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This paper studies the effects of lottery preference on stock market participation at the macro level.

The authors use the abnormal search volume intensity for lottery-related keywords from the Baidu search engine to capture retail investors' lottery preference. To measure stock market participation, they use five different macro-level measures from various angles. They perform the time series regression analysis in their empirical study.

First, the validation tests show that the lottery preference index in this study is reasonable. Further, the authors find that lottery preference increases people's propensity to enter and trade in the stock market. Besides, they find that the effect on trading behavior is asymmetric, that is, high lottery preference has a more significant impact on trading behavior than low lottery preference. However, lottery preference has no significant effect on the stockholding.

This paper contributes to the growing literature that examines the determinants of stock market participation and the role of lottery/gambling preference in the financial market. It also provides direct and novel evidence for Statman's (2002) conclusions about the similarity of lottery players and stock traders.

The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.

Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.

A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.

The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.