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.

股票投资的大部分犹豫是由于股票价格的明显波动。如果有一个预测器可以准确预测股票的最终交易价格，那么投资股票市场就可以保证了。因此，我们提出了一个可信赖的混合模型，通过级联多元自适应回归样条（MARS）和深度神经网络（DNN）来预测股票的收盘价。使用了高频 KOSPI 数据集，并应用了定制的预处理算法来清理数据。然后将 MARS 应用于这些干净的数据，并将 MARS 保留的属性传递给 DNN 进行训练。此类应用已实现高达 92% 的收盘价预测准确度。因此，我们的混合模型成功地在不影响准确性的情况下减少了维度特征，因为它提供了比单独的 MARS 和 DNN 更好的结果。数据增强也被用于进一步验证此应用程序的结果。用于性能评估的主要指标是相关性（RHO）和 R2 值。