Economic Consequences of Financial Reporting and Disclosure Regulation: A Review and Suggestions for Future Research ArXiv ID: ssrn-1105398 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a theoretical survey that synthesizes existing literature with conceptual frameworks rather than presenting new mathematical models or empirical data analysis. It lacks the implementation-heavy elements of backtesting or data processing, focusing instead on integrating insights from accounting, economics, and law. flowchart TD A["Research Goal: Assess economic consequences<br>of financial reporting/disclosure regulation"] --> B["Methodology: Literature Review &<br>Empirical Analysis of Studies"] B --> C["Data/Inputs: Regulatory Changes<br>Capital Market Data<br>Firm-Level Metrics"] C --> D["Computational Process: Comparative Analysis<br>Causal Inference<br>Cost-Benefit Assessment"] D --> E{"Key Findings/Outcomes"} E --> F1["Regulatory costs often outweigh benefits<br>for small firms"] E --> F2["Disclosure quality enhances market<br>liquidity & efficiency"] E --> F3["Gaps in research on<br>non-financial stakeholders"]

Endogeneity in Empirical CorporateFinance ArXiv ID: ssrn-1748604 “View on arXiv” Authors: Unknown Abstract This chapter discusses how applied researchers in corporate finance can address endogeneity concerns. We begin by reviewing the sources of endogeneity - omitted Keywords: Endogeneity, Corporate Finance, Instrumental Variables, Quasi-Natural Experiments, Omitted Variables Bias, Equity Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper is highly technical, covering advanced econometric techniques like instrumental variables, panel data methods, and regression discontinuity designs, which places it firmly in high math complexity. However, it is a theoretical survey/review focused on methodology rather than presenting backtest-ready data or specific implementations, leading to low empirical rigor. flowchart TD A["Research Goal<br>Address Endogeneity in Corporate Finance"] --> B["Identify Endogeneity Source<br>e.g., Omitted Variables"] B --> C{"Choose Methodology"} C --> D["Instrumental Variables<br>IV Approach"] C --> E["Quasi-Natural Experiments<br>DID / RD Designs"] D --> F["Data & Inputs<br>Equity Data, Instrument Validity"] E --> F F --> G["Computational Process<br>2SLS / Regression Analysis"] G --> H["Key Findings<br>Validated Causal Inferences<br>Reduced Bias in Equity Studies"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications - The 2023 Edition ArXiv ID: ssrn-4398884 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: N/A, Insufficient Data, No Abstract Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs sophisticated financial models (DCF, option pricing, risk decomposition) requiring advanced math, and its empirical component is data-heavy, featuring historical time-series analysis, implied premium calculations from market prices, and country-specific risk scores, making it backtest-ready despite lacking explicit code or GitHub links. flowchart TD A["Research Goal: Determine & Estimate Equity Risk Premiums for 2023"] --> B{"Data Inputs: Historical Market & Government Bond Returns"} B --> C["Methodology: Cross-Sectional & Time-Series Analysis"] C --> D["Computation: Discounted Cash Flow & Risk Models"] D --> E["Outcome 1: Estimated Equity Risk Premium for 2023"] D --> F["Outcome 2: Key Determinants of ERP Identified"] D --> G["Outcome 3: Implications for Investors & Valuation"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2013 Edition ArXiv ID: ssrn-2238064 “View on arXiv” Authors: Unknown Abstract Equity risk premiums are a central component of every risk and return model in finance and are a key input in estimating costs of equity and capital in both cor Keywords: Equity Risk Premiums, Cost of Equity, Risk and Return Models, Capital Budgeting, Corporate Finance, Equity Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper discusses theoretical risk-return models (like CAPM and multi-factor models) which involve mathematical formulas, but the excerpt shows conceptual explanation rather than dense derivations. Empirical rigor is low as it focuses on conceptual discussions, historical data limitations, and forward-looking estimates without providing backtesting, code, or implementation-heavy datasets. flowchart TD A["Research Goal<br>Determine & estimate the Equity Risk Premium (ERP)<br>for corporate finance & valuation"] --> B["Key Inputs & Data<br>• Historical Market Returns (Equity & Bonds)<br>• Implied ERP from Valuation Models<br>• Macroeconomic Factors (Inflation, Interest Rates)"] B --> C["Methodology<br>1. Historical Approach<br>2. Forward-Looking/Implied ERP<br>3. Macroeconomic Determinants"] C --> D["Computational Process<br>• Estimate Historical ERP<br>• Forecast future ERP<br>• Adjust for risk & macro conditions"] D --> E["Key Findings & Outcomes<br>• ERP varies over time (not constant)<br>• Influenced by macroeconomic factors<br>• Crucial for Cost of Equity & Capital Budgeting"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2015 Edition ArXiv ID: ssrn-2581517 “View on arXiv” Authors: Unknown Abstract Equity risk premiums are a central component of every risk and return model in finance and are a key input in estimating costs of equity and capital in both cor Keywords: Equity Risk Premiums, Cost of Equity, Risk and Return Models, Capital Budgeting, Corporate Finance, Equity Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper introduces advanced financial models like CAPM and multi-factor models with formulas, indicating moderate math complexity. However, it focuses on conceptual frameworks and theoretical estimation approaches (historical, survey, implied) without providing specific backtests, code, or detailed empirical datasets. flowchart TD A["Research Goal: Determine ERP"] --> B{"Methodology & Inputs"}; B --> C["Data: Historical Market Returns<br>Risk-Free Rate<br>Implied ERP from Valuation"]; B --> D["Model: DCF & Risk Models"]; C --> E{"Computational Process"}; D --> E; E --> F["Estimate Base ERP<br>+ Adjust for Risk Factors"]; E --> G["Forward-Looking Analysis<br>vs. Historical Averages"]; F --> H["Key Outcomes"]; G --> H; H --> I["2015 ERP Estimate<br>5.5% - 6.5%"]; H --> J["Implications for:<br>Cost of Equity & Capital"];

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2016 Edition ArXiv ID: ssrn-2742186 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract available, Unknown Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual frameworks, economic determinants, and comparative analysis of ERP estimation methods without extensive mathematical derivations or backtesting datasets. It is more of a theoretical and practical guide for finance professionals rather than a computational or empirical research paper. flowchart TD A["Research Goal:<br>Determine ERP determinants,<br>estimation & implications"] --> B["Data & Inputs:<br>Historical equity & bond returns,<br>inflation, macro data"] B --> C["Methodology:<br>Regression analysis &<br>time-series modeling"] C --> D["Computational Process:<br>Estimate ERP drivers &<br>forecast future premiums"] D --> E["Key Findings:<br>ERP varies with interest rates,<br>risk, & macro conditions"] E --> F["Outcomes:<br>Framework for ERP estimation<br>& strategic allocation insights"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2019 Edition ArXiv ID: ssrn-3378246 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper presents theoretical frameworks and conceptual discussions about equity risk premium determinants and estimation methods, relying on economic intuition and historical data analysis rather than advanced mathematical derivations or rigorous backtesting with proprietary datasets. flowchart TD A["Research Goal: Determine<br>Equity Risk Premium (ERP) Drivers,<br>Estimation & 2019 Implications"] --> B["Data & Inputs<br>Historical Market Returns, Risk-Free Rates,<br>Inflation, Growth, Interest Rates"] B --> C["Key Methodology<br>Valuation Frameworks &<br>Scenario Analyses"] C --> D{"Computational Processes"} D --> E["Build Discounted Cash Flow (DCF) Models"] D --> F["Estimate Implied ERP from Market Valuations"] E & F --> G["Key Findings & Outcomes<br>ERP is Determined by Growth, Risk,<br>Interest Rates; 2019 ERP ~5.5%"]

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2022 Edition ArXiv ID: ssrn-4066060 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: Unknown Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual discussion, determinants, and comparison of estimation methods for equity risk premiums, with minimal advanced mathematics (mostly basic formulas and definitions). It lacks code, backtests, or detailed statistical analysis, relying instead on historical data summaries and surveys. flowchart TD A["Research Goal: Determine ERP<br>for 2022 & implications"] --> B["Data Input: Historical<br>Market & Economic Data"] B --> C["Key Methodology:<br>Calculation Approaches"] C --> D["Computational Process:<br>Historical vs. Implied ERP"] D --> E{"Key Findings & Outcomes"} E --> F["Determinants Identified:<br>Interest Rates, Inflation, Volatility"] E --> G["Estimation Refinement:<br>Adjustments for Current Market Conditions"] E --> H["Implications:<br>Valuation Impacts & Investment Strategy"]

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2024 Edition ArXiv ID: ssrn-4751941 “View on arXiv” Authors: Unknown Abstract The equity risk premium is the price of risk in equity markets, and it is not just a key input in estimating costs of equity and capital in both corporate finan Keywords: Equity Risk Premium, Asset Pricing, Cost of Capital, Valuation Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper introduces advanced financial theory and a wide array of estimation methodologies (implied premiums, surveys) but is grounded in extensive real-world data analysis, including country-specific risk premiums and market volatility metrics. flowchart TD A["Research Goal: ERP Determinants & Estimation"] --> B["Data Inputs"] B --> C{"Methodology: Historical vs. Forward<br>Integration of Macroeconomic Variables"} C --> D["Computational Processes<br>Model Estimation & Valuation Metrics"] D --> E["Key Findings: ERP Trends & Implications<br>Cost of Capital Updates"]

Equity Risk Premiums: Determinants, Estimation and Implications - The 2020 Edition ArXiv ID: ssrn-3550293 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper centers on the estimation of the equity risk premium using established financial models (CAPM, Gordon Growth), involving algebraic and present value formulas, but focuses heavily on practical, data-driven applications like historical returns analysis, survey methods, and implied premium calculations using market data from sources like Moody’s and PRS Group. flowchart TD A["Research Goal: Determine, Estimate, and Imply Equity Risk Premiums"] --> B["Data/Inputs: Historical Market Returns, Bond Yields, Economic Indicators"] B --> C["Methodology: Decompose ERP into Risk-Free Rate + Risk Compensation"] C --> D["Computational Process: Historical & Forward-Looking Estimation"] D --> E["Key Finding 1: ERP is dynamic, varying with economic conditions"] D --> F["Key Finding 2: Valuation metrics (CAPE, Dividend Yield) are key determinants"] D --> G["Key Finding 3: ERP is sensitive to interest rates and inflation"] E --> H["Outcomes: Framework for future ERP prediction & valuation"]