Abstract

This article uses public perceptions to forecast short-term fluctuations in asset prices. Based on four billion tweets scraped between 2009 and 2019, I perform textual analysis to construct daily sentiment indices. The sentiment indices allow us to forecast stock volatility jumps as well as expected jump levels. The implications of forecasting volatility jumps are substantive. First, volatility jumps have a significant effect on option prices. Second, changes in the volatility path lead to large (negatively related) changes in the prices’ future trajectory. Determining what information causes jumps allows for better risk management and more accurate asset pricing models.

摘要

本文利用公众认知来预测资产价格的短期波动。基于 2009 年至 2019 年间抓取的 40 亿条推文，我进行文本分析以构建每日情绪指数。情绪指数使我们能够预测股票波动率的跳跃以及预期的跳跃水平。预测波动性跳跃的影响是实质性的。首先，波动率跳跃对期权价格有显着影响。其次，波动路径的变化导致价格未来轨迹发生较大（负相关）变化。确定导致跳跃的信息有助于更好的风险管理和更准确的资产定价模型。