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AbstractWe investigate the differences in market microstructure between U.S. and non‐U.S. stocks cross‐listed on the New York Stock Exchange using a sample of 316 pairs of matched stocks. We find that non‐U.S. stocks have wider spreads and larger adverse‐selection costs than U.S. stocks even after controlling for macro‐level institutional differences. Regression analysis shows that spreads and adverse‐selection costs are negatively correlated with institutional ownership and analyst followings. Thus, the higher spreads and adverse‐selection costs for non‐U.S. stocks can be partly explained by the lower institutional ownership and analyst following of non‐U.S. stocks. In addition, we find that although the spreads and adverse‐selection costs for non‐U.S. stocks are significantly higher before the implementation of Regulation Fair Disclosure (FD), the differences become even greater after Regulation FD, suggesting that Regulation FD has improved the information environment for U.S. stocks.

This paper presents an integrated time series model to analyze the interdependence and volatility among five major Asian stock markets, including Taiwan, Hong Kong, Korea, Singapore, and Japan. The model accounts for autoregression, cross correlation, error correction term, and GARCH effect. The evidence indicates that these five Asian stock markets follow at least one common stochastic trend. The stock returns for four of these Asian markets are contemporaneously correlated with those of Japan, while their correlations with the US stock returns take a one-day lag. Our evidence also shows some dynamic adjustment involving an error correcting process. Finally, the GARCH effect is present in all of the variance equations although we fail to find the GARCH-in-mean supported by the data.

ABSTRACTEconometric methods used in foreign exchange rate forecasting have produced inferior out‐of‐sample results compared to a random walk model. Applications of neural networks have shown mixed findings. In this paper, we investigate the potentials of neural network models by employing two cross‐validation schemes. The effects of different in‐sample time periods and sample sizes are examined. Out‐of‐sample performance evaluated with four criteria across three forecasting horizons shows that neural networks are a more robust forecasting method than the random walk model. Moreover, neural network predictions are quite accurate even when the sample size is relatively small.