Additional cautionary notes about the Pearson's correlation coefficient

In this paper we draw attention to two usually-neglected problems with the use of the Pearson's correlation coefficient for ordinal scores. We refer to the questions of substantial differences between the marginal distributions of the two variables to be correlated, and the number of ties on each one of the distributions or on both. Finally, we propose a correction factor for the Pearson's correlation coefficient when the number of ties is large. Some empirical examples are given in order to demonstrate these points.     

Nachhaltiges Marketing im Schweizer Detailhandel: Das Beispiel Migros

Zusammenfassung  Wird das Thema Nachhaltigkeit vom Marketing mit allen seinen Aspekten aufgegriffen, hat das Auswirkungen auf die gesamte Wertsch?pfungskette. Die Schweizer Einzelhandelskette Migros begann bereits im Jahr 1973 damit ihren Kunden Produkte aus integrierter Produktion anzubieten. L?ngst ist daraus ein umfassendes nachhaltiges Konzept entstanden, dessen Kern ein eigenes Label dokumentiert. Somit gelingt es der Migros die drei zentralen Aspekte des nachhaltigen Handelns — ?kologische, soziale und ?konomische Dimensionen — umzusetzen.     

Visitation at capacity-constrained tourism destinations: Exploring revenue management at a national park

This study explores issues related to the introduction of revenue management principles in the mechanism of allocating permits to visitors. Specifically, the study looks at ways in which backcountry hikers in Grand Canyon National Park, a World Heritage Site listed among America’s most visited tourism attractions, value a particular allocation mechanism for a permit application. From a stratified random sampling scheme with a 76% response rate, over 1400 overnight backcountry hikers reported the potential for considerable increased revenues. At the same time the findings indicate that certain demographics and user groups will not participate in the modifications, and thus, may be less likely to get a permit when they apply for one. Any policy implementation from such an analysis needs to consider the implications of privileging those who are both willing and able to pay more for access, and effectively excluding others from the same opportunity.     

Multi level categorical data fusion using partially fused data

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.     

Robust risk aggregation with neural networks

We consider settings in which the distribution of a multivariate random variable is partly ambiguous. We assume the ambiguity lies on the level of the dependence structure, and that the marginal distributions are known. Furthermore, a current best guess for the distribution, called reference measure, is available. We work with the set of distributions that are both close to the given reference measure in a transportation distance (e.g., the Wasserstein distance), and additionally have the correct marginal structure. The goal is to find upper and lower bounds for integrals of interest with respect to distributions in this set. The described problem appears naturally in the context of risk aggregation. When aggregating different risks, the marginal distributions of these risks are known and the task is to quantify their joint effect on a given system. This is typically done by applying a meaningful risk measure to the sum of the individual risks. For this purpose, the stochastic interdependencies between the risks need to be specified. In practice, the models of this dependence structure are however subject to relatively high model ambiguity. The contribution of this paper is twofold: First, we derive a dual representation of the considered problem and prove that strong duality holds. Second, we propose a generally applicable and computationally feasible method, which relies on neural networks, in order to numerically solve the derived dual problem. The latter method is tested on a number of toy examples, before it is finally applied to perform robust risk aggregation in a real‐world instance.     

The Accrual Anomaly: Accrual Originations,Accrual Reversals,and Resolution of Uncertainty*

We combine a fundamental property of accruals and a behavioral phenomenon to provide an explanation for the accrual anomaly. The fundamental property is accruals that originate must subsequently reverse. The behavioral phenomenon is individuals tend to underestimate the variance of noisy signals in various domains of decision making. We argue that originating accruals represents a noisier signal than reversing accruals because the uncertainty of whether originating accruals will eventually convert into cash is high, while there is no uncertainty regarding reversing accruals. If investors underestimate the variance of originating accruals but understand reversing accruals, then originating accruals will be mispriced while reversing accruals will not. To test this prediction, we first develop and empirically validate a novel method for ex ante detecting accrual originations and their reversals. Then we document that investors face increased uncertainty when accruals originate and decreased uncertainty when accruals reverse, and we provide evidence that only originating accruals are mispriced. We further demonstrate that our findings can be useful for improving the accrual-based trading strategy by ex ante detecting and removing accrual reversals from extreme accrual portfolios. Overall, we provide a behavioral explanation for the accrual anomaly that is consistent with the mispricing of originating accruals only.