Shambhavi Mishra
ABSTRACT
Predicting mid-price movements of stocks is a difficult task. It is mainly owing to the fact a financial times series is often non-stationary, chaotic, and a dynamic mixture of several factors. This work provides a thorough investigation on the use of statistical features and mid-price prediction via kernel adaptive filtering algorithms. First, we apply the process of feature expansion (FE) to build a procedure that is effective and efficient in terms of price prediction. Subsequently, we use these features to improve upon the accuracy of the prediction model. We work on several years of data from the National Stock Exchange (NSE-50) for experimentation. The proposed solution is comprehensive as it includes the utilization of multiple feature engineering techniques and mid-price prediction using the Kernel Adaptive Filtering class of algorithms. We conduct comprehensive studies on ten different Kernel Adaptive Filtering algorithms and show that these techniques outperform similar methods in the literature. This work contributes to stock analysis research by providing extensive design and evaluation of the predictive capability, feature engineering, and data pre-processing approaches.
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Index Terms
- Mid Price Prediction via Statistical Feature Expansion and Kernel Adaptive Filtering
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