Tang Wei, Yang Shuili, Khishe Mohammad
School of Economics and Management, Xi'an University of Technology, Xi'an, 710054, Shaanxi, China.
School of Accounting and Finance, The Open University of Shaanxi, Xi'an, 710119, Shaanxi, China.
Heliyon. 2023 Aug 31;9(9):e19431. doi: 10.1016/j.heliyon.2023.e19431. eCollection 2023 Sep.
Financial accounting information systems (FAISs) are one of the scientific fields where deep learning (DL) and swarm-based algorithms have recently seen increased use. Nevertheless, the application of these hybrid networks has become more challenging as a result of the heightened complexity imposed by extensive datasets. In order to tackle this issue, we present a new methodology that integrates the twin adjustable reinforced chimp optimization algorithm (TAR-CHOA) with deep long short-term memory (DLSTM) to forecast profits using FAISs. The main contribution of this research is the development of the TAR-CHOA algorithm, which improves the efficacy of profit prediction models. Moreover, due to the unavailability of an appropriate dataset for this particular problem, a newly formed dataset has been constructed by employing fifteen inputs based on the prior Chinese stock market Kaggle dataset. In this study, we have designed and assessed five DLSTM-based optimization algorithms, for forecasting financial accounting profit. The performance of various models has been evaluated and ranked for financial accounting profit prediction. According to our research, the best-performing DL-based model is DLSTM-TAR-CHOA. One constraint of our methodology is its dependence on historical financial accounting data, operating under the assumption that past patterns and relationships will persist in the future. Furthermore, it is important to note that the efficacy of our models may differ based on the distinct attributes and fluctuations observed in various financial markets. These identified limitations present potential avenues for future research to investigate alternative methodologies and broaden the extent of our findings.
财务会计信息系统（FAISs）是深度学习（DL）和基于群体的算法最近使用频率增加的科学领域之一。然而，由于大量数据集带来的复杂性增加，这些混合网络的应用变得更具挑战性。为了解决这个问题，我们提出了一种新方法，将双可调增强黑猩猩优化算法（TAR-CHOA）与深度长短期记忆（DLSTM）相结合，以使用FAISs预测利润。本研究的主要贡献是开发了TAR-CHOA算法，该算法提高了利润预测模型的有效性。此外，由于缺乏针对该特定问题的合适数据集，我们基于之前的中国股票市场Kaggle数据集采用15个输入构建了一个新的数据集。在本研究中，我们设计并评估了五种基于DLSTM的优化算法，用于预测财务会计利润。对各种模型在财务会计利润预测方面的性能进行了评估和排名。根据我们的研究，表现最佳的基于深度学习的模型是DLSTM-TAR-CHOA。我们方法的一个局限性是它依赖于历史财务会计数据，其运作假设过去的模式和关系将在未来持续存在。此外，需要注意的是，我们模型的有效性可能会因在各种金融市场中观察到的不同属性和波动而有所不同。这些已确定的局限性为未来研究提供了潜在途径，以研究替代方法并扩大我们研究结果的范围。
