Comparative Study of Machine Learning Techniques for Stock Market Price and Optimizing Its Cumulative Strategy Returns

In the present study our main focus is on the probable of making forecast of Facebook stock market prices by machine learning algorithms based on historical data. In recent years several tools and methods have been planned and used to check the accuracy of forecasting model. This paper presents a comparative study of machine learning techniques such as Support vector machine (SVM), artificial neural network (ANN) and logistic regression. Further, to optimize the buy or sell strategy we have evaluated cumulative returns and cumulative strategy returns with simulating train test data split. It has been observed that ANN model outperforming as compared to other models. Further, maximum cumulative returns with the cumulative strategy are obtained at split of 90:10. It gives the best performance of the stock with the 32.04% cumulative strategy return. The result suggests that, to optimize the performance of Facebook stock market one can predicted the stock price with proposed strategy in future.