Library/Machine Learning in Finance

A curated list of papers that apply machine learning in finance

This post maintains a curated list of papers that use machine learning for financial applications.

The link on the title of the paper is a direct download link when available. Otherwise, it takes you to the journal page for the paper.

Click on the image to see a 15-slide deck with an overview of the paper.

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Xu, X., & Liu, W.-H. (2024). Forecasting the equity premium: Can machine learning beat the historical average? Quantitative Finance, 24(10), 1445-1461.

This paper asks whether modern machine learning methods can beat the historical average benchmark in forecasting the U.S. equity premium. They evaluate 17 models, including OLS, 15 machine learning methods, and a forecast combination, using monthly S&P 500 excess returns from 1926 to 2020 and an out-of-sample period from 1957 to 2020, with both macroeconomic predictors and technical indicators. The striking result is that machine learning often looks powerful in sample, especially tree-based models such as XGBoost, but that performance largely disappears out of sample: in the main expanding-window test, only PCR produces a positive out-of-sample R-squared, and the historical average still has the better success ratio and the best market-timing Sharpe ratio. The paper’s practical message is that aggregate equity-premium prediction is a small-sample, low-signal-to-noise problem where model complexity can easily become overfitting; the right benchmark is not whether a model explains the past, but whether it reliably improves on the historical average in real time.

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Yilmaz, S., & Sefer, E. (2025). Pairs trading with time-series deep learning models. The Journal of Finance and Data Science, 11, 100177.

Yilmaz and Sefer study whether modern time-series deep learning models can improve generalized pairs trading by predicting the direction of factor-model residuals rather than relying only on classical asset-wise mean reversion. They compare a relative-value Ornstein-Uhlenbeck baseline with AdaBoost, LSTM, and several transformer-based models, including Informer, Autoformer, iTransformer, Scaleformer, and Chronos, using survivorship-bias-aware S&P 500 data and a 20-asset cryptocurrency sample. The central finding is that transformer-based residual prediction generally delivers higher risk-adjusted performance than the baseline: iTransformer leads the S&P 500 backtest with a Sharpe ratio of 1.94 versus 0.57 for relative value, while Scaleformer and iTransformer lead the crypto tests with Sharpe ratios around 2.2. The paper’s practical message is that the advantage comes not only from better forecasts but also from panel-level learning and more selective trading under transaction costs, though the results still require caution because the backtest uses simplified cost assumptions and does not fully model market impact, borrow constraints, or bid-ask spreads.

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Nardari, Federico and Schüssler, Rainer Alexander, Ensembles of Portfolio Rules (June 29, 2024). Available at SSRN: https://ssrn.com/abstract=4217088

Nardari and Schüssler’s paper proposes FLEXPOOL, a utility-based ensemble framework for combining heterogeneous portfolio rules rather than selecting a single “best” rule. Each candidate rule contributes its assigned portfolio weights and subsequent pseudo out-of-sample returns, and the framework chooses convex weights across rules to maximize discounted realized investor utility, allowing recent performance to matter more when market conditions change. Empirically, across U.S. stock allocation and market timing from 1977 to 2020, the ensemble generates higher certainty-equivalent returns than individual rules and simple combination benchmarks, suggesting that portfolio construction can benefit from treating allocation rules themselves as diversifiable components.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273

Gu, Kelly, and Xiu’s “Empirical Asset Pricing via Machine Learning” asks whether machine learning can improve the measurement of equity risk premiums, both in the cross-section of stocks and in aggregate market timing. Its central result is that flexible models, especially trees and neural networks, produce stronger out-of-sample forecasts and investment performance than traditional linear methods because they can capture nonlinear interactions among familiar predictors like momentum, liquidity, volatility, and valuation. The paper’s point is not that ML magically explains expected returns, but that it can be a better measuring instrument for risk premia when disciplined by validation and out-of-sample testing.