Towards a new approach to predict business performance using machine learning
| Autor: | Qi-lin Cao, Chen Zhang, Yue-gang Song |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050208 finance
business.industry Computer science Cognitive Neuroscience 05 social sciences Financial ratio Experimental and Cognitive Psychology 02 engineering and technology Decision maker Machine learning computer.software_genre Fuzzy logic Support vector machine Data set Management strategy Artificial Intelligence 0502 economics and business 0202 electrical engineering electronic engineering information engineering Constrained least squares Performance prediction 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| Zdroj: | Cognitive Systems Research. 52:1004-1012 |
| ISSN: | 1389-0417 |
| DOI: | 10.1016/j.cogsys.2018.09.006 |
| Popis: | Financial ratio plays a crucial role in business performance prediction, but the ability of the decision maker to use this method in adjusting management strategy has been extensively ignored. In this paper we attempt to build a fuzzy chance constrained least squares twin support vector machine (FCC-LSTSVM) to predict the business performance through the financial ratios. Specifically, machine learning techniques are utilized to build the models and 796 listed companies in China are selected as the data set. We find that different efficiencies are performed for different models with the same industry and different effectiveness are shown for different predicting time periods with the same method. In addition, the predicting achievements of business performance depend on the types of industries. This paper has extent significance both in theoretical development and managerial practices. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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