Corporate Governance and Firm Valuation ArXiv ID: ssrn-754484 “View on arXiv” Authors: Unknown Abstract Gompers et al. [“Gompers, P., Ishii, J., Metrick, A., 2003. Corporate governance and equity prices. Quarterly Journal of Economics 118, 107-155”] created G-Index, Keywords: Corporate Governance, G-Index, Shareholder Rights, Equity Valuation, Antitakeover Provisions, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses standard regression models and index construction without advanced mathematics, but is heavily data-driven with comprehensive datasets (Compustat, ISS), large sample sizes (2538 firms), and detailed backtesting of governance indices against firm valuation metrics like Tobin’s Q. flowchart TD A["Research Goal: Does stronger<br>shareholder rights affect firm valuation?"] --> B["Data Source: Institutional Shareholder Services"] B --> C{"Key Methodology"} C --> D["Construct G-Index<br>from 24 antitakeover provisions"] C --> E["Collect firm financials &<br>equity prices 1990-1999"] D --> F["Regression Analysis: Tobin's Q"] E --> F F --> G["Key Finding: Firms with<br>stronger shareholder rights<br>have higher valuations"]

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:<br/>Does investor attention drive buying<br/>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<br/>e.g., CRSP/TAQ"] B --> B2["Attention Proxies<br/>News mentions & Abnormal volume"] B --> B3["Investor Classification<br/>Individual vs. Institutional"] C --> C1["Event Study Design<br/>Focus on high-attention days"] C --> C2["Regression Analysis<br/>Trading volume vs. attention"] D --> D1["Net Buy Calculation<br/>Aggregate flows by investor type"] D --> D2["Control for Fundamentals<br/>Liquidity, Returns, Volatility"] E --> F1["Confirmation: Individuals<br/>buy high-attention stocks"] E --> F2["Institutional Behavior<br/>Contrast or indifference"] E --> F3["Implication<br/>Attention-driven anomalies"]

ModernFinancevs. BehaviouralFinance: An Overview of Key Concepts and Major Arguments ArXiv ID: ssrn-746204 “View on arXiv” Authors: Unknown Abstract Modern Finance has dominated the area of financial economics for at least four decades. Based on a set of strong but highly unrealistic assumptions its advocate Keywords: Modern Finance, Financial Economics, Economic Assumptions, Economic Models, Theoretical Critique, Academic/Financial Economics Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper appears to be a conceptual overview comparing two theoretical frameworks in finance, likely involving descriptive arguments and literature review rather than advanced mathematical models or empirical backtesting. flowchart TD A["Research Goal:<br>Compare Modern vs. Behavioral Finance"] --> B["Methodology:<br>Literature Review & Conceptual Analysis"] B --> C["Inputs:<br>Historical Assumptions &<br>Empirical Anomalies"] C --> D{"Computational Process:<br>Theoretical Framework Comparison"} D --> E["Modern Finance<br>Assumptions: Rationality, Efficiency"] D --> F["Behavioral Finance<br>Assumptions: Psychology, Biases"] E --> G["Key Findings:<br>Strong theoretical models<br>but limited real-world predictive power"] F --> G

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:<br>Reconcile EMH with Behavioral Finance"] --> B["Methodology:<br>Empirical Asset Pricing Tests"] B --> C{"Data Inputs:<br>US Equities (CRSP/Compustat)"} C --> D["Computational Process:<br>Estimate Risk-Adjusted Returns"] D --> E{"Outcomes / Findings"} E --> F["Markets are adaptive<br>Efficiency evolves over time"] E --> G["Behavioral anomalies<br>arise from market shocks"] E --> H["Asset pricing models<br>must incorporate adaptiveness"]

Fundamental Indexation ArXiv ID: ssrn-713865 “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: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper presents a straightforward, intuitive strategy (fundamental indexing) with minimal mathematical derivations, but heavily relies on empirical backtests, real-world benchmark comparisons, and data analysis to challenge capitalization-weighted norms. flowchart TD A["Research Goal:<br/>Test if capitalization-weighted indexes<br/>are truly optimal"] --> B["Methodology:<br/>Compare Cap-Weighted vs.<br/>Fundamental Indexation"] B --> C["Data: Equities &<br/>Fundamental Metrics"] C --> D["Computation:<br/>Mean-Variance Optimization<br/>& Portfolio Simulation"] D --> E["Key Finding:<br/>Fundamental Indexation<br/>Outperforms Cap-Weighting"] E --> F["Outcome:<br/>Rejection of passive indexing<br/>as mean-variance efficient"]

Special Purpose Vehicles and Securitization ArXiv ID: ssrn-713782 “View on arXiv” Authors: Unknown Abstract This paper analyzes securitization and more generally special purpose vehicles (SPVs), which are now pervasive in corporate finance. The first part of the paper Keywords: Securitization, Special Purpose Vehicles (SPVs), Corporate finance, Off-balance sheet financing, Asset transfer, Structured Products Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper develops a theoretical model to explain SPV usage, contributing to math complexity, but its primary strength is the empirical testing of model implications using unique credit card securitization data. flowchart TD A["Research Goal: Analyze SPV and Securitization<br/>Functionality in Corporate Finance"] --> B["Data Collection<br/>Corporate Filings & Regulatory Databases"] B --> C["Methodology: Structural Analysis<br/>Off-balance Sheet Asset Transfer Review"] C --> D["Computational Process<br/>Risk Exposure & Leverage Calculation"] D --> E{"Key Findings"} E --> F["Increased Leverage &<br/>Systemic Risk Accumulation"] E --> G["Regulatory Arbitrage &<br/>Reduced Transparency"] E --> H["Complexity in<br/>Structured Products"]

Special Purpose Vehicles and Securitization ArXiv ID: ssrn-684716 “View on arXiv” Authors: Unknown Abstract Firms can finance themselves on- or off-balance sheet. Off-balance sheet financing involves transferring assets to “special purpose vehicles” (SPVs), Keywords: Off-balance sheet financing, Special Purpose Vehicles (SPVs), Asset transfer, Corporate finance, Balance sheet management, Corporate Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper relies on game theory and contractual theory, but the math presented is relatively conceptual rather than dense with advanced proofs or LaTeX. It demonstrates strong empirical rigor by using unique credit card securitization data to test theoretical predictions, focusing on real-world implementation and data analysis. flowchart TD A["Research Goal<br>Assess SPV impact on corporate finance & credit"] --> B["Methodology"] B --> C{"Data Sources"} C --> D["1. SEC EDGAR<br>ABS/SPV filings"] C --> E["2. Moody's/Refinitiv<br>Corporate credit data"] C --> F["3. Bloomberg<br>Balance sheet metrics"] D & E & F --> G["Computational Process<br>Fixed Effects Regressions"] G --> H["Key Outcomes/Findings"] H --> I["1. Off-balance sheet<br>reduces leverage ratios"] H --> J["2. SPV issuance<br>lowers funding costs"] H --> K["3. Risk transfer<br>affects corporate credit"]

Does Corporate Governance Predict Firms’ Market Values? Evidence from Korea ArXiv ID: ssrn-311275 “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 Keywords: corporate governance, OLS regression, instrumental variables, firm value, ownership structure, Equities (Corporate Governance) Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper primarily uses OLS and instrumental variable (IV) regression methods without advanced mathematical derivations, placing math complexity at a low level. However, it demonstrates high empirical rigor with a clear backtest-ready design, including a proprietary index (KCGI), instrumental variables based on Korean legal rules, and sensitivity checks on market value metrics. flowchart TD A["Research Question: Does Corporate Governance<br>predict Korean firms' market value?"] A --> B["Data & Inputs<br>Firm-level governance & value data from Korea"] B --> C["Methodology: Core Analysis"] C --> D["OLS Regression<br>Initial association estimates"] C --> E["Instrumental Variables<br>Address endogeneity, estimate causal effect"] D & E --> F["Key Findings<br>Governance index significantly explains<br>and likely causes higher firm value"]

Corporate Governance in India - Evolution and Challenges ArXiv ID: ssrn-649857 “View on arXiv” Authors: Unknown Abstract While recent high-profile corporate governance failures in developed countries have brought the subject to media attention, the issue has always been central to Keywords: corporate governance, agency theory, board independence, executive compensation, shareholder rights, Equities Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a descriptive, qualitative review of corporate governance history and challenges in India, using citations from existing literature rather than presenting original mathematical models, simulations, or backtest-ready data. flowchart TD RQ["Research Question: How has corporate governance in India evolved and what are its key contemporary challenges?"] MET["Methodology: Systematic Literature Review & Conceptual Analysis"] DATA["Data Inputs: Academic papers, policy documents, corporate reports, high-profile case studies"] COMP["Computational Process: Thematic analysis of governance mechanisms and synthesis of challenges"] FIND["Key Findings: Evolution from owner-centric to regulated governance; persistent challenges in board independence, shareholder rights, and executive compensation"] RQ --> MET --> DATA --> COMP --> FIND

Research Ranking ofFinanceDepartments: A Modified Citation Approach ArXiv ID: ssrn-646185 “View on arXiv” Authors: Unknown Abstract We provide a research ranking of academic finance departments that incorporates both a quantitative and qualitative dimension in its methodology. Based on the Keywords: academic finance, research ranking, methodology, N/A Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper presents a methodological ranking framework using citation counts and editorial board metrics, which is conceptual and involves counting methods rather than advanced mathematical models. Empirical rigor is modest, relying on hand-collected citation data and predefined journal lists, but lacks implementation-heavy elements like backtesting or code. flowchart TD A["Research Goal<br>Rank Finance Depts<br>Modified Citation Approach"] --> B["Data Collection<br>JCR/Ft50 Journals<br>Faculty Rosters"] B --> C["Citation Analysis<br>Quantitative Dimension<br>Raw Citation Count"] B --> D["Journal Quality Weighting<br>Qualitative Dimension<br>Impact Factor/Reputation"] C --> E["Modified Citation Score<br>Normalized Aggregation"] D --> E E --> F["Final Department Ranking<br>Research Productivity Scores"] F --> G["Key Findings<br>Top Departments Identified<br>Methodology Validation"]