TOWARD STOCK PRICE FORECASTING ON NEWLY ACQUIRED BELEX DATASET

Keywords:
stock price forecasting, machine learning, linear regression, support vector regression, multi-layer

Abstract:
Currently there is a lot of research with the aim of applying machine learning in stock price prediction as one of the challenging trends in fintech. Fintech helps to other stakeholders to make better/optimal financial decisions which will improve their wealth and satisfaction. In this paper, the first steps in examining the potential application of basic machine learning algorithms for the prediction of stock prices on the Belgrade Stock Exchange (BELEX) are presented. The results showed that future research is needed to increase dataset and improve quality of data in order to adapt modern and more advanced machine learning methods to achieve higher accuracy in forecasting stock prices. The additional value of this research is the database that combines data from the BELEX, indicators of the company’s success and economic parameters of Serbia's growth in the period from 2010 to 2022 and is the basis not only for this but also for future research

CITATION:

IEEE format

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L. Barjaktarović, M. Barjaktarović, M. Todorović, A. Obradović, “Toward stock price forecasting on newly acquired belex dataset,” in FINIZ 2023 - Sustainable development as a measure of modern business success, Belgrade, Singidunum University, Serbia, 2023, pp. 154-161. doi: 10.15308/finiz-2023-154-161 

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Barjaktarović, L., Barjaktarović, M., Todorović, M., Obradović, A. (2023). Toward stock price forecasting on newly acquired belex dataset. Paper presented at FINIZ 2023 - Sustainable development as a measure of modern business success. doi:10.15308/finiz-2023-154-161

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