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Tag: after-work

pythonpapervolatilitylstmafter-work

Introducing GARCH-Informed Neural Networks for Volatility for after-work quants

Xu, Liechty, Benthall, Skar-Gislinge & McComb (2024) train an LSTM with a loss that mixes true variance and a GARCH teacher—PINN logic for vol. Not investment advice.

pythonpaperensemble

portfolio

after-work

Introducing Expert Aggregation for Financial Forecasting for after-work quants

Remlinger, Alasseur, Brière & Mikael (2021) mix thirteen Gu–Kelly–Xiu-style experts with Bernstein Online Aggregation so long-short books adapt when models rotate. Not investment advice.

pythonpapervolatilitygarchafter-work

Introducing Deep Learning Enhanced Realized GARCH for after-work quants

Liu, Wang, Tran & Kohn (2023) put an LSTM inside realized GARCH so latent variance depends nonlinearly on lagged RV, returns, and past variance. Bayesian SMC. Not investment advice.

pythonpapercorrelationfuturesafter-work

Introducing Conditional Correlations and Principal Regression Analysis for Futures for after-work quants

Karami, Benichou, Benzaquen & Bouchaud (2019) show that past trends in a futures eigen-factor (≈10 days) raise or cut instantaneous correlations—indexes vs bonds dominate. Risk map. Not investment advice.

pythonpapermicrostructurelstmafter-work

Introducing Universal features of price formation in financial markets: perspectives from Deep Learning for after-work quants

Sirignano & Cont (2018) train one LSTM on pooled NASDAQ books and beat per-stock models—even on names never seen in training. Universality + path dependence. Not investment advice.

pythonpaperquantautomlafter-work

Introducing Quant 4.0 for after-work quants

Guo, Wang, Ni & Shum (2023) sketch Quant 4.0: automated modelling, explainable AI, and knowledge-driven methods after Quant 1.0–3.0 workshops, alpha factories, and deep nets. Map, not a bot. Not investment advice.

pythonpapertransformerportfolioafter-work

Introducing Portfolio Transformer for Attention-Based Asset Allocation for after-work quants

Kisiel & Gorse (2022) train a Transformer to output portfolio weights that maximise cost-adjusted Sharpe—no separate return forecast step. Plain-language intro. Not investment advice.

pythonpapersurveyasset-pricingafter-work

Introducing From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing for after-work quants

Ye, Goswami, Gu, Uddin & Wang (2024) survey how ML reshapes empirical asset pricing: factors → flexible g(x), portfolios, GNNs, RL, and the usual overfitting/explainability traps. Reading map. 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.

pythonchatgptchatgpt-workresearch-agentafter-workstart-using

How to Start Using ChatGPT Work/Tools (Not Old Operator)

Skip retired Operator and old Agent-mode names. Use ChatGPT Work, tools, and cloud browser for one supervised after-work task.

pythonperplexityresearch-agentafter-workstart-using

Getting Started with Perplexity for Research

Use Perplexity as a cited research searcher—not a coding agent—and run one sourced brief after work.

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.

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