On the dark side of the market: identifying and analyzing hidden order placements

Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders’ use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ, we propose a simple method to conduct statistical inference on the location of hidden depth and to test economic hypotheses. Analyzing a wide cross-section of stocks, we show that market conditions reflected by the (visible) bid-ask spread, (visible) depth, recent price movements and trading signals significantly affect the aggressiveness of ’dark’ liquidity supply and thus the ’hidden spread’. Our evidence suggests that traders balance hidden order placements to (i) compete for the provision of (hidden) liquidity and (ii) protect themselves against adverse selection, front-running as well as ’hidden order detection strategies’ used by high-frequency traders. Accordingly, our results show that hidden liquidity locations are predictable given the observable state of the market

Limit order books and trade informativeness

In the microstructure literature, information asymmetry is an important determinant of market liquidity. The classic setting is that uninformed dedicated liquidity suppliers charge price concessions when incoming market orders are likely to be informationally motivated. In limit order book markets, however, this relationship is less clear, as market participants can switch roles, and freely choose to immediately demand or patiently supply liquidity by submitting either market or limit orders. We study the importance of information asymmetry in limit order books based on a recent sample of thirty German DAX stocks. We find that Hasbrouck’s (1991) measure of trade informativeness Granger-causes book liquidity, in particular that required to fill large market orders. Picking-off risk due to public news induced volatility is more important for top-of-the book liquidity supply. In our multivariate analysis we control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. JEL Classification: G14 Keywords: Price Impact of Trades , Trading Intensity , Dynamic Duration Models, Spread Decomposition Models , Adverse Selection Ris

On the Dark Side of the Market: Identifying and Analyzing Hidden Order Placements

Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders’ use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ, we propose a simple method to conduct statistical inference on the location of hidden depth and to test economic hypotheses. Analyzing a wide cross-section of stocks, we show that market conditions reflected by the (visible) bid-ask spread, (visible) depth, recent price movements and trading signals significantly affect the aggressiveness of ’dark’ liquidity supply and thus the ’hidden spread’. Our evidence suggests that traders balance hidden order placements to (i) compete for the provision of (hidden) liquidity and (ii) protect themselves against adverse selection, front-running as well as ’hidden order detection strategies’ used by high-frequency traders. Accordingly, our results show that hidden liquidity locations are predictable given the observable state of the market.limit order market, hidden liquidity, high-frequency trading, non-display order, iceberg orders

Hidden Limit Orders and Liquidity in Order Driven Markets

This paper analyzes the rationale for the submission of hidden limit orders, and compares opaque and transparent limit order books. In my sequential model, the limit order trader may be informed with some probability. Both informed and large uninformed liquidity suppliers submit hidden orders in order to decrease the informational impact of their large orders, while ensuring a large trading volume. As they cannot adopt such a strategy in the transparent market, I find that pre-trade opacity improves market liquidity, and the welfare of the participants. My model further yields empirical predictions on the use and revelation of hidden orders in opaque markets.

High‐Frequency Trading and the New Stock Market: Sense And Nonsense

The stock market has been transformed during the last 25 years. Human suppliers of liquidity like the NASDAQ dealers and NYSE specialists have been replaced by algorithmic market making; stocks that once traded on a single venue now trade across twelve exchanges and a multitude of alternative trading systems. New venues like dark pools, and new participants like high‐frequency traders, have emerged to take on prominent roles. This new market has had more than its share of controversy and regulatory scrutiny, particularly in the wake of Michael Lewis’s bestseller Flash Boys. In this article, the authors analyze five of the most controversial new market practices, including various high‐frequency trading strategies and dark pool activities. They set out a simple conceptual framework based on adverse selection and agency problems, and apply that framework to assess the welfare effects of each of the five practices. While much that is criticized is indeed objectionable, other controversial practices are much more complex than popularly imagined and may in fact be socially desirable. They conclude by evaluating a range of potential reforms to equity market structure

Higher Order Expectations, Illiquidity, and Short-term Trading

We propose a theory that jointly accounts for an asset illiquidity and for the asset price potential over-reliance on public information. We argue that, when trading frequencies differ across traders, asset prices reflect investors' Higher Order Expectations (HOEs) about the two factors that influence the aggregate demand: fundamentals information and liquidity trades. We show that it is precisely when asset prices are driven by investors' HOEs about fundamentals that they over-rely on public information, the market displays high illiquidity, and low volume of informational trading; conversely, when HOEs about fundamentals are subdued, prices under-rely on public information, the market hovers in a high liquidity state, and the volume of informational trading is high. Over-reliance on public information results from investors' under-reaction to their private signals which, in turn, dampens uncertainty reduction over liquidation prices, favoring an increase in price risk and illiquidity. Therefore, a highly illiquid market implies higher expected returns from contrarian strategies. Equivalently, illiquidity arises as a byproduct of the lack of participation of informed investors in their capacity of liquidity suppliers, a feature that appears to capture some aspects of the recent crisis.Expected returns, multiple equilibria, average expectations, over-reliance on public information, Beauty Contest.

Price pressures

We study price pressures in stock prices—price deviations from fundamental value due to a risk-averse intermediary supplying liquidity to asynchronously arriving investors. Empirically, twelve years of daily New York Stock Exchange intermediary data reveal economically large price pressures. A $100,000 inventory shock causes an average price pressure of 0.28% with a half-life of 0.92 days. Price pressure causes average transitory volatility in daily stock returns of 0.49%. Price pressure effects are substantially larger with longer durations in smaller stocks. Theoretically, in a simple dynamic inventory model the ‘representative’ intermediary uses price pressure to control risk through inventory mean reversion. She trades off the revenue loss due to price pressure against the price risk associated with remaining in a nonzero inventory state. The model’s closed-form solution identifies the intermediary’s relative risk aversion and the distribution of investors’ private values for trading from the observed time series patterns. These allow us to estimate the social costs—deviations from constrained Pareto efficiency—due to price pressure which average 0.35 basis points of the value traded. JEL Classification: G12, G14, D53, D6

Copula-based dynamic conditional correlation multiplicative error processes : [Version 18 April 2013]

We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks