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The Stock Price Crash Risk Prediction by Neural Network

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  • Qunfeng Liao

Abstract

The prediction of stock price crash risk is an important and widely studied topic in both accounting and finance, since crash risk has a significant impact on shareholders, creditors, managers, investors, and regulators. In this paper, I develop a neural network crash risk prediction model that has not been explored before. In addition, I compare the performance of the neural network model with the logistic model and random forecast. I show that the neural network crash risk prediction model provides a significant improvement in prediction accuracy over logistic regression and random forecast. The results indicate that the neural network methodology is a good alternative to predict stock price crash risk. The prediction of stock price crash risk is an important and widely studied topic in both accounting and finance, since crash risk has a significant impact on shareholders, creditors, managers, investors, and regulators. In this paper, I develop a neural network crash risk prediction model that has not been explored before. In addition, I compare the performance of the neural network model with the logistic model and random forecast. I show that the neural network crash risk prediction model provides a significant improvement in prediction accuracy over logistic regression and random forecast. The results indicate that the neural network methodology is a good alternative to predict stock price crash risk. Key words- stock price crash risk; neural network;

Suggested Citation

  • Qunfeng Liao, 2016. "The Stock Price Crash Risk Prediction by Neural Network," Accounting and Finance Research, Sciedu Press, vol. 5(2), pages 1-61, May.
    • Handle: RePEc:jfr:afr111:v:5:y:2016:i:2:p:61
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    References listed on IDEAS

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    Cited by:

    1. Han, Muye & Hao, Zhicheng & Zhao, Yukun, 2024. "Stock price crash risk prediction based on high-low frequency dual-layer graph attention network," International Review of Economics & Finance, Elsevier, vol. 96(PB).

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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