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A number of US firms voluntarily de‐listed their stock from the Tokyo Stock Exchange (TSE) during the years 1977–97. We examine changes in trading volume, return volatility and implicit bid‐ask spreads in the U.S. stock exchange surrounding the de‐listing, and find evidence of an increase both in trading volume and bid‐ask spreads, particularly when the analysis is conditioned upon (a) trading volume on the TSE prior to de‐listing and (b) whether the de‐listing firm had operations in Japan. We also examine the daily stock price movement of the de‐listed firms and find a significantly negative price movement at the time of the de‐listing announcement, and also around the actual date of de‐listing. The results suggest a negative price response reflecting both a temporary information effect and also a more permanent valuation effect. Preliminary tests suggest that the latter is not related to the decrease in liquidity.

This paper explored the new features of emerging stock markets, in order to point out the most associated indicators of increasing stock return volatility, which may lead to instability of emerging markets. The study covers a sample of five geographical areas of emerging economies, including Mexico, Korea, South Africa, Turkey, and Malaysia. It used the backward multiple‐regression technique to examine the relationship between monthly changes of stock price indices as dependent variable and the associated predicting local as well as international variables, which represent possible causes of increasing price volatility and initiating crises in emerging stock markets. The study covered monthly data for a period of forty‐eight months from January 1997 to December 2000. The study revealed that stock trading volume and currency exchange rate respectively represent the highest positive correlation to the emerging stock price changes; thus represent the most predicting variables of increasing price volatility. International stock price index, deposit interest rate, and bond trading volume were moderate predicting variables for emerging stock price volatility. While changes in inflation rate showed the least positive correlation to stock price volatility, thus represents the least predicting variable.

The purpose of this paper is to investigate the lead‐lag relationships between three major stock markets (Tokyo, London, and New York) over the period 1997‐2007, using the return‐volatility variable. The study aims to use new data to test how one national stock market affects another national stock market, which is one focus of the market integration literature.

The paper employs the traditional regression model because three stock markets (Tokyo, London, and New York) are in different time zones and trading takes place sequentially. Specifically, the intraday return is calculated from the daily open and close prices.

This paper finds strong evidence that three stock markets are significantly interdependent: Tokyo leads London and New York; London leads New York and Tokyo; and New York leads Tokyo and London. In particular, the tie between London and New York is the strongest. Most of the author's results are consistent with those of previous studies.

First, there may exist profitable investment strategies in one market by observing the performance of another market that was just closed. Second, regulators should pay close attention to not only the domestic market but also the foreign markets and be ready to deal with adverse situations accordingly. Third, achieving international diversification in portfolio management may become more challenging because of high correlations between markets.

This paper extends the existing literature in market integration by using return volatility to test how one market affects another market. Our new evidence confirms that there are high degrees of linkages between Tokyo, London, and New York.

Big Bang, the deregulation of the London Stock Exchange in October of 1986, was accompanied by changed industry structure, altered regulatory environment, and new trading practices. In particular, trading was automated, and the floor of the Exchange was rapidly abandoned. Such massive discontinuities have produced opportunities in other industries. Although they may prove temporary, opportunities are arising to seek competitive advantage in London. Given the information‐intensive nature of securities trading, and the great differences in resources and experience with technology enjoyed by different firms, many of these advantages will entail the use of information technology.

The purpose of this paper is to determine the best conditional asset pricing model for the Tokyo Stock Exchange sample by utilizing long‐run daily data. It aims to investigate whether there are any other firm‐specific variables that can explain abnormal returns of the estimated asset pricing model.

The individual firm sample was used to conduct various cross‐sectional tests of conditional asset pricing models, at the same time as using test portfolios in order to confirm the mean variance efficiency of basic unconditional models.

The paper's multifactor models in unconditional forms are rejected, with the exception of the five‐factor model. Further, the five‐factor model is better overall than the Fama and French model and other alternative models, according to both the Gibbons, Ross, and Shanken test and the Hansen and Jagannathan distance measure test. Next, using the final conditional five‐factor model as the de facto model, it was determined that the turnover ratio and the size can consistently predict Jensen's alphas. The book‐to‐market ratio (BM) and the past one‐year returns can also significantly predict the alpha, albeit to a lesser extent.

In the literature related to Japanese data, there has never been a comprehensive test of conditional asset pricing models using the long‐run data of individual firms. The conditional asset pricing model derived for this study has led to new findings about the predictability of past one‐year returns and the turnover ratio.

This paper studies the exchange rate exposure of investments by the United States, Japan and Germany in the London International Stock Exchange (LSE) from 1982 to 1991. Japanese and German investments are fully exposed to their own exchange rates, and the US is “supernominally” exposed to its own exchange rate. No significant changes in exposure are associated with the Plaza and Louvre Accords. The 1987 worldwide stock market crash exhibits a significant decrease in US exposure, and increase in German exposure. US, Japanese and German investments are also fully exposed to their own exchange rates for the periods before and after the 1986 “Big Bang” in London, except that US investments are “supernominally” exposed in the pre‐Big Bang period.

The purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.

Six liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.

The result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.

In the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.

Evaluation of the foreign listing decision involves many complexities since it impacts a firm's financing, investment, and marketing decisions. In this paper, we identify major costs and benefits of foreign listing based on the available evidence and suggest evaluation of the foreign listing decision using an Adjusted Present Value method. We also discuss implications of some recent regulatory changes on the costs and benefits of foreign listing.

The study seeks to find the effect of financial leverage on the firm performance of non-financial companies listed in the Tokyo stock market.

The study collected data from 263 companies in the automobile and industrial producer sectors listed on the Tokyo stock exchange between 2001 and 2021. The generalized method of moments was used to estimate the effect of leverage on financial performance due to its ability to overcome the problems of endogeneity and autocorrelation.

The study found that the equity multiplier has a positive and statistically significant effect on return on assets (ROA), return on equity (ROE) and earning per share (EPS). The study discovered that the interest coverage ratio has a positive and statistically significant effect on ROA, ROE, EPS and Tobin’s Q. The results revealed that the degree of financial leverage and debt to earnings before interest, taxes, depreciation and amortization (EBITDA) have a negative and statistically significant effect on ROE, EPS and Tobin’s Q. The study also found that the capitalization ratios of the firms have a negative and statistically significant effect on ROA, ROE, EPS and Tobin’s Q.

The use of debt financing, which presents financial leverage, indicates that the companies can make enough earnings to pay off the interest and principal (debt service obligations), which were shown by the interest coverage ratio, as well as to pay all the long-term fixed expenses, which were shown by the fixed charge coverage ratio. Interest and fixed charge coverage have a positive statistically significant effect on the financial performance of automobile and industrial producer companies.

The study focused on the effect of financial leverage on financial performance by relying on pecking and trade-off theories to contribute to the existing body of literature in finance.

This study aims to analyse, from a corporate finance and governance perspective, the reasons why managers decide to delist their companies from a stock exchange. On the basis of the five hypotheses of voluntary delisting, this study examines why listed companies delist themselves voluntarily in the construction and real estate sectors.

By using actual data to examine contractors and real estate companies listed on the Tokyo Stock Exchange between 2004 and 2014, this study analyses whether these companies delist themselves voluntarily. The pooled binary logit model is used as the statistical method.

In both the construction and real estate sectors, the concentration of shareholders has a significantly positive effect on voluntary delisting, thus supporting the transfer of wealth effect hypothesis. In construction, market capitalisation has a significantly negative effect on voluntary delisting, thus supporting the maintenance cost reduction hypothesis. In the real estate sector, the ratio of market capitalisation to total assets has a significantly negative effect on voluntary delisting, thus supporting the undervalue elimination hypothesis.

By comparing the construction and real estate sectors, this study reveals both unique and common reasons for voluntary delisting in each sector. It also offers valuable insights to managers, regulators setting standards in securities markets and investors.