DEA performance assessment of Greek listed metallurgical firms

Tsolas, Ioannis E.

doi:10.1108/jm2-07-2010-0031

Cited by 3 publications

(1 citation statement)

References 60 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DEA has been applied in various fields including health organizations (Jorge et al , 2013), strategic decision-making (Saen and Azadi, 2011) and performance evaluation of metallurgical firms (Tsolas, 2014). Wide applicability and use of DEA is due to its ability to handle multiple input and output parameters for evaluating the relative efficiency.…”

Section: Principal Component Analysis–data Envelopment Analysismentioning

confidence: 99%

A PCA-DEA framework for stock selection in Indian stock market

Jothimani

Shankar

Yadav

2017

JM2

View full text Add to dashboard Cite

Purpose Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization. Design/methodology/approach Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process. Findings The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE. Research limitations/implications One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased. Practical implications Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry. Originality/value This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

show abstract

Section: Principal Component Analysis–data Envelopment Analysismentioning

confidence: 99%

A PCA-DEA framework for stock selection in Indian stock market

Jothimani

Shankar

Yadav

2017

JM2

View full text Add to dashboard Cite

show abstract

A business strategy, operational efficiency, ownership structure, and manufacturing performance: The moderating role of market uncertainty and competition intensity and its implication on open innovation

Handoyo¹,

Suharman²,

Ghani³

et al. 2023

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

Potential carbon emission abatement cost recovery from carbon emission trading in China

Wang

Xian

Zhang

et al. 2016

JM2

View full text Add to dashboard Cite

Purpose This study aims to provide an estimation of carbon dioxide (CO2) emission abatement costs in China’s industry sector during the period of 2006-2010, and additionally provide an ex-post estimation of CO2 abatement cost savings that would be realized if carbon emission permits trading among different industry sectors of 30 provinces in China during the same period were allowed, to answer the question that whether the industrial carbon emission abatement cost can (partially) be recovered from carbon emission trading in China. Design/methodology/approach The joint production framework associated with the environmental technology is utilized for formulating the models for estimating abatement costs and simulating emission permits trading scheme. Several data envelopment analysis-based models that could deal with both the desirable and undesirable outputs within the above framework are utilized for abatement cost saving estimation. The weak disposability assumption and variable returns to scale assumption are applied in the modelling. Findings In China’s industry sector, during 2006-2010, the estimated CO2 emission abatement cost was 1,842 billion yuan, which accounts for 2.45 per cent of China’s total industrial output value; the emission abatement cost saving from emission permits trading would be 315 billion yuan, which accounts for 17.12 per cent of the emission opportunity abatement cost; and additional 1,065.95 million tonnes of CO2 emission reductions would be realized from emission permits trading, and this accounts for 4.75 per cent of the total industrial CO2 emissions. Research limitations/implications The estimation is implemented at the regional level, i.e. the emission permits trading subjects are the whole industry sectors in different Chinese provinces, because of the data limitation in this study. Further estimation could be implemented at the enterprise level to provide a deeper insight into the abatement cost recovery from emission permits trading. Practical implications The estimation models and calculation process introduced in this study could be applied for evaluating the efficiency and effectiveness of pollutant emission permits trading schemes from the perspective that whether these market-based abatement policy instruments help to realize the potential abatement cost savings. Originality/value To the best of the authors’ knowledge, no study has provided the estimation of CO2 emission abatement cost and the estimation of CO2 abatement cost saving effect from emission permits trading for China’s industry sector. This study provides the first attempt to fill this research gap.

show abstract

DEA performance assessment of Greek listed metallurgical firms

Cited by 3 publications

References 60 publications

A PCA-DEA framework for stock selection in Indian stock market

A PCA-DEA framework for stock selection in Indian stock market

A business strategy, operational efficiency, ownership structure, and manufacturing performance: The moderating role of market uncertainty and competition intensity and its implication on open innovation

Potential carbon emission abatement cost recovery from carbon emission trading in China

Contact Info

Product

Resources

About