Hybrid Corporate Performance Prediction Model Considering Technical Capability
Hybrid Corporate Performance Prediction Model Considering Technical Capability
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DOI：
10.3390/su8070640
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发表时间：
2016-07
期刊：
影响因子：
3.9
通讯作者：
Joonhyuck Lee;Gabjo Kim;Sangsung Park;Dong-Sik Jang
中科院分区：
文献类型：
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作者：
Joonhyuck Lee;Gabjo Kim;Sangsung Park;Dong-Sik Jang
许多研究都试图预测公司业绩和股票价格，以提高投资盈利能力的定性方法，如德尔菲法。然而，数据处理技术和机器学习算法的发展导致了在各种管理学科领域开发定量预测模型的努力。我们提出了一个定量的公司业绩预测模型，该模型应用支持向量回归（SVR）算法来解决训练数据的过拟合问题，并可应用于回归问题。该模型基于训练数据对支持向量回归机的训练参数进行优化，利用遗传算法实现对多变的市场和管理环境的可持续预测。技术密集型公司在整个经济中所占的份额越来越大。这些公司的业绩和股票价格受其财务状况和技术能力的影响。因此，我们综合运用财务指标和技术指标来建立预测模型。在这里，我们使用时间序列数据，包括44家电子和IT公司的财务，专利和企业绩效信息。然后，我们对这些公司的业绩进行了预测，作为对所提出的模型的预测性能的实证验证。
Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR) algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.