F in BPM : A Framework for Portfolio Management-based Financial Investor Behavior Perception Model (2024)

Abstract: The goal of portfolio management is to simultaneously maximize the accumulated return and also to control risk. In consecutive trading periods, portfolio manager needs to continuously adjust the portfolio weights based on the factors which can cause price fluctuation in the market. In the stock market, the factors affecting the stock price can be divided into two categories. The first is price fluctuations caused by irrational investment of the speculators. The second is endogenous value changes caused by operations of the company. In recent years, with the advancement of artificial intelligence technology, reinforcement learning (RL) algorithms have been increasingly employed by scholars to address financial problems, particularly in the area of portfolio management. However, the deep RL models proposed by these scholars in the past have focused more on analyzing the price changes caused by the investment behavior of speculators in response to technical indicators of actual stock prices. In this research, we introduce an RL-based framework called FinBPM, which takes both the factor pertaining to the impact on operations of the company and the factor of the irrational investment of the speculator into consideration. For our experimentation, we randomly selected twelve stocks from the Dow Jones Industrial Index to construct our portfolio. The experimental results reveal that, in comparison to conventional reinforcement learning methods, our approach with at least 13.26% increase over other methods compared. Additionally, it achieved the best Sharpe ratio of 2.77, effectively maximizing the return per unit of risk.

## introduction many studies conclude that stock movements follow a random walk @xcite figure 1 : aapl reported on august 2 that "apple heads for largest q3 revenue drop since 2016 as iphone sales slow", and the stock price dropped significantly a few days later. 2015). with the development of artificial intelligence techniques, many scholars attempt to predict stock price movements or trends based on machine learning @xcite @xcite . however, it also turns out that accurately predicting future market prices remains difficult. therefore, rather than directly forecasting prices, our work transforms the problem into predicting investment flows based on investor behavior. by modeling endogenous and exogenous behavioral factors influencing stock price, we aim to emulate macro-level investor dynamics that drive market prices. this agent-based, behavior-centric perspective circumvents the need to absolute valuation, instead focusing on more tractable signals correlated with crowd behavior. investor behavior is driven by two key factors: irrational price fluctuations from speculative market timing @xcite , and fundamental company value @xcite . numerous models exploit irrational price fluctuations @xcite @xcite @xcite . while price and volume data can capture irrational fea-tures @xcite , these signals alone do not consider endogenous information from events like earnings surprises, mergers, or corporate actions @xcite . such value-relevant events are often disclosed through financial news and social media, as well as substantially impact market movements @xcite . models such as s-reward @xcite , sarl @xcite , and profit @xcite incorporate news text to estimate intrinsic value and inform decisions. however, they rely on correlating news with prices rather than directly extracting fundamental value, losing the core purpose of fundamental value discovery. figure 1 gives the correlation between aapl stock price trends and news. when apple was reported to have slowed down iphone sales on august 2, the stock price fell for several days. additionally, using individual tweets or headlines provides limited information @xcite , as not all texts equally impact value @xcite . these limitations motivate research info efficiently utilizing financial text with prices to model investor behavior. an integrative model of investor behavior requires incorporating market transaction data with textual sources reflecting intrinsic value. our research addresses these limitations by: 1) directly parsing semantic signals of intrinsic value from news content rather than just price correlations; 2) leveraging full articles to extract richer insights versus restricted headlines or tweets; 3) filtering text to focus on the most relevant endogenous valuation drivers. this allows more targeted modeling of how financial news influences investor demand through fundamental value, complementing price data that captures exogenous speculation. ## background this paper presents a framework for portfolio management that incorporates investor behavior forecasting which combines financial text processing techniques and price volatility characteristics techniques. we provide an overview of the relevant technologies for each component, a new financial investor behavior perception module for enhancing investment portfolio management tasks. financial text processing techniques. financial text processing techniques involve the use of natural language processing (nlp) to analyze financial text data such as financial statements, news articles, and social media posts. textrank @xcite ) is a graph-based algorithm for text summarization and keyword extraction. it constructs a graph of relationships between sentences in the text and calculates the importance of sentences based on their similarities. pegasus @xcite ) is a pre-trained language generation model that uses the transformer architecture and is trained on large-scale text data. the aim of pegasus is to generate high-quality text summaries, compressing long texts into concise summaries. in the context of financial news pro-cessing, using textrank can help extract the most important sentences from the full text, enabling faster access to key information. on the other hand, pegasus can summarize the entire article, providing a concise summary to help users quickly grasp the core content of the article. finbert @xcite ) is a language model pre-trained on financial text data. it is based on bert (bidirectional encoder representations from transformers) @xcite , but pretrained on a large corpus of financial documents like earnings reports, analyst reports, news articles, and financial forums. achieves state-of-the-art results on many financial nlp benchmarks. price volatility characteristics techniques. historical stock prices, trading volume and other information can be used to predict stock prices @xcite . lstm has been widely used for time series prediction due to its ability to handle long sequences using gating mechanisms @xcite . however, it faces challenges with increasingly long sequences and poor performance in extreme cases. informer uses a transformer architecture that can process both long and short sequences while effectively learning and extracting features at different time scales @xcite . it also incorporates attention mechanisms and adaptive lengths to further improve prediction performance. analyzing social media data or the sentiment of news reports is also a way to predict stock prices @xcite . ## problem description we shall describe the traditional model corresponding to the portfolio optimization problem. let @xmath0, s 2 , . . . , s n } denote a set of n stocks. in portfolio optimization we design a stock trading model to generate maximum cumulative return over all the stock trades across n stocks within a time period t . the cumulative return corresponding to any stock s i at any time step τ depends on the state of the stock at that time step {s iτ } and the trading action applied to s i , denoted as a[s i ] τ . we establish our state space {s iτ } based on profit @xcite . this research utilizing finrl @xcite , an open-source framework, to systematically construct the action space a[s i ] τ and rewards function r . and the holdings n[s i ] τ . m[s iτ ] have financial news related to s i released during a t -day lookback period. ## proposed framework our proposed framework consists of two different modules. they are financial investor behavior perception module and investment decision module. broadly speaking, the aim of perception module is to provide an idea about the current state of the stock. the perception module outputs an index that is exploited by the investment decision network to determine the trading action at any time t. figure 2 gives the framework of finbpm. each module of finbpm is described as follows. ## experiment setup in this section, we first provide an overview of the dataset used in our experiment. subsequently, we introduce into the various baseline methods employed in our experimental setup, elaborating on the hardware and parameter configurations. ## results and discussion figure 3 gives the cumulative return for finbpm versus baseline methods. the red line (finbpm) shows exhibiting stable growth overall, with only a minor drawdown around the 60th trading day. in contrast, other methods display larger fluctuations in the second half of the trading period, while finbpm maintains a relatively steady upward trajectory. this highlights advantage of finbpm in risk control and consistent returns, particularly in the latter trading days, where even rmr sees volatile shocks. the stable growth trend of finbpm throughout the timeline, with minimal drawdown, demonstrates its robustness in portfolio management. overall, the experimental results validate the advantages of finbpm in dual investor behavior modeling and reinforcement learning optimization for enhanced portfolio management. ## effect of news on trading in order to better observe the impact of news information on stock trading, we first traded a single stock. that is, there is no linkage between stocks, and only buy, hold, and sell operations are made for a certain stock. we selected eight target stocks such as aapl and jpm for the experiment, and compared the impact of using financial news signals and not using them on stock buying and selling decisions. for each stock, we conducted five experiments with and without news data, and effect of α in equation 3 : we also investigated the variation of cumulative return for three extreme values of α (i.e. the parameter controlling the contribution of news content in estimating overall index as described in equation 3 ). the value of cumulative return with α = 0.1 is 35.19%. and with α = 1, it is 1.72% in our approach. ## conclusions and future work in this research, we propose finbpm, a portfolio management framework based on predicting investor behavior using reinforcement learning. it leverages linguistic models to comprehensively analyze intrinsic value information of companies from news text, and captures irrational volatility characteristics from price and trading volume data. extensive ablation experiments are conducted to determine the optimal processing of financial text for maximizing intrinsic value signals under portfolio management. the results demonstrate the strengths of finbpm in integrating textual and time series signals for investor behavior modeling and portfolio management optimization. experiments show that finbpm gains 13.26% returns over state-ofthe-art models, while controlling maximum drawdown to 10.10%. in the future, we will support high-frequency trading strategy and explorer more financial market trading.