Conference on High-Frequency Trading
Conference on High-Frequency Trading

Titles and abstracts

Torben Andersen (Northwestern University)

Intraday Trading Invariance in Foreign Exchange Futures

Prior work of Andersen, Bondarenko, Kyle and Obizhaeva (2015) establishes that the intraday trading patterns in the E-mini S&P 500 futures contract are consistent with the following invariance relationship: The return variation per transaction is log-linearly related to trade size, with a slope coefficient of -2. This association applies both across the intraday diurnal pattern and across days in the time series. The factor of proportionality deviates sharply from prior hypotheses relating volatility to transactions count or trading volume. This paper documents that a similar invariance relation holds for foreign exchange futures. However, the log-linear association is not fixed, but shifts over time reflecting an, all else equal, declining trend in the average trade size. The findings are remarkably robust across the full set of currency contracts explored, providing challenges to market microstructure research to rationalize these tight intraday and intertemporal interactions among key market activity variables.

Co-authored with Oleg Bondarenko, University of Illinois at Chicago.


Jean-Philippe Bouchaud (CFM, Paris)

The square root law of price impact and the intrinsic fragility of financial markets

We will review the accumulating empirical evidence for an approximately square-root impact of a metaorder. Interestingly, this square-root law appears to be universal, i.e. to a large extent ndependent of markets (futures, equities, volatility, Bitcoin), microstructure and epochs (pre and post HFT). This suggests that this law must originate from a simple and robust statistical mechanism. We propose a dynamical theory of the latent market liquidity that predicts that the average supply/demand profile is V shaped and vanishes around the current price, leading to the square-root impact. This result only relies on mild assumptions about the order flow and on diffusive prices. We test our arguments numerically using a minimal model of order flow and provide further theoretical predictions that can be compared to further experimental observations. Our scenario suggests that markets are intrinsically prone to liquidity crises and puts in perspective the recent debate on the role of HFT liquidity.


Jonathan Brogaard (University of Washington)

High-Frequency Trading Competition

Using a firm-identified limit-order book dataset we show that competition among high-frequency trading firms (HFT) influences liquidity. HFT entries increase liquidity. The reverse is true for exits. Market participants’ behavioral changes are consistent with competitive pressure. HFT entries increase total HFT market share and take market share from incumbents. After HFT entry (exit), incumbent HFT spreads tighten (widen). Trading revenue suggests competition reduces HFT firm profitability. Impacts are larger in markets with fewer incumbents. The results show that part of the value of HFT comes from its competitiveness.


Rama Cont (Imperial College London & CNRS, Paris)

Algorithmic trade execution and intraday market Dynamics

''Optimal execution'' are typically derived assuming an exogenous Price process which is unaffected by the trading behaviour of market participants. On the other hand, in intraday price behavior in electronic markets reveals evidence of the price impact of algorithmic order flow, an extreme example being the 'Flash Crashes' repeatedly observed in such markets. We propose a simple model for analyzing the feedback effects which  arise in a market where participants use market signals to minimize the impact of their trade execution. We show that commonly used execution algorithms which aim at reducing market impact of trades can actually lead to unintended synchronization of participants' order flows, increase their market impact and generate large « self-exciting » intraday  swings in volume and volatility. We show that such bursts may occur even in absence of large orders, and lead to a systematic underperformance of 'optimal execution' strategies. These results call for a critical assessment of "optimal execution" algorithms and point to a notion of order flow toxicity distinct from information asymmetry or adverse selection.


Thierry Foucault (HEC Paris)

Data Abundance and Asset Price Informativeness

Investors can acquire either raw or processed information about the payoff of risky assets. Information processing filters out the noise in raw information but it takes time. Hence, investors buying processed information trade with a lag relative to investors buying raw information. As the cost of raw information declines, more investors trade on it, which reduces the value of processed information, unless raw information is very unreliable. Thus, a decline in the cost of raw information can reduce the demand for processed information and, for this reason, the informativeness of asset prices in the long run.


Frank Hatheway (NASDAQ)

We have all become High Frequency Traders: What are some implications?

Competitive and regulatory forces in the U.S. have resulted in almost all equity executions being handled using sophisticated electronic trading systems.  Empirical evidence from Nasdaq shows that order submission patterns once restricted to proprietary trading firms, the prototypical High Frequency Trader, are now observed in orders originating from almost all types of market participants. One aspect of the widespread automation of trading is that the use of "price taker" algorithms has become increasingly prevalent. The implications for the market where each algorithm's order placement decision is dependent on other algorithms' order placement decisions is not well understood. Some consequences of widespread "price taking" behavior are seen every trading day as well as on occasional events such as the May 6, 2010 and August 24, 2015 market breaks.   

The public policy discussion around market structure needs a better understanding of how the automated price setting mechanism works under the current structure and would work under future alternative market structure designs.


Terrence Hendershott (UC Berkely)

Price Discovery Without Trading: Evidence from Limit Orders

Adverse selection in financial markets is traditionally measured by the correlation between the direction of market order trading and price movements. We show this relationship has weakened dramatically with limit orders playing a larger role in price discovery and with high-frequency traders’ (HFTs) limit orders playing the largest role. HFTs are responsible for 60–80% of price discovery, primarily through their limit orders. HFTs’ limit orders have 50% larger price impact than non-HFTs’ limit orders, and HFTs submit limit orders 50% more frequently. HFTs react more to activity by non-HFTs than the reverse. HFTs react more to messages both within and across stock exchanges.


Andrei Kirilenko (Imperial College London)

Latency in Automated Trading Systems

Time in an automated trading system does not move in a constant deterministic fashion. Instead, it is a random variable drawn from a distribution. This happens because messages enter and exit automated systems though different gateways and then race across a complex infrastructure of parallel cables, safeguards, throttles and routers into and out of the central limit order books. Add to it market fragmentation and you get a pretty complex picture about the effects of latency on price formation.


Pete Kyle (University of Maryland)

Dimensional Analysis and Market Microstructure Invariance

In this talk we focus on the combination of dimensional analysis, leverage neutrality, and a principle of market microstructure invariance to derive scaling laws expressing transaction costs functions, bid-ask spreads, bet sizes, number of bets, and other financial variables in terms of dollar trading volume and volatility. The scaling laws are illustrated using data on bid-ask spreads and number of trades for Russian stocks. These scaling laws provide useful metrics for risk managers and traders; scientific benchmarks for evaluating controversial issues related to high frequency trading, market crashes, and liquidity measurement; and guidelines for designing policies in the aftermath of financial crisis.


Albert Menkveld (VU Amsterdam)

High-Frequency Trading around Large Institutional Orders

Liquidity suppliers lean against the wind. We analyze whether high-frequency traders (HFTs) lean against large institutional orders that execute through a series of child orders. The alternative is that HFTs go “with the wind” and trade in the same direction. We find that HFTs initially lean against orders but eventually turn around and go with them for long-lasting orders. This pattern explains why institutional trading cost is 46% lower when HFTs lean against the order (by one standard deviation) but 169% higher when they go with it. Further analysis supports recent theory, suggesting HFTs “back-run” on informed orders.


Mark Podolskij (Aarhus University)

Testing for the maximal rank of the volatility process in noisy diffusion models

In this talk we present a test for the maximal rank of the volatility process in continuous diffusion models observed with noise. Such models are typically applied in mathematical finance, where latent price processes are corrupted by microstructure noise at ultra high frequencies. Using high frequency observations we construct a test statistic for the maximal rank of the time varying stochastic volatility process. We will show the asymptotic mixed normality of the test statistic and obtain a consistent testing procedure. Finally, we demonstrate some numerical and empirical illustrations.


Philip Protter (Columbia University)

High Frequency Trading and Insider Trading

The attorney general for New York State, Eric Schneiderman, said at one point that he believed that high frequency trading (in the sense of co-location, that is to say extremely high frequency trading) is used for insider trading. Inspired by his remarks we purport to indicate via a mathematical model how this could come to pass. We use the newly developed theory (by Y. Kchia and this speaker) on the enlargement of filtrations via a stochastic process to show how continual infinitesimal peaks at the order book can beget a type of insider trading, thereby explaining the casual observation of the attorney general.


Chris Rogers (University of Cambridge)

High-frequency data: why are we looking at this?

High-frequency financial data is certainly a `big data' problem, with all of the associated issues: what are the stylized facts of the data? what are we trying to do with the data? what are appropriate models? Industry approaches get the first two of these questions, but do badly on the third. Most academic studies do badly on all three. For example, it is a fairy tale that we can propose a time-invariant model for the evolution of high-frequency data, estimate the parameters of this model, and then apply the conclusions of an analysis that assumes that the paramters were known with certainty.  In this talk, I will try to identify what we might want to do with high-frequency data, critique some existing research agendas, and illustrate a possible way of dealing with the problem of optimally liquidating a given position before a given time.


Mathieu Rosenbaum (University Paris 6)

How to predict the consequences of a tick value change? Evidence from the Tokyo Stock Exchange pilot program

The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick value change on the microstructure of an asset. To that purpose, we analyze the pilot program on tick value modifications started in 2014 by the Tokyo Stock Exchange in light of this methodology. We focus on forecasting the future cost of market and limit orders after a tick value change and show that our predictions are very accurate. Furthermore, for each asset involved in the pilot program, we are able to de ne (ex ante) an optimal tick value. This enables us to classify the stocks according to the relevance of their tick value, before and after its modification.

This is joint work with Charles-Albert Lehalle and Weibing Huang.


(above in alpabetical order of speakers)

HFT2016 | Astrid Kollros  | Unversität Wien  | Oskar-Morgenstern-Platz 1  | 1090 Vienna  | Austria  |