THE INTRADAY INTERDEPENDENCE STRUCTURE BETWEEN U.S. AND JAPANESE EQUITY MARKETS

Contrary to the efficient market hypothesis, previous research documents a significant correlation between lagged U.S. close-to-close stock market returns and current open-to-close Japanese equity market returns. We find that the significant correlation is limited to the first hour of Japanese trading, with subsequent hourly returns independent of lagged U.S. returns. This evidence suggests that the documented significant correlation is attributable to a sticky Japanese opening value associated with the use of nonsynchronous index data.     

New Findings Regarding Day-of-the-Week Returns over Trading and Non-Trading Periods: A Note

This paper decomposes daily close to close returns into trading day and non-trading day returns. We discover that all of the average negative returns from Friday close to Monday close documented in the literature for stock market indexes occurs during the non-trading period from Friday close to Monday open. In addition, average trading day returns (open to close) are identical for all days of the week. January/firm size/turn-of-the-year anomalies are shown to be interrelated with day-of-the-week returns.     

Empirical Identification of Non-Informational Trades Using Trading Volume Data

This paper empirically identifies non-informational and informational trades using stock returns and trading volume data of the U.S., Japanese, and U.K. stock markets and five individual firms. We achieve the identification by imposing a restriction from theoretical considerations. Our results show that trading volume is mainly driven by non-informational trades, while stock price movements are primarily driven by informational trades. We also find that, around the 1987 stock market crash, trading volumes due to non-informational trades increased dramatically, while the decline in stock market prices was due mainly to informational trades. Increases in volatilities both in returns and in trading volumes during and after the crash are mainly due to non-informational trades. Regarding the trading volume-serial correlation in the stock returns relationship, we find evidence that is consistent with theoretical predictions that non-informational components can account for high trading volume accompanied by a low serial correlation of stock returns.     

Price discovery in the round-the-clock U.S. Treasury market

We evaluate the efficacy of price discovery in the round-the-clock U.S. Treasury market. Using a comprehensive intraday database, we explore informational role of trades over the 24-hour day. We find that information asymmetry is generally highest in the preopen period and lowest in the postclose period. Information asymmetry in the overnight period is comparable to that in the regular trading period. However, on days with macroeconomic announcements, information asymmetry peaks shortly after the news release at 8:30. Moreover, information asymmetry is higher on Monday morning and higher immediately before than after the open of U.S. Treasury futures trading. Although volume is low after hours and trading cost is relatively high, overnight trading generates significant price discovery. Results suggest that overnight trading activity is an important part of the Treasury price discovery process.     

Is VIX an investor fear gauge in BRIC equity markets?

This study examines the intertemporal relationships between CBOE market volatility index (VIX) and stock market returns in Brazil, Russia, India, and China (BRIC), and between VIX and U.S. stock market returns, to uncover if VIX serves as an investor fear gauge in BRIC and U.S. markets. We conduct the VIX-returns analysis for the 1993–2007 period.Our results suggest a strong negative contemporaneous relation between daily changes (innovations) in VIX and U.S. stock market returns. This relation is stronger when VIX is higher and more volatile. A significant negative contemporaneous relation between VIX and equity returns also exists for China and Brazil during 1993–2007 and for India during 1993–1997. Similar to the U.S. market, the immediate negative relation between the Brazilian stock returns and VIX changes is much stronger when VIX is both high and more volatile. Our results also indicate a strong asymmetric relation between innovations in VIX and daily stock market returns in U.S., Brazil, and China, suggesting that VIX is more of a gauge of investor fear than investor positive sentiment. However, the asymmetric relationship between stock market returns and VIX is much weaker when VIX is large and more volatile. These results have potential implications for portfolio diversification and for stock market and option trading timing in the equity markets of Brazil, India, and China. Overall, our results indicate that VIX is not only an investor fear gauge for the U.S. stock market but also for the equity markets of China, Brazil, and India.     

Economic and financial crises and the predictability of U.S. stock returns

We argue that the use of publicly available and easily accessible information on economic and financial crises to detect structural breaks in the link between stock returns and macroeconomic predictor variables improves the performance of simple trading rules in real time. In particular, our results suggest that accounting for structural breaks and regime shifts in forecasting regressions caused by economic and financial crises has the potential to increase the out-of-sample predictability of stock returns, the performance of simple trading rules, and the market-timing ability of an investor trading in the U.S. stock market.     

Presidential Address: The Cost of Active Investing

I compare the fees, expenses, and trading costs society pays to invest in the U.S. stock market with an estimate of what would be paid if everyone invested passively. Averaging over 1980–2006, I find investors spend 0.67% of the aggregate value of the market each year searching for superior returns. Society's capitalized cost of price discovery is at least 10% of the current market cap. Under reasonable assumptions, the typical investor would increase his average annual return by 67 basis points over the 1980–2006 period if he switched to a passive market portfolio.     

A comprehensive investigation on the predictive power of economic policy uncertainty from non-U.S. countries for U.S. stock market returns

This study uses economic policy uncertainty (EPU) indices for ten developed countries, three diffusion models, and five combination methods to forecast excess returns in the U.S. stock market. It shows empirically that, over the period January 1997 to January 2022, non-U.S. EPU indices have better predictive power for U.S. equity market excess returns than the U.S. EPU index itself. This illustrates how economic information from international markets can affect the U.S. stock market. This finding challenges the extensively recognized view that the U.S. is where important market signals are initially transmitted to other markets, suggesting that this belief is incomplete. Our outcomes are robust to a battery of tests covering model selection, model specification, forecast horizons, and the pandemic period, and their economic values are assessed. The findings are essential for the financial field to confront future fierce situations and crises.     

Valuation effects of international stock exchange listings

A comprehensive data set consisting of 346 U.S. firm stock listings on ten different stock exchanges is examined in order to determine the valuation consequences of listing on a foreign stock exchange. For the sample of U.S. firms listing abroad, abnormal returns in U.S. trading were: (1) positive around the date of acceptance on the foreign exchange; (2) negative on the first trading day; and (3) negative in the post-listing period for firms listing on the Tokyo and Basel exchanges. Tests for the equality of stock return variances between event periods and market model estimation periods failed to reveal a definitive impact.     

Market Efficiency and the Returns to Simple Technical Trading Rules: New Evidence from U.S. Equity Market and Chinese Equity Markets

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in U.S. markets and also compare the excess returns between U.S. market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in U.S. equity index after 1975. During the 1990s break-even costs turned to be negative, –0.06%, even failing to beat a buy-holding strategyin U.S. equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.     

The Response of Dynamic Herd Behavior to Domestic and U.S. Market Factors: Evidence from the Greater China Stock Markets

ABSTRACT

We investigate the dynamic reaction of stock market herding in China, Hong Kong, and Taiwan to unexpected shocks from domestic and U.S. market factors. In China and Taiwan, herding is more pronounced, and the investors tend to herd with the rising stock market returns. Overconfident investors will herd on the subsequent trading days under market stress. Compared with the response to the domestic market factors, the responses of herding in the Greater China stock market to the U.S. market factors are weaker. After the 2007–8 financial crisis, the U.S. market factors highly explain the forecast error variance of herding in the Shanghai A-share and Taiwan markets.     

Multimarket Trading and Liquidity: Theory and Evidence

We develop a new model of multimarket trading to explain the differences in the foreign share of trading volume of internationally cross‐listed stocks. The model predicts that the trading volume of a cross‐listed stock is proportionally higher on the exchange in which the cross‐listed asset returns have greater correlation with returns of other assets traded on that market. We find robust empirical support for this prediction using stock return and volume data on 251 non‐U.S. stocks cross‐listed on major U.S. exchanges.     

Trading and Returns under Periodic Market Closures

This paper studies how market closures affect investors' trading policies and the resulting return-generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U-shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open-to-open returns than close-to-close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs.     

Transmission of Stock Returns and Volatility between the U.S. and Japan: Evidence from the Stock Index Futures Markets

In this paper, we examine the nature of transmission of stock returns and volatility between the U.S. and Japanese stock markets using futures prices on the S&P 500 and Nikkei 225 stock indexes. We use stock index futures prices to mitigate the stale quote problem found in the spot index prices and to obtain more robust results. By employing a two-step GARCH approach, we find that there are unidirectional contemporaneous return and volatility spillovers from the U.S. to Japan. Furthermore, the U.S.'s influence on Japan in returns is approximately four times as large as the other way around. Finally, our results show no significant lagged spillover effects in both returns and volatility from the Osaka market to the Chicago market, while a significant lagged volatility spillover is observed from the U.S. to Japan. This revised version was published online in August 2006 with corrections to the Cover Date.     

Exchange rates,interventions, and the predictability of stock returns in Japan

Using monthly Japanese data for the period 1991–2005, we examined the link between exchange rate movements and stock returns. We found that exchange rate movements per se do not help to explain stock returns. There is, however, evidence of in-sample predictability if one accounts for the interventions of the Japanese monetary authorities in the foreign exchange market. This evidence does not indicate a violation of market efficiency insofar as investors cannot use information on interventions to systematically improve the performance of simple trading rules based on out-of-sample forecasts of stock returns.     

Why Do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements

This article explores the fundamental factors that affect cross-country stock return correlations. Using transactions data from 1988 to 1992, we construct overnight and intraday returns for a portfolio of Japanese stocks using their NYSE-traded American Depository Receipts (ADRs) and a matched-sample portfolio of U. S. stocks. We find that U. S. macroeconomic announcements, shocks to the Yen/Dollar foreign exchange rate and Treasury bill returns, and industry effects have no measurable influence on U.S. and Japanese return correlations. However, large shocks to broad-based market indices (Nikkei Stock Average and Standard and Poor's 500 Stock Index) positively impact both the magnitude and persistence of the return correlations.     

Transmission of shocks from cross-listed markets to the return and volatility of domestic stocks

This paper examines the transmission of information from German and the U.S. markets to domestic markets using daily price and volume data of 264 stocks from 26 countries that are traded in their home country and cross-listed outside their home market as depository receipts (DRs); in the German market as Global Depository Receipts (GDRs) and in the U.S. as American Depository Receipts (ADRs). We identify days with significant news arrivals in a market through minimum thresholds for both significant absolute price change and trading volume. DR returns and volatilities are affected by the shocks in the markets where they are cross-listed controlling for domestic shocks. Contemporaneous and/or lagged shocks to the cross-listed markets are transmitted to domestic stock returns and volatilities. South American DRs are affected mostly by U.S. shocks, while Eastern European DRs show greater reaction to the German shocks.     

HIGH-TECH IPOS: A TALE OF TWO CONTINENTS

The typical price behavior of an initial public offering (IPO), consisting of a price upsurge on the first trading day followed by subpar performance in the (longer-run) after-market, is one of the most intriguing puzzles in corporate finance. This study focuses on high-tech IPOs in Europe and the U.S. over the period 1998–2001, both to compare the European and U.S. IPO markets and to determine how the price behavior of high-tech IPOs compares to that of IPOs in general. Average initial-day returns were 39% and 64% for the European and U.S. samples, respectively. The median returns were significantly lower, however, indicating that the sample averages are affected by a small group of exceptionally strong performers. But, for the first full year of trading, the median market-adjusted returns were negative for both samples. Not surprisingly, this substandard aftermarket performance was most apparent in companies that failed to generate operating profits.
As with IPOs in general, high-tech IPOs showed higher initial-day returns in "hot" markets than in "cold." Strong first-day performance was a good predictor of IPO volume in the high-tech market, with strong first-day returns triggering a flood of IPOs in subsequent months. Overall, then, the authors' study concludes that the price behavior of high-tech IPOs provides an exaggerated version of the general tendency of IPOs to be underpriced initially but underperform over the longer term.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

French and U.S. trading of cross-listed stocks around the period of U.S. decimalization: Volume, spreads, and depth effects

We analyze how U.S. decimalization affects stocks cross-listed in France (Euronext) and the U.S. (NYSE). The French stocks examined are much larger than the non-U.S. stocks examined in prior studies of decimalization, and their U.S. trading is likely to be dominated by institutions. So, we explore whether a reduction in depths in the U.S. due to decimalization makes the U.S. market less competitive for institutions trading these French stocks. We find evidence consistent with the above. First, the average NYSE trade size for these stocks relative to that on Euronext declines substantially after decimalization. Second, we categorize individual trades by the number of shares traded. We find that mainly driven by large trades, the NYSE proportion of trading of French firms declines markedly after decimalization. Third, using regression analysis, we find that the decline in the U.S. share of institutional trading volume is significantly positively related with the decline in NYSE depths relative to Euronext, and the decline is greater for French firms. Overall, we find consistent results indicating a migration of institutional order flow in French firms to France after NYSE decimalization. Also, intraday analysis indicates that the institutional volume in both France and the U.S. is greatest when both the markets are open.