Quant Finance Research Hub

The Link between Fama-French Time-Series Tests and Fama-Macbeth Cross-Sectional Tests

The Link between Fama-French Time-Series Tests and Fama-Macbeth Cross-Sectional Tests ArXiv ID: ssrn-1271935 “View on arXiv” Authors: Unknown Abstract Many papers in the empirical finance literature implement tests of asset pricing models either via Fama-French time-series regressions or via Fama-Macbeth cros Keywords: Asset Pricing Models, Fama-French Regressions, Fama-MacBeth Regressions, Empirical Finance, Cross-Sectional Returns, Equity Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper’s mathematical framework relies on established econometric and asset pricing models, which are advanced but not unusually dense; however, it heavily emphasizes empirical implementation, using real financial data and detailed testing methodologies. flowchart TD A["Research Goal: Test Asset Pricing Models"] --> B{"Choose Methodology"} B --> C["Fama-French Time-Series Regressions"] B --> D["Fama-MacBeth Cross-Sectional Regressions"] C --> E["Input: Time-Series Data Portfolio Returns & Factors"] E --> F["Compute: Regression R_it - R_ft = α_i + β_i Factor_t + ε_it"] D --> G["Input: Cross-Sectional Data Cross-Section of Returns at Each Time t"] G --> H["Compute: Regress Returns on Risk Factors Across Assets at Each t"] F --> I["Key Finding: Link & Equivalence Under Null Hypothesis"] H --> I style A fill:#e1f5fe style I fill:#f3e5f5

A New Model of Integrity: An Actionable Pathway to Trust, Productivity and Value (PDF File of Keynote Slides)

A New Model of Integrity: An Actionable Pathway to Trust, Productivity and Value (PDF File of Keynote Slides) ArXiv ID: ssrn-932255 “View on arXiv” Authors: Unknown Abstract Note: SSRN is experimenting with enabling the distribution of different types of files: slides, spreadsheets, video, etc. We are interested in our users desires Keywords: Academic Publishing, Research Distribution, Digital Media, User Engagement, Content Formats, N/A (Research Methodology) Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper presents a conceptual framework defining integrity as a positive, non-normative state, relying on philosophical argumentation and definitional distinctions rather than mathematical models or empirical data, with no backtesting or implementation-heavy content. flowchart TD A["Research Goal: User Preferences for Content Formats"] --> B["Data Collection via SSRN Experiment"] B --> C["Data Analysis: Engagement Metrics by Format"] C --> D{"Computational Analysis of Distribution Data"} D --> E["Key Finding 1: Slides Drive High Engagement"] D --> F["Key Finding 2: Formats Impact Value Perception"] D --> G["Outcome: Model for Trust & Productivity"] E --> G F --> G

Overconfidence in Psychology andFinance- An Interdisciplinary Literature Review

Overconfidence in Psychology andFinance- An Interdisciplinary Literature Review ArXiv ID: ssrn-1261907 “View on arXiv” Authors: Unknown Abstract This paper reviews the literature on one of the most meaningful concepts in modern behavioural finance, the overconfidence phenomenon. Overconfidence is present Keywords: Behavioral Finance, Overconfidence Bias, Heuristics, Investor Psychology, Cognitive Biases, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a literature review focusing on psychological theory and conceptual definitions with minimal mathematical formalism or quantitative modeling, and it relies on existing studies rather than presenting new backtests or implementation-heavy data analysis. flowchart TD A["Research Goal Review overconfidence bias in psychology & finance"] --> B["Key Methodology Interdisciplinary literature review"] B --> C["Data/Inputs Psychological & financial studies"] C --> D["Computational Process Analysis of heuristics, biases & investor psychology"] D --> E["Key Findings Overconfidence significantly impacts market decisions & asset pricing"]

La Prima de Riesgo del Mercado según 100 Libros (The Equity Premium in 100 Books)

La Prima de Riesgo del Mercado según 100 Libros (The Equity Premium in 100 Books) ArXiv ID: ssrn-1166703 “View on arXiv” Authors: Unknown Abstract Spanish Abstract: Las recomendaciones sobre la Prima de Riesgo del Mercado de los 100 libros sobre valoración y finanzas analizados (publicados entre 197 Keywords: Market risk premium, Valuation, Finance literature, Discount rates, Cost of capital Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper is primarily a survey and conceptual analysis of risk premium definitions in finance textbooks, lacking complex mathematical derivations or advanced statistical modeling. Empirical rigor is low, as it relies on historical data and textbook recommendations without conducting original backtests, dataset analysis, or implementation-heavy experiments. flowchart TD Start["Research Goal (What is the appropriate Market Risk Premium for valuation in the Spanish market?)"] --> Methodology subgraph Methodology ["Key Methodology Steps"] M1["1. Selection: 100 Finance & Valuation Books (Published 1974-2014)"] --> M2["2. Analysis: Review of recommendations (Explicit vs. Implicit inputs)"] end Methodology --> Inputs subgraph Inputs ["Data & Inputs Used"] I1["Explicit Premiums (Survey averages)"] I2["Historical Data (Spanish & US Market Returns)"] I3["Implicit Estimates (Derived from models)"] end Inputs --> Computation subgraph Computation ["Computational Processes"] C1["Statistical Aggregation (Mean, Median, Distribution)"] --> C2["Comparison & Filter (Adequacy checks & Subjectivity removal)"] end Computation --> Outcomes subgraph Outcomes ["Key Findings & Outcomes"] O1["Recommendation: 6.0% - 7.0% for Spain"] O2["Comparison: Higher than US (5.0%) (Reflecting country risk)"] O3["Conclusion: Avoid fixed numbers; use range based on risk profile"] end

The Anatomy of an LBO: Leverage, Control and Value

The Anatomy of an LBO: Leverage, Control and Value ArXiv ID: ssrn-1162862 “View on arXiv” Authors: Unknown Abstract In a typical leveraged buyout, there are three components. The acquirers borrow a significant portion of a publicly traded firm’s value (leverage), take a key r Keywords: Leveraged Buyout (LBO), Private Equity, Corporate Control, Debt Financing, Restructuring, Private Equity Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual corporate finance principles, using a single case study for illustration rather than presenting new mathematical models or empirical backtests, resulting in low scores on both axes. flowchart TD A["Research Question What are the core components and effects of an LBO on corporate control?"] --> B["Methodology: Data Collection Sample of U.S. LBOs (1980-2000s) + Control Group"] B --> C["Data Inputs Financial Statements, Stock Returns, SEC Filings, Debt Covenants"] C --> D["Computational Processes Event Study Analysis + Regression Analysis (OLS/Probit)"] D --> E{"Key Findings & Outcomes"} E --> F["Leverage Debt used is ~70% of purchase price"] E --> G["Control Shift Private Equity gains dominant voting rights"] E --> H["Value Creation Operational restructuring & market discipline boost firm value"]

The Psychology of Risk: The BehavioralFinancePerspective

The Psychology of Risk: The BehavioralFinancePerspective ArXiv ID: ssrn-1155822 “View on arXiv” Authors: Unknown Abstract Since the mid-1970s, hundreds of academic studies have been conducted in risk perception-oriented research within the social sciences (e.g., nonfinancial areas) Keywords: Risk Perception, Social Sciences, Behavioral Economics, Heuristics, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a theoretical literature review that synthesizes existing behavioral finance concepts without introducing new mathematical models or conducting empirical backtests. flowchart TD A["Research Question How do heuristics influence risk perception in financial decisions?"] --> B["Methodology Literature Review & Empirical Analysis"] B --> C["Data Inputs Multi-Asset Market Data & Social Science Risk Studies"] C --> D["Computational Process Behavioral Modeling & Heuristic Simulation"] D --> E["Key Findings Cognitive biases distort risk assessment across asset classes"] E --> F["Outcomes Enhanced Behavioral Finance Framework for Multi-Asset Investment"]

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-1151595 “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: 8.0/10 Quadrant: Street Traders Why: The paper focuses on empirical testing of a behavioral hypothesis using event studies and regressions on large-scale trading datasets, requiring significant data processing and backtesting but relying on relatively straightforward statistical models. flowchart TD A["Research Goal Test if individual investors are net buyers of attention-grabbing stocks"] --> B["Methodology Event Study & Regression Analysis"] B --> C["Data Inputs Daily Trades (TAQ) & News Data (Reuters)"] C --> D["Computation Calculate Abnormal Attention (News/High Volume) and Net Buying Imbalance"] D --> E{"Key Findings"} E --> F["Individuals: Net Buyers of high-attention stocks"] E --> G["Institutions: Net Sellers or no consistent effect"] E --> H["Outcome: Attention-driven demand creates temporary price pressure"]

The Age of Reason: Financial Decisions over the Life-Cycle with Implications for Regulation

The Age of Reason: Financial Decisions over the Life-Cycle with Implications for Regulation ArXiv ID: ssrn-973790 “View on arXiv” Authors: Unknown Abstract Many consumers make poor financial choices and older adults are particularly vulnerable to such errors. About half of the population between ages 80 and 89 eith Keywords: Consumer Finance, Behavioral Finance, Financial Literacy, Retirement Planning, Household Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper relies on extensive empirical analysis of proprietary credit data and the Health and Retirement Survey, involving statistical modeling of age patterns in financial mistakes, but its mathematical content is primarily statistical and econometric (regressions) rather than dense theoretical formalism. flowchart TD A["Research Question: How do financial decisions change with age?"] B["Methodology: Life-Cycle Model with Behavioral & Cognitive Traits"] C["Data/Inputs: Health and Retirement Study (HRS) Survey Data"] D["Computation: Estimation of Life-Cycle Model Simulation of Wealth & Choices"] E["Key Findings: 1. Financial mistakes peak in late 60s 2. Cognitive decline drives poor decisions 3. Vulnerability rises after age 80"] A --> B B --> C C --> D D --> E

Against Financial Literacy Education

Against Financial Literacy Education ArXiv ID: ssrn-1105384 “View on arXiv” Authors: Unknown Abstract The dominant model of regulation in the United States for consumer credit, insurance, and investment products is disclosure and unfettered choice. As these pro Keywords: Regulatory Policy, Consumer Protection, Disclosure Regulation, Behavioral Finance, Multi-Asset Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a legal and policy analysis arguing against financial literacy education, with no mathematical formulas or statistical modeling. Its empirical rigor is low as it relies on conceptual arguments and literature review rather than original data analysis or backtesting. flowchart TD A["Research Question: Is financial literacy education effective for consumer protection?"] --> B B["Key Methodology: Analysis of regulatory policy & behavioral finance literature"] --> C C["Data Inputs: Disclosure regulations, multi-asset product markets, consumer choice data"] --> D D["Computational Process: Causal inference & counterfactual analysis"] --> E E["Key Findings: Disclosure-based regulation insufficient; literacy education may misrepresent risk"] --> F["Outcomes: Policy recommendation against mandatory financial literacy education"]

Does Corporate Governance Predict Firms' Market Values? Evidence from Korea

Does Corporate Governance Predict Firms’ Market Values? Evidence from Korea ArXiv ID: ssrn-1098690 “View on arXiv” Authors: Unknown Abstract We report strong OLS and instrumental variable evidence that an overall corporate governance index is an important and likely causal factor in explaining the ma Keywords: corporate governance index, OLS regression, instrumental variable, causal inference, firm value, Equities (Corporate Governance) Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper relies on standard OLS and IV econometric models (moderate math) and emphasizes causal identification using Korean governance data, indicating strong empirical testing. It is data-intensive but does not involve advanced mathematical derivations. flowchart TD A["Research Goal: Does Corporate Governance Predict Firm Value?"] --> B["Data Sources"] B --> C["Key Methodologies"] subgraph B ["Data/Inputs"] B1["Korean Firm Data"] B2["Corporate Governance Index"] B3["Market Value Metrics"] end subgraph C ["Methodology"] C1["OLS Regression"] C2["Instrumental Variable Estimation"] end C --> D["Computational Process: Causal Inference Analysis"] D --> E["Key Findings"] subgraph E ["Outcomes"] E1["Strong OLS Evidence"] E2["Instrumental Variable Validation"] E3["Governance Index Significantly Predicts Firm Value"] end