We investigate the long-run return performance of non-U.S. firms that raise equity capital in U.S. markets. Overall, between 1982 and 1996, our sample of 333 global equity offerings with U.S. depositary receipt (ADR) tranches from 35 countries in Asia, Latin America, and Europe under-perform local market benchmarks of comparable firms by 8%–15% over the three years following issuance. We show that differences in long-run returns are related to the scope and magnitude of investment barriers that induce segmentation of capital markets around the world. While companies from markets with significant investment barriers for foreigners that issue equity on major U.S. exchanges outperform their benchmarks, those from segmented markets that issue equity in the U.S. by way of Rule 144A private placements significantly under-perform. We also show that inter-market competition for order flow in the post-issuance period affects long-run return performance. Post-issuance buy-and-hold abnormal returns are most significantly and positively related to the offering's ability to generate a larger share of U.S. trading volume.

Researchers are increasingly using data from the Nasdaq market to examine pricing behavior, market design, and other microstructure phenomena. The validity of any study that classifies trades as buys or sells depends on the accuracy of the classification method. Using a Nasdaq proprietary data set that identifies trade direction, we examine the validity of several trade classification algorithms. We find that the quote rule, the tick rule, and the Lee and Ready (1991) rule correctly classify 76.4%, 77.66%, and 81.05% of the trades, respectively. However, all classification rules have only a very limited success in classifying trades executed inside the quotes, introducing a bias in the accuracy of classifying large trades, trades during high volume periods, and ECN trades. We also find that extant algorithms do a mediocre job when used for calculating effective spreads. For Nasdaq trades, we propose a new and simple classification algorithm that improves over extant algorithms.

This study directly tests the ability of several competing methods to identify market buy and sell orders using intra-day quote and trade prices, and identifies factors that affect the accuracy of the methods. Lee and Ready's (1991) algorithm performs about the same as the tick test, but the performance of both methods is worse than expected. The results show that the use of either algorithm to classify trades can lead to significantly biased estimates of effective spreads and signed volume, but the tick test provides better estimates of effective spreads and signed volume than Lee and Ready's method.

While theoretical models predict a decrease in the cost of capital from depositary receipt offerings, the economic benefits of this liberalization have been difficult to quantify, Using a sample of 126 firms from 32 countries, we document a significant decline of 42% in the cost of capital. In addition, we show the decline is driven by the ability of U.S. investors to span the foreign security prior to cross-listing. Our findings support eh hypothesis that financial market liberalizations have significant economic benefits.

This paper argues that inferring long-horizon asset return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable. We illustrate the problems that can arise by reexamining the findings of Bekaert and Hodrick(1992), who detected evidence of in-sample predictability in international equity and foreign exchange markets using VAR methodology for a variety of countries from 1981–1989. The VAR predictions are significantly biased in most out-of-sample forecasts and are conclusively outperformed by a simple benchmark model at horizons of up to six months. This remains true even after corrections for small sample bias and the introduction Bayesian parameter restrictions. A Monte Carlo analysis indicates that the data are unlikely to have been generated by a stable VAR. This conclusion is supported by an examination of structural break statistics. We show that implied long-horizon statistics calculated from the VAR parameter estimates are very unreliable.

This paper examines the association between block ownership and market liquidity. Blockholders are believed to have access to private, value-relevant information via their roles as monitors of firms' operations. consistent with this, we find that firms with greater blockholder ownership, either by managers or external entities, have larger quoted spreads, effective spreads, adverse selection spread components, and smaller quoted depths.