Re-visiting collaborative behavior in supply networks – structural embeddedness and the influence of contextual changes and sanctions

This study considers decision making beyond a dyadic buyer-supplier context to the network context. Decisions made by firms are shaped by behavioral norms within the supply network as perceived by the decision makers. Firms can perceive themselves to be part of a collaborative regime or one in which the potential for non-cooperation is high. Further, the ability to put sanctions on non-cooperating firms could shape the overall behavioral patterns in the network. To gain further insights into these aspects and their interactive effects on firm behavior, our study investigates decision-making in supply network by means of behavioral experiments. By organizing practicing managers in a supply network, the study investigates the role of structural embeddedness, incentive structure, and sanctioning mechanisms on the level of collaboration. The results of this study confirm that while sanctions are detrimental for collaborative behavior in a supply network, they play an important role when the underlying norms of governance of such a network are perturbed. The results show that structural embeddedness provides a context that aids adaptive collaborative behavior by firms that are part of the supply network. Once the incentive structure is altered such that there is a higher payoff from defection, the adaptive collaborative behavior is replaced by a behavior in which firms try to maximize their returns and forego collaborative decision-making behavior.     

A nonparametric approach to identifying a subset of forecasters that outperforms the simple average

Empirical studies in the forecast combination literature have shown that it is notoriously difficult to improve upon the simple average despite the availability of optimal combination weights. In particular, historical performance-based combination approaches do not select forecasters that improve upon the simple average going forward. This paper shows that this is due to the high correlation among forecasters, which only by chance causes some individuals to have lower root-mean-squared error (RMSE) than the simple average. We introduce a new nonparametric approach to eliminate forecasters who perform well based on chance as well as poor performers. This leaves a subset of forecasters with better performance in subsequent periods. The average of this group improves upon the simple average in the SPF particularly for bond yields where some forecasters may be more likely to have superior forecasting ability.     

Analyzing user sentiment in social media: Implications for online marketing strategy

This article examines restaurant customers’ online activity following visits to restaurants. Differences in customers’ opinions based on gender and location are discussed. Sentiment analysis was used to analyze customers’ social media behavior in terms of liking, rating, and reviewing restaurants. User‐generated reviews and comments about experiences influence potential customers’ decisions. The results of this study show that gender and location of customers influence restaurant ratings. This article shows that sentiment analysis (using Natural Language Toolkit and TextBlob) can help marketers by providing a useful tool for big data analysis. Sentiment analysis can be used to interpret customer behavior and highlight how presales, sales, and after‐sales strategies can be improved.     

Pane e Cioccolata: The Impact of Native Attitudes on Return Migration

This paper addresses the link between native attitudes and return migration. We exploit the variation in xenophobia using information on media consumption by migrants in Italy. A widely documented crime provides a quasi‐experimental setting to identify the impact of Italian attitudes on migrants’ settlement intentions. Our results suggest a significant effect of anti‐immigrant attitudes on the intended duration of stay in the host country. The impact is more pronounced for low‐skilled migrants, which has consequences for how migration affects the long‐run convergence between sending and destination countries.     

Quantification of political risk with multiple dependent sources

In this paper, we develop a model using a conditional Poisson process for measuring the effect of a countable number of mutually dependant political risks on the outcome of foreign direct investment. We also apply a Bayesian updating process that makes it possible to re-estimate the model's parameters as new information becomes available. We then show how the model can be operationalized and provide a comparative example related to foreign direct investment. (JEL G31, D81, F21).     

Regulations, Supervision Approaches and Acquisition Likelihood in the Asian Banking Industry

We investigate the association between the regulatory and supervision framework and the likelihood of being involved in acquisitions either as acquirer or target in the Asian banking industry over the period 1998–2004. The sample consists of 473 commercial banks operating in 9 markets, a total of 2,693 yearly observations. We use information from the new database of the World Bank and examine various regulations such as the level of accounting and information disclosure requirements, the restrictions on bank activities, the degree of official disciplinary power, and capital adequacy requirements. Our results suggest that the latter two influenced the probability of becoming a target or acquirer; however, restriction on activities and disclosure requirements had no impact on the acquisition likelihood.     

The Traditional Hedging Model Revisited with a Nonobservable Convenience Yield

This article examines the hedging of constrained commodity positions with futures contracts. We extend the study of Adler and Detemple (1988a, 1988b) to include a partial information framework where the convenience yield is not observable. As a consequence, futures prices depend on investor's beliefs regarding the value of the convenience yield, and every component of the hedge is impacted by these beliefs. We achieve a decomposition of the demand that clarifies the impact on the optimal hedge of the beliefs, the spot price and the risk‐free rate as well as the hedging horizon.     

Commercial Real‐Estate Inventory and Theory of Storage

Real estate, like commodities, belongs to the category of alternative investments. Even though the characteristics of real estate are fairly different from those of crude oil or copper, inventory plays a crucial role in both cases. Our goal in this study is to investigate whether the theory of storage, as developed by Kaldor (1939) and Working (1949), then thoroughly analyzed for various commodities, can be extended to real estate. We take advantage of recent Futures data on the main U.K. commercial property index as well as a proprietary inventory database in order to test in this setting, and for the first time to our knowledge, the validity of the theory of storage for this asset class. The empirical evidence, covering data of the period 2008–2010, suggests that its main results can interestingly be extended to commercial real estate. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark     

Banks' risk race: A signaling explanation

Many observers argue that one of the major causes of the 2007–2009 recession was the abnormal accumulation of risk by banks. This paper provides a signaling explanation for this race for risk. If banks' returns can be observed while risk cannot, the less efficient banks can hide their type by taking more risks and paying the same returns as the more efficient banks. The latter can signal themselves by taking even higher risks and delivering bigger returns. The game presents several equilibria that are all characterized by excessive risk taking as compared to the perfect information case.