Quantitative Strategies

High-Throughput Asset Pricing ArXiv ID: 2311.10685 “View on arXiv” Authors: Unknown Abstract We apply empirical Bayes (EB) to mine data on 136,000 long-short strategies constructed from accounting ratios, past returns, and ticker symbols. This ``high-throughput asset pricing’’ matches the out-of-sample performance of top journals while eliminating look-ahead bias. Naively mining for the largest Sharpe ratios leads to similar performance, consistent with our theoretical results, though EB uniquely provides unbiased predictions with transparent intuition. Predictability is concentrated in accounting strategies, small stocks, and pre-2004 periods, consistent with limited attention theories. Multiple testing methods popular in finance fail to identify most out-of-sample performers. High-throughput methods provide a rigorous, unbiased framework for understanding asset prices. ...

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

Detection of False Investment Strategies Using Unsupervised Learning Methods ArXiv ID: ssrn-3167017 “View on arXiv” Authors: Unknown Abstract Most investment strategies uncovered by practitioners and academics are false. This partially explains the high rate of failure, especially among quantitative h Keywords: quantitative finance, investment strategies, backtesting bias, market efficiency, quantitative strategies Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper introduces a complex unsupervised learning algorithm involving probability distributions and multiple testing corrections, but lacks specific implementation details, code, or detailed backtesting results, focusing more on theoretical and statistical methodology. flowchart TD A["Research Goal:<br>Detect false quantitative investment strategies"] --> B["Methodology:<br>Unsupervised Learning (e.g., Clustering)"] B --> C["Data Inputs:<br>Strategy Returns, Factor Loadings, Backtest Metrics"] C --> D["Computational Process:<br>Identify Outliers & Anomalies in Strategy Space"] D --> E["Key Findings:<br>Strategies are often noise; high failure rate due to backtesting bias"]