用情绪调整概率度量提升半导体股预测准确率
A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting
- 构建情绪调整概率度量，纠正新闻过热或不足带来的偏差
- 在美股半导体标的上实现更高精度的收益率与波动率预测
- 将金融中的概率测度变换引入NLP，适合量化金融研究者
本文提出一种新型自然语言处理方法，用于预测股票收益与波动率。针对美国半导体行业标的，设计了一种新的情感得分公式，以捕捉日内新闻对下一周期股价变动和波动的影响。该方法通过修正新闻偏见、记忆效应和权重分布，并融合情绪方向变化，显著提升了预测准确性。更重要的是，首次将资产定价领域的概率测度变换工具应用于NLP预测中，构建了基于概率空间权重再分配的‘情绪调整概率度量’，旨在校正新闻过度传播或不足的问题。
原文摘要 · Abstract (English)
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.
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