利用机器学习技术预测小额信贷机构的财务业绩

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-08-02 DOI:10.1108/jm2-10-2023-0254
Tang Ting, Md Aslam Mia, Md Imran Hossain, Khaw Khai Wah
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引用次数: 0

摘要

目的鉴于学者、从业人员和政策制定者越来越重视金融可持续性,本研究旨在探索机器学习技术在预测小额信贷机构(MFIs)财务业绩方面的适用性。为了预测小额信贷机构的财务业绩,作者对训练和测试数据集采用了一系列机器学习回归方法。这些方法包括线性回归、偏最小二乘法、逐步选择的线性回归、弹性网、随机森林、量化随机森林、贝叶斯脊回归、K-近邻和支持向量回归。研究结果研究结果表明,随机森林模型是最合适的选择,优于所考虑的其他模型。随机森林模型的有效性因具体情况而异,特别是训练和测试数据集比例之间的平衡。更重要的是,研究结果发现,业务自给自足是影响小额信贷机构财务业绩的最关键因素。这些见解为旨在预测其长期财务可持续性的小额金融机构提供了宝贵的指导。投资者和捐赠者在选择潜在受助对象时,也可以利用这些发现做出明智的决策。此外,从业人员和政策制定者也可以利用这些发现来识别潜在的财务表现漏洞。 原创性/价值 本研究通过使用全球数据集来研究预测小额信贷机构财务表现的最佳模型,这在现有的小额信贷文献中是一个相对稀缺的主题。此外,它还使用了先进的机器学习技术,以深入了解影响小额金融机构财务业绩的因素。
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Predicting the financial performance of microfinance institutions with machine learning techniques

Purpose

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

Design/methodology/approach

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

Findings

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

Research limitations/implications

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

Originality/value

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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