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.