Stochastic dominance analysis of CTA funds

Phoon, Kok Fai; Wong, Wing‐Keung

doi:10.1007/s11156-012-0284-1

Cited by 31 publications

(17 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the bear market, risk averters show an increased preference for old economy stocks, while risk seekers show a reduced preference for Internet stocks. These results are not consistent with prospect theory, and indicate that investors exhibit reverse S-shaped utility functions [64][65][66][67][68].…”

Section: Empirical Studiescontrasting

confidence: 60%

Management Science, Economics and Finance: A Connection

Chang¹,

McAleer²

2016

Int J Econ Manag Sci

View full text Add to dashboard Cite

Section: Empirical Studiescontrasting

confidence: 60%

Management Science, Economics and Finance: A Connection

Chang¹,

McAleer²

2016

Int J Econ Manag Sci

View full text Add to dashboard Cite

“…The SD theory can be used in many areas, including indifference curves (Wong, 2006(Wong, , 2007Ma and Wong, 2010;Broll, Egozcue, Wong, and Zitikis, 2010), two-moment decision model (Broll, Guo, Welzel, and Wong, 2015;Guo, Wagener, and Wong, 2018), moment rule (Chan, Chow, Guo, and Wong, 2018), economic growth (Chow, Vieito, and Wong, 2018), diversification Egozcue, Fuentes García, Wong, and Zitikis, 2011;Lozza, Wong, Fabozzi, and Egozcue, 2018). It can also be applied to many different assets, including stock (Fong, Lean, and Wong, 2008), fund Zumwalt, 2007, 2012;Wong, Phoon, Lean, 2008), futures (Lean, McAleer, Wong, 2010;Lean, Phoon, Wong, 2012;Qiao, Clark, Wong, 2012;Qiao, Wong, Fung, 2013;Lean, McAleer, Wong, 2015;Clark, Qiao, Wong, 2016), Warrant (Chan, de Peretti, Qiao, Wong, 2012;Wong, Lean, McAleer, Tsai, 2018), Option (Abid, Mroua, and 2009), wine (Bouri, Gupta, Wong, and Zhu, 2018), warrants (Chan, de Peretti, Qiao, and Wong, 2012), gold Zhu, 2015, 2018;Hoang, Zhu, El Khamlichi, and Wong, 2019) , property market (Qiao, Wong, 2015;Tsang, Wong, Horowitz, 2016).…”

Section: Stochastic Dominancementioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Academics and practitioners can apply different risk measures estimators and test statistics in many different areas in the presence of big data, and large finite samples that might not be classified as big data. The literature in applying different risk measures estimators and test statistics includes Gasbarro et al (2007), Lean et al (2007Lean et al ( , 2010Lean et al ( , 2012Lean et al ( , 2015, Chan et al (2012), Qiao et al (2012Qiao et al ( , 2013, Bai et al (2013), Qiao and Wong (2015), Hoang et al (2015aHoang et al ( , 2015b, among many others.…”

Section: Risk Measuresmentioning

confidence: 99%

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

Chang

McAleer

Wong

2018

JRFM

Self Cite

View full text Add to dashboard Cite

Abstract:The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses research issues that are related to the various disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas.

show abstract

Stochastic dominance analysis of CTA funds

Cited by 31 publications

References 53 publications

Management Science, Economics and Finance: A Connection

Management Science, Economics and Finance: A Connection

Optimal Solution Techniques in Decision Sciences: A Review

Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections

Contact Info

Product

Resources

About