Examining the Effect of Short-Term Affect on Farmers’ Risk and Time Preferences in Financial Decision-Making

Farmers regularly make intertemporal decisions under risk or uncertainty. To improve how farmers behave when faced with decisions that have financial consequences, there is a need for a deeper understanding of farmers’ risk and time preferences. While the relationship between individual components of affect and risk preferences is well documented, the same cannot be said for holistic measures of affect on one hand, and for affect and time preferences on the other hand. The data analysed in this paper is the 2014–2015 Indonesian Family Life Survey Wave 5. The survey included experimental measures designed to elicit both risk and time preferences from the same subjects. We analysed the data using limited dependent variable regression models. Our findings strengthen what is known about the affect infusion model. With increased pleasant affect, farmers’ willingness to take risks increases significantly. The results also suggest that pleasant affect is associated with increased odds that farmers will choose future rewards in the long horizon but had no statistically significant effect on the short horizon. The practical implications are that an experience of pleasant affect before decision-making may cause the decision-maker (DM) to perceive a prospect as having high benefits and low risks. Pleasant affect may also induce lower sensitivity towards losses and play the role of a buffer which reduces the immediate negative impact of information that otherwise would prevent the DM from focusing on the long-term. JEL Classifications: C93, D81, D91

Multi-parameter predictive shift schedule of automatic mechanical transmission for electric bus

Shift schedule is crucial in improving the dynamic and economic performance of electric vehicles (EVs) equipped with automatic mechanical transmission (AMT). As the driver, vehicle, and road constitute a closed-loop inseparable system, identifying the states of both vehicle and road is fundamental to realizing optimal shift schedule. However, the existing shift strategies neglect the coupling relationship of multiple parameters to the shift strategy. To minimize this gap, this paper presents a novel multi-parameter shift schedule based on model predictive control. Firstly, cubature Kalman filters (CKF) algorithm is employed to accurately estimate vehicle quality and road slope, which could improve the energy economy of EVs. Secondly, an artificial neural network (ANN) is adopted to forecast the compound future short horizon driving conditions, which contains the perdition information of vehicle velocity and road slope. Meanwhile, the AMT predictive shift schedule based on the above estimated and forecast information is constructed, which used dynamic programming to optimize in the rolling horizon. Simulation study results indicate that the ANN-based predictive approach shows better performance on accuracy and robustness than that of Markov chain, and the electricity consumption over China typical urban driving cycle (CTUDC) is further reduced by 6.79% than that of multi-parameter rule-based shift schedule.

Portfolio diversification benefits of alternative currency investment in Bitcoin and foreign exchange markets

This study examines the portfolio diversification benefits of alternative currency trading in Bitcoin and foreign exchange markets. The following methods are applied for the analysis: the spillover index method of Diebold and Yilmaz (Int J Forecast 28(1): 57–66, 2012. 10.1016/j.ijforecast.2011.02.006), the spillover asymmetry measures of Barunik et al. (J Int Money Finance 77: 39–56, 2017. 10.1016/j.jimonfin.2017.06.003), and the frequency connectedness method of Barunik and Křehlík (J Financ Econom 16(2): 271–296, 2018. 10.1093/jjfinec/nby001). The findings identify the presence of low-level integration and asymmetric volatility spillover as well as a dominant role of short horizon spillover among Bitcoin markets and foreign exchange pairs for six major trading currencies (US dollar, euro, Japanese yen, British pound sterling, Australian dollar, and Canadian dollar). Bitcoin is found to provide significant portfolio diversification benefits for alternative currency foreign exchange portfolios. Alternative currency Bitcoin trading in euro is found to provide the most significant portfolio diversification benefits for foreign exchange portfolios consisting of major trading currencies. The findings of the study regarding spillover dynamics and portfolio diversification capabilities of the Bitcoin market for foreign exchange markets of major trading currencies have significant implications for portfolio diversification and risk minimization.

A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers

Explosive growth in the number of users on various social media platforms has transformed the way firms strategize their marketing activities. To take advantage of the vast size of social networks, firms have now turned their attention to influencer marketing wherein they employ independent influencers to promote their products on social media platforms. Despite the recent growth in influencer marketing, the problem of network seeding (i.e., identification of influencers to optimally post a firm’s message or advertisement) neither has been rigorously studied in the academic literature nor has been carefully addressed in practice. We develop a data-driven optimization framework to help a firm successfully conduct (i) short-horizon and (ii) long-horizon influencer marketing campaigns, for which two models are developed, respectively, to maximize the firm’s benefit. The models are based on the interactions with marketers, observation of firms’ message placements on social media, and model parameters estimated via empirical analysis performed on data from Twitter. Our empirical analysis discovers the effects of collective influence of multiple influencers and finds two important parameters to be included in the models, namely, multiple exposure effect and forgetting effect. For the short-horizon campaign, we develop an optimization model to select influencers and present structural properties for the model. Using these properties, we develop a mathematical programming based polynomial time procedure to provide near-optimal solutions. For the long-horizon problem, we develop an efficient solution procedure to simultaneously select influencers and schedule their message postings over a planning horizon. We demonstrate the superiority of our solution strategies for both short- and long-horizon problems against multiple benchmark methods used in practice. Finally, we present several managerially relevant insights for firms in the influencer marketing context. This paper was accepted by J. George Shanthikumar, big data analytics.