A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting

TL;DR

This paper proposes a Hype-Adjusted Probability Measure for NLP-based stock return forecasting, improving accuracy by correcting news bias and sentiment shifts, and extending financial probability tools to NLP applications.

Contribution

It introduces a novel Hype-Adjusted Probability Measure and a sentiment score equation for better stock return and volatility forecasting using NLP.

Findings

Enhanced forecast accuracy by addressing news bias and sentiment shifts

Effective correction of news bias through the Hype-Adjusted Probability Measure

Extension of asset pricing probability tools to NLP forecasting

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.