An integrated efficiency evaluation of China stock market

Abstract

Several studies employ data envelopment analysis (DEA) in the area of portfolio optimization or stock market efficiency evaluation, however, they have not extended two-stage network DEA to further decompose the efficiency of firms on the stock market and understand the origin of performance. Because of China’s rising role in global stock market, the current research utilizes the sample of 140 firms extracted from China CSI 300 Index from 2012 to 2016 and proposes an additive two-stage network DEA model to evaluate the performance of China listed firms derived from production and financial production process with fundamental and technical analysis approaches. Analytic hierarchy process and second order cone programming are developed to determine the weights and to solve the nonlinear problem in the additive efficiency decomposition model. In addition to, future data estimated from regression analysis is applied in the DEA model to predict the performance. The main findings are as follows: The production process is more important in defining the overall efficiency of a firm on the stock market; There are significant differences in the distribution of efficiencies among various group industries; The health care, information technology or energy industry has a predicted outstanding performance in general.

Keywords:

• Data envelopment analysis
• additive efficiency decomposition
• second order cone programming
• China stock market

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The World Bank Database

2 Capital structures (current ratio, debt to equity ratio, leverage ratio) between financial and non-financial firm has different meanings. For example, high leverage ratio in financial firm is normal, but in non-financial one this presents the distress or financial burden. Then financial firms, banks are excluded from sample to avoid effects on DEA scores.

3 Annual Report of China’s Healthcare Industry, 2017

4 Key China Energy Statistics, 2016