Library/Systematic Trading

A curated list of papers on quantitative and systematic trading strategies

This post maintains a curated list of papers on quantitative and systematic trading strategies.

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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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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Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.

Moskowitz, Ooi, and Pedersen’s paper documents “time series momentum”: the tendency for an asset’s own past return to predict its future return across liquid futures and forward markets. Using 58 instruments across commodities, currencies, equity indexes, and bonds, they show that assets with positive returns over the past year tend to keep rising over the next month, while assets with negative returns tend to keep falling, with partial reversal at longer horizons. A diversified trend-following strategy earns strong abnormal returns that are not explained by standard asset-pricing factors or cross-sectional momentum alone, and it performs especially well during extreme market moves. The paper also links these profits to market structure: speculators appear to ride trends while hedgers take the other side, suggesting that time series momentum reflects both gradual price adjustment and compensation for absorbing hedging pressure.

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Faber, Meb, A Quantitative Approach to Tactical Asset Allocation (February 1, 2013). The Journal of Wealth Management, Spring 2007.

Faber’s paper proposes a simple tactical asset allocation rule: hold an asset class when its monthly price is above its 10-month moving average, and move that sleeve to cash when it falls below. Tested across equities, foreign stocks, bonds, commodities, and REITs, the rule does not try to forecast returns with complexity; instead, it uses trend as a practical risk filter that reduces exposure during major bear markets. The main result is that long-run returns remain broadly comparable to buy-and-hold, while drawdowns and volatility fall sharply, especially in diversified multi-asset portfolios. The paper’s appeal is its discipline: a transparent, low-turnover rule that turns asset allocation into a repeatable process for controlling downside risk.