Financial stock market forecast using evaluated linear regression based machine learning technique

Abstract

The objective of Stock Market Forecasting (SMF) is to forecast the future value of a company's financial stocks. The availability of Machine Learning (ML), particularly obtains forecasts regarding specific prices of current stock market indexes through training on actual high economic value, constitutes a significant improvement in stock market prediction automation. ML itself utilizes a wide range of models to improve as well as evaluate prediction. Regarding the nonlinearities as well as discontinuities of the factors that are anticipated to effect stock markets, the selection of a modest price of global financial data is frequently acknowledged as a significant basic phase in any stock market prediction model. The fundamental emphasis of the study is the application of Evaluated Linear Regression based Machine Learning (ELR-ML) technique to forecast stock financial values of the Standard and Poor's 500 (s & p 500) index with Open, close, low, high, and volume factors.