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Tag: paper

pythonpaperdeep-learningstat-arbafter-work

Introducing Deep Learning Statistical Arbitrage for after-work quants

Guijarro-Ordonez, Pelger & Zanotti (2021) use deep nets to extract residual spreads for statistical arbitrage. Plain-language intro. Not investment advice.

pythonpaperdeep-hedgingderivativesafter-work

Introducing Deep Hedging for after-work quants

Bühler, Gonon, Teichmann & Wood (2018) train neural nets to hedge derivatives under frictions instead of relying on Black–Scholes deltas. Plain-language intro. Not investment advice.

pythonpaperdeep-learningclassificationafter-work

Introducing Classification-based Financial Markets Prediction DNN for after-work quants

Dixon, Klabjan & Bang (2017) recast market prediction as classification with deep neural nets instead of return regression. Plain-language intro. Not investment advice.

pythonpapercnndeep-learningafter-work

Introducing CNNPred for after-work quants

Hoseinzade & Haratizadeh (2018) propose CNNPred—CNN models for stock market prediction from crafted feature representations. Plain-language intro. Not investment advice.

pythonpaperfactor-zoomultiple-testingafter-work

Introducing …and the Cross-Section of Expected Returns for after-work quants

Harvey, Liu & Zhu (2016) raise the multiple-testing bar for factors—often summarised as needing much higher t-stats than 1.96. Plain-language intro. Not investment advice.

pythonpapervaluemomentumafter-work

Introducing Value and Momentum Everywhere for after-work quants

Asness, Moskowitz & Pedersen (2013) show value and momentum premia—and their negative correlation—across markets and asset classes. Plain-language intro. Not investment advice.

pythonpapertime-series-momentumtrend-followingafter-work

Introducing Time Series Momentum for after-work quants

Moskowitz, Ooi & Pedersen (2012) document time-series momentum: trend-follow each asset’s own past returns. Plain-language intro. Not investment advice.

pythonpapermultiple-testingalphaafter-work

Introducing Thousands of Alpha Tests for after-work quants

Giglio & Xiu (2021) provide a multiple-testing toolkit for thousands of alphas—guardrails against fake discovery. Plain-language intro. Not investment advice.

pythonpaperfactor-zoolassoafter-work

Introducing Taming the Factor Zoo for after-work quants

Feng, Giglio & Xiu (2020) tame the factor zoo with double-selection LASSO: does your new factor add incremental information? Plain-language intro. Not investment advice.

pythonpapermomentumspatio-temporalafter-work

Introducing Spatio-Temporal Momentum for after-work quants

Tan, Roberts & Zohren (2023) study spatio-temporal momentum—combining time and cross-asset structure. Plain-language intro. Not investment advice.

pythonpapermomentummean-reversionafter-work

Introducing Slow Momentum with Fast Reversion for after-work quants

Wood, Roberts & Zohren (2021) explore slow momentum with fast reversion—multi-horizon trend vs mean-reversion features. Plain-language intro. Not investment advice.

pythonpapermomentumjegadeesh-titmanafter-work

Introducing Returns to Buying Winners and Selling Losers for after-work quants

Jegadeesh & Titman (1993) document cross-sectional momentum: buy recent winners, sell losers over 3–12 month horizons. Plain-language intro. Not investment advice.

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