Berislav ŽmukORCID logo 0000-0003-3487-1376 and Hrvoje JošićORCID logo 0000-0002-7869-3017

University of Zagreb - Faculty of Economics and Business
Zagreb, Croatia

INDECS 18(4), 471-489, 2020
DOI 10.7906/indecs.18.4.7
Full text available here.

Received: 15th June 2020.
Accepted: 14th July 2020.
Regular article


In recent years machine learning algorithms have become a very popular tool for analysing financial data and forecasting stock prices. The goal of this article is to forecast five major stock market indexes (DAX, Dow Jones, NASDAQ, Nikkei 225 and S&P 500) using machine learning algorithms (Linear regression, Gaussian Processes, SMOreg and neural network Multilayer Perceptron) on historical data covering the period February 1, 2010, to January 31, 2020. The forecasts were made by using historical data in different base period lengths and forecasting horizons. The precision of machine learning algorithms was evaluated with the help of error metrics. The results of the analysis have shown that machine learning algorithms achieved highly accurate forecasting performance. The overall precision of all algorithms was better for shorter base period lengths and forecast horizons. The results obtained from this analysis could help investors in determining their optimal investment strategy. Stock price prediction remains, however, one of the most complex issues in the field of finance.

machine learning, neural networks, stock market indices prediction

JEL:C53, G17

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