Extraction of Implied Information from Options and Its Impact on the Stock Market:A Machine Learning Perspective
How to extract implied information from options and study its impact on the returns of underlying stocks has always been a concern in both academia and industry.Existing research primarily relies on a single dimension:Either moneyness or maturity structure,to extract implied information,such as implied volatility,implied skewness,or implied tail risk,etc.How to extract implied information simultaneously from both dimensions,and how to extract common information factors from numerous pieces of information,are the focal points of this study.To address these issues,this paper utilizes a method combining principal component analysis with machine learning to extract implied information from the options volatility surface,and tests its predictability on the returns of the underlying stocks.Unlike traditional methods,PCA-LASSO can capture the time-varying nature of implied option information,while also refining common driving factors of different types of information,thus providing better predictive power for stock returns.
index optionoption implied informationprinciple component analysismachine learning
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