Best proxy to determine firm performance using financial ratios: A CHAID approach

IF 0.4 Q4 ECONOMICS
Muhammad Yousaf, S. Dey
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引用次数: 3

Abstract

Abstract The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm’s performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy’s efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.
使用财务比率确定公司业绩的最佳代理:CHAID方法
摘要本研究的主要目的是研究不同代理之间的企业绩效最佳预测指标。从汽车、建筑和制造业抽取了287家捷克公司的样本。这些公司的面板数据是从Albertina数据库中获取的,时间为2016年至2020年。公司绩效的三个不同指标,资产回报率(RoA)、股权回报率(RoE)和使用资本回报率(RoCE)被用作因变量。包括三个代表公司业绩的指标在内,16个财务比率是基于以前的文献来衡量的。部署了一种基于机器学习的决策树算法——卡方自动交互检测器(CHAID),以衡量每个代理的功效,并检查企业绩效的最佳代理。保持了70:30的划分规则,这意味着数据集的70%用于训练,其余30%用于测试。结果显示,在目标指标中,资产回报率(RoA)被检测为预测财务业绩的可靠指标。研究结果和方法将有助于决策者、利益相关者、学者和管理者做出战略商业决策和预测财务业绩。
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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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
10
审稿时长
38 weeks
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