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

被引:8
作者
Al Ali, Amal [1 ]
Khedr, Ahmed M. [2 ,3 ]
El Bannany, Magdi [4 ,5 ]
Kanakkayil, Sakeena [2 ]
机构
[1] Univ Sharjah, Informat Syst Dept, Sharjah 27272, U Arab Emirates
[2] Univ Sharjah, Comp Sci Dept, Sharjah 27272, U Arab Emirates
[3] Zagazig Univ, Math Dept, Zagazig 44519, Egypt
[4] Umm Al Quwain Univ, Coll Business Adm, Umm Al Quwain 536, U Arab Emirates
[5] Ain Shams Univ, Fac Business, Dept Accounting & Auditing, Cairo 11566, Egypt
关键词
financial distress prediction (FDP); long short term memory (LSTM); genetic algorithm (GA); machine learning (ML); GENETIC ALGORITHM; RATIOS; MARKET; BANKS;
D O I
10.3390/ijfs11010038
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页数:15
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