Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations

Abstract

We have extracted different types of indicator, price and temporal features.Previous instances and correlation between features are used to design CNN.We predict the hourly direction of 100 Stocks Borsa Istanbul Stock Market.Proposed method outperforms the CNN that uses randomly ordered features.On average we perform 56.3% Macro Average F-Measure rate on 100 stocks. Stock market price data have non-linear, noisy and non-stationary structure, and therefore prediction of the price or its direction are both challenging tasks. In this paper, we propose a Convolutional Neural Network (CNN) architecture with a specifically ordered feature set to predict the intraday direction of Borsa Istanbul 100 stocks. Feature set is extracted using different indicators, price and temporal information. Correlations between instances and features are utilized to order the features before they are presented as inputs to the CNN. The proposed classifier is compared with a CNN trained with randomly ordered features and Logistic Regression. Experimental results show that the proposed classifier outperforms both Logistic Regression and CNN that utilizes randomly ordered features. Feature selection methods are also utilized to reduce training time and model complexity.