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This paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design.

Against this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies.

The empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness.

Further investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation.

In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities.

As the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.

This paper aims to examine how olfactory imagery, triggered by scent brand names prior to smelling, influences scented-product purchase intention.

Five studies were conducted. Logistic regression analysis was used to predict likelihood of olfactory imagery formation. ANOVA and t-test analyses were used for scent brand name group comparisons, and serial mediation analysis was used to test how scent brand names impact purchase intention through olfactory imagery vividness and the (dis)confirmation between imagined (i.e. expected) and experienced scents.

Scent name familiarity stimulates olfactory imagery formation. Scent brand name specificity (e.g. “Lavender Bouquet” vs. “Floral Bouquet”) influences purchase intention, with specific names leading to lower purchase intention, because they generate vivid olfactory imagery and induce a disconfirmation between imagined and experienced scents.

Branding scents on products should be a strategic product design decision. Surprisingly, although specific scent brand names trigger vivid olfactory imagery and precise scent expectations, they mitigate purchase intention and thus are riskier. General scent brand names are safer.

This research contributes by extending the literature on the effect of verbal cues on scent perception by considering the role of scent brand name specificity on purchase intent. It also adds to work on how olfactory imagery influences purchase intention by incorporating olfactory imagery vividness. Finally, it proposes and tests an underlying cognitive mechanism to explain the relationship between scent brand names and purchase intention.