YouTubers’ networking activities during the 2016 South Korea earthquake

While YouTube has become an important social networking platform for risk and crisis communications, research into its role and use during environmental disasters has not been conducted. The present study investigates how Korean YouTubers reacted during the 2016 Gyeongju earthquakes. Two kinds of data were collected from the most popular YouTube clips on the earthquake (from AfreecaTV and YonHap news): the most frequently used words in the replies to the comments and the networked forums among YouTubers. Findings suggest that YouTube has become a channel for quickly increasing public awareness of crises and for promoting safety strategies. InDegree Centralization is higher in professional videos. Hierarchy measure’s value is one, which means the reply-to network is not two-way. There is sparse close-knittedness in amateur videos. Community clusters emerge in amateur videos over time. The results also reveal that YouTubers on AfreecaTV are more likely to express emotional opinions than their counterparts on YonHap news are.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Does local political support influence financial markets? A study on the impact of job approval ratings of political representatives on local stock returns

Using data on job approval ratings of governors, U.S. senators, and the president, we find that firms located in states with high approval ratings outperform firms located in states with low approval ratings by .64% per month. Furthermore, this relationship is stronger when investors are actively involved in politics, when local politicians are closer to the center of political power, for small firms that have a larger proportion of local investors, and for financially strong areas where investors are ready to execute investments in local stocks. Overall, our study shows that investors’ political sentiment is important in determining stock returns.     

South Korea’s outward direct investment and its dyadic determinants: Foreign aid,bilateral treaty and economic diplomacy

This study investigates the factors that affect South Korean outward foreign direct investment (FDI) in developing countries. Most previous studies focus on monadic factors and do not consider how and to what extent bilateral relationships between South Korea and the host countries affect the investment decisions of Korean firms. The current study finds that interstate factors such as South Korea's international investment treaties with and official development assistance to host countries have positive effects on FDI to these countries, while presidential visits have strong and statistically significant effects on FDI only in countries located in non-Asian regions, especially the African continent. The findings suggest that the effects of bilateral relations on South Korea's FDI vary depending on the geographic location of the host country.     

Industry Shock Expectations,Interindustry Linkages,and Merger Waves: Evidence from the Hospital Industry

It is well known that mergers often occur in waves, and this paper develops a new mechanism for merger waves: expectations over industry shocks. We develop a simple test of this explanation and use it to explore the role of expectations in the context of the 1990s hospital merger wave. Managed care such as Health Maintenance Organizations (HMOs) started to become popular in the late 1980s and ultimately became an important player in the health insurance market. Our empirical analysis shows that the expected increase in the popularity of HMOs was partly responsible for the hospital merger wave of the 1990s: hospitals feared that the “innovation” of managed care in the downstream insurance market would penetrate the upstream hospital market and responded to this belief by merging. Our results show the importance of incorporating expectations and interindustry linkages into the understanding of merger waves.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Value at risk forecasting for volatility index

Forecasts of values at risk (VaRs) are made for volatility indices such as the VIX for the US S&P 500 index, the VKOSPI for the KOSPI (Korea Stock Price Index) and the OVX (oil volatility index) for crude oil funds, which is the first in the literature. In the forecasts, dominant features of the volatility indices are addressed: long memory, conditional heteroscedasticity, asymmetry and fat-tails. An out-of-sample comparison of the VaR forecasts is made in terms of violation probabilities, showing better performance of the proposed method than several competing methods which consider the features differently from ours. The proposed method is composed of heterogeneous autoregressive model for the mean, GARCH model for the volatility and skew-t distribution for the error.     

Effects of Lightness‐Location Congruency on Consumers’ Purchase Decision‐Making

A considerable body of psychological and neuroscientific research has demonstrated the existence of robust sensory correspondences between various features, attributes, or dimensions of experience in different sensory modalities. Despite findings indicating the importance of sensory correspondences to human information processing, research on purchase decision‐making has not to date focused sufficiently on this phenomenon. The present study examines how the lightness of packaging colors, and the location of products on a display shelf interact to affect consumers’ purchase decision‐making via perceived visual heaviness. As predicted, a display with light (dark) colored products positioned in the upper (lower) shelf positions increases shoppers’ perceptual fluency and facilitates their visual search, thus leading to the suggestion that “light” (heavy) locations are most appropriate for light (dark) colored products. Moreover, the lightness‐location congruent display is shown to influence people's choice behavior positively as well. This research also demonstrates that when consumers consider the lightness (in terms of their weight) of the products, they are more likely to choose light (vs. dark) colored products located in the upper shelf positions. These results therefore demonstrate that consumers’ purchase decision‐making may be promoted by in‐store environments designed to be congruent with their sensory correspondences.