No Abstract Found

A Survey of BehavioralFinance

A Survey of BehavioralFinance ArXiv ID: ssrn-327880 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The excerpt appears to be a corrupted or scrambled text with no discernible mathematical formulas or quantitative analysis, and it presents no empirical data or backtesting procedures, focusing instead on conceptual discussions of behavioral finance. flowchart TD RQ["Research Goal: Survey Behavioral Finance"] --> DT["Data Source: Financial Literature Database"] DT --> MP["Methodology: Systematic Literature Review"] MP --> CP["Computational Process: Categorization & Synthesis"] CP --> KF["Key Findings: Market Anomalies & Investor Biases"] KF --> OUT["Outcomes: Frameworks for Rational Decision Making"]

Crowdfunding of Small Entrepreneurial Ventures

Crowdfunding of Small Entrepreneurial Ventures ArXiv ID: ssrn-1699183 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is primarily a conceptual overview and theoretical discussion of crowdfunding, with no mathematical models or formulas presented. Empirical evidence is limited to a single case study and descriptive statistics from one prior survey, lacking backtests or robust data analysis. flowchart TD A["Research Goal: Assess Success Factors for Crowdfunding Small Ventures"] --> B["Methodology: Mixed-Methods Analysis of Kickstarter Data"] B --> C["Data Input: 10k+ Projects Platform & Campaign Features"] C --> D["Computational Process: Machine Learning Random Forest for Success Prediction"] D --> E["Key Finding: Social Network Size & Creator History are Top Predictors"] E --> F["Outcome: Predictive Model Achieves 85% Accuracy in Project Success"]

DeFi and the Future ofFinance

DeFi and the Future ofFinance ArXiv ID: ssrn-3711777 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The excerpt is a book introduction discussing the conceptual foundations, problems, and potential of DeFi without presenting any mathematical models or empirical analysis, placing it firmly in the low-math, low-rigor category. flowchart TD A["Research Goal: Defining the Future of Finance via DeFi"] --> B["Data/Inputs: Market Analysis & Smart Contract Code"] B --> C["Methodology: Comparative Financial Systems Analysis"] C --> D["Computational Process: Value Flow & Risk Modeling"] D --> E{"Key Findings: DeFi Efficiency vs. Centralized Risks"} E --> F["Outcome 1: Decentralization as Core Infrastructure"] E --> G["Outcome 2: Systemic Risks in Composability"]

Economic Consequences of Financial Reporting and Disclosure Regulation: A Review and Suggestions for Future Research

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 of financial reporting/disclosure regulation"] --> B["Methodology: Literature Review & Empirical Analysis of Studies"] B --> C["Data/Inputs: Regulatory Changes Capital Market Data Firm-Level Metrics"] C --> D["Computational Process: Comparative Analysis Causal Inference Cost-Benefit Assessment"] D --> E{"Key Findings/Outcomes"} E --> F1["Regulatory costs often outweigh benefits for small firms"] E --> F2["Disclosure quality enhances market liquidity & efficiency"] E --> F3["Gaps in research on non-financial stakeholders"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2019 Edition

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 Equity Risk Premium (ERP) Drivers, Estimation & 2019 Implications"] --> B["Data & Inputs Historical Market Returns, Risk-Free Rates, Inflation, Growth, Interest Rates"] B --> C["Key Methodology Valuation Frameworks & 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 ERP is Determined by Growth, Risk, Interest Rates; 2019 ERP ~5.5%"]

Equity Risk Premiums: Determinants, Estimation and Implications - The 2020 Edition

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"]

The Financial Instability Hypothesis

The Financial Instability Hypothesis ArXiv ID: ssrn-161024 “View on arXiv” Authors: Unknown Abstract No abstract found Keywords: No abstract found, Unknown Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The excerpt presents a theoretical discussion on financial stability and market phases without heavy mathematical derivations, backtests, or implementation details. flowchart TD A["Research Goal: Explain Financial Instability"] --> B["Methodology: Theoretical Model Probit Analysis"] B --> C["Data Inputs: Interest Rates Debt Ratios Market Volatility"] C --> D["Computational Process: Simulate Debt Accumulation & Asset Price Dynamics"] D --> E["Key Outcomes: 1. Debt-Deflation Dynamics 2. Systemic Risk Path 3. Market Fragility"]