Technical gazette, Vol. 31 No. 1, 2024.
Original scientific paper
https://doi.org/10.17559/TV-20230307000415
A Hybrid CNN and LSTM based Model for Financial Crisis Prediction
Zhengjun Liu
; School of Information Engineering, Jiangxi Vocational College of Industry and Engineering, 337099 Pingxiang, China
Xiping Liu
orcid.org/0000-0002-0230-8004
; School of Information Management, Jiangxi University of Finance and Economics, 330013 Nanchang, China
*
Liying Zhou
; College of Engineering and Management, Pingxiang University, 337055Pingxiang, China
* Corresponding author.
Abstract
The detection and prediction of financial crises in listed companies are crucial for investors to mitigate potential losses. Traditional prediction methods primarily rely on financial indicators, yet they often overlook valuable insights hidden in financial text. To address this limitation, our study explores the integration of financial indicators and financial text from annual reports to enhance financial crisis prediction. We propose a two-step approach, leveraging a Convolutional Neural Network (CNN) model to extract features from financial indicators and utilizing a Long Short-Term Memory (LSTM) network with attention mechanism to capture the underlying semantics in financial text. Subsequently, we combine the extracted features from both sources for effective classification. Through extensive experiments with various models, we demonstrate the efficacy of our combined approach in achieving optimal prediction results. Our findings highlight the importance of considering financial text alongside traditional financial indicators for enhanced financial crisis detection and prediction. The proposed methodology contributes to the existing literature and offers valuable insights for investors and financial analysts seeking more accurate and comprehensive risk assessment tools.
Keywords
convolutional neural networks; financial crisis predication; financial text; financial indicator; long short-term memory
Hrčak ID:
312899
URI
Publication date:
31.12.2023.
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