Forecasting high-frequency stock returns: a comparison of alternative methods

Identificador:  imarina:9242970

Handle: https://hdl.handle.net/20.500.11797/imarina9242970

Autores:  Akyildirim, E; Bariviera, AF; Nguyen, DK; Sensoy, A

Resumen:
We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naive Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.