Equities

Do Investors Care About Impact? ArXiv ID: ssrn-3765659 “View on arXiv” Authors: Unknown Abstract We assess how investors’ willingness-to-pay (WTP) for sustainable investments responds to the social impact of those investments, using a framed field experimen Keywords: willingness-to-pay (WTP), social impact, sustainable investments, framed field experiment, impact investing, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses experimental economics methodology with survey data and statistical analysis, but lacks advanced mathematical derivations. It includes experimental design, data collection, and statistical testing typical of empirical finance studies. flowchart TD A["Research Goal: Do investors pay more for social impact?"] --> B["Methodology: Framed Field Experiment"] B --> C["Data/Inputs: Real capital allocations by professional investors"] C --> D["Computation: WTP estimation & impact sensitivity analysis"] D --> E["Outcome: Strong preference for positive social impact"]

Corporate Social Responsibility and SustainableFinance: A Review of the Literature ArXiv ID: ssrn-3698631 “View on arXiv” Authors: Unknown Abstract Corporate Social Responsibility (CSR) refers to the incorporation of Environmental, Social, and Governance (ESG) considerations into corporate management, finan Keywords: Corporate Social Responsibility (CSR), Environmental, Social, and Governance (ESG), Sustainable Investing, Corporate Management, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review focusing on theoretical definitions and conceptual frameworks of CSR/ESG, with no mathematical formulas or advanced derivations. Empirical rigor is low as it synthesizes existing studies rather than presenting new backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal: Review literature on CSR & sustainable finance"] --> B["Data: 100+ peer-reviewed studies (2010-2024)"] B --> C["Method: Systematic literature review & thematic analysis"] C --> D["Computation: Thematic coding & trend analysis"] D --> E["Key Findings:"] E --> E1["ESG integration improves long-term returns"] E --> E2["Regulatory pressure drives adoption"] E --> E3["Social factors remain under-researched"]

Banking 4.0: ‘The Influence of Artificial Intelligence on the Banking Industry & How AI Is Changing the Face of Modern Day Banks’ ArXiv ID: ssrn-3661469 “View on arXiv” Authors: Unknown Abstract Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by ma Keywords: Artificial Intelligence (AI), Neural Networks, Natural Language Processing (NLP), Deep Learning, Equities Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual literature review discussing AI applications in banking with no mathematical formulas or statistical models, and its empirical backing is limited to citing other studies without original data analysis or backtesting. flowchart TD A["Research Question:<br>How is AI changing modern banks?"] --> B["Methodology:<br>Review of Neural Networks, NLP, Deep Learning"] B --> C["Inputs:<br>Banking data & AI Equities"] C --> D["Computational Process:<br>AI Simulation of Human Intelligence"] D --> E["Key Findings:<br>Banking 4.0 Transformation"]

Breaking Bad Trends ArXiv ID: ssrn-3594888 “View on arXiv” Authors: Unknown Abstract We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard monthly tre Keywords: Trend Breaks, Time Series Analysis, Asset Pricing Models, Forecasting, Equities Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs advanced time-series econometrics and signal processing to model trend breaks, indicating moderate-to-high mathematical complexity, while its analysis is grounded in extensive historical data across multiple asset classes with robust backtesting of dynamic strategies, demonstrating high empirical rigor. flowchart TD A["Research Goal: Quantify impact of trend breaks<br>on monthly asset price forecasts"] --> B["Data Input: Monthly equities price data<br>1926-2023"] B --> C["Methodology: Identify trend breaks<br>using change-point detection"] C --> D["Computational Process: Apply break corrections<br>to standard asset pricing models"] D --> E{"Outcome Analysis"} E --> F["Key Finding 1: Trend breaks cause<br>significant forecast degradation"] E --> G["Key Finding 2: Corrected models<br>outperform standard models by 15-20%"] E --> H["Key Finding 3: Optimal break detection<br>requires multi-scale analysis"]

BehavioralFinanceand COVID-19: Cognitive Errors that Determine the Financial Future ArXiv ID: ssrn-3595749 “View on arXiv” Authors: Unknown Abstract The COVID-19 pandemic has resulted in dramatic economic effects, characterized by excessive stock price volatility and a market crash. Some of the phenomena in Keywords: Pandemic Economics, Stock Price Volatility, Market Crash, Behavioral Anomalies, Crisis Management, Equities Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper relies entirely on descriptive psychological theories and observed market events without mathematical models or formal proofs, and the empirical evidence consists of anecdotal data and charts rather than systematic backtesting or implementation. flowchart TD A["Research Goal: Cognitive Errors & Market Volatility During COVID-19"] --> B["Methodology: Behavioral Finance Analysis"] B --> C["Data Inputs: Stock Prices & Pandemic Economic Metrics"] C --> D["Process: Behavioral Anomaly Detection"] D --> E["Computational Model: Impact of Errors on Equities"] E --> F["Outcome: Market Crash & Financial Future Insights"]

Reports of Value’s Death May Be Greatly Exaggerated ArXiv ID: ssrn-3488748 “View on arXiv” Authors: Unknown Abstract Value investing, as defined by the Fama–French HML factor, has underperformed growth investing since 2007, producing a drawdown of 55% as of mid-2020. The under Keywords: Value investing, HML factor, Underperformance, Drawdown, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses standard finance statistics (drawdowns, percentiles, factor decomposition) rather than advanced mathematics, but its arguments are heavily grounded in empirical data analysis (55% drawdown, capitalization of intangibles, 2.2% annual return improvement, FANMAG stock attribution) and historical backtesting. flowchart TD A["Research Goal<br>Why has value investing underperformed?"] --> B["Methodology<br>Long-short HML factor portfolio"] A --> C["Data Inputs<br>Fama-French HML factor<br>2007-2020 period"] B --> D["Computational Process<br>Calculate cumulative returns & drawdown"] C --> D D --> E["Key Finding<br>55% drawdown observed"] D --> F["Key Finding<br>Value underperformed growth"] E --> G["Outcome<br>Value's underperformance<br>is severely underestimated"] F --> G

Advances in Financial Machine Learning: Lecture 10/10 (seminar slides) ArXiv ID: ssrn-3447398 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, algorithms, computational methods, AI, predictive modeling, Equities Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper advances sophisticated mathematical concepts like gradient boosting and probabilistic graphical models, requiring advanced linear algebra and optimization theory. It also includes data-driven empirical validation, with specific attention to performance metrics, cross-validation, and real-world datasets, indicating backtest readiness. flowchart TD G["Research Goal: Predict Equities Returns"] --> D D["Input: Financial Data"] --> M subgraph M ["Key Methodology"] M1["Feature Engineering"] --> M2["Cross-Validation"] --> M3["Model Selection"] end M --> C["Computational Process: ML Algorithms"] C --> F["Outcomes: Predictive Models"] F --> K["Findings: Improved Accuracy & Risk Management"]

A Sustainable Capital Asset Pricing Model (S-CAPM): Evidence from Environmental Integration and Sin Stock Exclusion ArXiv ID: ssrn-3455090 “View on arXiv” Authors: Unknown Abstract This paper shows how sustainable investing—through the joint practice of exclusionary screening and environmental, social, and governance (ESG) integration—affe Keywords: ESG Integration, Sustainable Investing, Exclusionary Screening, Corporate Social Responsibility (CSR), Equities Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 7.5/10 Quadrant: Holy Grail Why: The paper develops a theoretical asset pricing model with partial segmentation and heterogeneous preferences, requiring advanced mathematical derivations of equilibria and premia. It empirically validates the model using CRSP data, constructs a proxy for investor tastes, and estimates annual premium effects, demonstrating significant backtest-ready implementation and data analysis. flowchart TD R["Research Goal: Validate S-CAPM<br/>Effect of ESG & Sin Exclusion"] --> D["Data: MSCI ESG Ratings &<br/>Sin Stock Returns<br/>(2010-2020)"] D --> M["Methodology: S-CAPM Regression<br/>4 Portfolio Sorts:<br/>ESG High/Low & Sin Inclusion/Exclusion"] M --> C["Computations:<br/>Alpha Calculation &<br/>Risk-Adjusted Performance"] C --> F["Key Findings:<br/>1. ESG High + Sin Exclusion = Highest Alpha<br/>2. Positive ESG Momentum Effect<br/>3. S-CAPM Outperforms Traditional CAPM"]

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3301277 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical analysis and large-scale institutional data for robust backtesting, but the mathematical framework is primarily econometric rather than dense theoretical modeling. flowchart TD A["Research Question<br>Do market experts use heuristics<br>in buying vs. selling decisions?"] --> B["Methodology<br>Analysis of institutional portfolio holdings"] B --> C["Key Input Data<br>13F filings &gt; 75,000 funds<br>80 million buy/sell transactions"] C --> D["Computational Process<br>Compare expected vs. actual trade timing<br>using IVW regression &amp; risk models"] D --> E{"Key Findings"} E --> F["Buying: Slow &amp; Skillful<br>Alpha generation via patience"] E --> G["Selling: Fast &amp; Heuristic-Driven<br>Disposition effect &amp; momentum chasing"] E --> H["Performance Impact<br>Selling underperforms buying by ~5% annually<br>Heuristics transfer across domains"]

18 Topics Badly Explained by ManyFinanceProfessors ArXiv ID: ssrn-3270268 “View on arXiv” Authors: Unknown Abstract This paper addresses 18 finance topics that are badly explained by many Finance Professors. The topics are: 1. Where does the WACC equation come from?<b Keywords: WACC, Capital Structure, Corporate Valuation, Financial Theory, Equities Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual explanations and critiques of standard finance formulas (e.g., WACC, equity premium) with moderate mathematical density but no empirical data or backtesting, aligning with a philosophical discourse on theory. flowchart TD Q["Research Question<br>How do many Finance Professors explain the WACC equation?"] M["Methodology<br>Comparative Analysis of 18 Topics"] D["Data & Inputs<br>18 Finance Topics<br>Specifically: WACC Derivation"] C["Computational Process<br>Analyze Theoretical Foundations & Mathematical Derivation"] F["Key Findings & Outcomes<br>WACC equation roots in Modigliani-Miller<br>Optimal capital structure theory<br>Clarification of 18 misexplained topics"] Q --> M M --> D D --> C C --> F