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AbstractWe provide an overview of the literature investigating (retail) investors' demand for lottery‐like stocks. We summarize different sets of lottery proxies and discuss their implications for cross‐sectional pricing of individual stocks. We present empirical evidence and summarize the findings including (i) the robustness of the lottery demand effect using an extended data set, (ii) the economic underpinnings of the lottery demand effect, and (iii) the explanatory power of the lottery preference factor for established stock market anomalies.

PUBLISHED ; This chapter seeks to introduce partial least squares (PLS) practitioners to the generation and evaluation of predictions from their path models, both as a means of validating the practical usefulness of their models and for forecasting future outcomes.Studies in tourism and hospitality currently offer strong research utility by explaining how personal, regional, or commercial factors generally relate to positive outcomes for the industry. However, the practical utility of our studies is limited to general policy-making suggestions based on the significance of antecedent factors. Could we use our models to predict, say, a new subject's potential behavior in the future, or forecast how a particular region or commercial interest will fare in the coming year given new data? In addition, before we even have access to data on new subjects, can we use our existing samples to validate that our current models have the potential to make accurate predictions using new data? Being able to address these types of prediction problems would greatly magnify the practical utility of our models and better serve the immediate and quantifiable needs of governments, organizations, businesses, services, and persons involved in the tourism industry. Current approaches and metrics in PLS path modeling (PM) largely cannot answer these prediction-oriented questions about specific cases or scenarios, and are largely relegated to making highly generalized prognostications based on path significances.We believe that predictive PLS is one of the emerging and promising directions in PLS-PM. Although predictive techniques for PLS are still at a nascent stage of development, we hope this chapter brings you up to speed on the latest developments, informs you of predictive practices you can employ today, and gives you a foundation for following future developments in this exciting new direction for PLS-PM

AbstractOutliers can seriously distort the results of statistical analyses and thus threaten the validity of structural equation models. As a remedy, this article introduces a robust variant of Partial Least Squares Path Modeling (PLS) and consistent Partial Least Squares (PLSc) called robust PLS and robust PLSc, respectively, which are robust against distortion caused by outliers. Consequently, robust PLS/PLSc allows to estimate structural models containing constructs modeled as composites and common factors even if empirical data are contaminated by outliers. A Monte Carlo simulation with various population models, sample sizes, and extents of outliers shows that robust PLS/PLSc can deal with outlier shares of up to$$50\%$$50%without distorting the estimates. The simulation also shows that robust PLS/PLSc should always be preferred over its traditional counterparts if the data contain outliers. To demonstrate the relevance for empirical research, robust PLSc is applied to two empirical examples drawn from the extant literature.

Der statistische Diskussionbeitrag untersucht, ob und wie sich Erwartungen und Stimmungen in der Wirtschaft bilden bzw. von welchen volkswirtschaftlichen Größen sie abhängen. Als Methodik werden Partial Least Squares (PLS) Modelle genutzt, eine Modellklasse der Pfadanalyse mit latenten Variablen. Die verwendeten Daten wurden vom Ifo-Institut und aus der amtlichen Statistik entnommen.

Partial Least Squares (PLS) is an estimation method and an algorithm for latent variable path (LVP) models. PLS is a component technique and estimates the latent variables as weighted aggregates. The implications of this choice are considered and compared to covariance structure techniques like LISREL, COSAN and EQS. The properties of special cases of PLS (regression, factor scores, structural equations, principal components, canonical correlation, hierarchical components, correspondence analysis, three-mode path and component analysis) are examined step by step and contribute to the understanding of the general PLS technique. The proof of the convergence of the PLS algorithm is extended beyond two-block models. Some 10 computer programs and 100 applications of PLS are referenced. The book gives the statistical underpinning for the computer programs PLS 1.8, which is in use in some 100 university computer centers, and for PLS/PC. It is intended to be the background reference for the users of PLS 1.8, not as textbook or program manual

The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (a) can be truly characterized as a technique for structural equation modeling (SEM), (b) is able to correct for measurement error, (c) can be used to validate measurement models, (d) accommodates small sample sizes, (e) is able to provide null hypothesis tests for path coefficients, and (f) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.