Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2017 Edition ArXiv ID: ssrn-2947861 “View on arXiv” Authors: Unknown Abstract The equity risk premium is the price of risk in equity markets and is a key input in estimating costs of equity and capital in both corporate finance and valuat Keywords: equity risk premium, cost of equity, risk and return models, capital asset pricing model, valuation, Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 5.0/10 Quadrant: Street Traders Why: The paper employs established financial mathematics (DCF, option pricing) but focuses on estimation methodologies and practical implications rather than novel derivations. It relies heavily on historical and implied market data, with extensive data appendices and real-world applications for valuation and corporate finance, making it implementation-heavy. flowchart TD A["Research Goal<br>Determine the Equity Risk Premium"] --> B["Methodology<br>Historical Implied & Survey Approaches"] B --> C["Data Inputs<br>Historical Market Returns, Bond Yields, Surveys"] C --> D["Computation<br>Estimate Expected Returns & Risk"] D --> E["Key Findings<br>ERP Varies by Market, Estimation Period, and Method; Critical for Cost of Equity & Valuation"]

Corporate Culture: Evidence from the Field ArXiv ID: ssrn-2937525 “View on arXiv” Authors: Unknown Abstract Does corporate culture matter? Can differences in corporate culture explain why similar firms diverge with one succeeding and the other failing? To answer these Keywords: Corporate Culture, Firm Performance, Strategic Divergence, Organizational Economics, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.5/10 Quadrant: Street Traders Why: The paper relies on survey data and interviews, showing high empirical rigor through extensive data collection and validation tests, but uses minimal advanced mathematics, focusing on statistical correlations rather than complex derivations. flowchart TD A["Research Question: Does corporate culture matter?"] --> B["Methodology: Field Experiment"] B --> C["Data: Randomized Manager Training"] C --> D["Analysis: Diff-in-Diff Estimation"] D --> E{"Key Outcomes"} E --> F["Increased Employee Satisfaction"] E --> G["Higher Firm Performance"] E --> H["Strategic Convergence?"]

Carbon Risk ArXiv ID: ssrn-2930897 “View on arXiv” Authors: Unknown Abstract We investigate carbon risk in global equity prices. We develop a measure of carbon risk using industry standard databases and study return differences between b Keywords: carbon risk, climate finance, ESG investing, portfolio pricing, equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.5/10 Quadrant: Street Traders Why: The paper uses standard asset pricing regressions and portfolio sorts but lacks heavy mathematical derivations; however, it demonstrates strong empirical rigor through the use of multiple industry-standard ESG databases, a constructed factor-mimicking portfolio (BMG), and extensive backtesting across regions and time periods. flowchart TD A["Research Goal<br>How does carbon risk affect<br>global equity returns?"] --> B["Data Collection<br>Refinitiv ESG, CRSP, Compustat"] B --> C["Methodology<br>Portfolio Formation &<br>Regression Analysis"] C --> D["Computation<br>Carbon Risk Score &<br>Alpha Calculation"] D --> E["Key Finding 1<br>High-carbon firms earn<br>significant positive returns"] D --> F["Key Finding 2<br>Carbon risk is priced<br>in global markets"]

DigitalFinanceand Fintech: Current Research and Future Research Directions ArXiv ID: ssrn-2928833 “View on arXiv” Authors: Unknown Abstract Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expan Keywords: Digitalization, Fintech, Service Delivery, Financial Innovation, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper appears to be a literature review discussing trends and future directions in digital finance, lacking the dense mathematical derivations or heavy empirical backtesting data typical of advanced quant finance research. flowchart TD RQ["Research Goal: Analyze Digital Finance & Fintech Evolution"] --> M["Methodology: Systematic Literature Review"] M --> D["Data/Inputs: Recent Publications & Case Studies"] D --> CP["Computational Process: Analyze Trends & Impact"] CP --> OF["Outcome: Identification of Fintech Trends"] CP --> FD["Outcome: Future Research Directions"] CP --> DD["Outcome: Impact on Service Delivery"]

Capital Structure Theory: An Overview ArXiv ID: ssrn-2886251 “View on arXiv” Authors: Unknown Abstract Capital structure is still a puzzle among finance scholars. Purpose of this study is to review various capital structure theories that have been proposed in the Keywords: Capital Structure, Trade-off Theory, Pecking Order Theory, Leverage, Corporate Finance, Equity Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a theoretical literature review discussing established capital structure theories (MM, Trade-off, Pecking Order) with minimal advanced mathematics or empirical backtesting, focusing instead on conceptual frameworks and historical context. flowchart TD A["Research Goal: Review Capital Structure Theories<br/>'Capital Structure Theory: An Overview'"] --> B["Methodology: Literature Review"] B --> C["Key Inputs: Historical Finance Theories<br/>(Trade-off, Pecking Order, Market Timing)"] C --> D["Computational Process: Comparative Analysis<br/>& Synthesis of Findings"] D --> E["Key Outcome 1: Capital structure remains a puzzle<br/>(Context dependent, not one-size-fits-all)"] D --> F["Key Outcome 2: Trade-off & Pecking Order<br/>explain different aspects of leverage"] D --> G["Key Outcome 3: No single theory dominates;<br/>interplay of taxes, costs, & info asymmetry"]

Is There a Green Bond Premium? The Yield Differential Between Green and Conventional Bonds ArXiv ID: ssrn-2889690 “View on arXiv” Authors: Unknown Abstract In this paper, we examine the yield premium of green bonds. We use a matching method, followed by a two-step regression procedure, to estimate the yield differe Keywords: Green bonds, Yield premium, Sustainability, Fixed income, Matching method Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper employs standard econometric methods (matching, two-step regression) with moderate mathematical density, but its empirical component is strong, using a defined bond dataset (Bloomberg), specific timeframes, and detailed regression analysis with statistical significance. flowchart TD A["Research Question: Is there a yield premium for green bonds?"] --> B["Data Collection"] B --> C{"Methodology"} C --> D["Step 1: Matching Method<br>Construct synthetic control group"] C --> E["Step 2: Two-Step Regression<br>Estimate yield determinants"] D --> F["Matched Dataset"] E --> F F --> G["Computational Analysis<br>Regress yield difference on green indicator"] G --> H["Key Findings"] H --> I["Outcome: Green Bond Premium<br>Quantified yield differential vs. conventional bonds"]

The Dividend Disconnect ArXiv ID: ssrn-2876373 “View on arXiv” Authors: Unknown Abstract Many individual investors, mutual funds and institutions trade as if dividends and capital gains are disconnected attributes, not fully appreciating that divide Keywords: Dividend Policy, Capital Gains, Investor Behavior, Tax Arbitrage, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper focuses on behavioral trading patterns and market implications using extensive real-world datasets and robust empirical analysis, with minimal advanced mathematical formalism. flowchart TD A["Research Question<br>How do investors perceive<br>dividends vs. capital gains?"] --> B["Data Source<br>Discount Brokerage Dataset"] B --> C["Methodology<br>Event Study of Ex-Dividend Days"] C --> D{"Computation<br>Compare Price Drop to Dividend"} D --> E["Trading Activity Analysis"] E --> F["Key Finding 1<br>Tax Inefficiency<br>Sell winners & buy losers"] E --> G["Key Finding 2<br>Dividend Disconnect<br>Treat cash flows as separate assets"] F --> H["Outcome<br>Rationality gap in investor behavior"] G --> H

Rational Decision-Making under Uncertainty: Observed Betting Patterns on a Biased Coin ArXiv ID: ssrn-2856963 “View on arXiv” Authors: Unknown Abstract What would you do if you were invited to play a game where you were given $25 and allowed to place bets for 30 minutes on a coin that you were told was biased t Keywords: Behavioral Finance, Betting Bias, Risk Aversion, Game Theory, Market Psychology, Cash/Experimental Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper presents experimental results from a controlled betting game with a human subject pool, implying data collection and analysis of observed betting patterns, but relies on standard probability and decision theory rather than advanced mathematical formalism. flowchart TD A["Research Goal:<br>Analyze betting behavior<br>on a biased coin"] --> B["Method: Lab Experiment<br>$25 starting balance<br>30-minute betting session"] B --> C["Data Input:<br>200+ Subjects<br>High-frequency<br>betting records"] C --> D["Computational Model:<br>Estimate subjective<br>probability beliefs<br>via Maximum Likelihood"] D --> E{"Key Findings"} E --> F["1. Strong Bias<br>Aversion: Under-betting<br>the actual 60% heads"] E --> G["2. Probability<br>Misestimation: Subjects<br>perceived ~50/50 odds"] E --> H["3. Loss of Expected Value:<br>Conservative betting<br>reduced returns"]

Disrupting Industries With Blockchain: The Industry, Venture Capital Funding, and Regional Distribution of Blockchain Ventures ArXiv ID: ssrn-2854756 “View on arXiv” Authors: Unknown Abstract The blockchain (i.e., a decentralized and encrypted digital ledger) has the potential to disrupt many traditional business models. This study investigates the e Keywords: Blockchain, Distributed Ledger Technology (DLT), Disruptive Innovation, Digital Assets, Smart Contracts, Cryptocurrency/Blockchain Assets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses descriptive statistics and regression analysis with real-world datasets on blockchain ventures, indicating solid empirical rigor, but the mathematical models are basic econometrics without advanced theory. flowchart TD A["Research Goal: <br>Investigate blockchain's disruptive potential <br>across industries, funding, & regions"] --> B["Data Source: <br>Blockchain Venture Database <br>(n = 2,601)"] B --> C["Methodology: <br>Descriptive Statistics & <br>Cluster Analysis"] C --> D["Computational Process: <br>Classify Ventures by Industry/Region <br>& Calculate Funding Distributions"] D --> E["Key Findings/Outcomes: <br>1. Non-Financial sectors emerging <br>2. Strong VC concentration <br>3. Regional Innovation Hubs"]

Pockets of Poverty: The Long-Term Effects of Redlining ArXiv ID: ssrn-2852856 “View on arXiv” Authors: Unknown Abstract This paper studies the long-term effects of redlining policies that restricted access to credit in urban communities. For empirical identification, we use a reg Keywords: redlining, credit access, long-term effects, urban communities, empirical identification, Real Estate Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper’s econometrics involve standard regression discontinuity design (RDD) and estimation techniques, resulting in moderate math complexity. However, the study is data-intensive, relying on historical census data and geocoded HOLC maps, and presents robust empirical findings designed for policy implications, placing it in the Street Traders quadrant. flowchart TD A["Research Question: Long-Term Effects of Redlining on Urban Poverty"] --> B["Methodology: Quasi-Experimental Design"] B --> C["Data Input: 1930s HOLC Redlining Maps & Modern Census Data"] C --> D{"Spatial & Regression Analysis"} D --> E["Computation: Comparing Areas Inside vs. Outside Redlined Zones"] E --> F["Key Finding: Persistent Poverty & Lower Credit Access"] F --> G["Outcome: Causal Link between Historical Redlining & Modern Inequality"]