A data analytic approach to forecasting daily stock returns in an emerging market

Abstract

Forecasting stock market returns is a challenging task due to the complex nature of the data. This study develops a generic methodology to predict daily stock price movements by deploying and integrating three data analytical prediction models: adaptive neuro-fuzzy inference systems, artificial neural networks, and support vector machines. The proposed approach is tested on the Borsa Istanbul BIST 100 Index over an 8 year period from 2007 to 2014, using accuracy, sensitivity, and specificity as metrics to evaluate each model. Using a ten-fold stratified cross-validation to minimize the bias of random sampling, this study demonstrates that the support vector machine outperforms the other models. For all three predictive models, accuracy in predicting down movements in the index outweighs accuracy in predicting the up movements. The study yields more accurate forecasts with fewer input factors compared to prior studies of forecasts for securities trading on Borsa Istanbul. This efficient yet also effective data analytic approach can easily be applied to other emerging market stock return series.