Asset Evaluation Model Based on SVM Algorithm

DOI

10.2991/meess-18.2018.19How to use a DOI?

Keywords

Asset write-offs; support vector machine; bagging

Abstract

The purpose of asset write-offs by a firm is to provide an accurate valuation of the firm and to reveal its true business performance from the perspective of economic conditions. However, the decision to write-off assets might be manipulated by the manager of the firm and thus misguide the public to an incorrect firm value. The aim of this study is to provide quantitative prediction models for asset write-offs based on both firms’ financial and managerial incentive factors. The prediction is achieved in two stages, where the first stage conducts a binary prediction of the occurrence of asset write-offs by a firm, while the second stage predicts the magnitude of such asset write-offs if they took place. The prediction models are constructed by support vector machine (SVM) and logistic regression for the binary decision of asset write-offs, and support vector regression (SVR) and linear regression for the write-off magnitude. The performances of different models are compared in terms of various criteria. Moreover, the bagging approach is used to reduce the variance in samples to improve prediction performance. Computational results from empirical data show the prediction performances of SVM/SVR are moderately superior to their counterpart legit/linear models. Moreover, the prediction accuracy varies with the distinctive types of asset write-offs.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chi Zhang
AU  - Lei Yu
AU  - Hao Li
AU  - Zhechang Chang
AU  - Zhe Chen
PY  - 2018/08
DA  - 2018/08
TI  - Asset Evaluation Model Based on SVM Algorithm
BT  - Proceedings of the 2018 International Conference on Management, Economics, Education and Social Sciences (MEESS 2018)
PB  - Atlantis Press
SP  - 98
EP  - 101
SN  - 2352-5398
UR  - https://doi.org/10.2991/meess-18.2018.19
DO  - 10.2991/meess-18.2018.19
ID  - Zhang2018/08
ER  -