PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS: A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET

Anders Nõu, Darya Lapitskaya, Mustafa Eratalay () and Rajesh Sharma

No 135, University of Tartu - Faculty of Economics and Business Administration Working Paper Series from Faculty of Economics and Business Administration, University of Tartu (Estonia)

Abstract: For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In this paper, we make a thorough analysis of the predictive accuracy of different machine learning and econometric approaches for predicting the returns and volatilities on the OMX Baltic Benchmark price index, which is a relatively less researched stock market. Our results show that the machine learning methods, namely the support vector regression and k-nearest neighbours, predict the returns better than autoregressive moving average models for most of the metrics, while for the other approaches, the results were not conclusive. Our analysis also highlighted that training and testing sample size plays an important role on the outcome of machine learning approaches.

Keywords: machine learning; neural networks; autoregressive moving average; generalized autore- gressive conditional heteroskedasticity (search for similar items in EconPapers)
Pages: 52 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-fmk
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Persistent link: https://EconPapers.repec.org/RePEc:mtk:febawb:135

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