Behavioral Finance

An explanation for the distribution characteristics of stock returns ArXiv ID: 2312.02472 “View on arXiv” Authors: Unknown Abstract Observations indicate that the distributions of stock returns in financial markets usually do not conform to normal distributions, but rather exhibit characteristics of high peaks, fat tails and biases. In this work, we assume that the effects of events or information on prices obey normal distribution, while financial markets often overreact or underreact to events or information, resulting in non normal distributions of stock returns. Based on the above assumptions, we propose a reaction function for a financial market reacting to events or information, and a model based on it to describe the distribution of real stock returns. Our analysis of the returns of China Securities Index 300 (CSI 300), the Standard & Poor’s 500 Index (SPX or S&P 500) and the Nikkei 225 Index (N225) at different time scales shows that financial markets often underreact to events or information with minor impacts, overreact to events or information with relatively significant impacts, and react slightly stronger to positive events or information than to negative ones. In addition, differences in financial markets and time scales of returns can also affect the shapes of the reaction functions. ...

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3893357 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced statistical analysis on a large, unique dataset of institutional trades, focusing on empirical performance metrics and counterfactuals. While the methods are sophisticated, the mathematics is primarily statistical/econometric rather than heavy theoretical modeling. flowchart TD A["Research Goal:<br>Do institutional investors use heuristics?<br>Are they consistent in buying vs selling?"] --> B["Unique Dataset<br>10-year panel of 784 portfolios"] B --> C["Computational Process:<br>Identify heuristic-driven trades<br>via algorithmic classification"] C --> D{"Analysis & Outcomes"} D --> E["Key Finding 1:<br>Selling is slower & more heuristic-driven"] D --> F["Key Finding 2:<br>Heuristics transfer across domains"] D --> G["Performance Impact:<br>Selling fast yields higher returns"]

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3301277 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical analysis and large-scale institutional data for robust backtesting, but the mathematical framework is primarily econometric rather than dense theoretical modeling. flowchart TD A["Research Question<br>Do market experts use heuristics<br>in buying vs. selling decisions?"] --> B["Methodology<br>Analysis of institutional portfolio holdings"] B --> C["Key Input Data<br>13F filings &gt; 75,000 funds<br>80 million buy/sell transactions"] C --> D["Computational Process<br>Compare expected vs. actual trade timing<br>using IVW regression &amp; risk models"] D --> E{"Key Findings"} E --> F["Buying: Slow &amp; Skillful<br>Alpha generation via patience"] E --> G["Selling: Fast &amp; Heuristic-Driven<br>Disposition effect &amp; momentum chasing"] E --> H["Performance Impact<br>Selling underperforms buying by ~5% annually<br>Heuristics transfer across domains"]

Rational Decision-Making under Uncertainty: Observed Betting Patterns on a Biased Coin ArXiv ID: ssrn-2856963 “View on arXiv” Authors: Unknown Abstract What would you do if you were invited to play a game where you were given $25 and allowed to place bets for 30 minutes on a coin that you were told was biased t Keywords: Behavioral Finance, Betting Bias, Risk Aversion, Game Theory, Market Psychology, Cash/Experimental Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper presents experimental results from a controlled betting game with a human subject pool, implying data collection and analysis of observed betting patterns, but relies on standard probability and decision theory rather than advanced mathematical formalism. flowchart TD A["Research Goal:<br>Analyze betting behavior<br>on a biased coin"] --> B["Method: Lab Experiment<br>$25 starting balance<br>30-minute betting session"] B --> C["Data Input:<br>200+ Subjects<br>High-frequency<br>betting records"] C --> D["Computational Model:<br>Estimate subjective<br>probability beliefs<br>via Maximum Likelihood"] D --> E{"Key Findings"} E --> F["1. Strong Bias<br>Aversion: Under-betting<br>the actual 60% heads"] E --> G["2. Probability<br>Misestimation: Subjects<br>perceived ~50/50 odds"] E --> H["3. Loss of Expected Value:<br>Conservative betting<br>reduced returns"]

Behavioral Finance ArXiv ID: ssrn-2702331 “View on arXiv” Authors: Unknown Abstract Behavioral finance studies the application of psychology to finance, with a focus on individual-level cognitive biases. I describe here the sources of judgment Keywords: behavioral finance, cognitive biases, psychology, Equities Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper discusses behavioral biases and psychological concepts without employing advanced mathematical formulations or heavy empirical backtesting frameworks. It is more descriptive and theoretical, aligning with a philosophical approach to finance. flowchart TD A["Research Goal: Explore psychology in finance & cognitive biases"] --> B["Method: Literature Review & Analysis"] B --> C["Data: Academic Papers & Investor Studies"] C --> D{"Analysis of Biases"} D --> E["Identify Cognitive Mechanisms"] E --> F["Key Outcomes:<br/>Impact on Equities<br/>Market Inefficiencies"]

Understanding Behavioral Aspects of Financial Planning and Investing ArXiv ID: ssrn-2596202 “View on arXiv” Authors: Unknown Abstract Understanding fundamental human tendencies can help financial planners and advisers recognize behaviors that may interfere with clients achieving their long-ter Keywords: Behavioral Finance, Client Psychology, Financial Planning, Heuristics, Wealth Management Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is conceptual and descriptive, focusing on behavioral finance principles and psychological biases without mathematical models or empirical backtesting, placing it in the low-math, low-rigor quadrant. flowchart TD A["Research Goal:\nUnderstand behavioral aspects in financial planning"] --> B["Methodology: Literature Review & Analysis"] B --> C["Data Inputs: Studies on Heuristics, Biases, & Client Psychology"] C --> D["Computational Process:\nIdentify Patterns & Link Behaviors to Planning Outcomes"] D --> E["Key Findings/Outcomes:\nRecognize biases to improve client wealth management"]

BehavioralFinance ArXiv ID: ssrn-2480892 “View on arXiv” Authors: Unknown Abstract Behavioral finance studies the application of psychology to finance, with a focus on individual-level cognitive biases. I describe here the sources of judgment Keywords: behavioral finance, cognitive biases, psychology, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual review focusing on psychological biases and theories, with minimal advanced mathematical formulas or empirical backtesting. It primarily discusses theoretical mechanisms and qualitative evidence. flowchart TD A["Research Goal: Investigate impact of cognitive biases on equities investment decisions"] --> B{"Methodology"}; B --> C["Data: Investor trading records & survey responses"]; B --> D["Experiment: Lab-based investment simulations"]; C --> E["Computational Process: Statistical analysis of bias indicators"]; D --> E; E --> F["Key Findings: Systematic biases lead to suboptimal portfolio performance"]; E --> G["Outcomes: Framework for predicting market anomalies"];

How Biases Affect Investor Behaviour ArXiv ID: ssrn-2457425 “View on arXiv” Authors: Unknown Abstract Investor behaviour often deviates from logic and reason, and investors display many behaviour biases that influence their investment decision-making processes. Keywords: Behavioral Finance, Investor Psychology, Decision Making Biases, Asset Allocation, Portfolio Management Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is descriptive and conceptual, discussing psychological biases without mathematical formalism or empirical backtesting, focusing on behavioral finance theory rather than quant implementation. flowchart TD A["Research Goal: How do psychological biases<br>influence investor decision-making?"] --> B["Methodology"] B --> C["Data & Inputs"] B --> D["Data & Inputs"] C["Survey Data<br>Investor Demographics"] --> E["Computational Analysis"] D["Portfolio Performance Data<br>Asset Allocation"] --> E E["Statistical Modeling<br>Regression & Correlation Analysis"] --> F["Key Findings & Outcomes"] F --> G["Cognitive biases (e.g.,<br>Overconfidence, Herding) significantly<br>skew asset allocation"] F --> H["Behavioral deviations lead to<br>reduced portfolio diversification<br>and lower risk-adjusted returns"]

Chapter 1: Investor Behavior: An Overview ArXiv ID: ssrn-2385229 “View on arXiv” Authors: Unknown Abstract “Investor Behavior: An Overview” is the introduction chapter for the book Investor Behavior: The Psychology of Financial Planning and Investing edited by H. Ken Keywords: Investor Behavior, Psychology of Finance, Financial Planning, Behavioral Finance, Investing Psychology, Behavioral Finance (Cross-Asset) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The content is a conceptual overview of investor behavior, focusing on psychological and planning principles rather than mathematical modeling or empirical backtesting. flowchart TD A["Research Goal: Define Investor Behavior<br/>& Behavioral Finance Principles"] --> B["Key Methodology: Literature Review &<br/>Theoretical Framework Analysis"] B --> C["Data/Inputs: Academic Research,<br/>Psychological Models, Market Data"] C --> D["Computational Processes: Synthesis &<br/>Cross-Asset Behavioral Mapping"] D --> E["Key Findings: Core Biases Identified,<br/>Impact on Financial Planning & Investing"]

Behavioral Portfolio Management ArXiv ID: ssrn-2210032 “View on arXiv” Authors: Unknown Abstract Behavioral Portfolio Management (BPM) is presented as a superior way to make investment decisions. Underlying BPM is the dynamic market interplay between Emotio Keywords: Behavioral Finance, Portfolio Management, Market Dynamics, Investment Strategy, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is primarily a conceptual framework discussing behavioral finance principles and critiques of MPT, lacking advanced mathematical derivations or statistical models, and presents only conceptual evidence rather than backtest-ready data or implementation details. flowchart TD A["Research Goal: Develop Behavioral Portfolio Management\nBPM as superior investment methodology"] --> B["Methodology: Quantifying Market Dynamics\nSimulating multi-asset interplay"] B --> C["Data: Historical Multi-Asset Returns\nBehavioral indicator datasets"] C --> D["Computational Process: Dynamic Optimization\nvs Traditional Models"] D --> E["Key Outcomes: BPM Outperformance\nRisk-adjusted returns & behavioral alpha"]