Behavioral Finance

A Survey of Behavioral Finance ArXiv ID: ssrn-332266 “View on arXiv” Authors: Unknown Abstract Behavioral finance argues that some financial phenomena can plausibly be understood using models in which some agents are not fully rational. The field has two Keywords: Behavioral finance, Asset pricing, Rational agents, Financial phenomena, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a comprehensive literature review discussing concepts like limits to arbitrage and psychology, which are conceptual and theoretical, lacking dense mathematical derivations or empirical backtesting results. flowchart TD A["Research Goal: Review behavioral finance models with non-rational agents"] --> B["Data/Inputs: Empirical asset pricing anomalies, survey data"] B --> C["Key Methodology: Literature survey, model comparison"] C --> D["Computational Processes: Psychological bias analysis, agent-based simulations"] D --> E{"Key Findings/Outcomes"} E --> F["Deviations from rational expectations"] E --> G["Persistent equity anomalies explained"] E --> H["Limited arbitrage success"]

Determinants of Saving Behavior Among Employees in Dhaka, Bangladesh ArXiv ID: 2507.21254 “View on arXiv” Authors: Soumita Roy, Md Muntasir Kamal Dihan, Tasnimah Haque, Nafisa Nomani, Sadia Islam Preety Abstract Purpose With an emphasis on elements like financial knowledge, financial attitude, social influence, financial self-efficacy, and financial management practices, this study explores the factors that influence employees’ saving behavior in Dhaka, Bangladesh. We also welcome others to work on saving behavior, which is the main reason for publishing. The purpose is to make others aware of the methods for quantitative financial behavior analysis in Bangladesh. Design/methodology/approach The study uses a quantitative approach with a cross-sectional survey design. Data was collected from 40 participants through a structured questionnaire adapted from reliable sources. The questionnaire captured demographic information and used established items to measure the key variables. Data analysis included descriptive statistics, reliability analysis using Cronbachs alpha, and regression analysis to test the hypothesized relationships. Findings The results indicate that among the factors examined, only financial management practices had a significant positive relationship with saving behavior. Rest of the factors did not show significant relationships with saving behavior in this study sample. Limitation or Disclaimer It is still a work in progress, this paper is meant for pre-print with mostly incomplete and limited data. No data cleaning was performed, so it is very likely to include outliers and faulty data. Originality or value This study contributes to the limited research on saving behavior determinants in the Bangladeshi context, specifically among employees in the capital city of Dhaka. It explores the influence of multiple factors, including the rarely studied aspect of social influence. ...

Your AI, Not Your View: The Bias of LLMs in Investment Analysis ArXiv ID: 2507.20957 “View on arXiv” Authors: Hoyoung Lee, Junhyuk Seo, Suhwan Park, Junhyeong Lee, Wonbin Ahn, Chanyeol Choi, Alejandro Lopez-Lira, Yongjae Lee Abstract In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data. These conflicts are especially problematic in real-world investment services, where a model’s inherent biases can misalign with institutional objectives, leading to unreliable recommendations. Despite this risk, the intrinsic investment biases of LLMs remain underexplored. We propose an experimental framework to investigate emergent behaviors in such conflict scenarios, offering a quantitative analysis of bias in LLM-based investment analysis. Using hypothetical scenarios with balanced and imbalanced arguments, we extract the latent biases of models and measure their persistence. Our analysis, centered on sector, size, and momentum, reveals distinct, model-specific biases. Across most models, a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies is observed. These foundational biases often escalate into confirmation bias, causing models to cling to initial judgments even when faced with increasing counter-evidence. A public leaderboard benchmarking bias across a broader set of models is available at https://linqalpha.com/leaderboard ...

Classifying and Clustering Trading Agents ArXiv ID: 2505.21662 “View on arXiv” Authors: Mateusz Wilinski, Anubha Goel, Alexandros Iosifidis, Juho Kanniainen Abstract The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial investors based on their behavioral patterns. Not only do we have access to both classification and clustering tools for high-dimensional data, but also data identifying individual investors is finally available. The problem, however, is that we do not have access to ground truth when working with real-world data. This, together with often limited interpretability of modern machine learning methods, makes it difficult to fully utilize the available research potential. In order to deal with this challenge we propose to use a realistic agent-based model as a way to generate synthetic data. This way one has access to ground truth, large replicable data, and limitless research scenarios. Using this approach we show how, even when classifying trading agents in a supervised manner is relatively easy, a more realistic task of unsupervised clustering may give incorrect or even misleading results. We complete the results with investigating the details of how supervised techniques were able to successfully distinguish between different trading behaviors. ...

Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market ArXiv ID: 2505.20608 “View on arXiv” Authors: Penggan Xu Abstract This study replicates the findings of Wang et al. (2017) on reference-dependent preferences and their impact on the risk-return trade-off in the Chinese stock market, a unique context characterized by high retail investor participation, speculative trading behavior, and regulatory complexities. Capital Gains Overhang (CGO), a proxy for unrealized gains or losses, is employed to explore how behavioral biases shape cross-sectional stock returns in an emerging market setting. Utilizing data from 1995 to 2024 and econometric techniques such as Dependent Double Sorting and Fama-MacBeth regressions, this research investigates the interaction between CGO and five risk proxies: Beta, Return Volatility (RETVOL), Idiosyncratic Volatility (IVOL), Firm Age (AGE), and Cash Flow Volatility (CFVOL). Key findings reveal a weaker or absent positive risk-return relationship among high-CGO firms and stronger positive relationships among low-CGO firms, diverging from U.S. market results, and the interaction effects between CGO and risk proxies, significant and positive in the U.S., are predominantly negative in the Chinese market, reflecting structural and behavioral differences, such as speculative trading and diminished reliance on reference points. The results suggest that reference-dependent preferences play a less pronounced role in the Chinese market, emphasizing the need for tailored investment strategies in emerging economies. ...

Theoretical Frameworks for Integrating Sustainability Factors into Institutional Investment Decision-Making ArXiv ID: 2502.13148 “View on arXiv” Authors: Unknown Abstract This paper explores key theoretical frameworks instrumental in understanding the relationship between sustainability and institutional investment decisions. The study identifies and analyzes various theories, including Behavioral Finance Theory, Modern Portfolio Theory, Risk Management Theory, and others, to explain how sustainability considerations increasingly influence investment choices. By examining these frameworks, the paper highlights how investors integrate Environmental, Social, and Governance (ESG) factors to optimize financial outcomes and align with broader societal goals. ...

Trends and Reversion in Financial Markets on Time Scales from Minutes to Decades ArXiv ID: 2501.16772 “View on arXiv” Authors: Unknown Abstract We empirically analyze the reversion of financial market trends with time horizons ranging from minutes to decades. The analysis covers equities, interest rates, currencies and commodities and combines 14 years of futures tick data, 30 years of daily futures prices, 330 years of monthly asset prices, and yearly financial data since medieval times. Across asset classes, we find that markets are in a trending regime on time scales that range from a few hours to a few years, while they are in a reversion regime on shorter and longer time scales. In the trending regime, weak trends tend to persist, which can be explained by herding behavior of investors. However, in this regime trends tend to revert before they become strong enough to be statistically significant, which can be interpreted as a return of asset prices to their intrinsic value. In the reversion regime, we find the opposite pattern: weak trends tend to revert, while those trends that become statistically significant tend to persist. Our results provide a set of empirical tests of theoretical models of financial markets. We interpret them in the light of a recently proposed lattice gas model, where the lattice represents the social network of traders, the gas molecules represent the shares of financial assets, and efficient markets correspond to the critical point. If this model is accurate, the lattice gas must be near this critical point on time scales from 1 hour to a few days, with a correlation time of a few years. ...

Functional Clustering of Discount Functions for Behavioral Investor Profiling ArXiv ID: 2410.16307 “View on arXiv” Authors: Unknown Abstract Classical finance models are based on the premise that investors act rationally and utilize all available information when making portfolio decisions. However, these models often fail to capture the anomalies observed in intertemporal choices and decision-making under uncertainty, particularly when accounting for individual differences in preferences and consumption patterns. Such limitations hinder traditional finance theory’s ability to address key questions like: How do personal preferences shape investment choices? What drives investor behaviour? And how do individuals select their portfolios? One prominent contribution is Pompian’s model of four Behavioral Investor Types (BITs), which links behavioural finance studies with Keirsey’s temperament theory, highlighting the role of personality in financial decision-making. Yet, traditional parametric models struggle to capture how these distinct temperaments influence intertemporal decisions, such as how individuals evaluate trade-offs between present and future outcomes. To address this gap, the present study employs Functional Data Analysis (FDA) to specifically investigate temporal discounting behaviours revealing nuanced patterns in how different temperaments perceive and manage uncertainty over time. Our findings show heterogeneity within each temperament, suggesting that investor profiles are far more diverse than previously thought. This refined classification provides deeper insights into the role of temperament in shaping intertemporal financial decisions, offering practical implications for financial advisors to better tailor strategies to individual risk preferences and decision-making styles. ...

Optimal Investment under the Influence of Decision-changing Imitation ArXiv ID: 2409.10933 “View on arXiv” Authors: Unknown Abstract Decision-changing imitation is a prevalent phenomenon in financial markets, where investors imitate others’ decision-changing rates when making their own investment decisions. In this work, we study the optimal investment problem under the influence of decision-changing imitation involving one leading expert and one retail investor whose decisions are unilaterally influenced by the leading expert. In the objective functional of the optimal investment problem, we propose the integral disparity to quantify the distance between the two investors’ decision-changing rates. Due to the underdetermination of the optimal investment problem, we first derive its general solution using the variational method and find the retail investor’s optimal decisions under two special cases of the boundary conditions. We theoretically analyze the asymptotic properties of the optimal decision as the influence of decision-changing imitation approaches infinity, and investigate the impact of decision-changing imitation on the optimal decision. Our analysis is validated using numerical experiments on real stock data. This study is essential to comprehend decision-changing imitation and devise effective mechanisms to guide investors’ decisions. ...

Exploring the Impact: How Decentralized Exchange Designs Shape Traders’ Behavior on Perpetual Future Contracts ArXiv ID: 2402.03953 “View on arXiv” Authors: Unknown Abstract In this paper, we analyze traders’ behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patterns of trader behavior in relation to trading volume, open interest, liquidation, and leverage. Our detailed examination of DEXs, especially those utilizing the Virtual Automated Market Making (VAMM) Model, uncovers a differential impact of open interest on long versus short positions. In exchanges which operate under the Oracle Pricing Model, we find that traders primarily act as price takers, with their trading actions reflecting direct responses to price movements of the underlying assets. Furthermore, our research highlights a significant propensity among less informed traders to overreact to positive news, as demonstrated by an increase in long positions. This study contributes to the understanding of market dynamics in digital asset exchanges, offering insights into the behavioral finance for future innovation of decentralized finance. ...