Antecedents of green behavioral intentions: a cross‐country study of Turkey,Finland and Pakistan

Quality of life of the future generations depends on the efforts of current generations to protect environment. The purchase decisions based on their green behavioral intentions not only helps the society, but also helps companies to gain a green competitive advantage. In this study, the relationships between green satisfaction, green trust, green equity and behavioral intentions are examined. Data were collected from Turkey, Finland and Pakistan with self‐administered questionnaires regarding with green white goods. The hypotheses were tested with structural equation modeling. Results of a structural model reveal positive relationships green satisfaction, green trust and green brand equity across three countries. In addition, green brand equity plays a remarkable role in behavioral intentions towards green white goods in general. Managers should consider green trust, green satisfaction, and green brand equity while implementing green marketing strategies in a global marketplace.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Online donation behavior in nonprofit organizations: The role of congruent processing styles

With the advent of technology, donors are increasingly donating via online channels. This shift deems it necessary for nonprofit organizations to better understand the online donor behavior. As such, the purpose of this paper is twofold. First, we investigate if nonprofit organizations are perceived differently in terms of their cognitive and affective natures. Then, we examine how different consumer processing styles, i.e. cognitive and affective, interact with consumer perceptions of nonprofit cognitive/affective orientations in influencing their donation intentions. Our results indicate that consumers with a high need for cognition are more willing to donate to predominantly cognitive nonprofit organizations, while those with high need for emotion are more willing to donate to predominantly affective nonprofit organizations. Based on these results, we suggest that nonprofit organizations can garner more donations if they request funds from donors whose processing styles are congruent with the organization. Additional recommendations for future research are provided.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.