Academy notes
Articles and updates from SoloQuant Academy.
Tag: alt-data
Kirtac and Germano (2024) compare OPT, BERT, FinBERT, and a classic finance dictionary on U.S. news sentiment and next-day returns—after costs in spirit for careful readers. Not investment advice.
Tetlock’s classic result: pessimism in a major WSJ column lines up with temporary downward pressure and higher trading volume—foundational alt-data intuition before modern transformers.
Yang, UY & Huang’s HKUST FinBERT is pretrained on financial communications—an alternative NLP backbone for filings and earnings text, distinct from Araci’s sentiment FinBERT.
Ke, Kelly, and Xiu (2019) show a supervised way to turn news text into return-prediction signals with careful screening. Plain-language intro for after-work Python NLP learners. Not investment advice.
Feng et al. add intentional perturbations during training so stock-movement models generalise better under noisy price features—a robustness habit you can borrow for NLP signals too.
Araci’s FinBERT fine-tunes BERT for financial sentiment so domain jargon scores more sensibly—then you can wire a Hugging Face checkpoint into a tiny daily-news feature pipeline.
Xu et al. (2021) HIST mines shared industry/theme concepts—and hidden ones—on a stock graph to forecast trends. Plain-language intro for after-work Python learners. Not investment advice.
Bybee, Kelly, Manela & Xiu turn decades of WSJ text into topic attention series that track the economy—a plain-language template for NLP alt-data practice in Python.
