GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction

Abstract

Despite the obvious benefits and growing popularity of Machine Learning (ML) technology, there are still concerns regarding its ability to provide Financial Distress Prediction (FDP). An accurate FDP model is required to avoid financial risk at the lowest possible cost. However, in the Internet era, financial data are exploding, and they are being coupled with other kinds of risk data, making an FDP model challenging to operate. As a result, researchers presented several novel FDP models based on ML and Deep Learning. Time series data is are important to reflect the multi-source and heterogeneous aspects of financial data. This paper gives insight into building a time-series model and forecasting distress far in advance of its occurrence. To build an efficient FDP model, we provide a hybrid model (GALSTM-FDP) that incorporates LSTM and GA. Unlike other previous studies, which established models that predicted distress probability only within one year, our approach predicts distress two years ahead. This research integrates GA with LSTM to find the optimum hyperparameter configuration for LSTM. Using GA, we focus on optimizing architectural aspects for modeling the optimal network based on prediction accuracy. The results showed that our algorithm outperforms other state-of-the-art methods in terms of predictive accuracy.

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

“…Research conducted [27] integrated GA with LSTM to find the optimal hyperparameter configuration for LSTM. By using GA, focus on optimizing architectural aspects to model optimal networks based on predictive accuracy.…”

Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study

“…4(Wang et al, 2022) The impact of pledging controlling shareholders stock on raising financial distress prediction accuracy empirically examined by this study. 5(Ben Jabeur and Serret, 2023) This study examines the performance of 4 common feature selection techniques for financial distress prediction (t-test, stepwise logistic regression, stepdisc discriminant analysis and partial least square discriminant analysis).6(AlAli et al, 2023) In order to determine the best hypermeter configuration for LSTM, this research blends GA with LSTM. 7(Jiang et al, 2023a)…”