A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting
This work provides a novel forecasting method for financial analysts in the semiconductor industry, though it is incremental as it extends existing finance tools to NLP.
The paper tackles stock return and volatility forecasting for U.S. semiconductor stocks by introducing a Hype-Adjusted Probability Measure based on NLP sentiment analysis of intraday news, resulting in improved forecast accuracy by addressing news bias, memory, weight, and sentiment shifts.
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.