Almost Stochastic Dominance Research Articles

Revisiting and refining the comparison of conventional and islamic markets’ performance

ABSTRACT With a refined version of almost stochastic dominance (ASD), we explore the performance-comparison issues between traditional markets and Islamic markets in terms of industry and business condition. The empirical outcomes indicate that Shariah compliance makes both the consumer services and finance industries in Islamic markets outshine other industries and outperform the corresponding industries in traditional markets. Except for the period before the Global Financial Crisis (GFC) in 2008, Islamic markets prevail over traditional markets in bearish (2007–2012) and persistently bullish (2013–2017) periods. These contrasts indicate that Islamic markets have emerged as a superior investment alternative since the GFC.

Stochastic Multiple-Attribute Decision Making Method Based on Stochastic Dominance and Almost Stochastic Dominance Rules with an Application to Online Purchase Decisions

Although some stochastic multiple-attribute decision making (SMADM) methods based on the stochastic dominance (SD) rules have been proposed, there is still the limitation that the dominance relations between some pairs of alternatives cannot be identified. In this paper, almost stochastic dominance (ASD) rules are used as supplements of the SD rules to identify dominance relations between the pairs of alternatives, and a new method for SMADM based on the SD and ASD rules is proposed. In the method, a procedure for identifying the dominance relation between each pair of alternatives based on the SD and ASD rules is given. Then, according to the identified dominance relation, the priority degree that one alternative is superior to another alternative concerning each attribute is calculated. Further, according to the obtained priority degrees, an approach for ranking alternatives is proposed using the simple weighted method. Finally, the proposed method is applied to the selection of passenger car(s) based on online ratings, and a comparison between the proposed method and the existing methods based on a numerical example is given. The proposed method can obtain more precise ranking results of alternatives. ASD rules are important supplements of the SD rules for identifying dominance relations. Based on the SD and ASD rules, the proposed SMADM method is important for developing theories and methods for SMADM.

Almost stochastic dominance for poverty level in Central Java Province

The criteria for the domination of the distribution function has been used in the investment issues, momentum, agricultural production, and so on. One criteria of domination is stochastic dominance (SD). When this criteria is applied to the dominating area that has smaller value than the dominated area, then almost stochastic dominance (ASD) can be used. It this research, we apply the ASD criteria on data of expenditure per capita based on districts/cities in Central Java. Furthermore, we determine which year the expenditure per capita in the period 2009-2013 is the most dominating to know the level of poverty in Central Java. From the discussion, it can be concluded that the expenditure per capita in Central Java in 2013 dominates expenditure per capita in Central Java in 2009-2012. In other words, the level of poverty in Central Java in 2013 is lower than in 2009-2012.

A New Stochastic Dominance Degree Based on Almost Stochastic Dominance and Its Application in Decision Making

Traditional stochastic dominance rules are so strict and qualitative conditions that generally a stochastic dominance relation between two alternatives does not exist. To solve the problem, we firstly supplement the definitions of almost stochastic dominance (ASD). Then, we propose a new definition of stochastic dominance degree (SDD) that is based on the idea of ASD. The new definition takes both the objective mean and stakeholders’ subjective preference into account, and can measure both standard and almost stochastic dominance degree. The new definition contains four kinds of SDD corresponding to different stakeholders (rational investors, risk averters, risk seekers, and prospect investors). The operator in the definition can also be changed to fit in with different circumstances. On the basis of the new SDD definition, we present a method to solve stochastic multiple criteria decision-making problem. The numerical experiment shows that the new method could produce a more accurate result according to the utility situations of stakeholders. Moreover, even when it is difficult to elicit the group utility distribution of stakeholders, or when the group utility distribution is ambiguous, the method can still rank alternatives.

Almost stochastic dominance for risk averters and risk seeker

In this paper we first extend the theory of almost stochastic dominance (ASD) (for risk averters) to include the ASD for risk-seeking investors. We then study the relationship between ASD for risk seekers and ASD for risk averters. Recently, Tsetlin, et al.(2015) develop the theory of generalized ASD (GASD). We then briefly discuss the advantages and disadvantages of ASD and GASD.

Consistent Tests for Almost Stochastic Dominance

Leshno and Levy (2002) introduce the concept of the first and second order of almost stochastic dominance (ASD) for most decision makers. There are many studies investigating the properties of this concept. Many empirical applications are also conducted based on it. However, there is no formal statistical inference procedure up to now. In this paper, we aim to develop consistent test statistics for the first three order of ASD. Two numerical approaches are proposed to determine the critical values.

A Note on Almost Stochastic Dominance and Generalized Almost Stochastic Dominance

In this paper we first extend the theory of almost stochastic dominance (ASD) (for risk averters) to include the ASD for risk-seeking investors. We then study the relationship between ASD for risk seekers and ASD for risk averters. Recently, Tsetlin, et al. (2015) develop the theory of generalized almost stochastic dominance (GASD). We then briefly discuss the advantages and disadvantages of ASD and GASD.

Almost Stochastic Dominance and Efficient Investment Sets

A major drawback of Mean-Variance and Stochastic Dominance investment criteria is that they may fail to determine dominance even in situations when all “reasonable” decision-makers would clearly prefer one alternative over another. Leshno and Levy [1] suggest Almost Stochastic Dominance (ASD) as a remedy. This paper develops algorithms for deriving the ASD efficient sets. Empirical application reveals that the improvement to the efficient sets implied by ASD is substantial (64% reduction for FSD). Direct expected utility maximization shows that investment portfolios excluded from the ASD efficient set would not have been chosen by any investors with reasonable preferences.

Tractable Almost Stochastic Dominance

LL-Almost Stochastic Dominance (LL-ASD) is a relaxation of the Stochastic Dominance (SD) concept proposed by Leshno and Levy that explains more of realistic preferences observed in practice than SD alone does. Unfortunately, numerical applications of this concept, such as identifying if a given portfolio is efficient or determining a marketed portfolio that dominates a given benchmark, are computationally prohibitive due to the structure of LL-ASD. We propose a new Almost Stochastic Dominance (ASD) concept that is computationally tractable. For instance, a marketed dominating portfolio can be identified by solving a simple linear programming problem. Moreover, the new concept performs well on all the intuitive examples from the literature, and in some cases leads to more realistic predictions than the earlier concept. We develop some properties of ASD, formulate efficient optimization models, and apply the concept to analyzing investors’ preferences between bonds and stocks for the long run.

Bonds versus stocks: Investors’ age and risk taking

It has become increasingly popular to advise investors to relocate their funds from a primarily stock portfolio to a primarily bond portfolio as they get older. However, the well-known decision rules such as mean–variance or stochastic dominance rules are unable to explain this common practice. Almost stochastic dominance (ASD) and almost mean–variance (AMV) approaches are used to examine the dominance of stock and bond portfolios. ASD and AMV rules unambiguously support the popular practice of advising higher stock to bond ratio for long investment horizons. Hence, we provide an explanation to the practitioners’ recommendation within the expected utility paradigm.

Dynamic Lifecycle Strategies for Target Date Retirement Funds

Lifecycle funds offered to retirement plan participants gradually reduce their exposure to stocks as they approach the target date of retirement. We show that such deterministic switching rules produce inferior wealth outcomes for the investor compared to strategies that dynamically alter the allocation between growth and conservative assets based on cumulative portfolio performance relative to a set target. The dynamic allocation strategies proposed in this paper exhibit almost stochastic dominance (ASD) over strategies that switch assets unidirectionally without consideration of portfolio performance.

A Rational for the Payback Criterion: An Application of Almost Stochastic Dominance to Capital Budgeting

Textbooks on financial management have emphasized the shortcomings of the payback criterion for decades. However, empirical evidence suggests that in actual capital budgeting procedures the payback method is used quite regularly. Mostly, it is implemented supplementary to net present value or internal rate of return, but small companies tend to rely on payback times as single criterion. A convincing theoretical foundation for the observed use of the payback criterion is lacking. Consequently, our goal is to provide such an explanation for the payback criterion’s popularity. We demonstrate from a decision theoretical perspective how relying on payback times simplifies investment decisions in modern organizations. Gathering information from different management levels and ensuring the utilization of individual skills requires a multi-stage capital budgeting process. Accordingly, we consider fundamental organizational features of this process with respect to their impact on the payback method’s use. For this purpose, we built upon almost stochastic dominance (ASD) as modeling device. Firstly, we show that applying his concept allows to include the risk preferences of all relevant decision makers into the analysis. Secondly, we illustrate that the criteria derived from this model help conveying these preferences to those who do the preparatory work preceding the final decision. To some extent, these new criteria are generalizations of payback times. This finding provides a potential explanation for the payback’s persisting prominence.

Almost Stochastic Dominance: A Penchant for Risk

This paper shows that the Almost Stochastic Dominance (ASD) decision rule overlooks the effect of leverage on the variance of portfolio returns. If a portfolio dominates another by ASD then that portfolio bought with any amount of leverage will also be dominant by ASD. Therefore, there is no limit to the risk that a portfolio may have and still be dominant. The effect of leverage on returns is particularly important as new Exchange Traded Funds offer returns on an index which are enhanced by leverage. ASD cannot distinguish between index returns which are and are not enhanced by leverage, and therefore the decision rule could lead financial planners using ASD to recommend far too risky investments.

Probabilistic dominance criteria for comparing uncertain alternatives: A tutorial

This paper is a tutorial which demonstrates the current state-of-the-art methods for incorporating risk into project selection decision making. The projects under consideration might be R&D, IT, or other capital expenditure programs. We will show six decision making methods: 1. mean-variance (MV), 2. mean-semivariance, 3. mean-critical probability, 4. stochastic dominance, 5. almost stochastic dominance (ASD), and 6. mean-Gini. We will also describe the assumptions about the risk attitudes of the decision maker which are associated with each of the techniques. While all these methods have been previously applied elsewhere, this is the first paper which shows all of their applications in the project selection context, together with their interrelationships, strengths and weaknesses. We have applied all six techniques to the same group of five hypothetical projects and evaluated the resulting nondominated sets. Among the methods reviewed here, stochastic dominance is recommended because it requires the least restrictive assumptions. ASD and mean-Gini are recommended when stochastic dominance is not practical or when it does not yield definitive choices. MV, mean-semivariance, and mean-critical probability are shown to be flawed.

Preferred by “All” and Preferred by “Most” Decision Makers: Almost Stochastic Dominance

While “most” decision makers may prefer one uncertain prospect over another, stochastic dominance rules as well as other investment criteria, will not reveal this preference due to some extreme utility functions in the case of even a very small violation of these rules. Such strict rules relate to “all” utility functions in a given class including extreme ones which presumably rarely represents investors' preference. In this paper we establish almost stochastic dominance (ASD) rules which formally reveal a preference for “most” decision makers, but not for “all” of them. The ASD rules reveal that choices which probably conform with “most” decision makers also solve some debates, e.g., showing, as practitioners claim, an ASD preference for a higher proportion of stocks in the portfolio as the investment horizon increases, a conclusion which is not implied by the well-known stochastic dominance rules.