C. Sherman Cheung

Disclosure Environment and Listing on Foreign Stock Exchanges

The benefits of listing a companys stock on a foreign exchange to achieve better global market integration have been quite extensively examined. What has been overlooked in the finance literature is an attempt to explain why the New York Stock Exchange (NYSE) tends to be bypassed in favor of the London market and other exchanges when firms select foreign exchanges for listing. This paper explains the behavior of firms in their selection of foreign stock markets for listing by using a signalling model. Another purpose of this study is to address the current dispute between the NYSE and the Securities and Exchange Commission (SEC) regarding the desire of the NYSE to relax its registration requirements in order to gain more listings by foreign companies.

A note on the transmission of public informtion across inetrnational stock markets

Abstract Previous studies have established that public information cannot explain the decrease in return volatility during the closing of a stock exchange. The present study shows that the failure of the public information hypothesis in explaining the trading/nontrading volatility differential is merely a domestic phenomenon. The manifestation of public information in an international setting is very different. Specifically, the absence of price information due to the closing of the US market affects both volatility and trading volume in the Canadian market.

An Empirical Investigation of the Random Character of Annual Earnings

The conclusion that annual accounting earnings follow a random walk is fairly well established in the literature. Still, this result is not all that compelling. The early studies by Little (1962), Lintner and Glauber (1978), and Ball and Watts (1972) reached their conchusion primarily by examining the serial correlation of earnings changes.’ However, the analysis of serial correlation of changes in earnings makes the implicit and simplistic assumption that the underlying time series model is first-order autoregressive (AR[ 13). Later studies by Watts and Leftwich (1977); Lookabill(l976); and Albrecht, Lookabill, and McKeown (1977) reached the random walk conclusion by comparing the forecast accuracy of the random walk with the best fitted Box-Jenkins models. There are three problems with this approach. First, standard Box-Jenkins techniques generally truncate the data beyond the sample period in the time domain. Truncation leads to serious problems in the spectral estimation of the data sequence. This problem, called “aliasing,” is especially pronounced when the sampling period is relatively short, as in the case of annual earnings data. The effect can be compared with viewing an infinitely wide scenery through a window of finite width. The abrupt cutoff of the scene at the edge of the window introduces nonexistent high-frequency components in the spectral composition of the actual scene. As a result, relatively long time series are required. On the other hand, using long time series of annual earnings may result in poor forecasts because of structural change in the series. Second, and related to the first point, the random walk, unlike Box-Jenkins models, uses only the most recent year’s earnings data, thereby reducing the influence of extreme observations arising

A mean-Gini approach to asset allocation involving hedge funds

In response to common criticisms on the appropriateness of mean-variance in asset allocation decisions involving hedge funds, we offer a mean-Gini framework as an alternative. The mean-Gini framework does not require the usual normality assumption concerning return distributions. We also evaluate empirically the differences in allocation outcomes between the two frameworks using historical data. The differences turn out to be significant. The evidence thus confirms the inappropriateness of the mean-variance framework and enhances the attractiveness of mean-Gini for this asset class.

The Pricing of Exchange Rate Risk and Stock Market Segmentation: The Canadian Case

Empirical evidence by Eun and Resnick (1988), among others, has demonstrated the significance of exchange rate risk in the international asset allocation and they have noted that the risk is nondiversifiable. Yet, exchange rate risk was found by Jorion (1991) to be a risk factor that is not priced in the U.S. stock market. This study reexamines such counterintuitive results using data from the Toronto Stock Exchange. The evidence here weakly supports the pricing of the exchange rate risk. Further, the sample period in this study coincides with Jorions to ensure that both studies examine the pricing of the exchange rate risk in the same global economic environment. The significant pricing of exchange rate risk in Canada and the insignificant pricing in the U.S. imply the possibility of market segmentation.

International investment and currency risk

Abstract A normative model for selecting optimal international portfolios is presented. The model allows investors to pursue an active portfolio strategy while obtaining the benefits of international diversification. Because currency risk is explicitly recognized, its importance can be assessed by investors in making investment decisions.

CORRELATION BEHAVIOR OF EMERGING MARKETS

Using a market model of international equity returns, which fully incorporates the regime switching and heteroskedasticity effects, we conduct an empirical study on the asymmetric behavior of 31 emerging equity markets across the different regimes of both the global and the local markets. Asymmetric correlation is found to be much weaker than that among developed markets as documented in the recent studies. There is little evidence of performance enhancement by possessing information on asymmetric correlation in international asset allocation strategies involving emerging markets.

The uneasy case for real estate investments

Abstract Real estate investment has been generally accepted as a value-adding proposition for a portfolio investor. Such an impression is not only shared by investment professionals and financial advisors but also appears to be supported by an overwhelming amount of research in the academic literature. The benefits of adding real estate as an asset class to a well-diversified portfolio are usually attributed to the respectable risk-return profile of real estate investment together with the relatively low correlation between its returns and the returns of other financial assets. By using the regime-switching technique on an extensive historical dataset, we attempt to look for the statistical evidence for such a claim. Unfortunately, the empirical support for the claim is neither strong nor universal. We find that any statistically significant improvement in risk-adjusted return is very much limited to the bullish environment of the real estate market. In general, the diversification benefit is not found to be statistically significant unless investors are relatively risk averse. We also document a regime-switching behavior of real estate returns similar to those found in other financial assets. There are two distinct states of the real estate market. The low-return (high-return) state is characterized by its high (low) volatility and its high (low) correlations with the stock market returns. We find this kind of dynamic risk characteristics to play a crucial role in dictating the diversification benefit from real estate investment.