Order Flow Data Research Articles

Effects of Limit Order Book Information Level on Market Stability Metrics

Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model’s limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange (NYMEX). Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs.

Why is equity order flow so persistent?

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.

Why Is Equity Order Flow so Persistent?

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.

Information and Investor Behavior Surrounding Earnings Announcements

The goal of this paper is to analyze the impact of annual earnings announcements on the market through the order flow data in addition to the usual transaction data. In this respect, examining order flow data can potentially reveal valuable information that is not available from transaction data. In fact, the data allow us to test hypotheses about asymmetric information and investor behavior and to test if the behavior varies with investor sophistication. In addition, the paper tries to identify the determinants of the impact on a firm's value using assumptions about investor behavior.

Empirical properties of inter-cancellation durations in the Chinese stock market

Order cancellation process plays a crucial role in the dynamics of price formation in order-driven stock markets and is important in the construction and validation of computational finance models. Based on the order flow data of 18 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate the empirical statistical properties of inter-cancellation durations in units of events defined as the waiting times between two consecutive cancellations. The inter-cancellation durations for both buy and sell orders of all the stocks favor a $q$-exponential distribution when the maximum likelihood estimation method is adopted; In contrast, both cancelled buy orders of 6 stocks and cancelled sell orders of 3 stocks prefer Weibull distribution when the nonlinear least-square estimation is used. Applying detrended fluctuation analysis (DFA), centered detrending moving average (CDMA) and multifractal detrended fluctuation analysis (MF-DFA) methods, we unveil that the inter-cancellation duration time series process long memory and multifractal nature for both buy and sell cancellations of all the stocks. Our findings show that order cancellation processes exhibit long-range correlated bursty behaviors and are thus not Poissonian.

Effects of Limit Order Book Information Level on Market Stability Metrics

Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model's limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange (NYMEX). Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs.

The position profiles of order cancellations in an emerging stock market

Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price level in the limit-order book (LOB) and the position in the queue at each price level. We study the profiles of these two order cancellation positions through rebuilding the limit-order book using the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We find that the profiles of relative price levels where cancellations occur obey a log-normal distribution. After normalizing the relative price level by removing the factor of order numbers stored at the price level, we find that the profiles exhibit a power-law scaling behavior on the right tails for both buy and sell orders. When focusing on the order cancellation positions in the queue at each price level, we find that the profiles increase rapidly in the front of the queue, and then fluctuate around a constant value till the end of the queue. These profiles are similar for different stocks. In addition, the profiles of cancellation positions can be fitted by an exponent function for both buy and sell orders. These two kinds of cancellation profiles seem universal for different stocks investigated and exhibit minor asymmetry between buy and sell orders. Our empirical findings shed new light on the order cancellation dynamics and pose constraints on the construction of order-driven stock market models.

Random matrix approach to the dynamics of stock inventory variations

It is well accepted that investors can be classified into groups owing to distinct trading strategies, which forms the basic assumption of many agent-based models for financial markets when agents are not zero-intelligent. However, empirical tests of these assumptions are still very rare due to the lack of order flow data. Here we adopt the order flow data of Chinese stocks to tackle this problem by investigating the dynamics of inventory variations for individual and institutional investors that contain rich information about the trading behavior of investors and have a crucial influence on price fluctuations. We find that the distributions of cross-correlation coefficient Cij have power-law forms in the bulk that are followed by exponential tails, and there are more positive coefficients than negative ones. In addition, it is more likely that two individuals or two institutions have a stronger inventory variation correlation than one individual and one institution. We find that the largest and the second largest eigenvalues (λ1 and λ2) of the correlation matrix cannot be explained by random matrix theory and the projections of investors' inventory variations on the first eigenvector u(λ1) are linearly correlated with stock returns, where individual investors play a dominating role. The investors are classified into three categories based on the cross-correlation coefficients CVR between inventory variations and stock returns. A strong Granger causality is unveiled from stock returns to inventory variations, which means that a large proportion of individuals hold the reversing trading strategy and a small part of individuals hold the trending strategy. Our empirical findings have scientific significance in the understanding of investors' trading behavior and in the construction of agent-based models for emerging stock markets.

Order Flow and the Real: Indirect Evidence of the Effectiveness of Sterilized Interventions

This study presents indirect evidence of the effectiveness of sterilized interventions in Brazil based on the complete records of daily customer order flow data reported by Brazilian dealers, as well as foreign exchange intervention data over a time span of 10 years (2002-2011). We find that the effect of USD sales by end-users on the BRL/USD was much stronger on days in which the BCB did not intervene in the spot foreign exchange market.

Modeling Informed State in Fixed Income Market: Empirical Evidence from HFT Data

Motivated by findings in the existing literature on the information content of order flows, we specify Probability of Informed Trading – Asymmetric Autoregressive Conditional Duration (PIN-AACD) model to identify the ‘informed state’ in the Fixed Income Markets. Using tick-by-tick price and order flow data, we obtain the estimates for the probability of informed (PIN) state throughout the trading day. The trade direction (buy versus sell orders) and the duration between trades are modeled jointly. The model allows the Probabilities of Good News and Bad News to vary each interval. Results show that intraday patterns of the PIN estimates are consistent with the scheduled arrival of public information or news. Further, we show that PIN is positively related to overall market depth. The effect of realized volatility on information asymmetry is unexpectedly found to be negative. We also find positive correlation between news and trading volume. The study intends to support trading and sales teams in identifying informed trades.

Order characteristics and the sources of commonality in prices and liquidity

Using electronic order flow data for a sample of NYSE-listed stocks, we examine the relative importance of program traders, institutional traders, retail traders, and exchange members in driving commonality in order flow, returns, and liquidity. Using principal components analysis, we find that program trades and other institutional trades are the primary drivers of commonality in order flow and that these two order flow factors are significantly related to returns. Our results suggest that commonality is driven by the correlated trading decisions of professional traders, as executed through program trades, and not by correlated trading among retail traders.

Complex stock trading network among investors

We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extract detailed information of each executed order for each trading day and demonstrate that the trade size distributions for different trading days exhibit power-law tails and that most of the estimated power-law exponents are well within the Lévy stable regime. Based on the records of order matching among investors, we can construct a stock trading network for each trading day, in which the investors are mapped into nodes and each transaction is translated as a direct edge from the seller to the buyer with the trade size as its weight. We find that all the trading networks comprise a giant component and have power-law degree distributions and disassortative architectures. In particular, the degrees are correlated with order sizes by a power-law function. By regarding the size of executed order as its fitness, the fitness model can reproduce the empirical power-law degree distribution.

Hiding Behind the Veil: Informed Traders and Pre-Trade Opacity

We investigate exchange-based hidden orders from an information perspective. Using order flow data that categorizes each order submitter into one of many institutional and non-institutional categories, we test several hypotheses and document substantial evidence on a spectrum of issues in this context. First, institutional investors (both domestic and foreign) submit more hidden orders than non-institutional investors. Second, the contribution of hidden orders to overall price discovery is significantly greater than that of non-hidden orders. Third, traders with higher information levels are significantly more likely to hide a larger proportion of trades. Fourth, informed traders are significantly more likely to submit hidden orders than uninformed traders. Fifth, informed trader categories that submit hidden orders make significantly greater economic profits than those that do not; while, in contrast, uninformed trader categories make significantly lower economic profits when they submit hidden orders than when do not hide their orders. Finally, relative to other periods, informed trader categories submit more hidden orders in the five days before and after earnings announcements (an exogenous information-intensive period), while the uninformed trader categories do not change their hidden order submission strategies around earnings announcements. Overall, we present overwhelming evidence linking informed traders with the propensity for pre-trade opacity.

Herding and the role of foreign institutions in emerging equity markets

This study examines herding surrounding qualified foreign institutional investors (QFIIs) in an emerging equity market. Using five years of weekly order flow data, I find that, when QFIIs increase (decrease) their weightings in particular sectors, dealers', margin traders', and mutual funds' holdings/weightings likewise increase (decrease) during the same and following weeks, despite controls for return and trading momentum. This herding is potentially destabilizing as prices overshoot and mean revert highlighting the potential negative impact of this level of transparency and of the policy of disclosing QFII trading in emerging markets.

International order flows: Explaining equity and exchange rate returns

Macroeconomic models of equity and exchange rate returns perform poorly at high frequencies. The proportion of daily returns that these models explain is essentially zero. Instead of relying on macroeconomic determinants, we model equity price and exchange rate behavior based on a concept from microstructure–order flow. The international order flows are derived from belief changes of different investor groups in a two-country setting. We obtain a structural relationship between equity returns, exchange rate returns and their relationship to home and foreign equity market order flow. To test the model we construct daily aggregate order flow data from 800 million equity trades in the U.S. and France from 1999 to 2003. Almost 60% of the daily returns in the S&P100 index are explained jointly by exchange rate returns and aggregate order flows in both markets. As predicted by the model, daily exchange rate returns and order flow into the French market have significant incremental explanatory power for the daily S&P returns. The model implications are also validated for intraday returns.

Commercially Available Order Flow Data and Exchange Rate Movements: Caveat Emptor

Research suggests that customer order flow should help predict exchange rates. We make two contributions. First, we provide a review of the recent literature on order flow and exchange rate movements. Second, we critically evaluate the practical value of customer order flow data that are commercially available to the wider market, as well as the forecasting properties of inter‐dealer order flow. In line with microstructure theory, we find little evidence that the latter can forecast exchange rates, but our results also cast considerable doubt on the practical value to market practitioners of commercially available customer order flow data.

Tax Loss Selling, Window Dressing, and the January Effect: Evidence from NYSE Order Flow Data

Tax Loss Selling, Window Dressing, and the January Effect: Evidence from NYSE Order Flow Data

Internalization in European Equity Markets Following the Adoption of the EU MiFID Directive

The European Union9s Markets in Financial Instruments Directive (MiFID), that will be implemented by October 2007, significantly modifies the regulation of the European securities industry. It will allow, among other things, investment firms to act as systematic internalizers. A systematic internalizer is an investment firm dealing on its own account to execute client orders outside a regulated market or a multilateral trading facility (MTF). Specifically, the new regulation requires systematic internalizers to publish firm quotes on liquid shares when dealing for retail quantities. In short, systematic internalizers are market makers on liquid stocks who execute small trades. This article uses order flow and limit order book data in order to estimate the internalization rate (i.e., the portion of the total order flow that could be internalized), to estimate the internalization expected revenues, and to investigate the main factors affecting both the internalization rate and the magnitude of internalization revenues. Four main findings arise from our analysis. First, the internalization rate is negatively related to the stock turnover, that is more liquid stocks exhibit lower internalization rates. Second, the size and the variability of internalization trading revenues strongly depend on their composition. Third, spread revenues are highly concentrated in the most liquid stocks. Fourth, the value of the inventories for an average internalizer firm is limited relative to the stock turnover. <b>TOPICS:</b>Developed, exchanges/markets/clearinghouses, fundamental equity analysis

Internalization in European Equity Markets Following the Adoption of the EU Mifid Directive

The European Union's Markets in Financial Instruments Directive (MiFID), that will be implemented by October 2007, significantly modifies the regulation of the European securities industry. It will allow, among other things, investment firms to act as systematic internalizers. A systematic internalizer is an investment firm dealing on its own account to execute client orders outside a regulated market or a multilateral trading facility (MTF). Specifically, the new regulation requires systematic internalizers to publish firm quotes on liquid shares when dealing for retail quantities. In short, systematic internalizers are market makers on liquid stocks who execute small trades. This paper uses order flow and limit order book data in order to estimate the internalization rate (i.e., the portion of the total order flow that could be internalized), to estimate the internalization expected revenues, and to investigate the main factors affecting both the internalization rate and the magnitude of internalization revenues. To simulate the systematic internalization activity we collected detailed order flow data for 57 liquid stocks traded on the Italian Stock Exchange, which is a currently concentrated market. To be internalized, an order should jointly satisfy the following two requirements (expressly requested by the Level 1 law text). First, the quantity of the order should not be greater than the estimated standard market size (SMS). Second, the price limit of the order should be compatible with immediate execution by a systematic internalizer in respect of the best execution principle. Based on this procedure we identify internalized orders and compute estimates of internalization rate, gross trading revenues, spread revenues and positioning revenues on a per stock per day basis and on a per internalizer firm per day basis. Our main findings relate to (i.) the relationship between internalization rates and stocks turnover; (ii.) the size and variability of internalization trading revenues; (iii.) the value of the inventories for an internalizer firm.

The Impact of Pennies on the Market Quality of the Toronto Stock Exchange

AbstractUsing detailed order flow data from the Toronto Stock Exchange, this paper finds no evidence that a smaller tick size lessens market liquidity for either small or large traders. Rather, there is evidence of lower trading costs, faster time to order execution, and greater price continuity. Consistent with a penny tick allowing a finer pricing grid search, there is an increase in the number of Change Former Orders and cancellations.