The objective of this paper is to explore the necessary and sufficient conditions to obtain high consumer-brand identification (CBI) and high repurchase intentions (Rep). Different from most business research on CBI and Rep that is based on symmetric thinking, this paper uses asymmetric analytics and performs fuzzy set qualitative comparative analysis. The findings show that (1) although it is possible to identify the necessary conditions for very high consumer-brand identification and very high repurchase intentions, no combination of conditions is sufficient to achieve these outcomes; (2) affective drivers have more importance than cognitive drivers for obtaining high CBI; (3) the configuration solutions for high CBI include at least two antecedents;(4) high CBI is a sufficient but not necessary condition for high Rep; (5) high Rep can also be achieved if brand-self similarity and brand identity occur; and (6) memorable brand experiences alone may be enough to obtain high Rep.

This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

This article sets forth results from an exploratory empirical study that aimed to test the predictive validity of three classic location theories: central place theory, spatial interaction theory and the principle of minimum differentiation. Correlation, linear regression and analytical procedures in a Geographic Information System were used to reveal relationships between variables. The results show that all theories find significant support. It was possible to relate store location with distance to the centre, population density and competitor's location. It was also found that store location is related to consumer's proximity to the store (when stores are smaller) and the store's attractiveness to the consumer (when stores are larger).

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In December 2017, the CBOE and CME launched bitcoin futures, arguing that, similar to other futures, these contracts would provide more price transparency, price discovery, and a risk management tool for bitcoin. Using daily data from several sources, this paper investigates the hedging properties of CBOE Bitcoin futures during these initial months of trading. The results point out that bitcoin futures are effective hedging instruments not only for bitcoin, but also for other cryptocurrencies. Bitcoin futures can even cope with bitcoin tail risk, however they may leverage the existence of extreme losses for other currencies.