A comparative study of Different Machine Learning Regressors For Stock Market Prediction
This work addresses stock price forecasting for investors, but it is incremental as it applies existing methods to new data without novel breakthroughs.
The authors tackled stock market prediction by comparing nine machine learning regressors on NASDAQ data from ten companies to forecast next-day opening prices, achieving evaluation using MSE and R2 metrics.
For the development of successful share trading strategies, forecasting the course of action of the stock market index is important. Effective prediction of closing stock prices could guarantee investors attractive benefits. Machine learning algorithms have the ability to process and forecast almost reliable closing prices for historical stock patterns. In this article, we intensively studied NASDAQ stock market and targeted to choose the portfolio of ten different companies belongs to different sectors. The objective is to compute opening price of next day stock using historical data. To fulfill this task nine different Machine Learning regressor applied on this data and evaluated using MSE and R2 as performance metric.