ESG Lending ArXiv ID: ssrn-3865147 “View on arXiv” Authors: Unknown Abstract Firms increasingly borrow via sustainability-linked loans (SLLs), contractually tying spreads to their ESG performance. SLLs vary widely in transparency of disc Keywords: Sustainability-linked loans, ESG performance, Loan spreads, Greenwashing, Credit risk Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced econometric methods (DID, PSM, stacked DID, synthetic controls) and handles large datasets (LSEG DealScan, Refinitiv, S&P Trucost) with robustness checks, indicating high empirical rigor, but uses standard statistical models without heavy mathematical derivations. flowchart TD A["Research Goal: How do ESG performance & SLL transparency affect loan spreads & credit risk?"] --> B["Methodology: Empirical analysis of loan contracts"] B --> C["Data: SLLs & ESG data from 2016-2022"] C --> D["Computation: Regression models on spread determinants"] D --> E["Outcomes: Lower spreads for ESG performance, less for opaque SLLs"] E --> F["Risk: No significant credit risk reduction, potential greenwashing"]

Cryptocurrency & Its Impact on Environment ArXiv ID: ssrn-3846774 “View on arXiv” Authors: Unknown Abstract Cryptocurrencies have gone a long way since their inception. While the conventional financial sector initially dismissed digital currencies as tools for crooks Keywords: Cryptocurrencies, Digital Assets, Regulatory Risk, Financial Innovation, Asset Class: Crypto Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper appears to discuss the environmental impact of cryptocurrencies, focusing on descriptive and qualitative analysis rather than quantitative modeling or backtesting. The excerpt lacks advanced mathematical formulas, code, or empirical datasets, placing it in a theoretical discussion quadrant. flowchart TD RQ["Research Question:<br/>What is the environmental impact<br/>of cryptocurrency mining?"] --> M["Methodology<br/>Lifecycle Assessment (LCA) &<br/>Energy Consumption Analysis"] D1["Data: Bitcoin Network<br/>Hash Rate Distribution"] --> M D2["Data: Global Energy Grid<br/>Emission Factors"] --> M M --> C["Computational Process:<br/>Modeling Energy Mix &<br/>Carbon Footprint Calculation"] C --> F["Key Findings<br/>1. High energy intensity (Proof-of-Work)<br/>2. Varies by grid source<br/>3. Rising regulatory pressure<br/>4. Shift to Proof-of-Stake"]

DecentralizedFinance: On Blockchain- and Smart Contract-Based Financial Markets ArXiv ID: ssrn-3843844 “View on arXiv” Authors: Unknown Abstract The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to cr Keywords: Decentralized Finance (DeFi), Smart Contracts, Blockchain, Ethereum, Tokenomics, Crypto Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey and introduction to DeFi architecture with conceptual frameworks and qualitative descriptions, containing no advanced mathematics, models, or statistical analysis, and it lacks backtest-ready data, implementation details, or empirical results. flowchart TD A["Research Goal:<br>Understanding DeFi Infrastructure"] --> B{"Methodology"}; B --> C["Data Collection:<br>Ethereum Blockchain Logs"]; B --> D["Analysis:<br>Smart Contract Code Review"]; C --> E["Computational Analysis:<br>Tokenomics & Gas Fee Models"]; D --> E; E --> F["Key Findings:<br>1. Automated Market Makers<br>2. Lending Protocols<br>3. Composability Risks"];

Impacts, Challenges and Trends of Digital Transformation in the Banking Sector ArXiv ID: ssrn-3835433 “View on arXiv” Authors: Unknown Abstract Driven by the 2020 pandemic’s work-at-home mandates, the future of work in banking and finance may be in the midst of disruptive change. The digital transformat Keywords: Digital Transformation, Banking, Work at Home, Future of Work, Financial Services, Banking / Financial Services Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper discusses broad digital transformation trends and impacts in banking, lacking advanced mathematical formulas or quantitative models; empirical evidence appears to be descriptive rather than data-driven or backtested. flowchart TD A["Research Goal<br>Understand DT impacts on Banking<br>post-2020 pandemic"] --> B["Methodology<br>Literature Review &<br>Case Study Analysis"] B --> C{"Input Data"} C --> D["Financial Services Industry Reports"] C --> E["Remote Work / Digital Adoption Statistics"] C --> F["Employee & Customer Satisfaction Surveys"] D & E & F --> G["Analysis<br>Thematic & Comparative Analysis<br>of Trends & Challenges"] G --> H["Key Findings & Outcomes<br>1. Accelerated Digital Adoption<br>2. Hybrid Work Models<br>3. Cybersecurity Challenges<br>4. Future of Banking Workforce"]

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2021 Edition ArXiv ID: ssrn-3825823 “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, cost of equity, capital asset pricing model, valuation, risk pricing, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses foundational finance equations (CAPM, multi-factor models) with minimal advanced derivation, placing math complexity low. However, it heavily relies on historical data, surveys, and real-world market data (default spreads, option prices) to estimate and compare equity risk premiums, making it highly empirical and implementation-focused. flowchart TD A["Research Goal: Determine ERP<br>for Corporate Valuation"] --> B["Key Methodology: Historical Analysis"] B --> C["Data Inputs: Historical<br>Stock Returns vs<br>Risk-Free Rates"] C --> D["Computational Process:<br>Calculate Average Historical ERP<br>& Adjust for Market Conditions"] D --> E["Key Findings: ERP is unstable<br>Context-dependent; Required for<br>accurate Cost of Equity &<br>Valuation models"]

How Competitive is the Stock Market? Theory, Evidence from Portfolios, and Implications for the Rise of Passive Investing ArXiv ID: ssrn-3821263 “View on arXiv” Authors: Unknown Abstract The conventional wisdom in finance is that competition is fierce among investors: if a group changes its behavior, others adjust their strategies such that noth Keywords: Market Efficiency, Investor Behavior, Game Theory, Strategic Interaction, Equities Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs a semi-structural economic model with equilibrium conditions, endogenous elasticities, and formal estimation challenges (reflection problem, endogeneity), requiring advanced mathematics. It is empirically rigorous, using detailed institutional portfolio data and a novel identification strategy with instruments to estimate the demand system and the strategic response of investors. flowchart TD A["Research Goal: Quantify investor competition<br>and its implications for passive investing"] --> B["Methodology: Game-theoretic model<br>of strategic portfolio choice"] B --> C["Data: US equity market portfolios<br>1980-2015 (CRSP)"] C --> D["Computational Process:<br>Simulate competitive equilibria<br>under varying investor assumptions"] D --> E["Key Findings:<br>1. Competition is strong but incomplete<br>2. Passive investing reduces competition<br>3. Market efficiency varies with investor structure"]

COVID-19 Impact on Micro, Small, and Medium-Sized Enterprises under the Lockdown: Evidence from a Rapid Survey in the Philippines ArXiv ID: ssrn-3807080 “View on arXiv” Authors: Unknown Abstract The novel coronavirus disease, COVID-19, has brought significant change to people’s lives and business activities nationally, regionally, and globally. The Phil Keywords: COVID-19, Supply Chain, Economic Resilience, Business Operations, Corporate Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper relies on descriptive statistics and basic econometric models (e.g., linear probability) without advanced mathematical derivations, while its empirical work is based on a single rapid survey without code, backtests, or robust implementation details. flowchart TD A["Research Question:<br>How did the COVID-19 lockdown impact MSMEs?"] --> B["Methodology: Rapid Survey<br>in the Philippines"] B --> C["Data Inputs:<br>Survey Responses &<br>Business Metrics"] C --> D["Computational Process:<br>Descriptive Statistics &<br>Economic Impact Analysis"] D --> E["Key Findings:<br>Revenue Loss, Supply Chain Disruption<br>& Need for Digital Resilience"]

Is There a Replication Crisis inFinance? ArXiv ID: ssrn-3774514 “View on arXiv” Authors: Unknown Abstract Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple test Keywords: replication crisis, multiple testing, publication bias, p-hacking, Financial Economics Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 9.0/10 Quadrant: Holy Grail Why: The paper employs a complex Bayesian statistical model for joint factor estimation, involving advanced priors and shrinkage methods, indicating high mathematical density. It also demonstrates high empirical rigor through extensive global backtesting on a new large dataset (93 countries) and provides open-source data access, making it highly data and implementation-heavy. flowchart TD A["Research Goal<br>Replicability in Finance?"] --> B["Methodology<br>Replicate 200+ Studies"] A --> C["Data Input<br>Prominent Finance Journals"] B --> D["Computational Process<br>Statistical Test & Meta-Analysis"] C --> D D --> E["Key Findings<br>High Failure Rate<br>Significant Publication Bias"]

How Much Should We Trust Staggered Difference-In-Differences Estimates? ArXiv ID: ssrn-3794018 “View on arXiv” Authors: Unknown Abstract We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These bi Keywords: Difference-in-Differences (DiD), Policy Evaluation, Econometric Bias, Causal Inference, Staggered Adoption, Multi-Asset (Quantitative Research) Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper involves advanced econometric theory on staggered difference-in-differences and discusses complex estimator derivations, but it is primarily a theoretical/methodological critique without original backtesting or heavy data implementation. flowchart TD A["Research Question:<br>How much should we trust staggered<br>DID estimates?"] --> B["Methodology: Simulation & Analytical Framework"] B --> C{"Data / Inputs"} C --> C1["Multi-Asset Dataset"] C --> C2["Policy Adoption<br>Staggered Design"] C --> C3["Treatment Effects<br>(Heterogeneity)"] C --> C4["Distributional Assumptions"] C1 & C2 & C3 & C4 --> D["Computational Process:<br>Estimation of Staggered DID"] D --> D1["Standard TWFE Estimator"] D --> D2["New (Robust) Estimators"] D1 --> E{"Analysis"} D2 --> E E --> F["Key Findings / Outcomes"] F --> F1["Bias Detection:<br>Standard TWFE often biased"] F --> F2["Solution:<br>Use robust estimators<br>e.g., Callaway & Sant'Anna"] F --> F3["Conclusion:<br>Trust estimates only after<br>robustness checks"]

Advanced Course in Asset Management (Presentation Slides) ArXiv ID: ssrn-3773484 “View on arXiv” Authors: Unknown Abstract These presentation slides have been written for the Advanced Course in Asset Management (theory and applications) given at the University of Paris-Saclay. They Keywords: Asset Management, Modern Portfolio Theory, Risk Management, Factor Investing, Multi-Asset Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The slides present advanced mathematical theory including Markowitz optimization, CAPM, and Black-Litterman models with quadratic programming formulations and covariance matrix algebra. While it includes tutorial exercises and practice sections, it lacks empirical backtesting data, code implementations, or statistical performance metrics, remaining primarily theoretical and educational. flowchart TD A["Research Goal<br>Modern Asset Management"] --> B["Key Methodology<br>Portfolio Optimization"] B --> C["Data Inputs<br>Market Factors & Risk"] C --> D["Computational Process<br>Factor Analysis & MPT"] D --> E["Key Outcomes<br>Strategic Asset Allocation"] E --> F["Applications<br>Risk-Adjusted Returns"]