Does reporting quality affect systematic risk? Evidence from a regulatory lottery

On June 1, 1976, the Swedish parliament voted on a legislative bill that, if approved, would have substantially increased the quality of the corporate information disclosed to the public. Since parliament was hung, the outcome of the vote was decided by a lottery. The winning lottery ticket was a “No”, which meant that the bill was rejected. I exploit this lottery as an exogenous event to study if reporting quality affects systematic risk. I find that the rejection of the bill resulted in a negative abnormal return that persisted in the following day. This result suggests that there is a causal relationship between reporting quality and equity prices through its impact on systematic risk.     

Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?

User-generated content, particularly online product reviews by customers, provide marketers with rich data of customer evaluations of product attributes. This study proposes, benchmarks, and validates a new approach for inferring attribute-level evaluations from user-generated content. Moreover, little is known about whether and when insights from product reviews gained in such a way are consistent with traditional research methods, such as conjoint analysis and satisfaction driver analysis. To provide first insights into this question, the authors apply their approach to a dataset with almost one million product reviews from 52 product categories and run conjoint and satisfaction driver analyses for these categories. Results indicate that the consistency between methods largely varies across product categories. Initial exploratory analyses suggest that consistency might be higher for categories characterized by low experience qualities, high hedonic value, and high customer willingness to post online reviews—but further work will be necessary to validate these findings.     

The risk impacts of global natural and technological disasters

This research develops an expected risk model and estimates risk impacts of single or multiple disasters in terms of human fatality, injury, affected, and economic damage for nearly 200 nations. Both natural hazards, including geophysical, meteorological, hydrological, and technological disasters, such as industrial, transport, and structural, are considered. Relevant measures of expected risk, such as standard deviation, coefficient of variance, range, and rank are also calculated to assess a country's overall risk. Social-economic-physical factors from the World Development Index developed by the United Nations (UN) are then regressed with occurrences and risks of natural and tech disasters to seek plausible associations. The results show that (1) the model performs reasonably well in fitting observed and modeled risks and risk impacts, relatively better for natural disaster and affected people and economic damage; (2) while natural disasters are far more risky than tech ones in total risk impacts; specific risks for subgroups of natural or tech disasters vary widely in magnitude and by country; (3) high natural and/or tech risks concentrate in a small number of countries, such as China, India, Bangladesh in Asia; U.S., Mexico, Canada in North America; Turkey, Russia, France, Germany in Europe, and Algeria, Egypt, and Ethiopia in Africa, which are relatively large in population, fast in development, or advanced in industrialization; yet (4) high risk deviations per unit risk impacts reveal that many small, developing, and tech backward countries need to prepare even more for both natural and tech disasters. Finally, (5) while many country-level development factors, together with disaster occurrence, are statistically significant, only some can provide weak predictability of disaster risk impacts under robust regression. The research findings provide useful risk references to countries for resilient national policies for disaster preparation, mitigation, and recovery.