Systematic trading and backtesting
Why most backtests are overfit, and the statistical tools for telling a real edge from noise.
| Citation | Paper | Access | Difficulty | Score |
|---|---|---|---|---|
| Gu et al. (2020) | Empirical Asset Pricing via Machine Learning Review of Financial Studies | Free | Technical | 0 |
| Arnott et al. (2019) | A Backtesting Protocol in the Era of Machine Learning Journal of Financial Data Science | Free | Easy read | 0 |
| Frazzini et al. (2018) | Trading Costs SSRN Working Paper | Free | Moderate | 0 |
| Bailey et al. (2017) | The Probability of Backtest Overfitting Journal of Computational Finance | Free | Technical | 0 |
| Novy-Marx & Velikov (2016) | A Taxonomy of Anomalies and Their Trading Costs Review of Financial Studies | Free | Moderate | 0 |
| Harvey & Liu (2015) | Backtesting Journal of Portfolio Management | Free | Moderate | 0 |
| Bailey & Prado (2014) | The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality Journal of Portfolio Management | Free | Moderate | 0 |
| Bailey et al. (2014) | Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance Notices of the American Mathematical Society | Free | Moderate | 0 |
| Harvey & Liu (2014) | Evaluating Trading Strategies Journal of Portfolio Management | Free | Easy read | 0 |
| Hansen (2005) | A Test for Superior Predictive Ability Journal of Business and Economic Statistics | Paywalled | Technical | 0 |
| Lo (2002) | The Statistics of Sharpe Ratios Financial Analysts Journal | Free | Moderate | 0 |
| White (2000) | A Reality Check for Data Snooping Econometrica | Paywalled | Technical | 0 |
| Sullivan et al. (1999) | Data-Snooping, Technical Trading Rule Performance, and the Bootstrap Journal of Finance | Paywalled | Moderate | 0 |