Stock selection using data envelopment analysis
| DOI | https://doi.org/10.1108/02635570810914928 |
| Published date | 31 October 2008 |
| Pages | 1255-1268 |
| Date | 31 October 2008 |
| Author | Hsin‐Hung Chen |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Stock selection using data
envelopment analysis
Hsin-Hung Chen
Department of Business Administration, Cheng Shiu University,
Feng-Shan, Taiwan
Abstract
Purpose – The purpose of this study is to adopt data envelopment analysis (DEA) to construct
portfolios, and compare their return rates with the market index to examine whether DEA portfolios
created superior returns. In addition, this study investigated whether using the “size effect” as a stock
selection strategy is appropriate in Taiwan.
Design/methodology/approach – This study applied two DEA models to evaluate the efficiency
of the firms and construct portfolios by selecting stocks with high efficiency. Furthermore, the return
rates of the portfolios constructed by small-size firms, DEA models and market indices were compared
via empirical data analysis.
Findings – The results showed that size effect seems inappropriate as a stock selection strategy in
the Taiwan stock market. However, the portfolios constructed by DEA models achieved noticeable
superior returns.
Research limitations/implications – Future studies can apply DEA models to other stock
markets in different countries to confirm the effectiveness of DEA methods in stock selection.
Originality/value – This study is the first attempt to select stocks using DEA models and compares
the performances of the portfolios composed by DEA analysis, small-size firms and the stock market
indices. The proposed approach provides useful managerial implications in stock selection and insight
to improve financial efficiencies of corporations.
Keywords Data analysis, Financial services, Portfolioinvestment
Paper type Research paper
1. Introduction
The main objective of fund managers in financial service industry is to select stocks
with high-expected returns to lift the performance of their funds. However, the number
of stocks listed on stock markets is increasing. This trend has increased the challeng e
of selecting stocks to create a portfolio that will have superior returns. For example, the
New York Stock Exchange (NYSE) already contains more than 2,800 company stocks,
while the National Association of Securities Dealers Automated Quotations (NASDAQ)
stock market lists approximately 3,600 electronics companies in 2007. Government
funds and mutual fund managers in financial service industry thus face growing
challenges in properly screening these stocks.
Academics have long stated that competition among traders eliminates asset
mispricing. As a result, every stock is always correctly priced and efforts to outperform
simple random selection of stocks are destined to fail. Jensen (1968) demonstrated
that fund managers in financial service industry generally failed to outperform a
random selection of stocks. Other researchers continue to suggest that inv estors can
seldom achieve superior returns (Walker and Hatfield, 1996; Dellva and Olson, 1998;
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Stock selection
1255
Received 20 January 2008
Revised 30 April 2008
Accepted 17 July 2008
Industrial Management & Data
Systems
Vol. 108 No. 9, 2008
pp. 1255-1268
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570810914928
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