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作为经济领域较为新兴的研究方法,机器学习拓宽了经济学研究边界。那么,能否将其应用于中国上市公司的财务预测呢?本文认为,通过正确地选择模型和准确完整地收集数据,机器学习可以成为一种适用性强且效率高的预测方法,能够挖掘数据间的关系,研究非线性、不易解释的模型,预测准确度超过了传统计量经济模型。本文以机器学习为研究方法,以中国上市公司业绩爆雷预警应用为切入点,基于文献和文本挖掘选择预测变量,训练了决策树、Bagging、 AdaBoost、弹性网、Logistic回归5种模型。结果表明机器学习模型对上市公司业绩爆雷有较好的预警效果;集成学习模型Bagging和AdaBoost预测能力更强,弹性网模型最稳定。我们的研究为机器学习在财务管理领域应用提供了一些思路及建议。
Abstract：As a relatively new research method in the field of economics, machine learning has broadened the boundaries of economics research. Can it be applied to the financial forecast of Chinese listed companies? This paper argues that through correctly selecting data and models, machine learning can become a simple, precise and efficient forecasting method. It can not only study nonlinear forecasting models, whose accuracy exceeds the traditional econometric model; but also use big data resources to mine the relationship between features. To predict the performance-mine explosion of Chinese listed companies, this paper chooses machine learning as the research method, selects predicting variables based on literature research and text mining, trains five models of decision tree, Bagging, AdaBoost, elastic net and Logistic regression, and uses the validation data set to evaluate the prediction accuracy of each model. The results show that the machine learning model has a better early warning effect on the performance-mine explosion of Chinese listed companies. The ensemble learning models, Bagging and AdaBoost, have better forecasting accuracy, and the elastic net model is the most stable model. Moreover, important predicting variables have been selected.
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基本信息:
DOI：
中图分类号:F832.51;TP181;F275
引用信息:
[1]张宏斌,郭蒙.机器学习与财务预测——来自中国上市公司业绩爆雷预警应用的经验研究[J].金融学季刊,2020,14(04):135-154.
基金信息:
国家自然科学基金项目（71790603、71872186）;; 广东省基础与应用基础研究基金项目（2019A1515011409）;; 广东省软科学研究计划项目（2019A101002103）;; 广东省2019—2020年度会计科研课题（19-20*011）;; 广东省普通高校哲学科学专项（2019GXJK078）;; 2020广东省财政科研课题（Z202079）等项目的资助