Machine Learning for Stock Selection ArXiv ID: ssrn-3330946 “View on arXiv” Authors: Unknown Abstract Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning technique Keywords: Machine learning, Quantitative finance, Predictive accuracy, Quantitative Strategies Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper provides a conceptual overview of machine learning techniques in finance with minimal advanced mathematical derivations, focusing more on the debate and methodology rather than deep theoretical proofs. Empirical rigor is limited as it discusses general challenges like overfitting and proposes forecast combinations without presenting detailed backtest results, code, or specific implementation datasets. flowchart TD A["Research Goal: Evaluate ML for Stock Selection"] --> B["Data: Historical Prices, Fundamentals, Sentiment"] B --> C["Methodology: Train ML Models e.g., Gradient Boosting, Neural Networks"] C --> D{"Computational Process: Backtest on Out-of-Sample Data"} D --> E["Key Finding: ML Models Achieve High Predictive Accuracy"] D --> F["Key Finding: Significant Risk of Overfitting"] E & F --> G["Outcome: Mixed Results; Strategy Viability Depends on Rigorous Validation"] style A fill:#e1f5fe style G fill:#fff3e0

Strategic Rebalancing ArXiv ID: ssrn-3330134 “View on arXiv” Authors: Unknown Abstract A mechanical rebalancing strategy, such as a monthly or quarterly reallocation towards fixed portfolio weights, is an active strategy. Winning asset classes are Keywords: rebalancing, portfolio weights, momentum, risk-adjusted returns, asset allocation, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper presents several analytical derivations, including a two-period model and convexity/concavity arguments, which indicate moderate mathematical density. It also includes extensive empirical backtesting on long historical datasets (1927-2017) with specific drawdown analysis and risk metrics, demonstrating strong implementation and data reliance. flowchart TD A["Research Goal"] --> B["Rebalancing<br>vs. Buy-and-Hold"] B --> C["Data Inputs<br>Multi-Asset Classes"] C --> D["Methodology<br>Strategic Rebalancing<br>Monthly/Quarterly"] D --> E["Computational Process<br>Calculate Returns &<br>Risk-Adjusted Metrics"] E --> F["Key Findings<br>Active Strategy<br>Better Risk-Adjusted Returns"]

The Globalisation Experience and Its Challenges for the Philippine Economy ArXiv ID: ssrn-3332089 “View on arXiv” Authors: Unknown Abstract This paper analyses the extent and impact of globalisation in the Philippines in terms of trade, finance and migration. In the Philippines, trade globalisation Keywords: Globalization, Philippines, Trade finance, Migration, Economic development Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper is primarily descriptive and policy-focused, relying on basic economic indicators and descriptive statistics rather than advanced mathematical modeling or complex empirical methods. While it references empirical estimates, the methodology is not detailed, and there is no mention of backtesting, specific datasets, or implementation-heavy frameworks. flowchart TD A["Research Goal<br>Assess globalization impact<br>on Philippine economy"] --> B["Methodology: Mixed Methods"] B --> C["Data Inputs<br>Trade, Finance, Migration Stats"] C --> D["Analysis: Economic Impact Assessment"] D --> E["Key Findings:<br>1. Uneven Trade Benefits<br>2. Financial Volatility<br>3. Migration Dependency"] E --> F["Outcome: Policy Recommendations<br>for Sustainable Development"]

Economic Analysis of Widespread Adoption of CSR and Sustainability Reporting Standards ArXiv ID: ssrn-3315673 “View on arXiv” Authors: Unknown Abstract This report provides an economic analysis for a widespread adoption of corporate social responsibility (or sustainability) disclosure and reporting standards in Keywords: Corporate Social Responsibility, Sustainability Disclosure, ESG Reporting, Economic Analysis, Asset Class: Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper focuses on economic theory and policy analysis for CSR reporting standards without advanced mathematical derivations or empirical backtesting components. flowchart TD A["Research Goal: Economic impact of<br>widespread CSR/ESG reporting adoption"] --> B["Data & Inputs"] B --> C["Methodology Steps"] C --> D["Computational Processes"] D --> E["Key Findings & Outcomes"] subgraph B ["Data/Inputs"] B1["Financial Data: Equities"] B2["ESG & Sustainability Metrics"] B3["Reporting Standard Regulations"] end subgraph C ["Methodology Steps"] C1["Market Efficiency Analysis"] C2["Asset Pricing Models"] C3["Cost of Capital Assessment"] end subgraph D ["Computational Processes"] D1["Regression Analysis"] D2["Simulation Modeling"] D3["Comparative Analysis"] end subgraph E ["Key Findings"] E1["Capital Cost Reduction"] E2["Enhanced Market Liquidity"] E3["Long-term Value Creation"] end

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"]

Principles of SustainableFinance ArXiv ID: ssrn-3282699 “View on arXiv” Authors: Unknown Abstract Finance is widely seen as an obstacle to a better world. Principles of Sustainable Finance explains how the financial sector can be mobilized to counter this an Keywords: Sustainable Finance, ESG (Environmental, Social, Governance), Impact Investing, Risk Management, Climate Finance, Cross-Asset (Sustainable Investing) Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The text is a conceptual overview of sustainable finance, focusing on economic models, behavioral changes, and policy frameworks, with no advanced mathematical derivations or empirical backtesting evidence presented. flowchart TD A["Research Goal: Mobilize Finance for Sustainability"] --> B["Methodology: ESG Analysis & Risk Management"] B --> C["Data Inputs: Climate Data & Corporate ESG Reports"] C --> D["Computation: Cross-Asset Impact Modeling"] D --> E["Outcome: Sustainable Finance Principles"]

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

A Backtesting Protocol in the Era of Machine Learning ArXiv ID: ssrn-3275654 “View on arXiv” Authors: Unknown Abstract Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, th Keywords: Machine Learning, Investment Management, Quantitative Finance, Asset Pricing, Algorithmic Trading Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses on a research protocol for backtesting and data mining, with moderate empirical rigor involving practical concerns like overfitting and data scarcity, but lacks advanced mathematical derivations, centering instead on statistical concepts and real-world data challenges. flowchart TD A["Research Goal: Develop robust backtesting protocol for ML in finance"] --> B["Data: Cross-sectional stock data & fundamental features"] B --> C["Methodology: ML pipelines with walk-forward validation"] C --> D["Computation: Model training, hyperparameter tuning, & signal generation"] D --> E["Risk Controls: Transaction costs, liquidity constraints, & overfitting tests"] E --> F["Key Outcomes: Generalizable signals & realistic performance metrics"] F --> G["Implication: ML requires rigorous validation to avoid false discoveries"]

Fintech for Financial Inclusion: A Framework for Digital Financial Transformation ArXiv ID: ssrn-3245287 “View on arXiv” Authors: Unknown Abstract Access to finance, financial inclusion and financial sector development have long been major policy objectives. A series of initiatives have aimed to increase a Keywords: Financial Inclusion, Access to Finance, Financial Sector Development, Microfinance, Credit Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a policy-oriented framework discussing regulatory strategies and digital infrastructure, lacking mathematical formulas or statistical models; its empirical support relies on high-level case studies (e.g., India, Kenya) and aggregated data from sources like the World Bank, with no backtesting or implementation details. flowchart TD A["Research Goal:<br/>Framework for Digital Financial Transformation"] --> B["Data & Inputs:<br/>Policy Initiatives & Microfinance Data"] B --> C["Methodology:<br/>Thematic Analysis & Synthesis"] C --> D["Computational Process:<br/>Mapping Inclusion to Digital Tech"] D --> E["Key Findings:<br/>Fintech as Catalyst for<br/>Financial Sector Development"]

Advances in Financial Machine Learning: Lecture 1/10 (seminar slides) ArXiv ID: ssrn-3270329 “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, algorithmic trading, predictive analytics, data science, fintech, Multi-Asset / Quantitative Strategies Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The excerpt presents a high-level critique of econometric methods compared to machine learning, but it focuses on theoretical arguments and conceptual pitfalls rather than advancing novel mathematical techniques or presenting concrete backtesting results. flowchart TD A["Research Goal: Apply ML to Financial Markets"] --> B["Methodology: Identify Financial Signals & Features"] B --> C["Data Inputs: High-Frequency Trading & Market Data"] C --> D["Computation: Training Algorithms & Model Validation"] D --> E["Outcomes: Predictive Analytics for Multi-Asset Strategies"]