Informed trading and the price impact of block trades: A high frequency trading analysis

Using high frequency data from the London Stock Exchange (LSE), we investigate the relationship between informed trading and the price impact of block trades on intraday and inter-day basis. Price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery.     

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.     

A functional approach to the price impact of stock trades and the implied true price

We propose a functional approach to estimate the instantaneous price impact of a trade and to infer an implied true price. Our model expresses price impact as an S-shaped function of signed volume. It has two parameters, one for price impact and one for liquidity depth. The latter measures the differential impact of small and large trades. The price impact is instantaneous, that is, it occurs at the instant of trade execution. Our specification also permits the price impact of buys and sells to be asymmetric. We compute an implied true price from our model, and we find that it is closer than the quote midpoint to the unobservable true price. Our empirical analysis also shows that the effective spread, which is computed using the midpoint, has an upward bias, and that the implicit transaction costs may be lower than previous estimates.     

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.     

The immediate price impact of trades on the Australian Stock Exchange

We study the immediate price impact of a single trade executed in the Australian Stock Exchange (ASX). By ordering the top 300 stocks on the ASX in order of their free float market capitalization, a clear pattern emerges, with higher cap stocks experiencing lower price impact than lower cap stocks for the same traded volume. We investigate this relationship in detail, and show that the price impact and liquidity scale as a power of the market capitalization. This relationship is used to obtain a single market impact curve which shows average price shift as a function of volume traded. We obtain similar results for every year from 2001 to 2004.     

The determinants of the price impact of block trades: further evidence

This article extends previous literature which examines the determinants of the price impact of block trades on the Australian Stock Exchange. As previous literature suggests that liquidity exhibits intraday patterns, we introduce time of day dummy variables to explore time dependencies in price impact. Following theoretical developments in previous literature, the explanatory power of the bid–ask spread, a lagged cumulative stock return variable and a refined measure of market returns are also examined. The model estimated explains approximately 29 per cent of the variation in price impact. Block trades executed in the first hour of trading experience the greatest price impact, while market conditions, lagged stock returns and bid–ask spreads are positively related to price impact. The bid–ask spread provides most of the explanatory power. This suggests that liquidity is the main driver of price impact.     

Stock liquidity premium with stochastic price impact and exogenous trading strategy

This paper provides a model which helps explain the variability of stock liquidity premium. Liquidity is modelled as a time-varying price impact and includes both permanent as well as temporary price impacts. Liquidity premium is defined as an additional expected return that stock should yield to compensate an investor for the potential loss of wealth utility caused by price impact costs. The numerical results presented show that liquidity premium varies with expected net stock return, return volatility and, to a lesser extent, with returns on risk-free bonds. Liquidity premium is a growing and convex function of liquidity costs, and temporary price impact has a more severe effect on liquidity premium than the permanent one.     

Duration, volume and volatility impact of trades

This paper presents a framework to model duration, volume and returns simultaneously, obtaining an econometric reduced form that incorporates causal and feedback effects among these variables. The methodology is applied to two groups of stocks, classified according to trade intensity. We find that: (1) all stocks exhibit trading volume clustering (which is significantly higher for frequently traded stocks); (2) times of greater activity coincide with a higher number of informed traders present in the market only for the frequently traded stocks; (3) the more frequently traded stocks converge more rapidly (in calendar time) to their long-run equilibrium, after an initial perturbation.     

Liquidity,volume and price efficiency: The impact of order vs. quote driven trading

As equity trading becomes predominantly electronic, is there still value to a traditional, intermediated dealer system? We address this question by comparing the impact of the organization of trading on volume, liquidity, and price efficiency in a quote-driven dealer market and in an order-driven limit order book. Small order price impacts are higher and large order price impacts are lower in a dealer market. Prices are more efficient in the limit order book, except when the level of informed trading is high. Volume is higher in a limit order market, making this system most attractive for trading venues.     

Time series momentum trading strategy and autocorrelation amplification

This paper investigates why general Moving Average (MA) trading rules are widely used by technical analysts and others. We assume general stationary processes for prices and we derive the autocorrelation function for an MA trading rule. Based on our results, we conjecture that autocorrelation amplification is one of the reasons why such trading rules are popular. Using simulated results, we show that the MA rule may be popular because it can identify price momentum and is a simple way of assessing and exploiting the price autocorrelation structure without necessarily knowing its precise structure. This paper then, provides empirical evidence of autocorrelation amplification using 15-year daily price data for 11 major international stock indices.     

Informed trading,information uncertainty,and price momentum

In this paper we investigate the effects of informed trading (PIN) and information uncertainty in determining price momentum. We find that trading strategies based on buying high-uncertainty good-news stocks and shorting high-uncertainty bad-news stocks work well when limited to high-PIN stocks, while stocks with low-PIN do not exhibit price continuations, even when the uncertainty level of those stocks is high. In contrast, momentum returns are always significant for high-PIN stocks, irrespective of information uncertainty. Overall, we show that the informed trading effect is both independent of and stronger than that of information uncertainty in determining price momentum.     

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.     

Drift dependence of optimal trade execution strategies under transient price impact

We give a complete solution to the problem of minimizing the expected liquidity costs in the presence of a general drift when the underlying market impact model has linear transient price impact with exponential resilience. It turns out that this problem is well-posed only if the drift is absolutely continuous. Optimal strategies often do not exist, and when they do, they depend strongly on the derivative of the drift. Our approach uses elements from singular stochastic control, even though the problem is essentially non-Markovian due to the transience of price impact and the lack in Markovian structure of the underlying price process. As a corollary, we give a complete solution to the minimization of a certain cost-risk criterion in our setting.     

Price impact of block trades: the curious case of downstairs trading in the EU emissions futures market

Using high-frequency data from the European Climate Exchange (ECX), we examine the determinants of price impact of €21 billion worth of block trades during 2008–2011 in the European carbon market. We find that wider bid-ask spreads and volatility are characterised by a smaller price impact. Larger levels of price impact are more likely to occur during the middle of the trading day, specifically the four-hour period between 11 a.m. and 3 p.m., than during the first or final hours. Purchase block trades induce a relatively smaller price impact on price run-up, while sell block trades exhibit a larger price impact on price run-up. We conclude that block trades on the ECX induce less price impact than in equity or conventional futures markets, and that a significant proportion of the effects contradict findings on block trades in those markets; thus, we provide the first evidence of the curious bent to block trading in the European Union emissions trading scheme.     

Understanding the price of volatility risk in carry trades

This paper investigates the cross-sectional pricing ability of the short- and long-run components of global foreign exchange (FX) volatility for carry trade returns. We find a negative and statistically significant factor risk price for the long-run component, but no significant pricing effect due to the short-run volatility component. We also document that the dynamics of the long-run component of global FX volatility are related to US macroeconomic fundamentals. Our results are robust to various parametrizations of the volatility models used to obtain the volatility components and they are invariant to alternative asset pricing testing methodologies and sample periods.     

High frequency trading and end-of-day price dislocation

We show that the presence of high frequency trading (HFT) has significantly mitigated the frequency and severity of end-of-day price dislocation. The effect of HFT is more pronounced on days when end of day price dislocation is more likely to be the result of market manipulation. Moreover, the effect of HFT is more pronounced than the role of trading rules, surveillance, enforcement and legal conditions in curtailing the frequency and severity of end-of-day price dislocation. We show our findings are robust to different proxies of the start of HFT by trade size, cancellation of orders, and co-location.     

Noise trading and the price formation process

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker screens. We find daily movements in our noise measure are positively associated with trading volume and market depth, and negatively related to the bid-ask spread. We find monthly movements in our noise measure are negatively associated with the probability of informed trading, and positively correlated with the arrival rate of uninformed traders. We also find the sensitivity of stock prices to net initiated order flow decreases in the level of noise trading. In addition we find that, after controlling for noise trading, the sensitivity of stock prices to net initiated order flow is significantly greater on Mondays. These empirical results consistently support the implications of various models of market microstructure, suggesting that our proxy provides useful information as a daily measure of noise trading.     

Information-based stock trading and managerial incentives: evidence from China's stock market

This paper uses stock price informativeness, or information-based stock trading, to help explain the pay–performance sensitivity (PPS) of chief executive officer (CEO) compensation in China's listed firms. We argue that higher stock price informativeness, which we measure by the probability of informed trading, helps and encourages shareholders to incentivize the top management team based on stock market performance. The regression results support our argument and show that a higher level of stock price informativeness is associated with higher CEO PPSs. Moreover, the impact of stock price informativeness on CEO incentives is stronger for privately controlled listed firms than it is for state-controlled listed firms. The results also hold when information asymmetry is approximated by the accuracy and dispersion of the earnings forecasts made by financial analysts.     

The impact of trading halts on liquidity and price volatility: Evidence from the Australian Stock Exchange

This study examines market behaviour around trading halts associated with information releases on the Australian Stock Exchange, which operates an open electronic limit order book. Using the Lee, Ready and Seguin (1994) pseudo-halt methodology, we find trading halts increase both volume and price volatility. Trading halts also increase bid-ask spreads and reduce market depth at the best-quotes in the immediate post-halt period. The results of this study imply that trading halts impair rather than improve market quality in markets that operate open electronic limit order books.     

Information-based trading and information propagation: Evidence from the exchange traded fund market

This paper studies the information-based trading of exchange-traded funds (ETFs) and the information propagation from the ETF market to its index. We find that the ETF trading triggered by asymmetric information and belief heterogeneity not only accelerates the ETFs' price discovery process but also increases the flow of information to the tracked index. Moreover, the price efficiency of the index also improves along with these two types of trading and their efficiency effects can be further enhanced by a speedier ETFs' price discovery. These observations portray the mechanism of the inter-market information propagation.