DEA portfolio selection in Malaysian stock market

Ismail, Khairu Amin; Rahman, Nik Muhammad Naziman Abd; Salamudin, Norhana; Kamaruddin, Badrul Hisham

doi:10.1109/icimtr.2012.6236492

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…In this study, five outputs and three inputs are chosen. The selection of input and output variables are based on Ismail et al (2012) and others major studies on the efficiency of financial sectors. The five input variables are market capital, total volume, dividend per share, financial leverage, price to book ratio.…”

Section: Data and Variablesmentioning

confidence: 99%

Productivity and efficiency analysis using DEA: Evidence from financial companies listed in Bursa Malaysia

Sharif

Hasan²,

Kurniasari

et al. 2019

10.5267/j.msl

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This study evaluates the technical efficiency, productivity change of financial companies listed in the Malaysian stock exchange (Bursa Malaysia) and examines the effects of productivity change on efficiency over the period 2007-2016. Moreover, this study also concentrates on the ranking of financial companies according to their efficiency scores. Data Envelopment Analysis (DEA) is utilized on a Malmquist Productivity Index in order to calculate the financial companies' efficiency scores. The results of this study show that some firms were fully efficient. The results implied that these companies were in optimal control of their inputs or resources to generate the maximum outputs. Also, the results indicate a tremendous productivity gain was mostly because of a positive shift in frontier technology and positive shift in technical efficiency. This study is significant because it helps to identify the efficient companies from the financial sector in Malaysia based on multiple inputs and outputs by using the DEA model. Common misspecification problems observed that instability of efficiency scores over productivity.

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Section: Data and Variablesmentioning

confidence: 99%

Productivity and efficiency analysis using DEA: Evidence from financial companies listed in Bursa Malaysia

Sharif

Hasan²,

Kurniasari

et al. 2019

10.5267/j.msl

View full text Add to dashboard Cite

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“…A set of 18 financial ratios is used as input and output parameters as shown in Table II. These parameters are supported by the work of Ismail et al (2012), Tehrani et al (2012), Hwang et al (2010), Edirisinghe and Zhang (2008), Feroz et al (2003) and Powers and McMullen (2000).…”

Section: Methodsmentioning

confidence: 69%

“…During the past decade, many researchers have used DEA as a strategy for stock selection in various stock exchanges. For instance, DEA was used to construct portfolio(s) of stocks from the property sector in Malaysian stock market (Ismail et al , 2012). The study was carried out for the period 2004-2005.…”

Section: Previous Workmentioning

confidence: 99%

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

Jothimani

Shankar

Yadav

2017

JM2

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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.

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“…Although DEA was invented to assess relative efficiency, its applicability as a multicriteria solution method (Stewart, 1996;Bouyssou, 1999;Cooper, 2005) and for portfolio selection in particular has been discussed (Dia, 2009;Opricovic and Tzeng, 2003) and applied (Chen, 2008;Ismail et al, 2012;Jothimani et al, 2018) by several authors.…”

Section: Related Literaturementioning

confidence: 99%

A framework for screening and portfolio selection in corporate venture capital

Herberger

Reinle

2020

SEF

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Purpose The purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture capital. Design/methodology/approach The use of the data envelopment analysis (DEA) enables the consideration of individual, heterogeneous and multidimensional decision criteria in portfolio selection and the preceding screening process by the investor. Findings The result of this method is a relative ranking of the PCs, with all the PCs considered serving as peer group. A weighting of individual criteria is not necessary because it is part of the functionality of DEA. The authors validate the proposed approach in a case study and show that it can be well combined with other models and theoretical frameworks. Practical implications The method is particularly useful in two cases. First, if a highly specialized investor wishes to use a variety of individual selection criteria for portfolio selection. Second, if an investor only has insufficient (financial) data on potential PCs, but still wants to make a (pre-) selection based on observable (qualitative) characteristics. This model helps to make consistent, intersubjectively comprehensible decisions based on valid decision criteria and helps to optimize the decision-making process in the context of portfolio selection in CVC. Originality/value This method allows the systematic selection of an attractive group from a large number of potential PCs, based on observable characteristics and taking into account individual strategic investment objectives, without having to make assumptions about underlying distributions or weights of decision criteria.

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DEA portfolio selection in Malaysian stock market

Cited by 10 publications

References 31 publications

Productivity and efficiency analysis using DEA: Evidence from financial companies listed in Bursa Malaysia

Productivity and efficiency analysis using DEA: Evidence from financial companies listed in Bursa Malaysia

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

A framework for screening and portfolio selection in corporate venture capital

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