Quant Finance Research Hub

Stock Returns, Aggregate Earnings Surprises, and BehavioralFinance

Stock Returns, Aggregate Earnings Surprises, and BehavioralFinance ArXiv ID: ssrn-380127 “View on arXiv” Authors: Unknown Abstract We study the stock market reaction to aggregate earnings news. Previous research shows that, for individual firms, stock prices react positively to earnings ne Keywords: Stock Market Reaction, Aggregate Earnings News, Event Study, Market Efficiency, Information Asymmetry, Equities Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses standard empirical finance econometrics (time-series regressions, correlation analysis) without highly advanced mathematical derivations, but is heavily data-driven with a 30-year Compustat sample and robust statistical tests. flowchart TD A["Research Goal Understand stock market reaction to aggregate earnings news"] --> B["Data: CRSP & Compustat Time Period: 1988-2017"] B --> C["Methodology: Event Study Construct SUE portfolios"] C --> D{"Key Computational Processes Abnormal Returns Calculation"} D --> E["Analyze Abnormal Returns vs Aggregate Earnings Surprise"] D --> F["Information Asymmetry Analysis Trading Volume Patterns"] E --> G["Key Findings/Outcomes"] F --> G subgraph G ["Key Findings/Outcomes"] G1["Market Underreacts to Aggregate Earnings News"] G2["Abnormal Returns Persist Post-Announcement"] G3["Support for Behavioral Finance Over Market Efficiency"] G4["Information Asymmetry Explains Delayed Reaction"] end style G fill:#e1f5e1,stroke:#2e7d32 style A fill:#e3f2fd,stroke:#1565c0 style B fill:#fff3e0,stroke:#ef6c00

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

Fundamental Indexation

Fundamental Indexation ArXiv ID: ssrn-604842 “View on arXiv” Authors: Unknown Abstract A trillion-dollar industry is based on investing in or benchmarking to capitalization-weighted indexes, even though the finance literature rejects the mean-vari Keywords: capitalization-weighted indexes, mean-variance, passive investing, benchmarking, portfolio optimization, Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper involves moderate mathematical finance concepts like portfolio optimization and benchmark analysis, but it is heavily data-driven, featuring extensive backtesting, real-world index performance comparisons, and discussion of implementation for a trillion-dollar industry. flowchart TD A["Research Goal Test: Does capitalization weighting violate mean-variance efficiency?"] --> B["Methodology Constrained Optimization vs. Capitalization Weighting"] B --> C["Input: Historical Returns U.S. Large Cap Equities"] C --> D["Computational Process Maximize Sharpe Ratio Under Optimization Constraints"] D --> E{"Key Finding 1: Efficiency Optimal Portfolio Sharpe Ratio > Cap-Weighted Portfolio?"} E -- Yes --> F["Outcome: Cap-weighting is Mean-Variance Inefficient"] E -- No --> G["Outcome: Cap-weighting is Mean-Variance Efficient"] F --> H["Key Finding 2: Performance Fundamental Indexation Outperforms Cap-Weighting"] G --> H H --> I["Key Takeaway Trillion-dollar cap-weighted industry is suboptimal vs. optimized portfolios"]

The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective

The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective ArXiv ID: ssrn-602222 “View on arXiv” Authors: Unknown Abstract One of the most influential ideas in the past 30 years is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally a Keywords: Efficient Markets Hypothesis, Market Efficiency, Asset Pricing, Informational Efficiency, Financial Theory, Equity Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper proposes a conceptual framework (Adaptive Markets Hypothesis) to reconcile EMH and behavioral finance using evolutionary principles, but it lacks mathematical derivations, empirical data, or backtesting details, focusing instead on theoretical exposition and implications for practice. flowchart TD A["Research Goal: Challenge EMH with Evolutionary Perspective"] --> B["Methodology: Literature Review & Theoretical Framework"] B --> C["Input Data: Historical Market Anomalies & Behavioral Studies"] C --> D["Process: Adaptive Markets Hypothesis Integration (Lo 2004)"] D --> E["Key Findings: 1. Markets are adaptive 2. Efficiency varies 3. Profit opportunities fluctuate with evolution"]

Fuel Hedging in the Airline Industry: The Case of Southwest Airlines

Fuel Hedging in the Airline Industry: The Case of Southwest Airlines ArXiv ID: ssrn-578663 “View on arXiv” Authors: Unknown Abstract Set in June 2001, the case places the student in the role of Scott Topping, Director of Corporate Finance at Southwest Airlines. Scott is responsible for the a Keywords: Corporate Finance Strategy, Hedging (Fuel), Risk Management, Financial Derivatives, Airline Economics, Equity (Transportation Sector) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a qualitative case study focused on corporate finance decision-making with minimal mathematical modeling, and while it includes some financial data and volatility metrics, it lacks backtesting or implementation details. flowchart TD A["Research Goal: Should SWA use fuel hedging?"] --> B["Data Inputs: 1. Historical Oil Prices 2. Futures/Options Prices 3. SWA Fuel Consumption"] B --> C["Methodology: Valuation of Hedging Strategies"] C --> D["Computational Process: Monte Carlo Simulation of Oil Price Scenarios"] D --> E{"Key Findings/Outcomes"} E --> F["SWA Hedging reduced volatility and saved costs vs. peers"] E --> G["Risk Management Framework justifies active hedging policy"] E --> H["Recommendation: Maintain/Expand Hedging Program"]

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"]

Introduction to Fast Fourier Transform inFinance

Introduction to Fast Fourier Transform inFinance ArXiv ID: ssrn-559416 “View on arXiv” Authors: Unknown Abstract The Fourier transform is an important tool in Financial Economics. It delivers real time pricing while allowing for a realistic structure of asset returns, taki Keywords: Fourier transform, asset pricing, financial economics, time series analysis, real-time pricing, Financial Derivatives Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper involves advanced mathematical concepts like Fourier transforms, complex numbers, and convolution, but it is a conceptual pedagogical piece focusing on methodology rather than providing empirical data, backtests, or implementation details for real-world trading. flowchart TD A["Research Goal: Use Fourier Transform for Real-Time Financial Pricing"] --> B["Key Methodology: Fast Fourier Transform FFT Algorithm"] B --> C["Data Inputs: Asset Return Time Series & Market Data"] C --> D["Computational Process: FFT of Return Distributions to Price Derivatives"] D --> E["Key Findings: Efficient Real-Time Pricing Model for Financial Derivatives"]

Theory of Capital Structure - a Review

Theory of Capital Structure - a Review ArXiv ID: ssrn-556631 “View on arXiv” Authors: Unknown Abstract This paper is a review of the central theoretical literature. The most important arguments for what could determine capital structure is the pecking order theo Keywords: Capital Structure, Pecking Order Theory, Corporate Finance, Financing Decisions, Theoretical Literature, Corporate Finance Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 1.0/10 Quadrant: Lab Rats Why: The paper presents advanced mathematical derivations, including state preference models, option pricing analogies, and multiple equations defining firm values and payoffs. However, it is purely theoretical with no backtesting, empirical data, or implementation details mentioned. flowchart TD A["Research Goal: Review of Central Capital Structure Theories"] --> B["Methodology: Systematic Literature Review"] B --> C["Data: Key Theoretical Models & Empirical Studies"] C --> D["Analysis: Comparative Synthesis of Theories"] D --> E{"Computational Process: Evaluate Theory Validity"} E --> F["Key Finding: Pecking Order Theory Dominates"] E --> G["Key Finding: Trade-off Theory Complements"] F --> H["Outcome: Unified Framework for Financing Decisions"] G --> H

Risk Management and Corporate Governance: The Case of Enron

Risk Management and Corporate Governance: The Case of Enron ArXiv ID: ssrn-468168 “View on arXiv” Authors: Unknown Abstract Enron Board’s Finance Sub-Committee’s approval of the first bankrupting Raptor transaction, Talon, is examined in as much detail as published documents allow. Keywords: Corporate Governance, Risk Management, Enron, Derivatives, Equities Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: This is a qualitative legal and organizational analysis of Enron’s corporate governance, focusing on board oversight and risk management, with no mathematical modeling or data-driven empirical testing. flowchart TD A["Research Goal"] --> B{"Methodology"} B --> C["Document Analysis"] C --> D["Input: SEC Filings & Board Meeting Minutes"] D --> E["Computational Process: Raptor Transaction Reconstruction"] E --> F["Key Findings/Outcomes"] F --> G["Governance Failure: Lack of Independent Oversight"] F --> H["Risk Failure: Inadequate Risk Management & Derivative Controls"]