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Stock volatility prediction Using TabNet based deep learning method

The significant feature of stock market maturity is stock volatility. Moreover, volatility is central determinant of market price behavior, so investigating the characteristics of stock market volatility is conducive to comprehend the price behavior of the stock market. In-depth study of the factors of stock market volatility will help investors make rational investments and promote the long-term health and stability of the national economy. With the development of sophisticated intelligent algorithms, machine learning methods are able to facilitate stock volatility prediction by effectively extracting features associated with core objectives. Based on the highly nonlinear stock market and the discrete nature of market data, this paper proposes a TabNet-based stock volatility prediction method. This method can encode discrete data and decrease model over-parameterization compared to traditional machine learning methods. Experiments confirm that this method has better performance than several classic machine learning algorithms, and with RMSPE as the evaluation index, the performance on the stock volatility dataset surpasses classic machine learning algorithms such as Naive Bayes and Xgboost.