Gamblers are frequently reminded to ‘gamble responsibly’. But these qualitative reminders come with no quantitative information for gamblers to judge relative product risk in skill-based gambling forms. By comparison, consumers purchasing alcohol are informed of product strength by alcohol by volume percentage (ABV%) or similar labels. This paper uses mixed logistic regression machine learning to uncover the potential variation in soccer betting outcomes. This paper uses data from four bet types and eight seasons of English Premier League soccer, ending in 2018. Outcomes across each bet type were compared using three betting strategies: the most-skilled prediction, a random strategy and the least-skilled prediction. There was a large spread in betting outcomes, with, for example, the per-bet average loss varying by a factor of 54 (from 1.1% to 58.9%). Gamblers’ losses were positively correlated with the observable betting odds across all bets, indicating that betting odds are one salient feature that could be used to inform gamblers about product risk. Such large differences in product risk are relevant to the promotion of responsible gambling.
This study introduces a Locally weighted Krill Herd-Support Vector Regression (KH-LSVR) model. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. In KH-LSVR, the KH optimizes the Locally weighted Support Vector Regression (LSVR) parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading seven ETF stocks on a daily basis over the period 2006-2012. The KH-LSVR's efficiency is evaluated through three different fitness functions, while its statistical and trading performance is benchmarked seven traditional SVR structures. The inputs of the SVR models are selected through a novel statistical technique that involves three hundred forty eight linear and non-linear predictors, and the Model Confidence Set (MCS) test proposed by Hansen et al. (2011). The trading application is designed in order to validate the robustness of the algorithm under study and to provide empirical evidence in favour or against the Adaptive Market Hypothesis (AMH). In terms of the results, the KH-LSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the time varying trading performance of the models under study validates the AMH theory.
Gambling is considered a public health issue by many researchers, similarly to alcohol or obesity. Statistical risk warnings on gambling products can be considered a public health intervention that encourages safer gambling while preserving freedom of consumer choice. Statistical risk warnings may be useful to gamblers, given that net gambling losses are the primary driver of harm and that gambling products vary greatly in the degree to which they facilitate losses. However, there is some doubt as to whether statistical risk warnings are, in their current form, effective at reducing gambling harm. Here, we consider current applications and evidence, discuss product-specific issues around a range of gambling products and suggest future directions. Our primary recommendation is that current statistical risk warnings can be improved and also applied to a wider range of gambling products. Such an approach should help consumers to make more informed judgements and potentially encourage gambling operators to compete more directly on the relative ‘price’ of gambling products.
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