Deep Learning Based Regression Approach for Algorithmic Stock Trading: A Case Study of the Bist30

Abstract

Today, one of the common uses of artificial intelligence is financial markets. In these markets, which are known asstock market, making price predictions for the future using machine learning and deep learning, making the rise and fallforecasts of indices, sectors and stocks are the main approaches used in this field. In the near future in the financialmarkets, artificial intelligence based software robots are expected to operate instead of people. For this purpose,learning models are developed by using trend and stock price movements. Validation studies such as accuracy, errorvalue and portfolio simulation are performed to demonstrate the performance of the developed models. In this study, aregression model using deep learning was developed to make adaptive buy-sell operations on the time series consistingof closing prices using data from Borsa İstanbul (BIST). The 2006-2015 range of the BIST30 index was used fortraining, the 2015-2018 range was used for testing, and the model portfolio value gained 39% on the test data for 694trading days and the trend direction was estimated with 82% accuracy.