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

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  • Na, Haejung
  • Kim, Soonho

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.

Suggested Citation

  • Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001944
    DOI: 10.1016/j.econlet.2021.109917
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    Cited by:

    1. Moitra, Agnij, 2024. "Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms," OSF Preprints y3mr6, Center for Open Science.

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    More about this item

    Keywords

    Artificial neural network; Informed investors; Stock price prediction; Market failure;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G4 - Financial Economics - - Behavioral Finance

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