Predicting Intraday Prices in the Frontier Stock Market of Romania Using Machine Learning Algorithms

Dan Gabriel Anghel

International Journal of Economics and Financial Research, 2020, vol. 6, issue 7, 170-179

Abstract: This paper investigates if forecasting models based on Machine Learning (ML) Algorithms are capable to predict intraday prices in the small, frontier stock market of Romania. The results show that this is indeed the case. Moreover, the prediction accuracy of the various models improves as the forecasting horizon increases. Overall, ML forecasting models are superior to the passive buy and hold strategy, as well as to a naïve strategy that always predicts the last known price action will continue. However, we also show that this superior predictive ability cannot be converted into “abnormal†, economically significant profits after considering transaction costs. This implies that intraday stock prices incorporate information within the accepted bounds of weak-form market efficiency, and cannot be “timed†even by sophisticated investors equipped with state of the art ML prediction models.

Keywords: Efficient market hypothesis; Intraday prices; Machine learning; Artificial intelligence; Trading strategies; Frontier stock market. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:arp:ijefrr:2020:p:170-179

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