well as in [W3] for a more detailed discussion. In the table, we present the results with and without CVR updates for both single stock trading and portfolio management tasks. The results demonstrate that implementing the CVR mechanism significantly improves decision-making quality across various tasks and market conditions.\n\n|Task|Asset|MarketTrend|Models|CR%|SR|MDD%|\n|----------------------|------------------|------------------|---------|---------|--------|---------|\n|SingleStock|GOOG|GeneralBullish|w/CVR|28.972|1.233|16.990|\n||||w/oCVR|-11.944|-0.496|29.309|\n||NIO|GeneralBearish|w/CVR|7.981|0.157|40.647|\n||||w/oCVR|-17.956|-0.356|55.688|\n|PortfolioManagement|(TSLA,MSFT,PFE)|Mixed|w/CVR|121.018|3.435|16.288|\n||||w/oCVR|20.677|0.987|23.975|\n\nTable C: Key metrics FINCON with vs. without implementing Conceptual Verbal Reinforcement（CVR）/ investment belief updates for over-trajectory risk control. The performance of FINCON with the implementation of CVaR won a leading performance in both single-asset trading and portfolio management tasks.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We sincerely appreciate all reviewers for their time and insightful feedback. We are glad that many reviewers found that: 

**Our framework is novel and our design of risk-control component is unique.**

- [bNfR] The introduction of FINCON presents a novel approach ... for financial decision-making tasks./ The dual-level risk control component ... is a unique addition.
- [pGXR] The paper tries to apply LLM agents to financial problems which is a very novel...

**Our approach is to be technically sound. And our empirical performance to be solid.**
- [rfBX] Integrating a manager-analyst hierarchical structure ... is useful. The study provides a strong empirical performance.
- [bNfR] Technically strong paper, with novel ideas, excellent impact on at least one area...
- [pGXR] The study is well structured and encloses the comparison with 6 SOTA competitors and the B&H baseline ... Specifically Figure 3 shows impressive results visible by a naked eye.


**Our contribution to financial decision-making field is significant.**
- [bNfR]The paper’s findings contribute significantly to the literature ... in financial decision-making and advancing the state-of-the-art in this interdisciplinary field
- [pGXR] The paper tries to apply LLM agents to financial problems ... promising field of study.

Again, we would like to thank you for your recognition.
Regarding limitations, beyond minor presentational issues and factual clarifications, two primary concerns emerged:

**More experiments**

- [rfBX] asked for FINCON'S performance in the period with significant market volatility.We have conducted additional performance evaluations for both single-asset trading and portfolio management tasks during a notably volatile time period, distinct from the experimental conditions in our original paper.

- [pGXR] on single-asset trading tasks and ablation studies focusing on the memory module and Conceptual Verbal Reinforcement (CVR). We have expanded the asset range and refined our experimental set-up to deliver the requested experiment outcomes. Furthermore, we provided the requested ablation study, which can help to illustrates the effectiveness of our memory module design as well as CVR mechanism simultaneously.

_**Please check out our detailed experimental results in the attached pdf file and replies below.**_

**Further explanation of working mechanism**
- [bNfR] "The inner workings of the multi-agent interactions and risk control mechanisms may not be fully transparent..." We have included a comprehensive explanation detailing how FINCON ensures full transparency in its multi-agent interaction workflow and risk-control mechanisms.
- [pGXR] "On lines 708-709 ... Where can I find the details of the implementation of this algorithm?”; "On lines 222-223 ... How is it supposed to work?" We have provided detailed explanations for all requested aspects of FINCON's operational mechanisms in our responses to the reviewers. If our paper is luckily accepted, we will incorporate this additional information into the revised manuscript.

In the meantime, we have also **clarified the ethical concern** from reviewer [rfBX]:  "The implications of applying such systems in real-world financial markets may need to be discussed, including potential ethical concerns and the impact on market dynamics." We've provided a detailed clarification addressing potential ethical concerns. We demonstrate that, other than undermining the market, implementing automated trading systems like FINCON can actually benefit the financial market dynamics. Our explanation includes comprehensive rationales supporting this conclusion.

Last, we would like to respectfully note that **our code was submitted to the Area Chair through an anonymized link via an author rebuttal, in full compliance with the review process guidelines.** If our submission is accepted, we commit to making our code publicly available. This release aims to provide valuable resources to researchers and developers in the financial technology and language agent design communities, fostering broader advancements in these fields. **Moreover, we would like to claim that our code is intended solely for academic and educational purposes. The code does not constitute financial, legal, or investment advice and is not intended to be a basis for any decision-making.**

Meta-review: This paper proposes a comprehensive agent-based approach to using LLMs to manage financial decision making, specifically, single-security and whole portfolio trading/investment decisions. The breakdown of tasks is nominally based on the organizational breakdown of investment organizations (e.g., with specialist analysts that focus on specific types of (multi-modal) information, a single portfolio manager than makes weighting and long/short/neutral decisions for individual securities, a risk mgmt agent). This decomposition, along with the use of textual reinforcement, blended with standard financial investment techniques provide the major contribution of the paper.

On the positive side, the paper provides a comprehensive use of LLMs, with, arguably, a fairly intuitive and useful approach to decomposition. The paper is fairly clearly written (though see below) and the results (at least as described in the rebuttal) seem fairly compelling.

On the negative side (and I agree with all of these points on my reading of the paper):

1. The reviewers express some concerns about the overall transparency of the approach (not to “users” but to researchers/readers of this paper). This is a complex system, and basically impossible to describe in a 9pp NeurIPS paper. The authors do a reasonable job of giving the high-level picture in the main body of the paper, but there are many, many details are pushed into a lengthy—total PDF of 40pp—appendix (including precisely how the LLM agents are prompted, how prompt optimization is conducted, almost all of the precise details about the experiments, etc.). To the extent that a NeurIPS paper should be reasonably self-contained, I think this paper fails (which is unavoidable because of the topic/complexity).

2. The results in the paper do not actually look convincing. E.g., Table 1, on my reading, shows that FINCON performs worse than at least one other strategy—and often worse than buy-and-hold—for most of the individual securities (if lower returns are not balanced by improvements in Sharpe and drawdown metrics, this is a bad result). It seems better on portfolio mgmt (Table 2) compared to some reasonable baselines, but leads to some serious questions about the results. In fact, this lead the original reviews to provide some very low scores. However, in the rebuttal, the authors provided updated (and I believe corrected results) that were much more impressive, so much so that the reviewers changed their (aggregate) assessment significantly. The results in the rebuttal are indeed much more compelling than those in the original submission.

On the first issue, I worry if NeurIPS is the right forum for this paper; but given the potential importance of the topic, the interest it might generate, and the fact that the appendix does make important details available, I think this can be overlooked.

On the second issue: I cannot recommend acceptance of the original manuscript. But if the updated results are incorporated and explained well (and the majority of them *must* be in the main body of the paper), then I think the paper would make a nice contribution. The authors must commit to making revisions along these lines. Moreover, a number of other, smaller issues raised by the reviewers in their reviews and the discussion, should be addressed if the paper is accepted. (This includes providing some evidence of the statistical significance of the comparisons being made against the competing methods.)
Decision: Accept (poster)