Today, it is required that the enterprise works on environmental conservation activities. Many enterprises struggle with these activities and announce the results. Under such social situation, we can suppose that the environmental information of the enterprise become the factor to have influence for the evaluation of the corporate value. This paper focuses on the greenhouse gas emission and the number of the environmental patent application as environmental information of the enterprise. The purpose of this paper is to examine the relations of these environmental measures and the corporate value in the stock market. Finally, this paper shows that the greenhouse gas emission and the number of the environmental patent application are associated with corporate value, and the greenhouse gas emission has explanatory power for stock volatility.

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Pythagorean expectation is a simple yet experimentally robust method that utilizes a single parameter for predicting the winning percentages of sports teams based on the score ratio. The score ratio can be defined as runs scored divided by runs allowed, and the Pythagorean probability can be identified as a parameter of logistic distribution. In this study, we aim to estimate the winning probability by incorporating actual sports data and to interpret the Pythagorean probability by using the generalized matching law of behavioral economics. In addition, the probabilistic models for runs scored and runs allowed, which can be considered as two independent Gumbel distributions, are estimated with maximum likelihood parameters from the actual data of a student baseball league. We compare four types of models: the Gumbel model, Poisson model, Pythagorean model, and histogram-based resampling model. For the Gumbel model, three types of methods are applied, namely maximum likelihood (ML), ordinary least squares (OLS), and EAP (expected a posteriori). Further, a series of Monte Carlo simulations shows that the histogram is best, and the Pythagorean is quite good; however, the Gumbel models slightly outweigh the Pythagorean model in terms of the mean square prediction error.

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This paper investigates stock price reaction to going concern withdrawal announcements in japan. The sample consists of 4 withdrawal announced between 2009 and 2016.Each announcement had to be free of potentially contaminating information that might have affected stock prices. Also, adequate stock price data had to be readily available for all firms. To measure the impact of the announcement, event study method was employed by which normal returns were compared to the raw returns during the announcement period, measuring abnormal returns. Normal returns were calculated for a period prior to the time closely surrounding the withdrawal announcement, adopting mean adjusted model, market adjusted model and market model. To more accurately measure the impact of the announcement, not daily returns but intra-daily returns were used. Results indicate an increase on abnormal returns attributable to the withdrawal announcement in all samples. But soon afterwards intra-daily abnormal returns are reversed in some samples. So the market is likely to over-reaction to withdrawal announcements.

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