Management Information, Decision Sciences, and Financial Economics: A Connection

Chang, Chia‐Lin; McAleer, Michael; Wong, Wing‐Keung

doi:10.2139/ssrn.3103807

Cited by 22 publications

(22 citation statements)

References 71 publications

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“…The paper investigated the preferences of risk averters to invest in warrants between China and Taiwan. Extensions include using other tools to compare the preferences for risk averters to invest in warrants between China and Taiwan, as well as investigate the preferences of other types of investors (see Chang et al [72][73][74][75][76] for further information).…”

Section: Discussionmentioning

confidence: 99%

Why Are Warrant Markets Sustained in Taiwan but Not in China?

2018

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This paper uses moment analysis, capital asset pricing model (CAPM) statistics, stochastic dominance (SD) test, and volume analysis to investigating why the market for Taiwan warrants can be sustained but not in China. Our moment analysis shows that buying in China warrants has a higher likelihood of losses. Our CAPM analysis shows that both the Sharpe ratio and Jensen index for warrants from the Chinese market are too negative. The Treynor index shows that Chinese warrants are highly volatile. Our SD analysis shows that risk averters prefer to invest in Chinese warrants compared to Taiwanese warrants, implying that the warrant issuers prefer to issue Taiwanese warrants than Chinese warrants. Using volume analysis, the Chinese warrant market is much more active, implying more speculative activities in China than in Taiwan. All the above could lead to China’s decision to close its warrant market. The findings in the paper are useful for academics, investors, and policy makers.

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Section: Discussionmentioning

confidence: 99%

Why Are Warrant Markets Sustained in Taiwan but Not in China?

2018

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“…Last, we note that there are many other areas in decision sciences that can use the approaches discussed in our paper to get the moment generating function, expectation and variance of different distributions, in this paper we also review some as discussed in the above. For more applications in decision sciences that can use the approaches discussed in our paper to get the moment generating function, expectation and variance of different distributions, readers may refer to Chang, McAleer, and Wong (2015, 2018a, 2018b, 2018c and Pho, Tran, Ho, and Wong (2019) for more information.…”

Section: Other Statistical and Econometric Modelsmentioning

confidence: 99%

Moment Generating Function, Expectation and Variance of Ubiquitous Distributions with Applications in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

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“…Last, we note that there are many other areas in decision sciences that can use the approaches discussed in our paper to get optimization solutions, in this paper we also review some as discussed in the above. For more applications in decision sciences that can use the approaches discussed in our paper to get optimization solutions, readers may refer to Chang, McAleer, and Wong (2015, 2018a, 2018b, 2018c) for more information.…”

Section: Statistical and Econometric Modelsmentioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

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Methods to find the optimization solution are fundamental and extremely crucial for scientists to program computational software to solve optimization problems efficiently and for practitioners to use it efficiently. Thus, it is very essential to know about the idea, origin, and usage of these methods. Although the methods have been used for very long time and the theory has been developed too long, most, if not all, of the authors who develop the theory are unknown and the theory has not been stated clearly and systematically. To bridge the gap in the literature in this area and provide academics and practitioners with an overview of the methods, this paper reviews and discusses the four most commonly used methods to find the optimization solution including the bisection, gradient, Newton-Raphson, and secant methods. We first introduce the origin and idea of the methods and develop all the necessary theorems to prove the existence and convergence of the estimate for each method.We then give two examples to illustrate the approaches. Thereafter, we review the literature of the applications of getting the optimization solutions in some important issues and discuss the advantages and disadvantages of each method. We note that all the theorems developed in our paper could be well-known but, so far, we have not seen any book or paper that discusses all the theorems stated in our paper in detail. Thus, we believe the theorems developed in our paper could still have some contributions to the literature. Our review is useful for academics and practitioners in finding the optimization solutions in their studies.

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Management Information, Decision Sciences, and Financial Economics: A Connection

Cited by 22 publications

References 71 publications

Why Are Warrant Markets Sustained in Taiwan but Not in China?

Why Are Warrant Markets Sustained in Taiwan but Not in China?

Moment Generating Function, Expectation and Variance of Ubiquitous Distributions with Applications in Decision Sciences: A Review

Optimal Solution Techniques in Decision Sciences: A Review

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