Testing loss aversion and diminishing sensitivity in review sentiment

This article analyzes the relationship between ratings and review sentiment by introducing, for the first time, the tenets of prospect theory. Specifically, we test loss aversion and diminishing sensitivity on a sample of 132,486 reviews and find that: first, negative deviations in ratings (receiving a service with worse performance than expected) bring about a higher impact on review sentiment than positive deviations of equal magnitude (receiving a service with better performance than expected), thus, confirming loss aversion; and second, regardless of whether the service received is better or worse than expected, variations in ratings closer to the reference point result in higher marginal impacts on sentiment than equivalent variations further away from the reference point, thus, proving diminishing sensitivity. These results have relevant theoretical implications related to the use of relative vs absolute measures and the cognitive bias involved, and managerial implications linked to meeting expectations and service recovery.     

Free-play impact by customer segment

Little is known about the effectiveness of casino free-play campaigns, despite hundreds of millions of dollars in annual redemptions. These costly play incentives are awarded to individual players, based largely on management’s evaluation of their historical play. Extant campaign-level research suggests these incentives may not be effective in driving spend per visit, but there has been no attempt to examine efficacy across player tiers (e.g., light, medium, and heavy users). Analysis of 365 days of performance data from a Las Vegas Strip casino produced varied results across tiers, but all tier-level findings indicated a failure to recover the face value of the free-play incentives. While no support was garnered for the house money effect, the results were consistent with the notion of loss aversion. The methodological approach outlined herein provides the means to critically evaluate free-play offers at the tier level, fast-tracking campaign optimization via more targeted revisions.     

对国际卡特尔的有效惩罚额度研究

对国际卡特尔行为实施有效威慑的一个关键问题是如何确立惩罚额度。本文认为，无论是以美国为代表的3倍损失赔偿，还是以欧盟为代表的单倍损失赔偿，都不是合理的惩罚标准。对国际卡特尔的合理的惩罚额度应使卡特尔成员承担其非法行为所带来的全部社会成本，包括国际卡特尔的超高定价、社会净损失、调查诉讼成本以及监禁成本。     

研究型购买者消费渠道转换模型研究

多渠道环境下,消费者试图通过增加信息搜索渠道数量,最终选择最为拟合自身需求的购买渠道。消费者特别是研究型购买者在产品既定的情况下会根据自身对消费渠道的感知价值决策是否采取渠道转换行为。本文通过文献回顾,提出感知价值的维度,并以其为自变量,根据期望效用理论设计研究型购买者的渠道转换模型,以及多渠道零售环境下的渠道策略。     

An Empirical Analysis of the Extreme Cherry Picking Behavior of Consumers in the Frequently Purchased Goods Market

Extreme cherry pickers are customers who seek price deals and excessively avail themselves of deep discount offers, which generates negative profits for retailers. This study uses market transaction and primary consumer survey data to provide insights into the determinants, prevalence, and profit impacts of such behavior in the frequently purchased goods market. We find that the extreme cherry picking segment is small (about 2% of all shoppers), but its relative value varies across stores, and consumers manifest this behavior only in secondary stores. An inverse U-shaped relationship marks consumers’ opportunity costs for cross-store price search and likelihood of extreme cherry picking behavior. Finally, we also find that a loss leader promotional strategy adds to retailers’ bottom lines, despite the pure loss generated by extreme cherry pickers.     

Accounting for optimism and pessimism in expected utility

We provide a preference foundation for decision under risk resulting in a model where probability weighting is linear as long as the corresponding probabilities are not extreme (i.e., 0 or 1). This way, most of the elegance and mathematical tractability of expected utility is maintained and also much of its normative foundation. Yet, the new model can accommodate the extreme sensitivity towards changes from 0 to almost impossible and from almost certain to 1 that has widely been documented in the experimental literature. The model can be viewed as “expected utility with the best and worst in mind” as suggested by Chateauneuf, Eichberger and Grant (Chateauneuf, Alain, Eichberger, Jürgen, Grant, Simon, 2007. Choice under uncertainty with the best and worst in mind: NEO-Additive capacities. Journal of Economic Theory 137, 538–567) or, following our preference foundation, interpreted as “expected utility with consistent optimism and pessimism”.     

Quantiles as optimal point forecasts

Loss functions play a central role in the theory and practice of forecasting. If the loss function is quadratic, the mean of the predictive distribution is the unique optimal point predictor. If the loss is symmetric piecewise linear, any median is an optimal point forecast. Quantiles arise as optimal point forecasts under a general class of economically relevant loss functions, which nests the asymmetric piecewise linear loss, and which we refer to as generalized piecewise linear (GPL). The level of the quantile depends on a generic asymmetry parameter which reflects the possibly distinct costs of underprediction and overprediction. Conversely, a loss function for which quantiles are optimal point forecasts is necessarily GPL. We review characterizations of this type in the work of Thomson, Saerens and Komunjer, and relate to proper scoring rules, incentive-compatible compensation schemes and quantile regression. In the empirical part of the paper, the relevance of decision theoretic guidance in the transition from a predictive distribution to a point forecast is illustrated using the Bank of England’s density forecasts of United Kingdom inflation rates, and probabilistic predictions of wind energy resources in the Pacific Northwest.     

Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting,incorporating realized measures

This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.