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Tag: deep-learning

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-learning

asset-returns

after-work

Introducing Deep Learning for Predicting Asset Returns for after-work quants

Feng, He & Polson (2018) study deep nets for predicting asset returns from characteristics. Plain-language intro. Not investment advice.

pythonpaperlobdeep-learningafter-work

Introducing Deep Learning for Limit Order Books for after-work quants

Justin Sirignano (2016) applies spatial neural nets to limit-order-book states—an early LOB deep-learning baseline. 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.

pythonpapermomentumdeep-learningafter-work

Introducing Enhancing Time Series Momentum Strategies Using Deep Neural Networks for after-work quants

Lim, Zohren & Roberts (2019) enhance time-series momentum with deep nets, Sharpe-oriented training, and volatility scaling. Plain-language intro. Not investment advice.

pythonpaperasset-pricingdeep-learningafter-work

Introducing Deep Learning in Asset Pricing for after-work quants

Chen, Pelger & Zhu (2019) estimate a deep SDF / pricing kernel from characteristics—end-to-end asset pricing without a hand-built factor zoo. Plain-language intro. Not investment advice.

pythonpaperdeep-learningasset-returnsafter-work

Introducing Deep Learning for Predicting Asset Returns for after-work quants

Feng, He & Polson (2018) study deep nets for predicting asset returns from characteristics. Plain-language intro. Not investment advice.

pythonpaperlobdeep-learningafter-work

Introducing Deep Learning for Limit Order Books for after-work quants

Justin Sirignano (2016) applies spatial neural nets to limit-order-book states—an early LOB deep-learning baseline. Plain-language intro. Not investment advice.

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-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.

pythonpaperdeep-learningmulti-factorafter-work

Introducing Factor Investing with a Deep Multi-Factor Model for after-work quants

Wei, Dai & Lin (2022) propose a deep multi-factor model for equity factor investing. Plain-language intro for after-work Python learners. Not investment advice.

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