It is a challenging task to correctly and accurately predict and estimate the stock prices to get the maximum profit in the current market, and it is also critically important for all financial institutions to obtain the optimal stock prices under the current fluctuation situation. In this study, we use different machine learning (ML) algorithms, such as random forest (RF), support vector machine (SVM), decision regression trees (DRT), and nonlinear regression (NR), to easily and correctly predict and estimate the current and future possible stock prices based on the historical stock data. By using some appropriate pre-data-processing techniques, the current stock prices could be accurately and quickly estimated via those models. In this research, different ML algorithms are designed and built to help decision makers working in the financial institutions to easily and conveniently predict the current stock prices. A comparison among different ML algorithms is performed to get the optimal one for the stock prices predictions. The minimum training and checking RMSE values for RF and DRT models can be accurate to 0.010 and 0.012.
Bai et al. (Fri,) studied this question.