Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM
Introduction
Markowitz (1952) introduced Mean–Variance Portfolio Model; moreover, Sharpe, 1964, Lintner, 1965, Mossin, 1966 subsequently referenced his model to propose the CAPM1 noting that expected return on a security is impacted by risk-free rate, expected market return, and beta of the security. Coupled with the model’s ability to predict expected stock return and formally link together the notions of risk and return, it is widely applied to help investors make investment decisions. However, investors care about how much return that they expect to earn. In other words, they are more interested in determining what factors influence the CAPM’s three fixed variables, and the level of importance of each individual factor. The CAPM only explained that three important factors impact expected stock return but a description of more detailed factors was not available. Therefore, through extensive literature review, this study identified the factors that were to be influenced by risk-free rate, expected market return, and beta of the security, and examined the level of importance of these factors in an effort to make up for the inadequacy of the CAPM.
Preceding studies on expected stock return were mostly focused on exploring the relationship between expected stock return and macroeconomic factors such as money supply (Bilson et al., 2001, Kwon and Shin, 1999, Mandelker and Tandon, 1985, Robichek and Cohn, 1974, Rogalski and Vinso, 1977), inflation (Balduzzi, 1995, Fama, 1981, Gultekin, 1983, Kim and In, 2005, Park and Ratti, 2000), and interest rates (Abugri, 2008, Domian et al., 1996, Geske and Roll, 1983, Kim and Wu, 1987). Yet, the results of these researches were not consistent in that the studies were conducted on unidirectional relationships. For investors, the message conveyed was simply what factors influence expected stock return and whether the influence was positive or negative. Consequently, these findings contribute little to the goal of constructing a complete set of expected stock return pricing model, and particularly a comprehensive analysis of factors and interactive relationships. In addition, studies on the relative weights among variables were insufficient, and MCDM was seldom used by researches for portfolio selection such as Ehrgott, Klamroth, and Schwehm (2004) and Lee, Tzeng, Guan, Chien, and Huang (2009).
Hence, the purpose of this study is to supplement the CAPM and that of previous findings on expected stock return in establishing an investment decision model by experts to provide investors with a reference of portfolio selection most suitable for investing effects to achieve the greatest returns. This research adopted the CAPM and a novel hybrid MCDM model consisting of combined DEMATEL with ANP and VIKOR. By reviewing literatures, we identified the sub-factors of risk-free rate, expected market return, and beta of the security in order to establish the investment decision model. Through a survey of experts, we employed DEMATEL technique to analyze the causal relationships between complex factors and then to build a network relation map (NRM) among criteria for portfolio evaluation. The weights of each factor of MCDM problem for selecting the best portfolio will then be derived by utilizing the Analytic Network Process (ANP) based on the NRM. Afterward, we ranked the data to recognize cardinal factors. Evaluation objects were taken from leadership companies of the hottest stocks in the semiconductor sectors: IC design, wafer foundry, and IC packaging. We then identified the most suitable investment by VIKOR and offered a complete depiction and testing of the decision model for the reference of investors.
The rest of this paper is organized as follows: in Section 2, we identify the sub-factors influencing risk-free rate, expected market return, and beta of the security on expected stock return pricing model in order to construct the evaluation criteria based on literature review. In Section 3, the depiction and application of the novel MCDM are included. Section 4 shows an empirical study of selecting the optimal portfolio by using the proposed evaluation model, and the results are discussed. The conclusions and remarks are provided in the final section.
Section snippets
Expected stock return pricing model
The purpose of this section is to identify the influential factors of expected stock return based on past literatures and discuss the regions of scarcity in these studies. To make up for such a gap, this study conducted a literature review of the CAPM’s three main factors – risk-free rate, expected market return, and beta of the security – for the sake of more accurately identifying the evaluation criteria affecting expected stock return.
A novel MCDM model with DEMATEL technique
As any criterion may impact each other, this study used the DEMATEL technique to acquire the structure of the MCDM problems. The weights of each criterion from the structure are obtained by utilizing the ANP. The VIKOR technique will be leveraged for calculating compromise ranking and gap of the alternatives. In short, the framework of evaluation contains three main phases: (1) constructing the network relation map (NRM) among criteria by the DEMATEL technique (2) calculating the weights of
Empirical case – using semiconductor portfolio as an example
In this section, an empirical study is displayed to illustrate the application of the proposed model for evaluating and selecting the best portfolio.
Conclusions and remarks
The CAPM is used all over in the financial field as an important reference in evaluating stock returns. Mathematical models have demonstrated that risk-free rate, expected market return, and beta of the security are influential of stock returns. However, it is uncertain how the sub-factors impact these three factors. Moreover, the level of importance of these three factors for estimating stock returns is also not mentioned, even though the understanding of the importance of these factors and
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