The Use of Machine Learning to Forecast Financial Performance: A Literature Review

Ahmed Abdulaziz Khudhur, A. Al-Alawi
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Abstract

The paper offers a comprehensive analysis of ten studies covering different facets of the application of artificial intelligence (AI) techniques for identifying financial performance. The financial stability of organizations is a major concern for decision-makers, particularly in the finance field. Diagnosing financial problems in the early stages can prevent further complications. Many of the previous papers have proved the reliability of machine learning in the prediction of financial performance. Therefore, the motivation of this systematic review is to find out how reliable is machine-learning in forecasting financial performance by exploring the pitfalls of machine-learning methods. Examining the models’ accuracies is not sufficient in determining the robustness of the methods applied, however, the harmony and quality of data used are examined as well. Financial performance is categorized as Bankruptcy and Insolvency. The financial datasets related to the study pertain to bankruptcy, data imbalance, feature dimensionality, forecasting insolvency, preprocessing issues, nonfinancial indicators, commonly used machine learning techniques, and performance metrics. Dealing with high dimensionality was suggested by feature extraction and feature selection. Whereas, data imbalance may be prevented by several techniques such as random sampling. The study's conclusions demonstrated the value of dimensionality reduction methods and data balance in data preprocessing. The study illustrates how critical and impactful when taking into consideration the mentioned strategies in enhancing the existent models. The scientific outcome of this work revolves around conceptualizing the cornerstone for building efficient models in predicting financial performance leading researchers to locate unexplored new research avenues.
使用机器学习预测财务业绩:文献综述
本文对十项研究进行了全面分析,这些研究涵盖了应用人工智能(AI)技术识别财务业绩的不同方面。组织的财务稳定性是决策者,尤其是财务领域决策者的主要关注点。在早期阶段诊断财务问题可以防止进一步的复杂化。之前的许多论文已经证明了机器学习在预测财务绩效方面的可靠性。因此,本系统综述的动机是通过探索机器学习方法的缺陷,了解机器学习在预测财务业绩方面的可靠性。要确定所应用方法的稳健性,仅考察模型的准确性是不够的,还要考察所使用数据的和谐性和质量。财务表现分为破产和资不抵债。与研究相关的财务数据集涉及破产、数据不平衡、特征维度、破产预测、预处理问题、非财务指标、常用机器学习技术和性能指标。特征提取和特征选择是处理高维度的方法。而数据不平衡可以通过随机抽样等几种技术来防止。研究结论证明了降维方法和数据平衡在数据预处理中的价值。这项研究说明,考虑到上述策略在增强现有模型方面的关键性和影响力。这项工作的科学成果围绕着建立预测财务业绩的高效模型的基石概念化,引导研究人员找到尚未探索的新研究途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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