Published papers

This study focuses on activist funds during the second wave of activism after the implementation of the Stewardship and Corporate Governance Codes in Japan. Based on reports of large-scale shareholdings (acquisitions of 5% shares or more) submitted by activist funds between 2014 and 2019, this study finds that the stock returns of target firms react positively to the intervention of activist funds. The positive correlation intensifies for funds classified as hostile or domestic. Unlike in the first wave of activism, activist funds do not necessarily target low-margin or undervalued firms in the second wave of activism. Comparing financial indicators of the targets of the pre- and post-acquisition periods of large blocks of shares by activist funds, return on assets tends to decline more than that of their matching peer control firms, while the dividend payout ratio tends to rise more. This study contributes to the related research in three ways. First, this study adds empirical evidence on hedge fund activism outside the U.S., which is still somewhat scant. Second, this study is the first in the English or Japanese language literature to focus on the second wave of activism in Japan. Third, this study presents empirical results that are distinct from those in prior studies focusing on the first wave, perhaps because institutional changes forced activist funds to focus more on engagement rather than selecting cheap firms.

The business conditions surrounding Japanese regional banks have deteriorated for decades amid continuing ultra-low interest rates. We examine stock price reactions of regional banks to information technology (IT) investment announcements and analyze the factors that affect market responses. We provide three main findings. First, regional banks’ stock prices as a whole do not respond significantly to IT investment announcements. Second, stock price responses to IT investment announcements are more positively correlated with the regional banks’ size and with the individual shareholders’ ratio. Third, regional banks’ stock prices respond more negatively when there's news regarding equity investments to FinTech companies.

This study compares the effect of sports sponsorship announcements on the stock returns of sponsors and their rivals between Japan and the United States. Event study analyses reveal that, whereas stock market reactions for Japanese sponsors were significantly positive during 1999-2010, those for their rivals were significantly negative. In the United States, however, both sponsors and rivals experienced increased market value. Sports sponsorships thus might be useful for sponsors to establish a competitive advantage over rivals in Japan, but not in the United States.

his study investigates how stock markets reacted to the postponement of the Tokyo Olympic Games, which were scheduled to be held in 2020. After the outbreak of the COVID-19 pandemic, the decision to postpone the Games was made on March 24, 2020. However, even before this decision was made, various news outlets had already reported the possibility of cancellation. Although Olympic Games had previously been cancelled five times, they had never been postponed. This event study based on the Fama French factor models provides no evidence that sponsor stock prices reacted differently from their matched firms to news related to the cancellation of the Tokyo Olympic Games. However, they reacted more positively to news of postponement, although the increase in stock returns did not compensate for the drop following the earlier news suggesting the possible cancellation of the Tokyo Olympic Games. Additionally, a cross-sectional regression shows that positive market responses to news of postponement were intensified for Worldwide Olympic Partners and sponsors with a high ratio of individual and foreign shareholders. Because investors did not believe that postponement of the Olympics would have paid off all of the sponsors’ costs, future sponsors will need to place more emphasis on potential deferral risk when making investment decisions.

This study explores the possibilities of applying Google Trends to exchange rate forecasting. Specifically, we construct a sentiment index by using Google Trends to capture market sentiment in Japan and the United States. We forecast the USD/JPY rates using three structural models and two autoregressive models and examine whether our sentiment index can improve the predictive power of these models. We also check the robustness of the main results using the Taylor rule-based model and rolling regression methodology. The data we use run from January 2004 to August 2018, treating January 2004 to February 2011 as the training sample and March 2011 to August 2018 as the forecast sample. We find that the addition of the sentiment index into these models decreases the mean squared prediction error. We also test the sentiment indices of different word numbers and find that the 25- and 30-word indices perform best; in particular, the 30-word index improves all the models tested in this study.

This study aims to improve the prediction accuracy of the exchange rate model by changing how indices that capture market sentiment are constructed. We construct the sentiment indices (SIs) for the Japanese and American markets using the Google search volume index (SVI) for financial terms listed in the Japanese dictionary. For these SVIs, we select keywords based on the correlation between weekly changes in the yen–dollar rate and the SVI. We use 30, 20, and 10 keywords that are replaced at three different frequencies: 3 months, 6 weeks, and weekly. The training period is from January 2013 to June 2015, and the forecast period is from July 2015 to December 2017. We perform a rolling regression, which keeps the length of the reference period constant at 2 and a half years, based on the interest rate parity and autoregressive models for the predictions. We compare the prediction accuracy using the mean squared prediction error, Clark and West's tests of equal predictive accuracy, and the direction of change test. When the SIs are updated every 3 months and 6 weeks, neither the interest rate parity model nor the autoregressive model shows improved prediction accuracy, even if the SI is added. However, when the SIs are updated weekly, prediction accuracy improves in both the interest rate parity and the autoregressive models as the number of words used to construct theSI increases. We conclude that frequently updated SIs can improve the short-term prediction accuracy, while SIs updated less frequently may not.

© 2019 IEEE. This study examines how target companies react to the Internet flaming and how the reactions affect their stock prices, based on the 154 flaming events targeting Japanese listed companies from 2009 to 2018. Among 154 flaming events, target companies ignored the flaming and did not take any actions in 80 cases while actions were taken in 74 events. These actions include 49 official apologies, 18 objections, and 7 deletions of comments without appropriate apologies. Using a probit model, we demonstrate that flamed companies are more likely to take actions if their stock prices drop immediately after the flaming, the incident is published in the newspaper, or they have higher PBR or sales growth. We also show whether the effect dies down in the short term depends on responses of the flamed companies. When a company apologizes or deletes comments, its stock price tends to decrease significantly immediately after the outbreak of the flaming, but this decrease does not continue after a few days. In contrast, when the company objects to the flaming, its stock price starts to decline a few days after the flaming outbreak and continues to further decline.