Prediction of stock index movement direction with multiple logistic regression and k-nearest neighbors algorithm

Abstract

In financial data mining, stock index movement direction prediction is a challenging classification problem, since stock index is affected by many economic and political factors. The accurate prediction of this problem is of interest to many researchers as it can serve as an early recommender system for short-term financiers. This study aims to predict daily upward or downward movement direction of Borsa Istanbul 100 (XU100) index with the aid of supervised machine learning algorithms based on classification. Problem we deal with includes whether on a specific day the XU100 index fall into up bucket or fall into down bucket For this purpose, the multiple logistic regression and K-nearest neighbors algorithm models are fitted using independent variables whose effect on XU100 index movement direction was statistically significant Lastly, the out-of sample predictions are compared with the actual movements in the stock market Performances are measured not only with accuracy but also other statistical metrics. According to the results obtained, logistic regression analysis achieves better predict performance with 8196 accuracy opposed to K-nearest neighbors algorithm on XU100 data over the given time period.