Diagnostic paper on LLM embeddings of WSB posts for volatility forecasting released
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Zhiyu Zheng released a 9-page quantitative-finance paper accepted at ICAIF '26, proposing a diagnostic protocol to test the true incremental information in LLM embeddings of Reddit r/WallStreetBets posts for volatility prediction. The study covers a panel of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample design. Adding embeddings to a HAR model reduced the contemporaneous mean squared error by 1.22% (95% CI [0.94, 1.49]). The gain was concentrated in 2015–2019 and was not significantly different from zero in 2020–2022; expanding sample coverage by 48% through off-hours and weekend posts did not yield reliable positive increments from the additional stock-days.
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- arXiv q-finarXiv Paper: Diagnosing the Incremental Information of Reddit WSB Post Embeddings for Volatility Prediction
This 9-page paper accepted by ICAIF '26 proposes a diagnostic protocol to examine the true source of incremental information from LLM embeddings of r/WallStreetBets posts for volatility prediction. The study covers panel data of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample test.
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