Backtesting Bias

AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market ArXiv ID: 2508.13429 “View on arXiv” Authors: Paulo André Lima de Castro Abstract Autonomous trading strategies have been a subject of research within the field of artificial intelligence (AI) for aconsiderable period. Various AI techniques have been explored to develop autonomous agents capable of trading financial assets. These approaches encompass traditional methods such as neural networks, fuzzy logic, and reinforcement learning, as well as more recent advancements, including deep neural networks and deep reinforcement learning. Many developers report success in creating strategies that exhibit strong performance during simulations using historical price data, a process commonly referred to as backtesting. However, when these strategies are deployed in real markets, their performance often deteriorates, particularly in terms of risk-adjusted returns. In this study, we propose an AI-based strategy inspired by a classical investment paradigm: Value Investing. Financial AI models are highly susceptible to lookahead bias and other forms of bias that can significantly inflate performance in backtesting compared to live trading conditions. To address this issue, we conducted a series of computational simulations while controlling for these biases, thereby reducing the risk of overfitting. Our results indicate that the proposed approach outperforms major Brazilian market benchmarks. Moreover, the strategy, named AlphaX, demonstrated superior performance relative to widely used technical indicators such as the Relative Strength Index (RSI) and Money Flow Index (MFI), with statistically significant results. Finally, we discuss several open challenges and highlight emerging technologies in qualitative analysis that may contribute to the development of a comprehensive AI-based Value Investing framework in the future ...

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"]