Forecasting stock price by hybrid model of cascading Multivariate Adaptive Regression Splines and Deep Neural Network

Much of the hesitation in stock investments is due to apparent volatility about the stock price. Had there been a predictor to accurately predict the final trading price of stocks, it could be an assurance to invest in the Stock Market. Thus we propose, a trustworthy hybrid model by cascading Multivariate Adaptive Regression Splines(MARS) and Deep Neural Network(DNN), to predict closing prices of stock. The high-frequency KOSPI data set has been used and a customized pre-processing algorithm has been applied to clean the data. MARS is then been applied on this clean data and the attributes retained by MARS are passed to a DNN for training. Such application has resulted up to 92% closing price prediction accuracy. Thus, our hybrid model successfully has reduced the dimensional feature without compromising on accuracy as it gave better results than MARS and DNNs individually. Data-Augmentation has also been used to further verify the outcome of this application. Main metrics used for performance evaluation are Correlation(RHO) and R2 value.

Automated summary

The proposed hybrid model combines MARS and DNN to predict stock closing prices with a high accuracy of up to 92% on the KOSPI dataset. The model successfully reduces feature dimensions and uses data augmentation to further validate the results.