Title: Research on enterprise financial investment risk prediction method based on binary tree clustering

Authors: Qian Cao

Addresses: Department of Economics and Management, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221116, China

Abstract: In order to overcome the problems of poor accuracy of enterprise financial investment risk prediction and poor fitting effect of operating profit margin, this paper proposes a risk prediction method of enterprise financial investment based on binary tree clustering. Firstly, binary tree clustering is used to classify financial data. Secondly, the binary tree is used to obtain the data probability density function, and then the maximum likelihood estimation method is used to solve the density objective function. Finally, the investment risk prediction results are obtained through the expectation maximisation method to realise the financial investment risk prediction. The experimental results show that the prediction accuracy of this method is as high as 99.06%, and the fitting effect of operating profit margin is good, which shows that this method can improve the prediction accuracy of enterprise financial investment risk.

Keywords: binary tree clustering; Bayes theorem; covariance matrix; financial investment risk.

DOI: 10.1504/IJRIS.2023.130212

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.2, pp.135 - 140

Received: 31 May 2022
Accepted: 14 Jul 2022
Published online: 06 Apr 2023 *

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