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Tag: after-work

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-learningclassification

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after-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.

pythonpapermachine-learningasset-pricingafter-work

Introducing Empirical Asset Pricing via Machine Learning for after-work quants

Gu, Kelly & Xiu (2019) bake off ML methods (trees, nets, and more) for the equity cross-section. Plain-language intro. Not investment advice.

pythonpaperdeeplobcnnafter-work

Introducing DeepLOB: Deep Convolutional Neural Networks for Limit Order Books for after-work quants

Zhang, Zohren & Roberts (2018) introduce DeepLOB—CNN (inception-style) plus LSTM on LOB snapshots for short-horizon mid-price moves. Plain-language intro. Not investment advice.

pythonbrowser-useresearch-agentagentsafter-workstart-using

How to Start Using Browser Use in Python

Install Browser Use, run one local Python agent that opens a real browser, and automate a tiny research or form-fill task after work.

pythonpaperportfolioautoencoderafter-work

Introducing Deep Portfolio Theory for after-work quants

Heaton, Polson & Witte (2016) propose Deep Portfolio Theory: encode–calibrate–validate hierarchical nets as a bridge from Markowitz. 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-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.