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This paper presents a case study of Renault Samsung Motors (RSM) that recently encounters dynamic changes unveiling various opportunities and challenges due to increasing complexity of the supply network with growing sales volume, diversifying models, and intensifying global competition. Such competitive environment puts constant pressure on the logistics operations to reduce supply costs and lead time, but the RSM has not been paying much attention to aligning interests of supply chain partners. In 2007, RSM’s effort to build partnership with new 3PLs turned abortive due to their unexpected default on the contract throwing RSM into confusion and disruptions. In this study, the problem was investigated by examining route planning process and incentive scheme of 3PL, and an optimization model was constructed to evaluate the performance of existing 3PL operation. The results indicate that transportation cost can be reduced by relocating consolidation centers, utilizing regional terminal and redesigning routing sequence. However, the research found that the key to successful implementation of the optimized solutions is in designing effective incentive system that induces partners to participate in continuous improvement initiatives.

The authors examined whether stocks with higher left-tail risk measures earn higher or lower futures returns. Specifically, the authors estimate the cross-sectional principal component of a battery of left-tail risk measures and analyze future returns on stocks with high principal component values. In contrast to finance theories on the risk–return trade-off relationship, the study results show that high left-tail risk stocks have lower future returns. This finding is robust to various left-tail risk measures and controls for other risk factors. Moreover, the negative relationship between the left-tail risk and returns is more pronounced for stocks that are actively traded by retail investors. This empirical result is consistent with behavioral theory that when investors make decisions based on experience, they tend to underweight the likelihood of rare events.

This study aims to explore empirical evidence of the impact of greenhouse gas (GHG) emissions on stock market volatility.

Using panel data of 35 Organization for Economic Co-operation and Development countries from 1992 to 2018, we conduct both fixed effects panel model and Prais-Winsten model with panel-corrected standard errors.

The authors document that there is a significant positive relationship between GHG emissions and stock market volatility. The results remain robust after controlling for potential endogeneity problems.

This study contributes to the literature in that it provides additional empirical evidence for the financial risk posed by climate change.