The Ability of Support Vector Machine (SVM) in Financial Recoverey Prediction

Document Type : Research Paper

Authors

1 PhD student in accounting, Babol branch, Islamic Azad University, Babol, Iran

2 Associate Professor of Management, Faculty of Management, Islamic Azad University, Babol, Iran

3 Assistant Professor of Accounting, Department of Accounting, Faculty of Economic and Administrative Sciences, University of Mazandaran

4 Assistant Professor of Accounting, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran

Abstract

One of the most important issues in the field of financial management is that investors can distinguish favorable investment opportunities from unfavorable ones. One way to help investors is to anticipate the financial recovery (exit from helplessness) of companies with financial distress. Therefore, this study intends to provide a model for predicting financial recovery using the support vector machine algorithm for companies listed on the Tehran Stock Exchange. To achieve this goal, 54 financial variables were determined using the Lars feature selection algorithm and to test the accuracy of the results of the proposed model, the support vector learning algorithm was used. For this purpose, in the period of 2001-2018, the information of 167 helpless companies that were out of financial helplessness and revived was extracted. The research findings show that the research model accurately predicts the recovery time of the financially helpless company from financial distress with 74% accuracy.

Keywords

References

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History

  • Receive Date: 06 April 2021
  • Revise Date: 24 September 2022
  • Accept Date: 25 February 2023

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