Putting Integrity into Finance: A Purely Positive Approach ArXiv ID: ssrn-2413334 “View on arXiv” Authors: Unknown Abstract The seemingly never ending scandals in the world of finance with their damaging effects on value and human welfare (that continue unabated in spite of all the v Keywords: Corporate Governance, Finance Scandals, Ethics, Risk Management, Stakeholder Value, General Finance Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper proposes a conceptual, normative theory of integrity with minimal mathematical formalism, relying instead on philosophical and ontological arguments. It explicitly states the lack of large-scale empirical studies and relies on anecdotal feedback, making it neither mathematically dense nor data/implementation-heavy. flowchart TD A["Research Goal: Why do finance scandals persist<br>despite known governance solutions?"] B["Methodology: Purely Positive Approach<br>Analyzes observable behaviors & incentives"] C["Data Inputs: Historical finance scandals<br>Corporate governance records<br>Stakeholder impact reports"] D["Computational Process: Identifying<br>systemic incentive misalignments<br>& integrity gaps"] E["Key Findings: <br>1. Integrity deficit as core risk<br>2. Stakeholder value vs shareholder value<br>3. Need for ethical risk management"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2014 Edition ArXiv ID: ssrn-2409198 “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 Premium, Cost of Equity, Risk and Return Models, Valuation, Capital Budgeting Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual discussion of risk premium determinants and methodological comparisons (historical, survey, implied) with minimal advanced mathematical derivation. While it discusses estimation approaches and financial implications, it does not present code, backtesting results, or detailed statistical implementations, placing it more in the conceptual/theoretical realm. flowchart TD A["Research Goal: Determinants, Estimation & Implications of ERP"] --> B["Key Data Inputs: Historical Returns, Macro-economic Variables, Survey Data"] B --> C["Methodology: Historical & Forward-Looking Estimation Models"] C --> D["Computational Process: Risk Premium Calculation & Adjustment"] D --> E{"Key Findings: ERP Sensitivity to Market Conditions & Valuation Impact"} E --> F["Implication: Cost of Equity & Capital Budgeting Decisions"]

What Courses Should Law Students Take? Harvard’s Largest Employers Weigh In ArXiv ID: ssrn-2397317 “View on arXiv” Authors: Unknown Abstract We report the results of an online survey, conducted on behalf of Harvard Law School, of 124 practicing attorneys at major law firms. The survey had two main ob Keywords: Law firms, Legal practice, Financial regulation, Corporate law, Survey analysis Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is an educational survey of lawyers about law school curriculum, containing no advanced mathematics or quantitative modeling, with empirical rigor limited to a simple online survey without backtesting or complex statistical analysis. flowchart TD A["Research Question: What Courses Should<br>Harvard Law Students Take?"] --> B{"Methodology"} B --> C["Survey of 124 Attorneys<br>at Major Law Firms"] C --> D["Statistical Analysis<br>of Survey Responses"] D --> E{"Key Findings"} E --> F["Priority Courses:<br>Financial Regulation & Corporate Law"] E --> G["Focus on Practical<br>Legal Practice Skills"]

Chapter 1: Investor Behavior: An Overview ArXiv ID: ssrn-2385229 “View on arXiv” Authors: Unknown Abstract “Investor Behavior: An Overview” is the introduction chapter for the book Investor Behavior: The Psychology of Financial Planning and Investing edited by H. Ken Keywords: Investor Behavior, Psychology of Finance, Financial Planning, Behavioral Finance, Investing Psychology, Behavioral Finance (Cross-Asset) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The content is a conceptual overview of investor behavior, focusing on psychological and planning principles rather than mathematical modeling or empirical backtesting. flowchart TD A["Research Goal: Define Investor Behavior<br/>& Behavioral Finance Principles"] --> B["Key Methodology: Literature Review &<br/>Theoretical Framework Analysis"] B --> C["Data/Inputs: Academic Research,<br/>Psychological Models, Market Data"] C --> D["Computational Processes: Synthesis &<br/>Cross-Asset Behavioral Mapping"] D --> E["Key Findings: Core Biases Identified,<br/>Impact on Financial Planning & Investing"]

SOX after Ten Years: A Multidisciplinary Review ArXiv ID: ssrn-2379731 “View on arXiv” Authors: Unknown Abstract We review and assess research findings from 120 papers in accounting, finance, and law to evaluate the impact of the Sarbanes-Oxley Act. We describe significan Keywords: Sarbanes-Oxley Act, corporate disclosure, audit quality, regulatory compliance, accounting standards, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review of existing research on SOX, discussing concepts and evidence without presenting new mathematical models or complex formulas. Its empirical work relies on synthesizing findings from other studies rather than conducting original backtests or data analysis. flowchart TD A["Research Goal:<br>SOX impact after 10 years"] --> B["Methodology:<br>Multidisciplinary Review"] B --> C["Data: 120 Papers<br>(Accounting, Finance, Law)"] C --> D["Process:<br>Review & Assess Findings"] D --> E{"Analysis<br>Focus Areas"} E --> F["Accounting<br>Disclosure Standards"] E --> G["Finance<br>Equities & Market"] E --> H["Law<br>Regulatory Compliance"] F & G & H --> I["Key Outcomes:<br>Audit Quality & SOX Impact"]

Shadow Banking ArXiv ID: ssrn-2378449 “View on arXiv” Authors: Unknown Abstract The rapid growth of the market-based financial system since the mid-1980s has changed the nature of financial intermediation. Within the system, “shadow banks” Keywords: Shadow Banking, Financial Intermediation, Market-Based Finance, Credit Supply, Banking Sector, Fixed Income / Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper appears to be a conceptual/theoretical overview of shadow banking, focusing on definitions and systemic issues rather than formal models or backtesting. The provided excerpt suggests high-level discussion without empirical data or implementation details. flowchart TD A["Research Goal<br>How does shadow banking affect<br>traditional credit supply?"] --> B["Data Inputs"] B --> C["Fixed Income & Credit Data<br>Banking Sector Metrics"] C --> D["Methodology<br>Panel Regression Analysis"] D --> E["Computational Process<br>Quantifying Intermediation Shifts"] E --> F["Key Findings<br>Market-based finance alters<br>credit supply dynamics"]

SOX after Ten Years: A Multidisciplinary Review ArXiv ID: ssrn-2343108 “View on arXiv” Authors: Unknown Abstract We review and assess research findings from 120 papers in accounting, finance, and law to evaluate the impact of the Sarbanes-Oxley Act. We describe significan Keywords: Sarbanes-Oxley Act, corporate disclosure, audit quality, regulatory compliance, accounting standards, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review synthesizing findings from over 120 studies, focusing on descriptive analysis, policy implications, and identifying research gaps rather than presenting new mathematical models or complex statistical methodologies. It discusses cost-benefit analysis conceptually but lacks deep statistical modeling, code, or backtesting, resulting in low scores on both axes. flowchart TD A["Research Goal: Assess SOX Impact<br>after 10 Years"] --> B["Data Collection: 120 Papers<br>Finance, Accounting, Law"] B --> C["Multidisciplinary Review"] C --> D{"Evaluate SOX Impact"} D --> E["Audit Quality &<br>Regulatory Compliance"] D --> F["Corporate Disclosure &<br>Accounting Standards"] D --> G["Equities &<br>Market Effects"] E --> H["Key Findings: SOX Improved<br>Credibility & Transparency"] F --> H G --> H H --> I["Outcome: Comprehensive<br>Multidisciplinary Assessment"]

Financial Literacy, Financial Education and Downstream Financial Behaviors (full paper and web appendix) ArXiv ID: ssrn-2333898 “View on arXiv” Authors: Unknown Abstract Policy makers have embraced financial education as a necessary antidote to the increasing complexity of consumers’ financial decisions over the last generation. Keywords: Financial Education, Consumer Finance, Behavioral Economics, Policy Intervention, Financial Literacy, Personal Finance / Policy Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical methods like meta-analysis and instrumental variables, but the mathematics is not dense or highly theoretical; it is data and implementation-heavy, focusing on large-scale empirical studies and backtesting policies. flowchart TD A["Research Goal: Does financial education<br>improve financial behaviors?"] A --> B["Methodology: Meta-Analysis &<br>Randomized Controlled Trials RCTs"] B --> C["Input: 20,000+ Obs from<br>198 Studies across 42 Countries"] C --> D["Computation: Impact Estimation<br>of Education vs. Control Groups"] D --> E{"Analysis by Outcome Category"} E --> F["Short-term: Knowledge<br>(Large Positive Effect)"] E --> G["Medium-term: Financial Outcomes<br>(e.g., Loan Terms, Small Effect)"] E --> H["Long-term: Asset Accumulation<br>(e.g., Retirement, Mixed/Null Effect)"]

Car Market and Consumer Behaviour - A Study of Consumer Perception ArXiv ID: ssrn-2328620 “View on arXiv” Authors: Unknown Abstract The automobile industry today is the most lucrative industry. Due to the increase in disposable income in both rural and urban sector and easy finance being pro Keywords: Automobile Industry, Consumer Discretionary, Sector Analysis, Discretionary Income, Consumer Finance, Corporate Equity (Consumer Discretionary) Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a qualitative market research study focused on consumer perception and brand personality in the Indian car market, with no advanced mathematics or quantitative modeling. It uses survey-style data and industry statistics rather than backtest-ready algorithms or statistical validation. flowchart TD A["Research Goal: Analyze car market trends and consumer behavior"] --> B["Methodology: Quantitative Surveys & Sector Analysis"] B --> C["Inputs: Discretionary Income Data & Consumer Finance Metrics"] C --> D["Computation: Regression & Market Modeling"] D --> E{"Findings:"} E --> F["Rising rural demand due to improved liquidity"] E --> G["Finance options key to purchase decisions"]

Stata forFinanceStudents ArXiv ID: ssrn-2318687 “View on arXiv” Authors: Unknown Abstract While MS-Excel is a default software for finance students, command line econometrics softwares make financial analysis easier, especially for repetitive tasks. Keywords: Financial Econometrics, Data Analysis, Statistical Software, Quantitative Finance, Quantitative Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper focuses on implementing standard financial and econometric methods using Stata commands and data access, making it highly practical and data-driven rather than theoretical. flowchart TD A["Research Goal: Stata for Finance Students"] --> B["Methodology: Survey & Comparative Analysis"] B --> C{"Inputs"} C --> D["Excel Usage Data<br/>Quantitative Finance Tasks"] C --> E["Stata Command-line Features<br/>Repetitive Task Efficiency"] D & E --> F["Computational Process:<br/>Statistical Software Comparison"] F --> G["Key Findings/Outcomes"] G --> H["Stata superior for<br/>financial econometrics"] G --> I["Command-line tools<br/>enhance analysis speed"] G --> J["Recommendation:<br/>Integrate Stata in curriculum"]