The information content of trading halts

We investigate the impact of trading halts of NYSE-listed stocks on informationally related securities that continue to trade during the period of the halt. Informational relationships are established for companies in the same four-digit SIC industry based on the correlation of returns, volume, volatility, and the adverse selection components of spreads. We find a significant liquidity impact on informationally related securities with spreads and price impact of trades having substantial increases. However, we also find that quoted depths, the number of trades, and trade volume significantly increase. Our results are consistent with the trading halt model of Spiegel and Subrahmanyam [2000. Asymmetric information and news disclosure rules. Journal of Financial Intermediation 9, 363–403] and with the informed trading model of Tookes [2008. Information, trading, and product market interactions: cross-sectional implications of informed trading. Journal of Finance 63, 379–413]. In addition, our results indicate that there is a common liquidity response of informationally related securities to firm-specific trading halts.     

The dynamics of dealer markets and trading costs

In this study we examine the temporal dynamics of dealer market share and their ramification for competition and trading costs using a large sample of NASDAQ securities. Our results show that although the total market share of the top five dealers is relatively stable over time, there is significant monthly variation in the composition of the top five dealers. We show that market share turbulence among top dealers is another form of competition that narrows bid–ask spreads, especially for stocks with less competitive market structure.     

Non-synchronous trading and testing for market integration in Central European emerging markets

The paper contributes to the literature on integration of stock markets by addressing the issue of non-synchronous trading. We argue that controlling for time differences in trading hours of stock markets is important and show that time-adjustment improves estimates of market integration. We also show that using weekly frequency does not sidestep the consequences of the time-match problem but leads to significant loss of information. We show that the nature of integration of stock exchanges operating in the Czech Republic, Hungary, and Poland with the stock markets of Germany, UK and US in the period 1994–2004 is very dynamic. Finally, the study shows that the autocorrelation of returns on the main market indices of these emerging markets have declined over time.     

The impact of competition and information on intraday trading

In a dynamic model of financial market trading multiple heterogeneously informed traders choose when to place orders. Better informed traders trade immediately, worse informed delay – even though they expect the market to move against them. This behavior generates intraday patterns with decreasing spreads, decreasing probability of informed trading (PIN), and increasing volume. We predict that policies that foster market entry improve the welfare of uninformed traders and lead to increased market participation by incumbent traders. Technological advances that lead to better signal processing also encourage market participation and increase volume but at the expense of uninformed traders’ welfare.     

Low-latency trading

We define low-latency activity as strategies that respond to market events in the millisecond environment, the hallmark of proprietary trading by high-frequency traders though it could include other algorithmic activity as well. We propose a new measure of low-latency activity to investigate the impact of high-frequency trading on the market environment. Our measure is highly correlated with NASDAQ-constructed estimates of high-frequency trading, but it can be computed from widely-available message data. We use this measure to study how low-latency activity affects market quality both during normal market conditions and during a period of declining prices and heightened economic uncertainty. Our analysis suggests that increased low-latency activity improves traditional market quality measures—decreasing spreads, increasing displayed depth in the limit order book, and lowering short-term volatility. Our findings suggest that given the current market structure for U.S. equities, increased low-latency activity need not work to the detriment of long-term investors.     

Does insider trading explain price run‐up ahead of takeover announcements?

This study empirically examines the impact of changes in substantial shareholdings ahead of 450 Australian takeover offers between the years 2000 and 2009. Previous studies have attributed a significant proportion of the price run‐up effect in takeover targets to insider‐trading behaviour. This study examines the contribution of a broad range of public information sources that are known to typically generate market anticipation, including the acquisition of toeholds ahead of takeover announcements. Our findings show no significant pre‐bid run‐up for takeover targets after considering these sources. We conclude from these results that previous findings attributing pre‐bid share price run‐up to illegal insider trading may overstate the existence of such conduct.     

Speculative trading and stock returns: A stochastic dominance analysis of the Chinese A-share market

The pricing of A-shares in China has long puzzled financial economists. This paper applies recent tests of stochastic dominance (SD) to examine whether differences in the return distributions of A- and B-shares in China are consistent with market efficiency. As SD is nonparametric, market efficiency can be examined without the joint test problem arising from misspecifications in the asset pricing benchmark. Our results show A-shares have second-order dominated B-shares from 1996 to 2005. This dominance was most significant during the market segmentation period, but has continued, albeit to a lesser extent even after the B-share market was opened to local investors in 2001. Our results are robust to using residual returns from an international asset pricing model instead of raw returns. We conclude that the superior performance of A-shares cannot be attributed to risk. The results are more likely due to a return bias caused by intense speculation among retail individuals under limited arbitrage.     

High frequency trading and the new market makers

This paper characterizes the trading strategy of a large high frequency trader (HFT). The HFT incurs a loss on its inventory but earns a profit on the bid–ask spread. Sharpe ratio calculations show that performance is very sensitive to cost of capital assumptions. The HFT employs a cross-market strategy as half of its trades materialize on the incumbent market and the other half on a small, high-growth entrant market. Its trade participation rate in these markets is 8.1% and 64.4%, respectively. In both markets, four out of five of its trades are passive i.e., its price quote was consumed by others.     

Hawkes model for price and trades high-frequency dynamics

We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by four kernels associated with, respectively, the trade arrival self-excitation, the price changes mean reversion, the impact of trade arrivals on price variations and the feedback of price changes on trading activity. It allows one to account for both stylized facts of market price microstructure (including random time arrival of price moves, discrete price grid, high-frequency mean reversion, correlation functions behaviour at various time scales) and the stylized facts of market impact (mainly the concave-square-root-like/relaxation characteristic shape of the market impact of a meta-order). Moreover, it allows one to estimate the entire market impact profile from anonymous market data. We show that these kernels can be empirically estimated from the empirical conditional mean intensities. We provide numerical examples, application to real data and comparisons to former approaches.     

Optimal high-frequency trading with limit and market orders

We propose a framework for studying optimal market-making policies in a limit order book (LOB). The bid–ask spread of the LOB is modeled by a tick-valued continuous-time Markov chain. We consider a small agent who continuously submits limit buy/sell orders at best bid/ask quotes, and may also set limit orders at best bid (resp. ask) plus (resp. minus) a tick for obtaining execution order priority, which is a crucial issue in high-frequency trading. The agent faces an execution risk since her limit orders are executed only when they meet counterpart market orders. She is also subject to inventory risk due to price volatility when holding the risky asset. The agent can then also choose to trade with market orders, and therefore obtain immediate execution, but at a less favorable price. The objective of the market maker is to maximize her expected utility from revenue over a short-term horizon by a trade-off between limit and market orders, while controlling her inventory position. This is formulated as a mixed regime switching regular/impulse control problem that we characterize in terms of a quasi-variational system by dynamic programming methods. Calibration procedures are derived for estimating the transition matrix and intensity parameters for the spread and for Cox processes modelling the execution of limit orders. We provide an explicit backward splitting scheme for solving the problem and show how it can be reduced to a system of simple equations involving only the inventory and spread variables. Several computational tests are performed both on simulated and real data, and illustrate the impact and profit when considering execution priority in limit orders and market orders.     

A profitable trading and risk management strategy despite transaction costs

We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision-making process using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, and switching among several investment positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for the equity market without short selling for 168 S&P 500-listed stocks during the 1998–2008 period and 213 Russell 2000-listed stocks during the 1995–2007 period. Our Monte Carlo simulation for both the various set of stocks and the interval of time confirms our findings.     

Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures

The increasing volume of messages sent to the exchange by algorithmic traders stimulates a fierce debate among academics and practitioners on the impacts of high-frequency trading (HFT) on capital markets. By comparing a variety of regression models that associate various measures of market liquidity with measures of high-frequency activity on the same dataset, we find that for some models the increase in high-frequency activity improves market liquidity, but for others, we get the opposite effect. We indicate that this ambiguity does not depend only on the stock market or the data period, but also on the used HFT measure: the increase of high-frequency orders leads to lower market liquidity whereas the increase in high-frequency trades improves liquidity. We hypothesize that the observed decrease in market liquidity associated with an increasing level of high-frequency orders is caused by a rise in quote volatility.     

Reputation and interdealer trading: a microstructure analysis of the Treasury Bond market

Trading generates not only information about the payoff of the assets traded, but also information about the traders themselves. Over time this information creates reputation. By using a unique dataset on the Treasury bond market, we derive a measure of reputation. This is then used to group dealers on the basis of their reputation and to analyze how they react to the reputation of other dealers. We show that the same type of trade, on the same asset, in the same market can generate different volume and volatility patterns depending on the type of dealers originating it. We also identify the “salient traders”. These traders, even if they do not originate the biggest volume of trade, have the highest impact on the market. These results have strong implications in terms of forecastability of future returns, volatility and overall trading volume because they show that most of the explanatory power of trades is due to salient traders.     

Endogenous informed trading in the presence of trading costs: Theory and evidence

The basic premise of the model we propose is that market frictions (trading costs) force traders with market-wide information to strategically choose which securities to trade in. We study the effect of recognizing trading costs on the choices of informed traders and the resulting statistical properties of security prices. Specifically, we show that (1) stocks with intermediate β's have the least informative prices, even though they are traded by the greatest number of informed traders; (2) for high β securities, the contemporaneous correlation of prices is close to the correlation in fundamental values; (3) a security with a higher β, higher volume of liquidity trading and lower idiosyncratic variance is more likely to lead another security. With market capitalization as a proxy for the level of liquidity trading, these specific predictions of the model on the lead–lag relationship are also shown to be strongly supported by the data.     

A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering

The probability of informed trading (PIN) is a commonly used market microstructure measure for detecting the level of information asymmetry. Estimating PIN can be problematic due to corner solutions, local maxima and floating point exceptions (FPE). Yan and Zhang [J. Bank. Finance, 2012, 36, 454–467] show that whilst factorization can solve FPE, boundary solutions appear frequently in maximum likelihood estimation for PIN. A grid search initial value algorithm is suggested to overcome this problem. We present a faster method for reducing the likelihood of boundary solutions and local maxima based on hierarchical agglomerative clustering (HAC). We show that HAC can be used to determine an accurate and fast starting value approximation for PIN. This assists the maximum likelihood estimation process in both speed and accuracy.     

Determinants of bid and ask quotes and implications for the cost of trading

Financial transaction costs are time varying. This paper proposes a model that relates transaction cost to characteristics of order flow. We obtain qualitatively consistent model results for different stocks and across different time periods. We find that an unusual excess of buyers (sellers) relative to sellers (buyers) tends to increase the ask (bid) price. Hence, the ask and bid components of spread change asymmetrically about the efficient price. For a fixed order imbalance surprise these effects are muted when unanticipated total volume is high. Unexpected high volatility in the transaction price process tends to widen the spread symmetrically about the efficient price. Our findings are consistent with predications from market microstructure theory that the cost of market making should depend on both the risk of trading with better-informed traders and inventory risk. We also find that order flow surprises have a significant impact on the efficient price and can also explain a substantial amount of persistence in the volatility of the efficient price. This dependence does not violate the efficient market hypothesis since the surprises, by definition, are not predictable.     

City goes dark: Dark trading and adverse selection in aggregate markets

We investigate the impact of dark trading on adverse selection in an aggregate market for trading UK stocks. Dark trading is linked to lower adverse selection risk and improved informational efficiency and liquidity in the aggregate market, even as liquidity declines in the lit market with dark trading. However, there is a trading value-based threshold when dark trading starts to induce adverse selection. We estimate that this threshold varies from around 9% for the most liquid stocks to 25% for the least liquid stocks. The overall average threshold for the 288 FTSE 350 stocks in our sample is 14%.     

Volume autocorrelation,information, and investor trading

This study investigates whether the widely documented daily correlated trading volume of stocks is driven by individual investor trading, institutional trading, or both. We find that at least 95% of NYSE and AMEX stocks exhibit statistically significant, positive serial correlation. Volume autocorrelation decreases with the level of institutional ownership of a stock. We also show that the rate of arrivals of new information to the market contributes to the clustering of trades. When there is high information flow to the market, institutional trading generates a more pronounced effect on volume autocorrelation than individual investor trading. Our results are broadly consistent with the predictions of trading volume patterns suggested by most theoretical models of stock trading and by empirical research on investor trading.     

Liquidity costs: Screen-based trading versus open outcry

The results reported in this paper challenge the popular belief that screen-based trading offered lower liquidity costs than the open-outcry approach during its first year of side-by-side operation in the U.S. financial derivatives market. Using time and sales data from the Chicago Board of Trade (CBOT) market profile data series, effective bid-ask spreads are estimated on the basis of daily and intraday measures of the Thompson-Waller and Smith-Whaley estimators. We find liquidity costs on the screen-based system vary with time and the level of floor trading activity. In particular, a one-tick market is observed just before the opening of the Chicago trading floor (6:30 to 7:30 am). However, subsequent intraday spreads exhibit the familiar “reverse J-shaped pattern”—highest following the opening of floor trading, declining until afternoon, and then increasing until close. Meanwhile, daily spread estimates average almost a quarter-tick higher on the screen-based market relative to the one-tick spread commonly associated with open outcry. This relationship remained robust across sample time-series and conservative price-change specifications. Since the study was conducted, electronic trading has become the predominant exchange medium for financial derivatives at the CBOT, following the example set in Europe's traditional futures exchanges, e.g. France's Matif, Germany's Deutsche Bourse and the U.K.'s Liffe.     

Information provision in financial markets

We set up a rational expectations model in which investors trade a risky asset based on a private signal they receive about the quality of the asset, and a public signal that represents a noisy aggregation of the private signals of all investors. Our model allows us to examine what happens to market performance (market depth, price efficiency, volume of trade, and expected welfare) when regulators can induce improved information provision in one of two ways. Regulations can be designed that either provide investors with more accurate information by improving the quality of prior information, or that enhance the transparency of the market by improving the quality of the public signal. In our rational expectations equilibrium, improving the quality of the public signal can be interpreted as a way of providing information about the anticipations and trading motives of all market participants. We find that both alternatives improve market depth. However, in the limit, we show that improving the precision of prior information is a more efficient way to do so. More accurate prior information decreases asymmetric information problems and consequently reduces the informativeness of prices, while a more accurate public signal increases price informativeness. The volume of trade is independent of the quality of prior information and is increasing in the quality of the public signal. Finally, expected welfare can sometimes fall as prior information or the public signal become more precise.