Reasoning on Time-Series for Financial Technical Analysis
Authors: Kelvin J.L. Koa
Jan Chen
Yunshan Ma
Zheng Huanhuan
Tat-Seng Chua
Venue: ICLR 2026 Poster (2026)
Keywords: Time-Series
Large Language Models
Stock Prediction
Primary area: learning on time series and dynamical systems

Abstract: While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of a time-series backbone model on the reasoning-based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves state-of-the-art forecasting accuracy, while the reasoning traces also perform well on evaluation metrics judged by industry experts.

Published as a conference paper at ICLR 2026
REASONING ON TIME-SERIES FOR FINANCIAL TECH- NICAL ANALYSIS
Kelvin J.L. Koa! Jan Chen Yunshan Ma Huanhuan Zheng Tat-Seng Chua' Nattional University of Singapore *Technical University of Munich
"Singapore Management University *City University of Hong Kong
kelvin.kode nus. edu, jan. wwwn@tun.deeee.... de, ysma smu. edu. sg, h. zhengecityy. edu....uuuuuuuuu.. edu.kk, .k, dcscts@nus edu.sg
ABSTRACT
While Large Language Models have been used to produce interpretable stock fore- casts, they mainly focus analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches be- tween domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), novel framework that combine verbal and la- tent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over liee-erres, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of time-series backbone model on the reasoning- baaoedd based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves sate-offthhear forecasting accuracy, while the reasoning traces also perform well on evaluation metrics judged by industry ex- perts. Our code is available at: https::: http:s////ithub..// com/ chen Sochhenjjan/VVV jan/VTA
INTRODUCTION
With the advent of Large Language Models (LLMs), an increasingly popular application is in finan- cial analysis (Wu et al. 2023 Xie et al. 2023). This spans wide range of tasks, including financial socision-making (Yu (et al.) 2025 2024), and market forecasting YYu et al. 2023 Koa et al., 2024). Majority of existing approaches primarily utilize the strong natural-language capabilities of LLMMs to analyze financial reports do sentiment analysis social texts (see Table I but neglects interpretable analysis stock price data, which arguably contain useful information for financial practitioners.
question answering (FinQA) (Lu et al. 2025b Qian et al. 2025). investment
The current solutions from the general time-series domain are yet sufficient for this task. Existing studies on time-series reasoning (Merrill et al.) 2024 Chow et al.) 2024) consistently report that LLMs struggle reason over raw time-series inputs. Meanwhile, time-series LLMs (Jin al. 2024 Liu et al. 2025a) often rely on reprogramming the embedding space, which produces time- time-series outputs but sacrifices verbal reasoning ability, which is an essential requirement for interpretable financial analysis. The closest effort is TimeCAP (Lee et al. 2025), which generates explanations by contextualizing the series with auxiliary information. However, its reasoning trace is derived from external data, and it produces classification label forecasts rather than full time-series trajectories.
Unlike other time-series data, financial time-series contains intrinsic interpretable signals which are widely studied by experts, known as Technical Analysis (Kirkpatrick II & Dahlquist 2010). We use these signals to verbally analyze financial time-series and produce interpretable stock forecasts.
The use of LLMs for time-series reasoning is hindered by three main challenges. Firstly, current LLMs have limited capabilities in time-series forecasts. Some works have tackled this by modifying the embedding space to produce time-series outputs (Jin al. 2024 Liu et al.) 2025a), but this comes at the cost of interpretability as the LLM loses its natural language capability. Secondly, on a
Equal contribution. Corresponding author.

---

Published as a conference paper at ICLR 2026
Table Comparison of relevant works. Our work contributes a novel explainable financial signal for practitioners and produces some insights into how time-series forecasting can be made interpretable.
Models
Domain
Input
Output
Finuncial LLMS
Fin-KI [ Li al. 2025h
Pinol ( Quan et al 1025
Financial Question Answering
Financial Reports
Textual Answers
FinMent
FinCod
luvestment Makinng
Text, Tabular, Audio News, Social Texts
Binary (Buy/Sell)
GPT-4 (Yo of mi A755
SP
Koa St dl. 2024
Market Dircction Forccasting
Binary or Quantile-Based
Time-Seriet LLMs
Time-I..M et
CALE
Time- Series Forecnsting Time-Serics Reasoning
General Time-Series
Time-Series Classification Reasoning
TimeCAP [Lee at 2013
Time-Series + Auxiliary
Ours
Time Time-Series Reasoning (Financial)
Verbal Technical Analysi (VTA)
Financial Time Seee-Series
Time Series Reasoning
higher level, current LLMs are not known to have the ability to do verbal reasoning on time- series to produce accurate forecasts (Merrill et al. [2024] Chow et al. (024). This involves understanding how to best analyze the predictive signals in the time-series data an unsupervised manner. Thirdly, the reasoning trace of the L.M further needs to be converted into time-series output to produce useful stock forecasts. LLMs are typically fine-tuned on next-token predictions (Radford et al. 2019), and using direct time-series outputs would not produce the best forecasts, which we verify empirically.
To address these problems, we present three key contributions. Firstly, we propose our Verbal Tech- nical Analysis (VTA) framework, which combines a backbone time-series model (which we termed as "latent thinking") with a reasoning LLM (termed as "verbal reasoning") to produce interpretable stock time-series forecasts. This framework combines the strong pattern processing ability of state- of-the-art time-series models and the strong reasoning ability of LLMS to produce forecasts that are both accurate and interpretable. Secondly, for reasoning over time-series, the stock time-series data is converted into textual annotations (Lin et al., (024) as inputs to the LLM. The reasoning trace is then optimized through modified Group Relative Policy Optimization (GRPO) objective (Shao 2024) that uses an inverse Mean Squared Error (MSE) reward scoring, which we termed as Time- GRPO. Thirdly, to produce time-series outputs from the reasoning traces, we condition (Ho & Salimans 2022) the generated outputs from the time-series model on the reasoning based attributes.
ct al.
To demonstrate the effectiveness of VTA, we perform extensive experiments across established stock baselines (Xu & Cohen] 2018) and additional stock data across the U.S., Chinese, and European markets. We show that our model forecasts achieve sfate-of-the-art results in prediction aceuracy, while also being interpretable. In addition, evaluation by industry experts show that the reasoning traces score high across various evaluation metrics from literature. Finally, to justify the practical capability of the model, we form Markowitz portfolios across the prediction length and show that the portfolios formed from VTA forecasts can also perform well on investment metrics.
2 RELATED WORKS
Financial Large Language Models. The rise of Large Language Models (LLMs) has spurred a growing body of research their application in finance. The earliest works focus developing general-purpose financial LLMs, such as BloombergGPT (Wu et al.) 2023) and FinMA (Xie et al., 2023], by finetuning on large set of financial corpora across multiple downstream tasks. Later works began to tackle the specific challenges of LLMs in finance. For example, works on finan- cial question-answering (FinQA) (Liu et al.] 20256; Qian et al., 2025) focus on teaching LLMs to analyze financial reports. which requires the ability to read structured financial tables and extract insights from complex documents (Zhu et al., 202T). LLMs for investment decision- makinng (Yu et al. 2025 2024) typically utilize multi-agent systems to handle different parts of an investment decision, including but not limited to document analysis, memory, risk control, etc. L.MS for finan- cial forecasting (Yu et al. 22023; Koa et al. (0244 seeks to predict the direction in which the market will go. Typically, these works analyze textual sources in order to understand the sentiment or finan- cial health of company. Our work positions itself in this field by learning to produce interpretable forward-looking signals from financial time-series data, which could benefit all applications above.
Time-Series Large Language Models. At the same time, there is also growing body of research in utilizing LLMs in the time-series domain (Kong et al.] (025). LLMs for time-series (Jin et al.)
2024
Liu et al. 2025a) leverage the large scale parameters and robust pattern recognition of L.LMS
2

---

Published as a conference paper at ICLR 2026
by fine-tuning them for time-series forecasting tasks. However, these approaches typically modify the embedding space the LLMs, making them lose their original language reasoning capabilities. Some works have also explored the ability of LLMs to reason
time-series data. It found that language models are "remarkably bad" at zero-shot time-series reasoning (Merrill et al. 2024), whereas fine-tuning them using latent encoder (Chow al. (024) shows
promising early results in reasoning over time-series for captioning. The closest work on time-series reasoning comes from TimeCAP (Lee et al. 2025), which perform forecasting by contextualizing the input time-series with auxiliary time- series information. It produces explanations by searching for similar historical contexts, and produces classification label forecasts, in textual form. Our work builds on this line of research by exploiting the predictive signals within financial time-series to produce interpretable time-series forecasts, providing some insights on the capabilities of LLMS in reasoning over time-series data.
VERBAL TECHNICAL ANALYSIS
The Verbal Technical Analysis (VTA) framework is shown in Figure There are three components: (1) In Time-Series Reasoning, we teach an LLM to verbally reason over the time-series inputs. This done through textual annotator to extract useful indicators, and proposed Time-Series Group Relative Policy Optimization Time-GGRP) method; (2) In Time-Series Forecasting, we train a backbone forecasting model, which can better learn from the complex low-level patterns in the time- series data; (3) In Joint Conditional Training, the time-series forecast is conditioned on the reasoning attributes, and the model is trained the conditional and unconditional forecasts concurrently.
Verbal Reasoning Sariess Bussoring
start
RL Beasaning
Reaconing Curpur
Anmotator
Reasoning
<aeb
Given prodied
-Mivvlue 3030
aad masn paot aoe bourrs
Reasoring Attributes
134413-59- 3400 -bore iu (4096-33) 3114,
Samplig
luse
Time-Serie Input Mnvynn
Latent Thinking [Time- Series Foreoettng)
Time Sortes Outpiny y mmm
Alanz Turt Tuken
Principal Embeddingg
Transforerr
9tea
Joint Conditional Training (Figure
Projecion
Cress Atanntonn
Feature Regulbrization
Output Condistency Loss kqupuu
Loss Apouure
Projeced Time Tokeess Yemr
Unconditiona Forecast 9,
Projetice F_m
Embboddings
Atcention
Transformer
Temporal Output
Figure 1: The Verbal Technical Analysis (VTA) framework. We first teach an LLM to reason over time-series data. The reasoning outputs are used to condition time-series forecasting model, to produce forecasts with similar attributes. This results in forecasts with interpretable reasoning traces.
3.1 PROBLEM FORMULATION
We consider the task of forecasting short-term future stock prices, based a historical window of T trading days. Let X = [x,TT41, X xt). where the input vector consists of the open price, high price, low price, volume traded, closing price and adjusted closing price, iee. x; Jo,, he 4e, Dt, Ct, Pt]. We aim to generate an output Y = {v. vy). which consists of the verbal reason- ing trace and the price trajectory over the next 7" trading days y = (pt+1, Pt42, ... .PtTT.).
3.2 TIME-SERIES REASONING
To teach LLM to reason time-series inputs, interpretable signals for forecasting. The LLM uses these indicators to reason over the time-series to make forecasts without any supervision data. This is achieved through proposed Time-Series
textual annotator to extract useful
3

---

Published as a conference paper at ICLR 2026
Group Relative Policy Optimization (Time-GRPO) method, which uses multi stage reinforcement learning (RL) pipeline, together with a modified GRPO objective (Shao et al. 2024).
The time-series input is first converted into textual annotations (Lin et al. 2024). which consist of its statistics information (Jin et al. 2024) (e.g., its mean, minimum and maximum values) and financial
technical indicators (Murphy 1999) (e.g., moving averages, momentum, etc.). Formally, we have:
X' f(X),
(1)
where f contains the annotation functions and X' are the annotated values. A full list of the financial technical indicators used, with their descriptions and calculations, are provided in Appendix B
Training Objectives. Using both the time-series X and their annotations X', we form prompt q, and let the LLM forecast the upcoming time-series sequence through verbal reasoning. The objective of the LLM is produce output o, which consists of sequence prediction &a and a verbal reasoning trace Vg. Formally, we denote the set of all task prompts as Q and a group of generated outputs as O {0r, 02; ...,,........ ,..)).). The time-series reasoning LLM policy my is then optimized across all groups using the following Time-GRPO objective:
Lime gipo (0) E
(O)g)
G
(o(0)) To (o:1g)
( To (o:1q) 1 -,, + A.)
min
A,, clip
B.D.K. (To||]Teet)
T
(2)
(o ())
Tea Oi (q)
log
1,
(3)
T (1
(o(o)qq)
where and 3 are hyper-parameters. A; denotes the advantage of the LLM policy, which is derived from a set rewards {ri, Th,,,,.'. ,Ta} that are associated with outputs O produced in each group:
A, T: mcan (21, .ra)) std (ri, 72, ... rra))
We utilize the format reward that was used in previous works (Guo et al. 2025). that enforces the model to always employ thinking process that is between < think >> and //think >> tags.
Ideally, the generated reasoning trace should also maximize the expected accuracy of the time-series forecasts. This is achieved by utilizing the Mean-Squared Error (MSE) score an additional reward:
MMSE (0) = 1/ (x e - -111))
(4)
where À is hyperparameter. The inverse MSE was used as the reward scores are to be maximized.
Training Pipeline. Following established practices in LLM fine-tuning literature (Guo et al. 2025
Ouyang al. 2022 Lu et al. 2024 Chow al. 2024), Time-GRPO utilizes multi-stage pipeline to fine-tune the time-seris reasoning LLM.
The first stage represents the cold-start phase (Guo al. 2025). As there were no "gold-standard" supervision data, this stage is used for generating the initial training samples, guided by the Lime.gpp objective. Empirically, we find that the forecasting performance would not significantly improve in this stage, but the process lets us generate training data for the next stage to fine-tune the base model.
The second stage focuses on teaching the model to produce more effective reasoning. This is achieved through rejection sampling, where we keep only the reasoning traces that lead to fore- casts with lower Mean Squared Error (MSE). To ensure better consistency of training samples, we also bucket the samples across different stocks and time-periods, and filter for those with MSE in the bottom th percentile in each bucket. The model is then trained on these filtered samples through the use of supervised fine-tuning (SFT).
The third stage optimizes the model for the best forecasting performance for our Technical Analysis (TA) task. Given that the model has leartt to reason over time-series data in the previous stage, this stage now aims to search for the best reasoning policy that can maximize the expected accuracy of the predicted time-series. For this stage, the model is also optimized using the Lime gnpo objective.
4

---

Published as a conference paper at ICLR 2026
3.3 TIME-SERIE FORECASTING
To perform time-series forecasting, we employ LLM-based time-series models. Works have shown that the powerful contextual modeling capabilities of LLMs can be effectively adapted for time-
series forecasting tasks (Zhou et al. [023] Chang et al. 2023 Cao et al. 2023). A key technique is to align the time-series and language distributions (Sun et [023] Jin et al. 2024) such that the
model is able to understand the context of time-series data (e.g. up. down, steady. etc.). For our
backbone model, we repurpose an LLM for cross-modal fine-tuning (Liu et al.
2025ap.
For this step, we first pass the time series input X through an embedding layer, followed by a multi- head attention layer, to obtain the projected time tokens Xime Next, words are usually close to each other in the LLM embedding space, and for non-text based tasks, it sufficient to keep cluster centers to reduce training costs (Sun et al. [023 Liu et al. (025a). To do this, we perform Principal Component Analysis (PCA) to retrieve the principal word embeddings D. Following this, we then pass the projected time tokens Xume and the principal word embeddings D through Multi-head Cross-Attentic layer. This lets align the time tokens and word embeddings in the forecasting model's 's embedding space to obtain the aligned cross modal text tokens, i.e.,
observed that similar
QK
Xext = Softmax
V
VC
(5)
where
X ime W
K = DW b
V DW.
Wg. We and W. are the projection matrices for query, key and value in the multi-headed attention layer, and C is the embedding dimension per attention head. X refers to the aligned text tokens.
Next, the projected time tokens Xume and aligned text tokens X passed through consecutive LLM transformer blocks. To guide modality alignment, after each transformer block in the tempo- ral and text branches, the embeddings pass through projection layer (Chen et al.. 2020) and matched via a feature regularization loss. This ensures that the text representations are consistent with the temporal dynamics each layer. Formally, given Fn time and F Fxx which are outputs of the n transformer block in the temporal and text branches, define the feature regularization loss
N Lieature (N- sim (eren (F (Fxx)) Prime
F time
where (NN-n) are the scaling hyperparameters, sim :) is the Ln regularization loss to ensure em- bedding similarity, and dimee Text are the projection layers in the temporal and textual branches.
At the end of the transformer blocks, the features are passed through a final dense layer to produce the temporal-basee and text-based outputs, Ytime and Yrext. These are also matched via L₁ loss:
foupuur sim(ý time Ftex))
The temporal-basee output Jime is extracted, which we denote as the time-series forecast & (X).
3.4 JOINT CONDITIONAL TRAINING
9
On its own, the time-series forecasting pipeline represents a black-box model, given that the em- bedding space of the LLM blocks have been modified, resulting in only time-series outputs. To preserve the interpretability of the time-series forecasts, we condition the time-series forecasts on the outputs produced by the reasoning model. At the same time, to also preserve the forecast accuracy, fine-tune tune the model to optimize for both the conditional and unconditional forecasts concurrently.
Px(x,
wwM
Linear
Time Series Reasoning
Po
May
thootho
Sc(K)
For this step. we first prompt for the reasoning output using the time-series reasoning policy
Figure Joint conditional training component.
5

---

Published as a conference paper at ICLR 2026
To. Next, we extract descriptive attribute classes (i.e., its maximum, minimum, mean values) from the generated time-series yo. which are used to condition (Dhariwal & Nichol, (2011) the time- series forecasts via joint conditional training (see Figure 2): For each label, we concatenate it with the time-series forecasts yo (X) from the time-series forecasting model. These inputs pass through separate linear layers for fine-tuning, and are then aggregated via projection layer generate the conditioned time-series forecasts, which we denote as y (X, c).
Finally, we a single neural network to parameterize both the conditional and unconditional mod- els (Ho & Salimans 2022). Both the unconditional and conditional pipelines are trained concur- rently by randomly setting с the unconditional class identifier Ø with a predefined probability Puncond- The model is then fine-tuned using MSE loss with the ground truth series y. We have:
Liorecast (0) = Ex.y, ()) -
(8)
ē~ fc, with probability 1 Puncond
Ø, with probability Puncend
During inference, our forecast is then a combination of the conditional and unconditional forecasts:
= s y (X, c) + (1 (1- (X),
(10)
where s a hyperparameter represetting the guidance scale, that controls the reasoning guidance.
EXPERIMENTS
We evaluate our Verbal Technical Analysis (VTA) model extensively across multiple datasets. The first is the ACL18 StockNet dataset (Xu & Cohen 2018), which includes historical
Dataset:
price data for 88 U.S. stocks that are selected to represent the top 8-10 companies by market capi-
talization in each of the major industries. The dataset spans the period of 01/09/2012 to 01/09/2017. This dataset is standard stock prediction benchmark that has been evaluated in multiple works
(Feng et al. 2018 Sawhney et al. 2020 Feng et al. 2021 Li al. 2023 Wang 2025)
Koa et al. 2023 Chen &
To further show the generalization ability of the model, we also collect additional stock data from across the U.S., Chinese, and European markets for testing. To ensure bias-free selection, we choose the stocks from well-known indices, i.e., the Dow Jones, the FTSE China A50 Index and the EURO STOXX 50. For these datasets, test on the time period from 01/01/2024 to 01/01/2025.
fime-series methods: Transformer Vaswani et al, 2017). Reformer (Kitaev et al. 2020), Informer (Zhou et al.] (021), Autoformer (Wu et al. 2021).
Baselines:
We compare against 12 statc-of-the
DLinear (Zeng et al. 2023). FiL.M (Zhou et al.) 2022). Crossformer (Zhang & Yan 2023). MICN [Wang al. 2023). Light (Campos al. [203)) TimesNet (Wu al.) 2022). TSMixer (Chen
ct al. 2023) and Ntn-Stationary Transformer (Liu et al.) 2022). We also compare with two LLM-
based time-series models: TimeLLM (Jin et al. (2024) and CALF (Liu et al.) 2025a). These models
are not explainable, including the two LLMs, which modify the embedding space for forecasting.
For evaluation against explainable models, we compare with reasoning LLMs: GPT-4.1 mini (Ope- nAI (025) and DeepSeek-RI (Guo et al. 2025).. To do so, we prompt these models to produce the time-series forecasts (Gruver et al. 2023] Wang et al. 2024) by reasoning on the time-series inputs.
All LLMs used in the VTA model, including the reasoning model and the transformer blocks for time-series forecasting, are trained using Low-Rank Adaptation (LoRA) (Hu) et al. 2022). Both input and output lengths T and 7" are set to 10, which is considered short-term
Implementation Details:
forecasting in time-series works (Li et al. 2022 Liu al. 2025a). Technical Analysis is typically utilized for short-term stock trading (Schwager 1995).
For the reasoning model, we use Qwen2.5-77 --B-struutt TTam 2024) as our base model. For the forecasting model, we use GPT-2 (Radford et al. 2019) as the base model. For hyperparameters, we set the conditional probability Puncood to 0.3 and the guidance scale to 0.1. More details on the experimental settings and computational resources used can be found in Appendix

---

Published as a conference paper at ICLR 2026
Table 2: Performance comparison. The best baselines underlined, and the best results are bolded.
StockNet
China A50
EUROSTONX
Dow Jones
All
Improvement
MAR
MSR
MAR
MSF
MAF
MSE
MAE
MSE
MAR
MSE
MAR
Large Language Models
GPTT4.1 mini CrenAl 2025
0.0846
0.1827
0.4875
0.3191
0,0997
0.2128
0,1340
0.2358
0,2014
0.2376
04453
0,1072
DeepSesk-R1
0.0788
0.1853
0.2920
0.3093
0.0776
0.2095
0.1227
0.2251
0.1428
0.2523
0.1750
0.0868
Time-Series Models
Informer Zhou el al. 2021
2.1816
0.9778
4,6823
1.1080
2.8986
1.0968
3.7031
1.1255
3.3672
1,0770
0.9650
0,8030
Tranfoment
201 7
1077
0.7762
3,9865
0.9784
2.1002
0.9042
2.8248
0.9194
2,60417
0.8946
0.9518
0. 0.7628
0.6475
4.3396
1.5656
2.3503
0.8173
2.3601
0.7963
0.900
0,7343
TSMixer
Chere mi M22.5
14774
08893
3,5863
1.1696
0.7227
2-5077
0.8473
2.19022
0.8700
09462
0.7561
Refommer KIuaverni
20223
1.2223
0.7628
2,4557
0.353
1.5342
0.8302
2,4280
0.9048
1,8995
0.8370
0.9300
0.7465
LightTS
Canioo 2 1i
0.008
0.5213
1.0634
0.5509
0.6100
0,5128
1.0994
0.6100
0.8467
0.5488
0.5000
0.6134
DLineur
0.1589
0.3021
0.3680
0.4126
0.1716
0.3189
0.2407
0.3566
0.2398
0.3475
0.5088
0.3896
FILM
2hru
0.0006
0.1927
0.2852
0.0894
0.2157
0.1246
0.2370
0.1449
0.2385
0.1873
0.1103
Non-sizionzyy
0.729
0.1822
0,2723
0.0861
0.2079
0.1207
0.1380
0.2300
0.146
0.0776
MICN
(Wang df 2015
0.0764
0.1878
0.2898
0,2922
0.0874
0.2108
0.1373
0.2405
0.1377
0.2328
0.1499
0.0888
Autefornnc Wirs di 2012
0.0748
0.1866
0.2427
0.2947
0,0653
0.2103
0.1132
0.2273
0,1290
0.2297
0.0868
0.0765
TimesNet Wurdrsil
0,0708
0,1789
0,2527
0 2900
0.0796
0.2022
0.1112
0.2203
0,1286
0.2229
0.0841
0.0482
Time-Seriex LL
LimeL.M Jinetal 2024
0.0704
0,1780
0.2439
0 0,2850
0.0776
0.1993
0.1127
0.2216
0.1262
0.2210
0.0663
0.0400
CALF (Liu 20253
0.0674
0.1738
0.2412
0.2433
0.0762
0.1957
0.1092
02181
0.1235
0.2180
0.0460
0.0267
Our Model
Avg
Avg
VTA (Ours)
0.0659
0.1701
0.2365
0.2737
0J0748
0.1929
0.1040
0.2120
0.1178
0.2122
0.4672
0.3417
5 RESULTS
Performance Comparison. Table 2 reports the forecasting results. We can observe the following:
The inference-only reasoning LLMs (i.e., GPT-4.1 mini, DeepSeck-RI) do not show very strong performances, as they are likely not fine-tuned for time-series forecasting. However, they were still able to beat some of the fine-tuned time-series models (e.g. Transformer, DLinear), which demonstrate some effectiveness of verbally reasoning over time-series inputs to do forecasting.
Among the time-series baselines, the biggest performance jump comes from the models which decomposes the data by trend and seasonality (i.e. FiLM, MICN, Autoformer and TimesNet). This could be attributed to the characteristics of stock prices, which are often affected by long- term and short-term business cycles (Dalio) (018). Another notable model that performs well is the statiiatt transformer, which might be attributed to the non-stationary behavior of stock price data (Malkiel 2019).
The time-series LLMs are able to surpass the performance of non-LLM-basedd models. These models typically align the LLM's internal word embeddings to time-series embeddings to do time-series forecasting. It is possible that the intrinsic knowledge of the LLM helps the model to understand the characteristics of stock data, thus allowing it to capture forecasting patterns better.
Our proposed VTA model demonstrates the best performance in stock forecasting in both MSE and MAE VTA explicitly combines internal (latent) understanding with external (verbalized) rea- soning. The empirical improvements suggest that integrating these two techniques can be benefi- cial for time-series forecasting. In addition to improved accuracy, the VTA model also produces interpretable reasoning traces for its forecasts, which do not exist in most baseline models.
More details of the statistical significance of the experiments can be found in Appendix C
25-78
4.00
Table 3: Ablation study of the LLM fine-tuning stages.
Sewend
3,50
Llama-3.1 3.1.30
Qwen-2.5.3B
Qwen-2.5.77 (Ouns)
3.25
MSE
0.1482
0,1707
D10449
Base Model
3.00
MAE
0.2543
0.2181
0,2040
Uama-3 1-88
Cold Start RL.
MSE
0.1475
0.1648
0.0941
MAE
0.2536
0,2153
0:2036
SFT For Reasoning
MSE
0.1168
0.1032
0.0893
Rellard
MAE
0.2267
0,1884
0. 1.1997
RL for
MSE
0.0955
0.0832
0.0686
Reasoning
MAE
0.2062
0.1745
0.1741
100
200
300
400
500
Coeditioning
MSH
0.0667
0.0672
0.0659
Stepss
(VTA)
MAE
0.1713
0.1710
0.1701
Figure 3: Correctness reward over steps.
7

---

Published as a conference paper at ICLR 2026
Ablation Study. We conduct an ablation study to demonstrate the effectiveness of the model design.
From Figure 3 we observe that the inverse MSE reward TMSE. a component of the Time-GRPO objective, increases across the number of training steps. This suggests that it is possible to learn verbal reasoning steps for time-series forecasting, which our VTA model was able to achieve.
From Table 3 we see that each fine-tuning stage helps to improve the results of the model. How- ever, the first RL fine-tuning, which uses the Time- GRme-GRPO objective, was not efficient by itself, showing small improvement of 1 1.6% over the base model in MSE averaged across all variants.
time-series, However, after rejection sampling and SFT teach the model how to reason the second RL fine-tuning, which uses the same Time-GRPO objective, produces an average im- provement of 20.3%. This highlights the usefulness of fine-tuning over multi-stage pipeline.
Finally, conditioning on the additional forecasting model helped to improve the performance fur- ther, showing the benefit of enhancing external verbal reasoning with internal latent understanding.
Contribution of Reasoning Component. To study the contribution of the reasoning component on forecasting performance, artificially corrupt the reasoning trace to observe their impact on the final forecasts. This was done in two ways: (1) Adversarial: We add adversarial noise to the technical indicator values; (2) Remove Information: Within the prompt, we force the LLM to not utilize certain indicators in its reasoning. The forecasting performances are reported in Figure 4
0.0680
0.0672
0.0 0.0676
0.0669
0.06/2
MSE
MSE
0.0666
0.0668
0.0663
0.0664
0.0660
0.0660
Adversarial Noise Multiplier (x Std)
Number of Indicators Removed
Figure 4: Change reasoning performance when the reasoning traces are corrupted.
In Figure 4 removing the indicators leads to clear degradation in performance, demonstrating that the reasoning traces provide genuinely useful guidance to the model. Adding adversarial noise also reduces overall performance but does not yield a consistent trend. A possible explanation is that, during joint conditional training, the model gradually learns to rely more heavily on the time-series forecasting component once detects that the reasoning signals have become unreliable.
Reasoning Quality. To evaluate the quality of the reasoning traces, we refer to past works on LLM explainability (Koa et al. 2024, Lin al. 2024) design a set of relevant metrics for our task. Each reasoning trace and its associated time-series forecast was scored on scale from 1 (poor) to 5 (excellent). The criteria are defined as follows:
Clarity: How clearly and succinetly the reasoning explains its analysis structured manner.
Depth: How well the reasoning incorporates explicit financial or technical indicators (e.g.,
MACD, RSI, Bollinger Bands, EMA) to meaningfully support its conclusions.
Accuracy: How precisely financial indicators are interpreted and technically described.
Coherence: How logically consistent and organized the reasoning is, ensuring clear alignment between analysis and conclusion.
Relevance: How directly and effectively the chosen indicators are linked to the stock-price fore- cast provided.
Using these metrics, we surveyed 25 industry experts with professional experience in financial mar- ket analysis, with backgrounds from organizations such as JPMorgan, UBS, Evercore, and Allianz Global Investors, etc. For each, we presented the model outputs from VTA (ours), GPT-4. mini, and Deepseck-RL. R1, showing both the forecasts and the textual reasoning. Respondents were blind to which model produced which output and were shown 15 randomly selected samples for evaluation.
8

---

Published as a conference paper at ICLR 2026
6PT-4.1 mini
DeepSeek R1
VTur(
4.04
3.43
3,07
3.74
3.37
3.05
3.03
3,00
3.71
3,59
Score
3
2,20
2.15
2.36
2.58
2.63
1.88
Clartty
Depth
Accuracy
Coherence
Relevonce
Figure 5: Performance of VTA on the financial time-series reasoning task.
From Figure 5] the results show that VTA achieved the highest average rating across all five metrics when compared to the other two LLMs. The best performance gains were observed in Depth, Ac- curacy, and Relevance. These criteria most directly reflect technical reasoning ability and the use of financial indicators. These findings suggest that our model, which was designed specifically for technical analysis, was able to produce reasoning outputs that were preferred by domain experts, demonstrating its practical strengths. The differences in Coherence and Clarity were smaller, which can be attributed to the fact that caneral-purpose LLMs like GPT-4.11 mini and Deepseek-RI good at producing fluent, well-structured text, even if they lack domain specialization. More details on this, including statistical significance and open-ended responses, are found in Appendix [.]
Case Studies. To illustrate the capabilities of VTA, we present some qualitative case studies to demonstrate its reasoning process. For the figures, the horizontal axis shows the time-steps while the vertical axis shows the scaled prices. In Figure the (downward) price correction and subsequent upward trend. In Figure 7 VTA correctly reason about the oscillating prices with slight uptrend. More case studies can be found in Appendix E
see that VTA was able to correctly reason about
The stock prices have been flactuating significantly, with recent upward trend. The moving averages momentum and RSI indicate weak buying pressure. MACD line slightly positive, but the Willims 1R and CCI suggest overbought conditions, Bollinger Bands price within the indicators are in the overbought region, which might lead to correction. Given the recent trend and indicators, it's likely the stock will correct and then continue the upward trend.
suggest docreasing trend,
show
indicating passible bounce. However, the stochastic
Predicting the next days, I'll assume slight correction followed by rehound, staying within the recent range. </think>
12
Figure 6: An example of VTA reasoning about slight correction and possible rebound in price.
<thinkc
The stock price has been fluctuating but seems to be range between 1. 1.349 and 1.483. The moving regagge supcest slight downward trend, while the momentum and indicate that the stock not an bourbought or oversold condition. The MACD line shows slight positive crossover, which could indicate potential buying opportunity. The Rollinger Bands suggest that the stock price near the band, which could indicate a potential bounce. Stochestic indicators are neutral range, not indicating strong trend either direction.
Grardizu
1.4
Given the recent price action and the technical indicators, the stock might continue to oscillate within the current lower Bollinger Band suggest slight upward bias. will predict range slightly above the recent lows but not exceeding the recent highs. </thinls
slightly increase. However, the positive MACD crossover and the proximity the
Figure 7: An example VTA reasoning about prices oscillating within range with slight uptrend.
Generalization Other Domains. In general, VTA is able to produce reasoning traces for any time-series data. To verify this, we run VTA on datasets from other domains. These include Healthcare (Wu et al.) 202T and Energy (Zhou et al.) 2021), which contains time-series data on ILI (influenza-like illness) cases and oil temperature respectively. The generated reasoning traces are shown in Figure g In these domains, we observe that the time-series do not contain any intrinsic interpretable signals to reason over, and VTA do not go beyond simple trend extrapolation in its rea- soning. It has also been shown in previous studies that large complex models do not meaningfully improve performance in these cases (Tan et al. 2024). For these cases, VTA are still able to pro- duce reasoning traces, but these do not contribute significant additional signals for the forecasting model. The forecasting performance of VTTA these datasets be found in Appendix G

---

Published as a conference paper at ICLR 2026
Healthcare (IU
Energy dataset):
pastern fuctuation, general decreasing Weighted
given data standarchzed transformer temperature
predic for moving avenage
valass are the negative range, indicating ng dedine infuurra--ii ilness, Given predict with some meady decine, so we can expect cont nuw, with some fluctuation, predict gradual decrease with some variabiity.
estrapelation
trend, it's reasonable continution this dedline, veriabiliy The
example, basit appreach of taking last and siightly adjusting recens sligtt decrease last velues, predict slight decrease the next hours, considering recent
sertes
weeks
shown
range
Figure 8: Examples of VTA reasoning traces on the Healthcare and Energy domains.
Portfolio Optimization. To justify the practical capability of VTA, we also evaluate the model in a real-life investment setting. Given that the model produces multi-step predictions, we are able to form portfolios that maximize the returns and minimize the volatility across the prediction length (Koa et al., 2023). This is achieved by performing Markowitz optimization (Markowitz) 1952) across the 10-day predictions. The portfolio is rebalanced daily, using the predicted returns and their covariance matrix.
For evaluation, compare against similar portfolios formed using the top-5 performing time-series models and all LLM baselines. The portfolio are compared on common investment metrics, such as their Sharpe ratio (Sharpe 1994).
The explanations of the portfolio metrics used are follows:
Returns: Measure the percentage change in portfolio value over a given period, indicating overall profitability.
Volatility: Captures the dispersion of returns over time, with higher volatility reflecting greater fluctuations and uncertainty.
Maximum Drawdown:
Represents the largest peak-to-trougs decline in portfolio value, highlighting the worst observed loss during the evaluation window.
Sharpe Ratio: Assesses risk-adjusted performance by comparing excess returns return volatility, where higher values indicate more efficient risk-taking.
We evaluate the portfolios across all datasets, and report the average results across each metric.
From Table we see that the portfolio con- structed using VTA predictions demonstrates strong overall performance. It ranks as the strongest baseline on the returns, volatility and maximum drawdown metrics, and the values are very close to the pe-performmig models for each. Notably, VTTA achieves the highest Sharpe ratio among all models, which represents the risk-adjusted returns. Given that the Sharpe ratio is one of the most com- mon measure of investment performance, this highlights the practical effectiveness of the VTA forecasts. More advanced financial analysis on the portfolios can be found in Appendix H
Table 4: Comparison on common portfolio metries.
Returns
Volatility
Drawdown
Sharpe
GPT-4. mini
0.1868
0.1226
-0.0947
1.3096
Deepseck-R
0,2069
0.1356
-0.1243
1.4074
HILM
0.2211
0.1184
-0.1085
1.4421
Non stationary
0,2122
0.1186
+0.0825
1.4430
MICN
0.1603
0.1193
-0.1094
1.1809
Autofomner
0.2495
0.1341
-0.1121
1.4736
TimesNer
0,1714
0.1198
-0,0947
1,2748
TimeLLM
0.2185
0.1199
-0,1040
1,5230
CALF
0,2019
0,1247
-0,0981
1.4566
VTA (Ours)
0.2409
0.1185
.0.0833
1.7190
6 CONCLUSION
In this work, we tackled the task of verbally reasoning over financial time-series data. This task is challenging as it switches between the timess and natural language domain for the stock price data and the reasoning step. To deal with this, we introduce our Verbal Technical Analysis (VTA) framework, which combines verbal and latent reasoning to produce interpretable time-series fore- casts. The framework utilizes Time-GRPO method to finetune the reasoning model, and con- ditions its forecasts on the reasoning attributes. We conducted extensive experiments and find VTA achieve state-of- afetttr-rt forecasting accuracy while producing high-quality reasoning traces.
From the observations in this work, we propose two possible future directions. The first is to in- corporate more stock characteristics into the VTA model (e.g.. trend and seasonality, non-stationary behavior). The second is to improve the alignment between reasoning and forecasting, possibly through the use of more advanced techniques from image conditioning control (Zhang al. 2023)
10

---

Published as a conference paper at ICLR 2026
7 ACKNOWLEDGEMENTS
This research is supported by the Asian Institute of Digital Finance (AIDF) and the NExT Research Centre. This research is also supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier I grant.
REFERENCES
David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S Jensen. Lightts: Lightweight time series classification with adaptive ensemble distillation. Proceedings of the ACM Management of Data, 1(2):1-27, 2023.
Defu Cao, Furong Jia, Sercan Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu, Tempo: Prompt-based generative pre-trained transformer for time series forecasting. arXiv preprint arXiv:2310.04948, 2023.
Ching Chang, Wen-Chih Peng. and Tien-Fu Chen. Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms. CoRR, 2023.
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister. Tsmixer: An all-mlp architecture for time series forecasting. arXiv preprint arXiv:2303.06053, 2023.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference machine learning, pp. 1597-1607. PmLR, 2020.
Weijun Chen and Yanze Wang. Dhmoe: Diffusion generated hierarchical multi-granular expertise for stock prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 11490-11499, 2025.
Winnie Chow, Lauren Gardiner, Haraldur T Hallgrímsson, Maxwell A Xu, and Shirley You Ren. Towards time series reasoning with lims. arXiv preprint 1eXX::2409.13766 2024.
Ray Dalio. Principles. Simon and Schuster, 2018.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794 2021.
Fuli Feng, Huimin Chen, Xiangnan He, Ding. Maosong Sun, and Tat-Seng Chua. Enhancing stock movement prediction with adversarial training. arXiv preprint arXiv: 1810.09936, 2018.
Fuli Feng, Xiang Wang, Xiangnan He, Ritchie Ng, and Tat-Seng Chua. Time horizon-aware model- ing of financial texts for stock price prediction. In Proceedings of the Second ACM International Conference on Al in Finance, pp. 1-8, 2021.
Nate Gruver, Mare Finzi, Shikai Qiu, and Andrew Wilson. Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems, 36: 16229922-19635, 2023.
Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Ying- han Shen, Shengjie Ma, Honghao Liu, et al. survey on au-aaee- arXiv preprint arXiv:2411.15594, 2024.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501. /2948, 2025.
Jonathan Ho and Tim Salimans. arXiv:2207.12599 2022.
Classifier-free
diffusion guidance.
arXiv preprint
Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3 2022.
11

---

Published as a conference paper at ICLR 2026
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin- Yu Chen, Yux- uan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen. Time-LMM: Time series forecasting by reprogramming large language models. In International Conference on Learning Representations (ICIR), 2024.
Charles D Kirkpatrick II and Julie R Dahlquist. Technical analysis: the complete resource for financial market technicians. FT press, 2010.
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. arXiv preprint arXiv:2001.04455 2020.
Kelvin JL Koa, Yunshan Ma, Ritchie Ng, and Tat-Seng Chua. Diffusion variational autoencoder for tackling stochasticity in multi-step regression stock price prediction. In Proceedings of the 32nd ACM International Conference Information and Knowledge Management, pp. 1087-1096, 2023.
Kelvin JL Koa, Yunshan Ma, Ritchie Ng, and Tat-Seng Chua. Learning to generate explainable stock predictions using self-reflective large language models. In Proceedings of the ACM Web Conference 2024, pp. 4304 4315, 2024.
Yaxuan Kong, Yiyuan Yang. Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, and Qingsong Wen. Time-mqa: Time series multi-task question answering with context enhancement. arXiv preprint arXiv:2503.01875, 2025.
Geon Lee, Wenchao Yu, Kijung Shin, Wei Cheng, and Haifeng Chen. Timecap: tualize, augment, and predict time series events with large language model agents. In Proceedings of the AAAI Conference Artificial Intelligence, 2025.
Learning to contex-
Shuqi Li, Weiheng Liao, Yuhan Chen, and Rui Yan. Pen: prediction-explanttio network to forecast stock price movement with better explainability. In Proceedings of the AAAI Conference Artificial Intelligence, volume 37, pp. 5187-5194, 2023.
Yan Li, Xinjiang Lu, Yaqing Wang. and Dejing Dou. Generative time series forecasting with dif- fusion, denoise, and disentanglement. Advances in Neural Information Processing Systems, 35: 55009-23022 2022.
Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, and Haifeng Chen. Decoding time series with llms: A multi-agent frame- work for cross-domain annotation. arXiv preprint arXiv:2410. 22.22220..742.... 2024.
Peiyuan Liu, Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, and Shu-Ta Tao Xia. Calf: Aligning iims for time series forecasting via cross-modal fine-tuning. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pp. 18915-18923, 2025a.
Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long. Non-stationary transformers: Exploring the stationarity in time series forecasting. Advances in neural information processing systems, 35: 9881-9893, 2022.
Zhaowei Liu, Xin Guo, Fangqi Lou, Lingfeng Zeng, Jinyi Niu, Zixuan Wang. Jiajie Xu, Weige Cai, Ziwei Yang, Xueqian Zhao, et al. Fin-rl: A large language model for financial reasoning through reinforcement learning. arXiv preprint arXiv:2505 16252, 2025b.
Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang. Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, al. Deepseek-vl: towards real-world vision-language understanding. arXiv preprint 240.050000 2024.
Burton G Malkiel. A random walk down Wall Street: the time-tested strategy for successful investing. WW Norton & Company, 2019.
Harry Markowitz. Portfolio selection. The Journal of Finance, 7(1):77-91, 1952. doi: https:
URL httLtts////// http://////oniinnbbrrrr.a wiley. com/doi hww.dd///bbbs// wttp:/.1.1111111111 62661 .2...1.66............1111111111. t501525.
12

---

Published as a conference paper at ICLR 2026
Mike A Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen, and Tim Althoff. Language models still struggle to zero-shot reason about time series. arXiv preprint arXiv:2404.11757 2024.
John J Murphy. Technical analysis of the financial markets: A comprehensive guide to trading
methods and applications. Penguin, 1999.
OpenAl. Introducing gpt-4.1 in the api, 2025. URL http:://opnaal.commndee gpt 4-17
Long Ouyang, Jeffrey Wu, Xu Jiang. Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to fol- low instructions with human feedback. Advances in neural information processing systems, 35:
27730-27744, 2022.
Lingfei Qian, Weipeng Zhou, Yan Wang, Xueqing Peng, Han Yi, Jimin Huang, Qianqian Xie, and Jianyun Nie. Finol: On the transferability of reasoning enhanced Ilms to finance. arXiv preprint arXiv:2502.08127. 2025.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, llya Sutskever, et al. Language models are unsupervised multitask learners. OpenAl blog, 1(8):9, 2019.
Ramit Sawhney, Shivam Agarwal, Arna Wadhwa, and Rajiv Shah. Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8415-8426, 2020.
Jack D Schwager. Technical analysis. John Wiley & Sons, 1995.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.
William Sharpe. The sharpe ratio. Journal of portfolio management, 21(1):49-58, 1994.
Chenxi Sun, Hongyan Li, Yaliang Li, and Shenda Hong. Test: Text prototype aligned embedding to activate I1m's ability for time series. arXiv preprint 0rX1v:2308.08241, 2023.
Mingtian Tan, Mike Merrill, Vinayak Gupta, Tim Althoff, and Tom Hartvigsen. Are language models actually useful for time series forecasting? Advances in Neural Information Processing Systems, 37:60162-60191 2024.
Qwen Team. Qwen2.5: A party of foundation models, September 2024. URL http::tp/// qwwewwenll github.ic/blog. qwen2. 57]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural informa- fion processing systems, 30, 2017.
Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao. Mien: Multi- scale local and global context modeling for long-term series forecasting. In The eleventh interna- tional conference learning representations, 2023.
Xinlei Wang, Maike Feng, Jing Qiu, Jinjin Gu, and Junhua Zhao. From news to forecast: Inte- grating event analysis in llm-based time series forecasting with reflection. Advances in Neural Information Processing Systems, 37:581 18-58153, 2024.
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting. Advances neural information processing systems, 14:22419-22433 2021.
Haixu Wu, Tengge Hu, Yong Liu. Hang Zhou, Jianmin Wang, and Mingsheng Long. Timesnet: Tem- poral 2d-variation modeling for general time series analysis. arXiv preprint 110002221..02.86, 2022.
13

---

Published as a conference paper at ICLR 2026
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prab- hamjan Kambadur, David Rosenberg, and Gideon Mann. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17566 2023.
Qianqian Xie. Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. Pixiu: A large language model, instruction data and evaluation benchmark for finance. Xrviv preprint 4344443306005443 2023.
Yumo Xu and Shay B Cohen. Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Anmual Meeting the Association for Computational Linguistics (Volume I: Long Papers), pp. 1970-1979, 2018.
Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets Ilm explaaiaee financial time series forecasting. arXiv preprint 2rX1v:2306.11002. 2023.
Yangyang Yu. Zhiyuan Yao, Haohang Li, Zhiyang Deng, Yuechen Jiang, Yupeng Cao, Zhi Chen, Jordan Suchow, Zhenyu Cui, Rong Liu, et al. Fincon: A synthesized Ilm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making. Advances in Neural Information Processing Systems, 0111133010-1370000 2024.
Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Jordan W Suchow, Denghui Zhang, and Khaldoun Khashanah. Finmem: A performance-enhanee I1m trading agent with layered memory and character design. IEEE Transactions Big Data, 2025.
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp. 11121-11128, 2023.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEF/CVF international conference computer vision, pp. 3836-3847, 2023.
Yunhao Zhang and Junchi Yan. Crossformer: Transformer utilizing cross- doos--immnniion dependency for multivariate time series forecasting. In The eleventh international conference learning representations, 2023.
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging alm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 014466955466622 2023.
Haoyi Zhou, Shanghang Zhang. Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference artificial intelligence, volume 35, pp. 1110--1111,, 2021.
Tian Zhou, Ziqing Ma, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, al. Film: Frequency improved legendre memory model for long-term time series forecasting, Advances in neural information processing systems, 35:12677-12690, 2022.
Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al. One fits all: Power general time series analysis by pretrained Im. Advances in neural information processing systems, 36:43322-43355 2023.
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang. Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance. In Proceedings of the 59th Annual Meeting of the Association for Computa- tional Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 3277-3287, 2021.
14

---

Published as a conference paper at ICLR 2026
Appendix
Table of Contents
Additional Experiment Details
15
A.1
Model and Training Hyperparameters
15
A.2
Computational Resources
16
B
List of Technical Indicators
16
Statistical Significance of Main Results
16
Details on Expert Evaluation
17
D.I
Supplementary Information
17
Survey Participants
17
E
Additional Case Studies
18
F
LLM-as-a-Judge
19
Comparison in other domains
19
Financial Analysis of Portfolios
20
H.1
The CAPM Regression Model
20
H.2 The
6- 6-Factor Model
21
A ADDITIONAL EXPERIMENT DETAILS
A.1 MODEL AND TRAINING HYPERPARAMETERS
This section summarizes the key hyperparameters used for training the Verbal Technical Analysis (VTA) model. All Large Language Model (LLM) components were trained using the Unsloth frame- work] which supports 4-bit quantization and Low-Rank Adaptation (LoRA) (Hu et al., 2022 The implementation of Group Relative Preference Optimization (GRPO) follows the principles outlined by the Hugging Face TRL library
Time-Series Reasoning. The reasoning model developed from Qwen2.5-7B-Instre using multi-stage training pipeline consisting of GRPO and supervised fine-tum mne-tuning (SFT). The maximum sequence length controlled via the max seq length parameter, and inference was performed with temperature of 0.2.
LoRA applied with specific lora-rank, targeting key modules such the attention pro- jections (q.proj, j_proj, v_proj, o.proj) and feed-forward layers (gate-proj, up_proj, down.proj). During the initial and final GRPO training phases-used for format learning-4 learning rate of 5 x 10-6 was used over two epochs. Each device processed a batch size of 4, with two gradient accumulation steps. For GRPO, four generations were produced per prompt, and the combined prompt and completion length was capped at 500 tokens. Rewards were based on adherence the target format and accuracy of the prediction.
15

---

Published as a conference paper at ICLR 2026
For SFT data generation, a rejection sampling mechanism was employed to select the top 10% Mean Squared Error (MSE) examples from a total of 100 buckets. This was followed by reasoning enhancement through SFT, using learning rate of 2 x 10 over two epochs. Here, the per-device batch size was reduced to 1, while increasing the number of gradient accumulation steps to 4.
Time-Series Forecasting. The forecasting component of the VTA model is adapted from GPT-2 and uses a fixed input and output sequence length of 10 days (denoted as T and T"). This component was trained for 20 epochs with a learning rate of 1 10-4 and batch size of 16. The architecture comprises 6 GPT-style transformer layers, as defined by the gpt.layers parameter.
LoRA was configured with rank of 8, a scaling factor (lora_a 1paa) of 32, and a dropout rate of 0.1. The joint conditional training was implemented with probability of unconditional training Puncond 0.30, and guidance scale of s 0.1. Additionally, alignment loss was incorporated using the statistical properties (i.e., minimum, maximum, and mean) of the predicted sequences.
A.2 COMPUTATIONAL RESOURCES
All experiments were conducted using 4x NVIDIA A5000 GPUs (24 GB VRAM each). Full reason- ing model training--nccine
cold-start reinforcement learning, supervised fine-tuning, and reward- guided GRPO-take approximately 120 GPU-hours for LLaMA 3. 1-8B, 100 GPU-hours for Qwen 2.5-7B, and 60 GPU-hours for Qwen 2.5-3B, using Unsloth's 's LoRA fine-tuning implementation with spu.memory.utille.. ion=0. 5 (requiring around 21 GB VRAM per process).
Once reasoning traces are generated, downstream forecasting runs are significantly more efficient. Training a forecasting model for single stock with approximately 1000 training and 250 test points takes around 3 minutes on single GPU (using ~2.7 GB VRAM). complete run over the full StockNet dataset requires approximately 4.5 hours on one GPU.
B LIST OF TECHNICAL INDICATORS
Table 5: A list of the financial technical indicators used in Time-GRPO.
Indicator
Description
Formula
Simple Moving Average (SMA)
Identifies trend by smoothing price data
SMA Ei=
Price,
over peried
Exponential Moving Average (EMA)
Measures the trend by smoothing price data greater weight recent prices
EMA, Price, a + EMA (1 aa)
Momentum
Tracks the speed of price changes for momentum
Momentum = Close, - Close,-n
Relative Strength Index (RSI)
Identifies overboughtfoversoldd condi- tions using average gains and losses Measures momentum via difference
RSI = 100
100
MACD Line
MACD EMA
EMAg
between short and long EMAs
Williams SR
Measures overhoughdloversold condi-
Williams %R (-100)
Highre High, CCis
Fiighnt
Lons
tions by comparing close to recent highs
Commodity Channel Index (CCI)
Identifies price deviations from mov- ing average for cyclical trends
CCI
Pris-MK D.01ExMen Deviation
Average Directional Index (ADX)
Measures trend strength using direc- tional movement
ADX = 100 x BMM DM |]
DoCt +DM-
Upper Band MA A Lower Band MA R
Bollinger Bands
Measures volatility using standard de- viations around moving average Measures momentum by comparing current close to range of highs and
Stochastic Oscillator
%.-
100
C STATISTICAL SIGNIFICANCE OF MAIN RESULTS
To assess whether the differences relative to the second- model in the model performance exper- iments (MSE, = 0.06737, MAE, = 0.17380) are statistically significant, we ran our model n = 10 times with different random seeds and performed one-sample, one-sided Student's (-tests under the
16

---

Published as a conference paper at ICLR 2026
alternative hypothesis that model'ss 's errors lower than those of the second-best model. Let and s denote the sample mean and sample standard deviation of the metric over the 10 runs.
where He is the error of the second-bess best model. We reject the null hypothesis for p < 0.05. Both
Table 6: One-sample, one-sided (-test results against the second-best model.
Metric
t
p (one-sided)
MSE 0.06661
0.00054
-4.42
8.3
MAE
0.17081
0.00047
200.0
4.5 x 10-9
tests yield p < 0.05, indicating that the reductions in error relative to the second-best model are statistically significant. For completeness, we report the full distribution over seeds as:
MSE 0.06661 + 0.00054, MAE 0. 10881 + 0.00047 (mean + std, n 10). We select the single best run (MSE 0.06594, MAE 0. 0.17008) for all subsequent evaluations.
D DETAILS ON EXPERT EVALUATION
D.1 SUPPLEMENTARY INFORMATION
We conducted significance tests for our expert evaluation results:
Table 7: Significance testing (paired (-tests). Values indicate p-values for pairwise comparisons.
Comparison
Clarity
Depth
Accuracy
Coherence
Relevance
Overall
VTA vs GPT-4.1 mini
0.0765
9.1 10
4.4 x 10-6
0.0280
1.6 x 10-3
1.3 x 10-6
VTA vs Deepseek RI
0.0024
1.8 x 10-7
3.1 x 10-5
0.0034
1.0 x 10-5
2.3 x 10-6
GPT-4.1 mini Deepseek RI
0.0555
0.0045
0.0247
0.4594
0.6854
0.8515
statistical analysis using paired f-tests shows that the differences between VTA and the other two models are significant at the 5% level for all criteria except Clarity, where the base LLMs already good at. Thus, the higher expert ratings for our model's 's reasoning are statistically robust.
In addition the quantitative scores, also allowed the experts to provide open-ended responses on the strengths and weaknesses of the models. We summarize the key points here:
Experts highlighted the strengths of VTA in using wider variety of relevant indicators and in providing conclusions that align with the explanation (e.g., "The analysis included variety of different indicators and used them to create a coherent story", "Interesting use and relevance of indicators") ") In contrast, outputs from Deepseek-RI and GPT-4. mini tended to be either vague in analysis depth or not clearly linked the price forecast, especially in the case of Deepseek-Ro R1.
On the other hand, experts suggested that VTA could further improve by discussing indicators in more detail and by using clearer formatting (e.g., "Should use bullet points for readability". "Should specify explicit indicator thresholds") While better formatting and longer explanations can be ad- dressed in future work, precise thresholds could be more difficult to enforce since the model learns them adaptively from data.
D.2 SURVEY PARTICIPANTS
For the expert evaluation, we have surveyed domain experts with professional experience in financial market analysis. A list of their surveyed background
follows (duplicates are removed):
J.P. Morgan, UBS, Allianz Global Investors. Evercore, Perella Weinberg Partners, Bain & Com- pany, McKinsey & Company, L.E.K. Consulting, H.I.G. Capital, Simon Kucher, Axxion S.A., TCG Corporate Finance, BayernLB. Spinone Capital, Phillip Nova, Check24, SD Guthrie, MSMIF, Asian Institute of Digital Finance, Harvard Business School
17

---

Published as a conference paper at ICLR 2026
E ADDITIONAL CASE STUDIES
<thinks
prices been fluctuating, but generally incressing over past days. moving averages suggest slight upward trend, but the momentum and RSI indicate potentaal reversal or pullback. the MACD line siighlly negative, sugresting bearish signal. the Williams 16R and CCI that the stock overbought, which could lead to correction. Dollinger Bands show the stock dlose to the lower band, indicating support. The stachastic indicators are the averbought region, which might to pullbakk.
235-
Given recent trend indicators, likelly stock will experience volatility may not see a significant increase the next 10 days, A pullback is possible, but the support from the Bollinger Bands and the recent upward trend suggest that the stock might not fall too much.
222
Predicting the exact prices challenging, but based the current trend and indicators, moderate Increase with some pullbacks is likell. </thinks
233
25
Figure 9; VTA reasoning about moderate increase (from the last price) with some pullbacks.
<thinko
fluctuating penerally trending upwards over past days. moving averages suggest a slight upward trend, with the exponential moving average showing more recent
upward trend. The momentum incicator has been positive, indicating upward momentum. The RSI above 50, suggesting the stock not oversold. The MACD line has been positive, indicating bullish trend. The Willams %R is in relatively low range, suggesting the stock is not overbought. The Bollingrr Bands show stock price within upper band, which could stochestic indicators are also in positive range, suggesting the stock is an uptrend.
1.-
122
potential
Given the current positive momentum and trend, and the fact that the stock price is near the upper Bollinger Band, might be expected to correct consolidate the near future. However, the overall trend remains positive. A reasonable prediction would be a slight correction followed by continuation of the upward trend.
Let's prodict the next 10 days' closing prices to be slightly lower than the currant price but still within recent </think>
180
Figure 10: VTA reasoning about prices to be slightly lower than last price but within recent range.
<think>
The stock has been showing trend recovery from its minimam price of -0.7999 to its maximum price moving averages suggest slight upward trand, with simple moving average (5MA)
Fracition
and exponential moving average [EMA] both indicating positive values. The mamentum and relative strength index (R51) also
indicating bullish
The MACD is positive, suggesting
bullish momentum. The Williams 588 and Commodity Channel Index (CCi) suggest that the stock is not
overbought in bullish phase. Bollinger Bands show the stock band, indicating a strong bullish trend. The stochastic indicators are also positive, sugpesting that the stock in uptrend.
within upper
Given these indicators, it likely that the stock will continue to rise in the next 10 days. However, the exact
predict precisely. We can assume slight increase closing prices, staying within the range of the current prices and the upper Bollinger Band. </thinks
15.6
162
Figure 11: VTA reasoning about a slight increase in price but staying within the current range.
<thinks
showing downward trend The moving averages suggest declining trend as well. The RSI is quite low, indicating the stock might be oversold. The MACD line also nogative, suggesting bearish trend. The williams '6R negative, indicating strong everselling, The Bollinger Bands show the stock price below the middle band, which is consitent with bearish trend. The Stochastic Oscillator alsa in the oversold region, which might suggest potential bounce.
lowest price 9 highest on day 3.
trend and indicators, likey the stock will continue dedline might experience a short term hounce. Howevar, the overall trand is bearish. will prodict a slight decline with potential short-term bounce.
Given
</thinke
25
30
300
125
154
Figure 12: VTA reasoning about a potential short-term bounce, which realized in the ground truth.
18

---

Published as a conference paper at ICLR 2026
<think>
increasing over past days, recent peak day 9: moving overages suggest a siigt upward trend, but the momentum and Williams S6R indicate some overbought conditions, The Bollinger Bands the
prices
Gna sCTun
above middle band, but yet upper band, sugpesting a potential for pullbckk However, territory, indicating bullish trend. Given the recent increase and the positive indicators, the stock is likely continue its upward trend, but with some polential for pullback.
300
and MACD are still positive
2.5
and educated guess. The prices are likely to remain above the recent lows but not necessanll break new highs. </thinko
Predicting prices challenging,
slight increase from the last price, with potential for pullback.
2.01
Figure 13: VTA reasoning about slight increase, with potential for a pullback (i.e., trend reversal).
F LLM-AS-A-JUDGE
To ensure reproducibility of the evaluation method, we further evaluated the quality of the reasoning generated by VTA using LLM-as-a-judge (Zheng al.
2023 Gu et al. (024). To do this, we first
randomly sample 1,000 reasoning traces for evaluation. The reasoning samples are then evaluated
using a stronger model as the judge, GPT-4.1 (OpenAl 2025). The samples judged the same metrics on scale 1-5 and the average scores over all samples is reported.
Table 8: Comparison on reasoning quality across different VTA variants and reasoning LLMs.
Clarity
Depth
Accuracy
Coherence
Relevance
Overall
GPT-4.1 mini
4.06
1.87
2.37
4.05
2.29
2.93
Deepseek-R1
3.95
3.02
2.97
3.98
3.25
3.43
VTA (Ours)
4.21
4.14
3.96
4.57
4.58
4.29
From Table 8 observe that there is clear improvement in all metrics from the inference-only reasoning models to our VTA model, showing the effectiveness the fine-tuning process. Impor- tantly, when comparing these LLM-as-judge results the human experts ratings, we also find that the relative differences are highly consistent: VTA shows the largest margin over baselines in Depth, Accuracy, and Relevance, while the smallest gap is seen Coherence and Clarity.
G COMPARISON IN OTHER DOMAINS
Table reports the performance comparison the Healthcare (ILI) and Energy (ETTh1) datasets.
Table 9: Performance comparison time-series from other domains.
II.I
ETTh1
MSE
MAE
MSE
MAE
Informer
1.7372
0.8669
0.3128
0.4848
Transformer
1.6827
0.8122
0.2505
0.4360
Crossformer
1.3320
0.7270
0.1298
0.2914
TSMixer
2.1163
0.9504
0.0877
0.2375
Reformer
1.5554
0.8134
0.2422
0.4020
LighTTS
2.3121
1.0612
0.0625
0.1921
DLinear
2.9160
1.3933
0.0404
0.1499
FiLM
2.6761
1.3856
0.0423
0.1543
stationary
1.4328
0.8538
0.0403
0.1530
MICN
1,3431
0.8732
0,3063
0.4649
Autoformer
1.7554
1.0350
0.0736
0.2146
TimesNet
1.0795
0.6910
0.0410
0.1572
CALF
1.4442
0.7409
0.0361
0.1411
VTA (Ours)
1.4366
0.7090
0.0346
0.1374
19

---

Published as a conference paper at ICLR 2026
Here, observe that the healthcare and energy datasets do not follow the same performance pat- terns we saw in financial time-series, which could be attributed to different defining characteristics. For example, models that benefit from reand-seasonal decomposition financial data do not show the same advantage here, likely because IIII and ETTh1 do not exhibit the same cyclical structure.
In these domains, especially the ILI dataset, our VTA model also does not exhibit performance that too far away from the time-series LLM model (CALF). It is possible that the additional reasoning traces provide limited benefit when there is much complex signals reason over outside of simple trend extrapolation, which was previously visualized in Figure 8
H FINANCIAL ANALYSIS OF PORTFOLIOS
To assess the robustness of our portfolio returns, we apply standard performance attribution models widely used in finance. These models allow separate returns that can be explained by common risk exposures from those that reflect potential strategy-speciff value.
Capital Asset Pricing Model (CAPM): A baseline model that relates portfolio returns to overall market returns. provides a measure of whether the strategy delivers excess returns (alpha) after adjusting for market risk (beta).
Fama-French multi-factor models: Extensions of CAPM that incorporate additional risk fac- tors, such company size, value versus growth, profitability, investment patterns, and momentum. These factors capture well-documented drivers returns beyond market exposure.
These models enables us to evaluate whether our results are explained by general market and factor exposures whether they demonstrate incremental performance beyond established benchmarks.
Following industry practice, we conduct an in-depth performance attribution study of the portfolio formed from our VTA method, using both CAPM and the Fama-French 6-factor model.
H.1 THE CAPM REGRESSION MODEL
For CAPM, the following model was applied the daily returns of our portfolio and the market:
(RVTA. RT,) a + B (Rmarket.t R2 + Et.
where:
R Ry,c): The excess return of our portfolio on day t.
Rmarket.t Ry The excess return of the market benchmark on day t.
Alpha (a): The regression intercept, which represents the portion the portfolio's 's return
that is not explained by market movements.
Beta (3): The regression slope, which measures the portfolio's systematic risk relative to the market.
Epsilon (e.): The error for day
A positive and statistically significant alpha is indicative of a superior strategy, whereas alpha of would mean that it has the same performance as the benchmark CAPM method. Additionally, we also report the R-squared of the methods against the benchmark, which show how much of the return variation can be explained by CAPM. For investors, low R-squared would be ideal as they show that the trading signals are less correlated, which reduces the idiosyncratic risk of the portfolio.
We regressed our daily portfolio returns against the excess market return for each region, using the representative market indices. Below is summary of results across the four datasets:
The CAPM regression results provide a useful first diagnostic for understanding the tisk-adjusted performance of our strategy. Across all four datasets, the strategy exhibits positive annualized alpha, which suggests that returns exceed those predicted by exposure to the overall market alone. How- ever, statistical significance is only achieved in the China A50 dataset, where the alpha is both high
20

---

Published as a conference paper at ICLR 2026
Table 10: CAPM Regression Results.
Dataset
Market Index Used
Beta (8)
Annualized Alpha (a)
Alpha p-value
R-squared
dowjones 30
Dow Jones Industrial (DJI)
0.6745
+3.41%
0.701
44.04%
stocknet
MSCI World (ACWI)
0.6312
+10.12%
0.198
36.28%
ssc_china_a50
FTSE China A50 ETF (2822.HK HK)
0.3562
+53.91%
0.008
26.72%
eurostoxx_50
EURO STOXX 50 (STOXX50F)
0.2804
+15.44%
0.120
15.93%
(+53.91%) and significant (p 0.008). In other markets, while alpha remains positive, the higher p-values suggest weaker evidence of systematic outperformance.
Beta values in the range of 0.28 0.67 indicate moderate market exposure. This shows that the strategy is not entirely market-neutral, but also not simply tracking index movements. This aligns with our use of short-term technical forecasts rather than macro-driven positions.
The R-squared values, which range from 15.93% to 44.04%, show that a significant portion of return variation is not explained by CAPM, particularly in the European and Chinese markets. This points to meaningful degree of idiosyncratic return generation, consistent with model that is extracting useful trading signals beyond traditional market risk.
H.2 THE FRENCH 6- F-FATTTR MODEL
We further evaluated performance using the Fama-French 6-factor model. This model expands CAPM by adding five more factors: market, size, value, profitability, investment, and momentum:
Rp R = a + (Rm Rÿ) + B2 SMB + 83 HML + 84 RMW + A5 CMA + SB WML +
Table 11: Fama-French 6-Factor Regression Results.
Dataset
Annualized Alpha (a)
Alpha p-value
R-squared
dowjones_30
+0.18%
0.985
38.77%
stocknet
+9.57%
0.213
39.37%
ftse eiee.china.cm a50
+73.40%
0.001
13.81%
eurostoxx_50
+13.67%
0.184
12.35%
The Hama-Frenchh 6-factor analysis gives a more granular view of performance. While positive alpha persists in all datasets, their signficance varies, ranging from 0.001 in China A50 (very significant) to 0.985 (not significant). One possible reason for the lack of significance could be due to the short period horizon of our test dataset (1 year).
The R-squared values, ranging from 12.35% to 39.37%, indicate that even after accounting for broader set of risk factors beyond CAPM, significant share of return variation remains unexplained, which good. In our work, technical analysis is typically designed for short-term optimization, capturing brief momentum, reversal, volume-based effects. Because of this short-term focus, our model is not expected to align closely with morgzooiion economic models like CAPM or Fama- French, which are typically evaluated over months quarters.
Overall, the results demonstrate that our approach does offer some complementary value to CAPM and Fema--rennh factors to be used as an interpretable, forward-looking portfolio signal.
21


Reviews:
[{"review_id": "AAgO2Szhun", "reviewer": "Reviewer_jfgi", "summary": "This paper proposes a novel framework called Verbal Technical Analysis (VTA), which combines verbal (LLM-based) and latent (time-series model-based) reasoning for interpretable financial time-series forecasting. The framework introduces a reinforcement learning training pipeline (Time-GRPO) to optimize reasoning quality, and conditioning mechanisms to guide forecasts based on reasoning outputs. VTA is evaluated on multiple datasets across U.S., Chinese, and European markets, achieving state-of-the-art results in both forecasting accuracy and interpretability.", "questions": "How well does VTA scale to longer prediction horizons or more volatile, high-frequency financial time-series?\n\nDoes the verbal reasoning always align with the actual forecast, or are there cases where they contradict, and how is that handled?\n\nHow does VTA perform under market regime shifts or structural breaks, such as during crises or unexpected economic events?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper proposes a novel framework, Verbal Technical Analysis (VTA), which effectively integrates large language models with time-series forecasting to produce both accurate and interpretable stock predictions.\n\nIt introduces an innovative reinforcement learning objective (Time-GRPO) using inverse MSE as a reward to improve the quality of verbal reasoning aligned with forecast accuracy.\n\nThe model generates natural language reasoning traces that are rated highly by financial domain experts, adding a valuable layer of explainability for real-world financial decision-making.\n\nThe evaluation is rigorous, including ablation studies, expert human assessment, and practical validation through portfolio optimization, demonstrating both technical soundness and practical utility.", "weaknesses": "The proposed training pipeline is multi-staged and complex, involving cold-start RL, supervised fine-tuning, and joint conditional training, which may limit reproducibility and increase implementation overhead.\n\nThe paper does not compare VTA’s reasoning or performance against human-crafted technical analysis rules or human analysts, which would be a natural baseline for such a task.\n\nThe paper does not compare VTA with financial time seires LLMs, e.g., Kronous, which are related to financial analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "zQ0VUvFoj3", "reviewer": "Reviewer_ndFL", "summary": "The authors propose a method for training a model that can reason over financial time series. The model works by jointly learning over textual and numerical representations of time series data. The authors conduct an evaluation by comparing the method to several other LM-based time series forecasting methods and conduct a human evaluation to measure the plausibility of the model's generated traces.", "questions": "- Why do we need dedicated financial time series reasoning? Why aren't the other times series LLMs useful for this job? \n- How does performance compare to simpler models? \"Are Language Models Actually Useful for Time Series Forecasting?\" Tan et al. 2024 shows that many LLM-based methods for time series forecasting are outperformed by simple baselines (like mean prediction). \n- Could the authors provide more information on how they tuned their baselines? Im concerned that the baseline models could outperform the proposed method given sufficient tuning.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Well-written, easy to understand\n- This paper contrasts with others in the area by combining neural representations of LMs with text embeddings. Most other papers focus only on time series expressed as text or on neural embeddings, not both.\n- Strong choice of baselines and third-party datasets\n- The use of experts for annotation is useful and lends confidence towards the model's performance", "weaknesses": "On the whole I think this is a very strong paper. However, without details on how baselines were tuned it's difficult for me to have confidence in the method's performance. If the authors can provide this information then I'll happily increase my score to an accept.   \n\nTo be clear, my primary concern is that the proposed method could be outperformed by (1) simple statistical methods or (2) strongly hyperparameter-tuned baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "c07tU1u3dv", "reviewer": "Reviewer_DJah", "summary": "This paper proposes Verbal Technical Analysis (VTA) — a hybrid reasoning framework that aims to make time-series stock forecasting interpretable by combining:\n- A verbal reasoning LLM fine-tuned via a proposed Time-GRPO objective (using inverse-MSE reward);\n- A latent time-series backbone model conditioned on the reasoning outputs through joint conditional training.\n\nExperiments across U.S., Chinese, and European stock datasets suggest VTA achieves better MSE/MAE than prior time-series LLMs (e.g., Time-LLM, CALF) and generates reasoning traces preferred by human financial experts. The paper also evaluates downstream portfolio performance using Sharpe ratio and CAPM/Fama-French models.", "questions": "1. How sensitive is VTA to the choice of base LLM (e.g., Qwen vs Llama)?\n\n2. Can the verbal reasoning traces be used independently for explainable trading, or are they only auxiliary features for the time-series decoder?\n\n3. Would random or adversarial reasoning degrade forecasting performance — i.e., is the reasoning genuinely informative?\n\n4. Is the “inverse-MSE reward” stable during optimization? Any reward hacking observed?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "pros:\n1. Interesting conceptual direction — bridging LLM reasoning and numerical forecasting is a timely and under-explored research frontier, particularly for financial data.\n\n2. Clear problem motivation — the authors highlight the lack of interpretable time-series reasoning methods and position VTA as a step toward explainable financial LLMs.\n\n3. Engineering completeness — the framework, ablations, and appendices (notably the extensive tables on pages 6–9 and expert evaluation in Appendix C) are well-executed and professional.\n\n4. Human evaluation effort — 25 finance professionals were surveyed; results show statistically significant preference for VTA reasoning over GPT-4.1 and DeepSeek-R1 (Table 7).\n\n5. Empirical results — consistent improvement across four datasets; the method performs competitively even under portfolio-level evaluation (Table 5).", "weaknesses": "1. The “Time-GRPO” formulation largely reuses GRPO (Shao et al., 2024) with an inverse-MSE reward; conceptually it is a direct extension rather than a fundamentally new RL objective. The “joint conditional training” (Figure 3) borrows heavily from classifier-free guidance (Ho & Salimans 2022) and conditional diffusion principles. The novelty lies more in application assembly than in algorithmic innovation.\n2. The paper never establishes whether the verbal reasoning causally improves forecasting accuracy, or merely adds correlated signals. The conditioning between textual reasoning and time-series output feels heuristic; no ablation demonstrates whether removing reasoning entirely (vs. random text conditioning) degrades performance beyond noise.\n3. The “expert evaluation” metrics (Clarity, Depth, etc.) are subjective and lack reproducibility; no public dataset of reasoning traces is released. The forecasting datasets are relatively small (StockNet, A50, EUROSTOXX 50). Improvements of ≈2–3% MSE over CALF (Table 2) may not be statistically meaningful beyond the reported seeds.\n4. Does not compare to simpler text-free multimodal adapters (e.g., cross-attention-based transformers with interpretable heads). The authors claim SOTA forecasting “and interpretability,” but do not quantify interpretability beyond expert survey averages.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "GcqPjtHclh", "reviewer": "Reviewer_6br1", "summary": "This paper presents verbal technical analysis (VTA) that integrates verbal reasoning through LLMs with latent time series forecasting to achieve interpretable financial prediction. The authors argue that existing LLM-based approaches primarily analyze textual financial information (e.g., reports or news), and VTA is distinct from them since it focuses on time series reasoning, transforming numerical data into natural language descriptions. Through extensive experiments across multiple stock datasets, VTA demonstrates state-of-the-art forecasting accuracy. In addition, VTA achieves superior expert evaluation scores compared to GPT and DeepSeek.", "questions": "- How does VTA perform when applied to non-financial time series data?\n- Does VTA show consistent reasoning patterns across markets (U.S. vs. China vs. Europe)?\n\nIn addition, please refer to the weaknesses above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Each component of the framework (time series reasoning, forecasting, etc) is well-motivated and properly designed. \n- Experiments are comprehensive, covering a wide range of datasets, baselines, and metrics.\n- The expert-based reasoning evaluation is practically valuable. \n- The authors provided their code anonymously for reproduciblity.", "weaknesses": "- Some parts of the paper are unclear:\n  - The discussion of Figure 1 should be expanded. Currently, the y-axis and evaluation criteria are ambiguous. It would be helpful to explicitly describe what each score measures and how they were computed.\n  - The notation *s* in Eq. (10) and Figure 3 should be aligned for consistency.\n  - Some terms in Table 5 (e.g., volatility, drawdown) should be clearly defined in the main text for non-finance readers.\n- The scope of the paper needs justification:\n  - The paper focuses only on financial datasets. Please elaborate on why the financial domain is particularly challenging (if so) for time series reasoning. \n  - Please discuss whether VTA could generalize to other domains (e.g., healthcare, energy).\n- There already exists a recent work that explores LLM-based time series explanation. The paper should clearly position VTA with respect to this approach by discussing or empirically comparing it.\n  - TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents (AAAI 2025) \n\n\nIn summary, while the idea is interesting and timely, the paper needs a clearer positioning with the existing work. ALso, several aspects of the presentation could be improved.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)