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A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting

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  • Zheng Cao
  • Helyette Geman

Abstract

This article introduces a Hype-Adjusted Probability Measure in the context of a new Natural Language Processing (NLP) approach for stock return and volatility forecasting. A novel sentiment score equation is proposed to represent the impact of intraday news on forecasting next-period stock return and volatility for selected U.S. semiconductor tickers, a very vibrant industry sector. This work improves the forecast accuracy by addressing news bias, memory, and weight, and incorporating shifts in sentiment direction. More importantly, it extends the use of the remarkable tool of change of Probability Measure developed in the finance of Asset Pricing to NLP forecasting by constructing a Hype-Adjusted Probability Measure, obtained from a redistribution of the weights in the probability space, meant to correct for excessive or insufficient news.

Suggested Citation

  • Zheng Cao & Helyette Geman, 2024. "A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting," Papers 2412.07587, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2412.07587
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    1. Zheng Cao & Xinhao Lin, 2024. "Theoretical and Empirical Validation of Heston Model," Papers 2409.12453, arXiv.org, revised Oct 2024.
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