Predicting stock prices based on informed traders’ activities using deep neural networks

Haejung Na and Soonho Kim

Economics Letters, 2021, vol. 204, issue C

Abstract: This study investigates the predictive power of informed traders’ activities in stock price movements by employing neural networks. Specifically, we examine whether informed investors’ trading activities can predict drastic changes in stock prices in the subsequent 5-day period. Our empirical results show that the probability of the model being correct can be as high as 74%. In addition, the simulated trading strategies based on our trained model lead to significantly positive risk-adjusted returns and show strong performance measures. Overall, we find that informed traders’ activities contain informational content and may provide actual investors with information that is useful for stock price prediction.

Keywords: Artificial neural network; Informed investors; Stock price prediction; Market failure (search for similar items in EconPapers)
JEL-codes: G12 G4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001944

DOI: 10.1016/j.econlet.2021.109917

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