A stochastic dominance analysis of yen carry trades

Yen carry trades have made headline news for over a decade. We examine the profitability of such trades for the period 2001–2009. Yen carry trades generated high mean returns and Sharpe ratios prior to the recent financial crisis. They continued to outperform major stock markets for the full sample period. Given the non-normality of carry trade returns, we apply non-parametric tests based on stochastic dominance (SD) to evaluate whether the high returns of yen carry trades are compatible with risk as reflected in returns on US and global stock market indices. We apply a general test for SD developed recently by Linton, Maasoumi and Whang (2005) to six currencies as well as portfolios of these currencies. For a large class of risk-averse investors, profits from yen carry trades cannot be attributed to risks.     

Universal price impact functions of individual trades in an order-driven market

The trade size ω has a direct impact on the price formation of the stock traded. Econophysical analyses of transaction data for the US and Australian stock markets have uncovered market-specific scaling laws, where a master curve of price impact can be obtained in each market when stock capitalization C is included as an argument in the scaling relation. However, the rationale of introducing stock capitalization in the scaling is unclear and the anomalous negative correlation between price change r and trade size ω for small trades is unexplained. Here we show that these issues can be addressed by taking into account the aggressiveness of orders that result in trades together with a proper normalization technique. Using order book data from the Chinese market, we show that trades from filled and partially filled limit orders have very different price impacts. The price impact of trades from partially filled orders is constant when the volume is not too large, while that of filled orders shows power-law behavior r?～?ω^α with α?≈?2/3. When returns and volumes are normalized by stock-dependent averages, capitalization-independent scaling laws emerge for both types of trades. However, no scaling relation in terms of stock capitalization can be constructed. In addition, the relation α?=?α_ω/α _r is verified for some individual stocks and for the whole data set containing all stocks using partially filled trades, where α_ω and α _r are the tail exponents of trade sizes and returns. These observations also enable us to explain the anomalous negative correlation between r and ω for small-size trades.     

Inferring the Components of the Bid-Ask Spread: Theory and Empirical Tests

The relation between the square of the quoted bid-ask spread and two serial covariances—the serial covariance of transaction returns and the serial covariance of quoted returns—is modeled as a function of the probability of a price reversal, π, and the magnitude of a price change, ?, where ? is stated as a fraction of the quoted spread. Different models of the spread are contrasted in terms of the parameters, π and ?. Using data on the transaction prices and price quotations for NASDAQ/NMS stocks, π and ? are estimated and the relative importance of the components of the quoted spread—adverse information costs, order processing costs, and inventory holding costs—is determined.     

What Drives Shareholder Reaction and Wealth Effect in Block Trades? Evidence from the Warsaw Stock Exchange

The main objective of this article is to present the determinants of shareholder reaction to block trades and their wealth effect on the Warsaw Stock Exchange. The positive abnormal returns obtained for the entire sample indicate that block trades create shareholder value. Shareholders reacted positively to block trades without a control transfer in the Polish market, and their reaction was stronger than in the US market. Abnormal returns of block trades concluded at a discount were twice as high as those for the entire sample. Moreover, cross-border block trades had a negative impact on shareholder value creation, as did financial investors as an acquirer. However, cumulative average abnormal returns (CAARs) were driven up by the relative power of minority shareholders (ocean) prior to the transaction. The absolute size of the block acquired by an investor was also observed to have a positive impact on price rises and abnormal returns.     

Informed Futures Trading and Price Discovery: Evidence from Taiwan Futures and Stock Markets

This article focuses on the information effects between the futures market and its spot market. Intraday data are used to investigate the lead-lag relationships between the returns and trading activity of Taiwan stock index futures and the spot returns. We focus on the transmission direction and the sources of information. Consistent with most previous studies, our results show that other than the contemporaneous relationship predicted by carry-cost theory and efficient market theory, futures returns significantly lead spot returns, which implies that informed trades may occur in the futures market. Using private transaction information, net open buy, as a proxy for futures trading activity and distinguishing different types of futures traders, we find that foreign institutional traders are the major source of informed trades because their trading has predictive power for future movements in both spot and futures prices. Traders in other categories are information laggards.     

Return Volatility,Trading Imbalance and the Information Content of Volume

In this paper, we examine the relationship between volume and return volatility using the transaction data. We introduce transaction and volume imbalance measures to capture the information content of trades. These two information measures are shown to have a strong explanatory power for return volatility and contain incremental information about the asset values over and above that conveyed by the size and frequency of trades. Also, return volatility is significantly correlated with the percentage of trading volume taking place at NYSE. This result suggests that NYSE trades are more informative and contribute more to price discovery. There is evidence that price discovery concentrates in more heavily traded stocks, particularly the Dow Jones Stocks. Finally, return volatility is found to be persistent at the intraday level. The persistence level is higher for less frequently traded stocks. Return volatility also exhibits temporal variations. In particular, return volatility is significantly higher in the opening half-hour for less frequently traded stocks. Thus, stocks with different frequencies of trades may follow different volatility processes.     

Asymmetry in Stock Returns Following Block Trades on the Australian Stock Exchange: A Note

A number of recent US. studies have examined the price impact of large (block) trades using intraday data. A major finding is that the price movement following block trades continues upwards following purchases but reverses following sales. This asymmetry in price behaviour, which suggests that block sellers pay a liquidity premium while block buyers do not, has been described as 'intriguing' and a 'key puzzle'. The purpose of this note is to determine whether the phenomenon exists on the Australian Stock Exchange. Evidence consistent with the 'puzzling' asymmetry is shown to exist when returns are measured from the block trade until the close of trade. Contrary to US. findings, which have shown that prices appear to reverse following both block purchases and sales in transaction time analysis, the asymmetry in price behaviour is also demonstrated to exist in transaction returns for the ASX. All results are found to be robust to a number of research design innovations and data partitions.     

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.     

Size and investment performance: a research note

This article examines the performance of actively managed Australian equity funds and the extent to which both fund size and manager size are related to risk-adjusted returns. Larger investment managers, by definition, engage in higher trade volume. The literature documents that transaction costs and trade difficulty increase with trade size, given difficulties associated with 'large' trades and their potential market impact on security prices. Therefore, ceteris paribus , large orders are consistent with lower levels of efficiency in trade execution and higher transaction costs. While larger investment managers may experience material disadvantages relative to their smaller counterparts, the Australian literature to date has largely ignored the issues of asset size and the long run performance of investment offerings. This article, employing returns and fund size data that control for survivorship bias, documents that while large retail active equity funds earn higher risk-adjusted returns (after expenses) than small funds, the difference in mean performance is not significantly different. In the institutional sphere, the study also finds no statistically significant performance differences (net of expenses) between funds on the basis of portfolio size. These findings suggest the hypothesis that performance declines with fund size is not supported empirically.     

Time and dynamic volume–volatility relation

This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive (VAR) model is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid–ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.     

HOURLY RETURNS,VOLUME, TRADE SIZE,AND NUMBER OF TRADES

Previous research identifies a causal relationship between returns and trading volume. But volume has two components: number of trades and number of shares per trade. In this paper the relationship between each of these volume components and returns is examined and the number of trades is found to be the dominant component. Results show that return activity in a period is associated with the level of trading frequency in a subsequent period and also with the number of shares in a subsequent period. This is consistent with small traders reacting to returns while professional traders largely ignore previous returns in their trading.     

Trading Better Versus Making More: Evidence from an Emerging Market

Using transaction data from Egypt, we examined the controversy over which investor—domestic or foreign—has superior trading performance in emerging markets. We account for informational and behavioral differences across investors by classifying them by origin and type and comparing their performance in trade execution versus profitability. Domestic institutions execute trades at the best prices with the greatest advantage against foreign institutions. This advantage is reduced when foreign institutions focus on large firms and trades. Profitability analysis revealed, however, that domestic investors accrue significant losses against foreign investors, suggesting that trading better does not necessarily translate into making more money.     

Short-run Returns around the Trades of Corporate Insiders on the London Stock Exchange

Previous work examined the long-run profitability of strategies mimicking the trades company directors in the shares of their own company, as a way of testing for market efficiency. The current paper examines patterns in abnormal returns in the days around these trades on the London Stock Exchange.
We find movements in returns that are consistent with directors engaging in short-term market timing. We also report that some types of trades have superior predictive content over future returns. In particular, medium-sized trades are more informative for short-term returns than large ones, consistent with Barclay and Warner's (1993) 'stealth trading' hypothesis whereby informed traders avoid trading in blocks.
Another contribution of this study is to properly adjust the abnormal return estimates for microstructure (spread) transactions costs using daily bid-ask spread data. On a net basis, we find that abnormal returns all but disappear.     

Evidence of a leadership role in the Chilean stock exchanges

In our study of the two leading stock exchanges in Chile—the Santiago Stock Exchange (SSE) and the Electronic Stock Exchange (ESE)—we find that the SSE exhibits a leadership role over the ESE in terms of transmission of returns and volatility. These results indicate that prior empirical studies showing that the New York Stock Exchange (NYSE) leads other exchanges is a result of market structure factors rather than a ‘U.S. effect’. The factors that lead to a leadership role are deeper liquidity, more effective monitoring, and a greater concentration of speculators. Markets possessing these factors will be the first to attract investors and will lead other exchanges. Although, the leadership role of the SSE creates a predictable flow of information between markets, the ability to create arbitrage opportunities is mitigated by transaction costs and the current order-processing mechanism in Chile. However, our results suggest that a market's leadership could result in arbitrage opportunities in countries with lower transaction costs or different order-processing mechanisms.     

Recent trends in trading activity and market quality

We explore the sharp uptrend in recent trading activity and accompanying changes in market efficiency. Higher turnover has been associated with more frequent smaller trades, which have progressively formed a larger fraction of trading volume over time. Evidence indicates that secular decreases in trading costs have influenced the turnover trend. Turnover has increased the most for stocks with the greatest level of institutional holdings, suggesting professional investing as a key contributor to the turnover trend. Variance ratio tests suggest that more institutional trading has increased information-based trading. Intraday volatility has decreased and prices conform more closely to random walk in recent years. The sensitivity of turnover to past returns has increased and cross-sectional predictability of returns has decreased significantly, revealing a more widespread use of quantitative trading strategies that allow for more efficient securities prices.     

Estimating daily volatility in financial markets utilizing intraday data

This study proposes a new approach to the estimation of daily realised volatility in financial markets from intraday data. Initially, an examination of intraday returns on S&P 500 Index Futures reveals that returns can be characterised by heteroscedasticity and time-varying autocorrelation. After reviewing a number of daily realised volatility estimators cited in the literature, it is concluded that these estimators are based upon a number of restrictive assumptions in regard to the data generating process for intraday returns. We use a weak set of assumptions about the data generating process for intraday returns, including transaction returns, given in den Haan and Levin [den Haan, W.J., Levin, A., 1996. Inferences from parametric and non-parametric covariance matrix estimation procedures, Working paper, NBER, 195.], which allows for heteroscedasticity and time-varying autocorrelation in intraday returns. These assumptions allow the VARHAC estimator to be employed in the estimation of daily realised volatility. An empirical analysis of the VARHAC daily volatility estimator employing intraday transaction returns concludes that this estimator performs well in comparison to other estimators cited in the literature.     

INTRADAY TRADING PATTERNS IN THE EQUITY OPTIONS MARKETS

Based on the analysis of data for over two million options transactions on the Chicago Board of Options Exchange, we find significant U-shaped intraday patterns in trading volume, transaction size, proportion of trades at the ask or bid, and other variables in the equity options market. These previously undocumented intraday temporal patterns are largely consistent with exogenous temporal influences on transactions volume in anticipation of and following nontrading periods. These patterns are also largely consistent with strategic trading endogenous to the market and are related to interactions between informed and liquidity or noise traders.     

Trading volume with private valuation: evidence from the ex-dividend day

We test a theory of the interaction between investors' heterogeneity,risk, transaction costs, and trading volume. We take advantageof the specific nature of trading motives around the distributionof cash dividends, namely the costly trading of tax shields.Consistent with the theory, we show that when trades occur becauseof differential valuation of cash flows, an increase in riskor transaction costs reduces volume. We also show that the nonsystematicrisk plays a significant role in determining the volume of trade.Finally, we demonstrate that trading volume is positively relatedto the degree of heterogeneity and the incentives of the variousgroups to engage in trading.     

Autocorrelation and partial price adjustment

Stock return autocorrelation contains spurious components—the nonsynchronous trading effect (NT) and bid–ask bounce (BAB)—and genuine components—partial price adjustment (PPA) and time-varying risk premia (TVRP). We identify a portion that can unambiguously be attributed to PPA, using three key ideas: theoretically signing and/or bounding the components; computing returns over disjoint subperiods separated by a trade to eliminate NT and greatly reduce BAB; and dividing the data period into disjoint subperiods to obtain independence for statistical power. Analyzing daily individual and portfolio return autocorrelations in sixteen years of NYSE intraday transaction data, we find compelling evidence that PPA is a major source of the autocorrelation.     

Time and the Process of Security Price Adjustment

This paper delineates the link between the existence of information, the timing of trades, and the stochastic process of prices. We show that time affects prices, with the time between trades affecting spreads. Because the absence of trades is correlated with volume, our model predicts a testable relation between spreads and normal and unexpected volume, and demonstrates how volume affects the speed of price adjustment. Our model also demonstrates how the transaction price series will be a biased representation of the true price process, with the variance being both overstated and heteroskedastic.