NASDAQ 100 INDEX ESTİMATE WİTH MACHİNE LEARNİNG ALGORİTHMS

Investors want to be informed about the future to ensure maximum profit and minimal damage before making an investment decision. Today, changes in the stock market are influenced not only by the economy but also by external factors, resulting in sudden ups and downs. Individual and institutional investors who make investment decisions follow stock indexes and take advantage of technical and basic analysis. In this study, the daily closing data of the NASDAQ index was brought back from 2016-2021 to the next day by using Linear, Polynomial, Sigmoid, Radial based Support Vector regressions, Random Forest Regression, K-nearest neighbors Regression algorithms. For model performance evaluation, it is determined that the RBF-DVR is the best prediction by comparing MSE, RMSE, MPE and R2 values