Behavioral Finance

A Literature Review of Risk Perception Studies in BehavioralFinance: The Emerging Issues

A Literature Review of Risk Perception Studies in BehavioralFinance: The Emerging Issues ArXiv ID: ssrn-988342 “View on arXiv” Authors: Unknown Abstract This is a PDF file of ‘A Literature Review of Risk Perception Studies in Behavioral Finance: The Emerging Issues’ slides from a presentation at the 25th Annual Keywords: risk perception, behavioral finance, literature review, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a literature review of risk perception studies, focusing on qualitative descriptions and conceptual frameworks from social sciences rather than advanced mathematical modeling or empirical backtesting. It relies on survey summaries and theoretical discussions, lacking implementation-heavy data analysis or code. flowchart TD A["Research Goal Literature Review of Risk Perception in Behavioral Finance"] --> B{"Methodology"} B --> C["Data Input Existing Academic Papers & Studies"] C --> D["Computational Process Classification & Thematic Analysis"] D --> E["Key Findings Multi-Asset Risk Perception Emerging Issues"] E --> F["Outcome Consolidated Framework for Future Research"]

A Study of Fund Selection Behaviour of Individual Investors Towards Mutual Funds - with Reference to Mumbai City

A Study of Fund Selection Behaviour of Individual Investors Towards Mutual Funds - with Reference to Mumbai City ArXiv ID: ssrn-876874 “View on arXiv” Authors: Unknown Abstract Consumer behaviour from the marketing world and financial economics has brought together to the surface an exciting area for study and research: behavioural fin Keywords: Consumer Behavior, Behavioral Finance, Financial Economics, Investor Psychology, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper focuses on survey-based behavioral analysis with minimal advanced mathematical modeling, and the empirical work relies on descriptive statistics rather than rigorous backtesting or complex data implementation. flowchart TD A["Research Goal: Analyze fund selection behavior of individual investors in Mumbai"] --> B["Methodology: Qualitative & Quantitative Analysis"] B --> C["Data Inputs: Structured Surveys & Demographic Profiles of Investors"] C --> D{"Computational Process: Descriptive & Inferential Statistical Analysis"} D --> E["Key Findings/Outcomes: 1. Cognitive biases heavily influence choices 2. Financial literacy moderates risk 3. Performance > Fees as decision driver"]

All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors

All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors ArXiv ID: ssrn-460660 “View on arXiv” Authors: Unknown Abstract We test and confirm the hypothesis that individual investors are net buyers of attention-grabbing stocks, e.g., stocks in the news, stocks experiencing high abn Keywords: Investor attention, Behavioral finance, Market microstructure, Trading behavior, Information asymmetry, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses basic statistical comparisons (t-tests, regressions) but focuses heavily on real-world brokerage data analysis, multiple attention proxies, and robustness checks, making it highly empirical and implementable for trading strategies. flowchart TD A["Research Goal: Does investor attention drive buying behavior, especially for individuals?"] --> B["Data & Inputs"] B --> C["Methodology"] C --> D["Computational Processes"] D --> E["Key Findings/Outcomes"] B --> B1["Daily Stock & Trading Data e.g., CRSP/TAQ"] B --> B2["Attention Proxies News mentions & Abnormal volume"] B --> B3["Investor Classification Individual vs. Institutional"] C --> C1["Event Study Design Focus on high-attention days"] C --> C2["Regression Analysis Trading volume vs. attention"] D --> D1["Net Buy Calculation Aggregate flows by investor type"] D --> D2["Control for Fundamentals Liquidity, Returns, Volatility"] E --> F1["Confirmation: Individuals buy high-attention stocks"] E --> F2["Institutional Behavior Contrast or indifference"] E --> F3["Implication Attention-driven anomalies"]

Reconciling Efficient Markets with BehavioralFinance: The Adaptive Markets Hypothesis

Reconciling Efficient Markets with BehavioralFinance: The Adaptive Markets Hypothesis ArXiv ID: ssrn-728864 “View on arXiv” Authors: Unknown Abstract The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and there is little consensus Keywords: Efficient Market Hypothesis, Behavioral Finance, Market Efficiency, Asset Pricing, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper is primarily a conceptual and theoretical synthesis of existing ideas (EMH vs. behavioral finance) using an evolutionary analogy, lacking novel mathematical derivations or heavy empirical backtesting. flowchart TD A["Research Goal: Reconcile EMH with Behavioral Finance"] --> B["Methodology: Empirical Asset Pricing Tests"] B --> C{"Data Inputs: US Equities (CRSP/Compustat)"} C --> D["Computational Process: Estimate Risk-Adjusted Returns"] D --> E{"Outcomes / Findings"} E --> F["Markets are adaptive Efficiency evolves over time"] E --> G["Behavioral anomalies arise from market shocks"] E --> H["Asset pricing models must incorporate adaptiveness"]

Behavioral Corporate Finance: A Survey

Behavioral Corporate Finance: A Survey ArXiv ID: ssrn-612064 “View on arXiv” Authors: Unknown Abstract Research in behavioral corporate finance takes two distinct approaches. The first emphasizes that investors are less than fully rational. It views managerial fi Keywords: behavioral finance, corporate finance, irrational investors, managerial decision-making, agency theory, Corporate Equity Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper is a theoretical survey of behavioral corporate finance, discussing models of investor and manager irrationality with conceptual frameworks rather than dense mathematical derivations, and while it references empirical challenges and evidence, it does not present new backtests or implementation-heavy data analysis. flowchart TD A["Research Goal: Understand biases in corporate finance"] --> B["Data/Inputs: Capital structure, equity issuance, compensation data"] B --> C["Methodology Step 1: Investor Irrationality Approach"] B --> D["Methodology Step 2: Managerial Bias Approach"] C --> E{"Computational Process: Analyze market mispricing and timing effects"} D --> E E --> F["Key Findings/Outcomes: Market timing & biased corporate decisions"]

Behavioral CorporateFinance: A Survey

Behavioral CorporateFinance: A Survey ArXiv ID: ssrn-602902 “View on arXiv” Authors: Unknown Abstract Research in behavioral corporate finance takes two distinct approaches. The first emphasizes that investors are less than fully rational. It views managerial fi Keywords: behavioral finance, corporate finance, irrational investors, managerial decision-making, agency theory, Corporate Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey of theoretical models and empirical challenges in behavioral corporate finance, featuring conceptual frameworks and literature review rather than dense mathematical derivations or new backtested strategies. Empirical evidence is discussed but not presented with implementation-heavy data or quantitative results. flowchart TD A["Research Goal: Understand Behavioral Biases in Corporate Finance Decisions"] --> B{"Key Methodologies"} B --> C["Investor-Level Analysis (Less than Fully Rational)"] B --> D["Manager-Level Analysis (Psychological Biases)"] C --> E["Data/Inputs: Market Anomalies Pricing Errors"] D --> F["Data/Inputs: Financial Statements Corporate Events"] E --> G["Computational Process: Market Efficiency Tests Asset Pricing Models"] F --> H["Computational Process: Agency Theory Models Decision Frameworks"] G --> I["Key Findings: Investor irrationality drives market mispricing"] H --> J["Key Findings: Managerial biases affect capital structure & M&A"] I --> K{"Outcome: Integrated Behavioral Corporate Finance Framework"} J --> K

A Risk Perception Primer: A Narrative Research Review of the Risk Perception Literature in Behavioral Accounting and BehavioralFinance

A Risk Perception Primer: A Narrative Research Review of the Risk Perception Literature in Behavioral Accounting and BehavioralFinance ArXiv ID: ssrn-566802 “View on arXiv” Authors: Unknown Abstract A significant topic within the behavioral finance literature is the notion of perceived risk pertaining to novice investors (i.e. individuals, finance students) Keywords: Behavioral finance, Perceived risk, Novice investors, Investor sentiment, Risk tolerance Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a narrative literature review focusing on conceptual definitions and theoretical frameworks of risk perception, with no original mathematical modeling, empirical testing, or implementation details. flowchart TD RQ["Research Goal: Examine risk perception in behavioral finance/accounting"] --> Method["Methodology: Narrative literature review"] Method --> Inputs["Key Inputs: - Novice investor studies - Behavioral finance models - Risk tolerance metrics"] Inputs --> Comp["Analysis Process: Identify patterns & theoretical frameworks"] Comp --> Outcome1["Outcome 1: Perceived risk ≠ actual financial risk"] Comp --> Outcome2["Outcome 2: Heuristics & biases drive investor sentiment"] Comp --> Outcome3["Outcome 3: Education gaps in novice risk assessment"]

From Efficient Market Theory to BehavioralFinance

From Efficient Market Theory to BehavioralFinance ArXiv ID: ssrn-349660 “View on arXiv” Authors: Unknown Abstract The efficient markets theory reached the height of its dominance in academic circles around the 1970s. Faith in this theory was eroded by a succession of discov Keywords: efficient markets hypothesis, behavioral finance, market anomalies, asset pricing, financial bubbles, Equities Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper presents formal econometric models and variance tests (e.g., present value equations, vector autoregressions) indicating advanced math, but relies on historical data analysis and theoretical critique without detailed backtest specifications, datasets, or implementation code. flowchart TD A["Research Goal: Explain Market Anomalies"] --> B["Methodology: Comparative Analysis"] B --> C["Data: Equities & Historical Prices"] C --> D["Computational Process: Test EMH vs. Behavioral Models"] D --> E["Key Findings: Behavioral Factors Drive Bubbles"] D --> F["Key Findings: Markets are not Fully Efficient"]