Application of Data Mining Method in Anticipating of Relationship between Liquidity and Profitability in Capital Market

Document Type : Original Article

Authors

• Hassan Chenari
• Roya Darabi

Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran

10.22067/ijaaf.2019.39251

Abstract

Two important subjects, liquidity, and profitability are under the special attention of scientists and financial managers of corporations. Liquidity as an effective factor in profitability has major importance that is interesting for corporations' financial managers. Therefore, this paper aims to apply the data mining technique in anticipating the relationship between liquidity and profitability in the capital market. This project is classified as library-type research work and based on data mining techniques (neural network, backup vector machine, and regression analysis). Here, the financial information of 147 corporations in the capital market from 2013 - 2018 was analyzed. The research method was inductive and posterior (using past information). This research is identified as descriptive–correlative. For analysis of data SPSS Modeler v. 18 and SPSS v. 23 programs were used. Research findings showed that neural networks and backup vector machines could predict the relationship between liquidity and profitability, but regression analysis could not anticipate it.

Keywords

• liquidity
• profitability
• neural network
• backup vector machine
• decision tree

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