A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting (2412.07587v6)

Abstract: This article introduces a Hype-Adjusted Probability Measure in the context of a new 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.

• The paper introduces a novel Hype-Adjusted Probability Measure using NLP for forecasting stock volatility in the semiconductor sector, incorporating dynamic sentiment components.
• The methodology integrates machine learning with advanced sentiment analysis, adjusting for news bias, weighting market components, and incorporating sentiment memory.
• Empirical results demonstrate the model's effectiveness, improving validation accuracy from 51.7% to 78.3% by integrating dynamic sentiment scores.

Hype-Adjusted Probability Measure for NLP Volatility Forecasting

The paper "Hype-Adjusted Probability Measure for NLP Volatility Forecasting" introduces a novel methodology in the field of financial market forecasting through the application of NLP. This approach focuses on the semiconductor sector and proposes a sentiment scoring mechanism that incorporates various dynamic components, thereby aiming to improve the accuracy of predicting market volatility.

The crux of the research lies in the development of a "Hype-Adjusted Probability Measure" which accounts for market sentiment drivers and adjusts sentiment scores considering news bias and time-memory effects. The research effectively integrates machine learning techniques with advanced sentiment analysis, thereby establishing a robust model that enhances the predictive outcomes of intraday news data on stock volatility.

Key Contributions and Methodological Advances

The primary contribution of the paper is the conception of a sentiment score equation that encapsulates:

1. Bias and Weight Adjustments: Implementing a framework that adjusts sentiment scores based on the bias inherent in news sources and weights corresponding to the market component.
2. Sentiment Memory: Incorporating the effect of historical news sentiment on current market behavior, taking into account decay and lagging impacts.
3. Recursive Sentiment Scoring: A recursive method is introduced for calculating the sentiment while considering past data’s memory effect. This improves the accuracy of sentiment prediction by dynamically adjusting weights.
4. Hype-Adjusted Probability Measure: This measure further refines volatility forecasting by modifying the probability measure to incorporate sentiment shifts, based on historical analysis of market responses to news hype.

Empirical Results

The authors demonstrate significant improvements in forecasting precision through their sentiment-enhanced model. The new NLP approach showed enhanced accuracy, lifting validation accuracy from 51.7% to 78.3% for optimal parameter settings. These quantitative results emphasize the impact of integrating dynamic sentiment scores and machine learning in predicting market responses.

Theoretical Implications and Future Directions

Theoretically, this research expands on existing sentiment analysis frameworks by mathematically formalizing the effects of market hype through the introduction of a hype-adjusted probability measure. This contributes a new dimension to financial modeling by acknowledging and quantifying the psychological elements represented by 'hype.'

The formulation of the hype-adjusted probability measure opens several pathways for future research. Enhancements could include further investigation into the bias model, incorporating additional neural mechanisms for better sentiment capture, and broadening the application of the measure beyond semiconductor stocks to other market sectors. Additionally, integrating real-time data from broader platforms could refine the model’s responsiveness to market dynamics.

Overall, this paper offers substantial advancements in the understanding and application of NLP-driven sentiment analysis for financial market forecasting, presenting both practical and theoretical expansions that could inform future research and applications within the field of financial econometrics.