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Tag: after-work

pythonpapergarchlstmafter-work

Introducing Unified GARCH-RNN Volatility Forecasting for after-work quants

Wei, Yang & Cui (2025) embed GARCH(1,1) inside GRU and LSTM cells—joint ω, α, β plus neural gates—on SPX, DJI and NASDAQ. Not investment advice.

pythonpapergarchvolatility

after-work

Introducing Realized GARCH for after-work quants

Hansen, Huang & Shek (2012) jointly model daily returns and realized measures: RV is a noisy observation of latent variance plus a leverage polynomial. High-frequency data into the GARCH workhorse. Plain-language intro. Not investment advice.

pythonpapervarcnnafter-work

Introducing Quantile CNN VaR for after-work quants

Petneházi (2019) turns a dilated causal CNN into a VaR machine with quantile (pinball) loss, and shows joint training on many stocks beats one-ticker nets. Plain-language intro. Not investment advice.

pythonpapervarriskafter-work

Introducing Neural Networks and Value at Risk for after-work quants

Arimond, Borth, Hoepner, Klawunn & Weisheit (2020) generate regime-switching returns for VaR: HMM init, a balanced RNN loss, and a data-hungry caveat. Not investment advice.

pythonpapervarriskafter-work

Introducing Neural Networks and Value at Risk for after-work quants

Arimond et al. (2020) run Monte-Carlo VaR from generative regime-switching nets. HMM init, a balanced bull/bear loss, and data-hungry RNNs. Plain-language intro. Not investment advice.

pythonpapercovariancehigh-frequencyafter-work

Introducing Large Covariance from Noisy High-Frequency Data for after-work quants

Dai, Lu & Xiu (2019) horse-race TSR, CSR and PCA factor covariances on noisy, asynchronous ticks with pre-averaging and thresholding. Plain-language intro. Not investment advice.

pythonpaperportfoliohrpafter-work

Introducing MVO vs HRP vs RL Portfolio Comparison for after-work quants

Sen et al. (2023) compare mean-variance, hierarchical risk parity, and deep Q-learning portfolios on NIFTY50 and Indian sector stocks. Plain-language intro. Not investment advice.

pythonpapergicscovarianceafter-work

Introducing Incorporating GICS into Portfolio Allocation for after-work quants

Fan, Furger & Xiu (2016) sort S&P 500 residuals by GICS, keep industry blocks, add Fama–French and sector ETFs on high-frequency data, then allocate. Plain-language intro. Not investment advice.

pythonpaperganbacktestafter-work

Introducing Improving Robustness of Trading Strategy Backtesting with Boltzmann/GANs for after-work quants

Lezmi, Roche, Roncalli & Xu (2020) use RBMs, GANs and Wasserstein models as market generators so backtest statistics have a distribution, not one lucky path. Plain-language intro. Not investment advice.

pythonpapercovarianceshrinkageafter-work

Introducing Honey, I Shrunk the Sample Covariance Matrix for after-work quants

Ledoit & Wolf (2004) shrink the noisy sample covariance toward a structured target so Markowitz weights stop exploding. Plain-language intro for after-work Python learners. Not investment advice.

pythonpapergenetic-algorithmforexafter-work

Introducing GA-MSSR for RoboTrading for after-work quants

Zhang & Khushi (2020) evolve FX technical-rule parameters with a fitness that mixes Sharpe and Sterling ratios to penalise variance and drawdowns. Lab study. Not investment advice.

pythonpaperdeeplobquantileafter-work

Introducing Extending Deep Learning Models for Limit Order Books to Quantile Regression for after-work quants

Zhang, Zohren & Roberts (2019) extend DeepLOB with quantile regression: CNN+LSTM branches predict long/short return quantiles from LOBs, then combine them. Plain-language intro. Not investment advice.

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