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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.

Using intraday prices for the S&P 500 and Nikkei Stock Average stock indexes and aggregate trading volume for the New York and Tokyo Stock Exchanges, we show how short-run comovements between national stock market returns vary over time in a way related to the trading volume and liquidity in those markets. We frame our analysis in the context of the heterogeneous-agent models of trading developed by Campbell, Grossman and Wang (1993) and Blume, Easley and O’Hara (1994) and Wang (1994) which predict that trading volume acts as a signal of the information content of a given price move. While we find that there exists significant short-run dependence in returns and volatility between Japan and the U.S., we offer new evidence that these return “spillovers” are sensitive to interactions with trading volume in those markets. The cross-market effects with volume are revealed in both close-to-open and open-to-close returns and often exhibit non-linear patterns that are not predicted by theory.

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 investigates Japanese stock returns for the Friday, Monday and Tuesday surrounding U.S. Monday holiday closures. The empirical results show that U.S. Monday closures have a statistically significant impact on Japanese stock return dynamics for surrounding trading days, but do not support the hypothesis that the U.S. Monday and Japanese Tuesday effects are related. Potential explanations for the occurrence and then disappearance of the Japanese Tuesday effect rely on market microstructure properties unique to the Tokyo market. The spillover effects from New York to Tokyo have been increased in density over time, which is attributed to market structural changes represented by the introduction of Nikkei 225 index futures on the SIMEX in 1986.

This study examines return and volatility spillovers from the US and Japanese stock markets to three South Asian capital markets – (i) the Bombay Stock Exchange, (ii) the Karachi Stock Exchange and (iii) the Colombo Stock Exchange. We construct a univariate EGARCH spillover model that allows the unexpected return of any particular South Asian market to be driven by a local shock, a regional shock from Japan and a global shock from the USA. The study discovers return spillovers in all three markets, and volatility spillovers from the US to the Indian and Sri Lankan markets, and from the Japanese to the Pakistani market. Regional factors seem to exert an influence on these three markets before the Asian financial crisis but the global factor becomes more important in the post-crisis period.

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.