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High Frequency Trading Acceleration using FPGAs

Abstract

This paper presents the design of an application specific hardware for accelerating High Frequency Trading applications. It is optimized to achieve the lowest possible latency for interpreting market data feeds and hence enable minimal round-trip times for executing electronic stock trades. The implementation described in this work enables hardware decoding of Ethernet, IP and UDP as well as of the FAST protocol which is a common protocol to transmit market feeds.

For this purpose, we developed a microcode engine with a corresponding instruction set as well as a compiler which enables the flexibility to support a wide range of applied trading protocols. The complete system has been implemented in RTL code and evaluated on an FPGA. Our approach shows a 4x latency reduction in comparison to the conventional Software based approach.

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The synchronized and long-lasting structural change on commodity markets: evidence from high frequency data

Abstract

This paper analyses the intraday co-movements between returns on several  commodity markets and on the stock market in the United States over the 1997-2011 period. By exploiting a new high frequency database, we compute various rolling correlations at (i) 1-hour, (ii) 5-minute,  (iii) 10-second, and (iv) 1-second frequencies. Using this database, we document a synchronized structural break, characterized by a departure from zero, which starts in the course of 2008 and continues thereafter. This is consistent with the idea that recent financial innovations on commodity futures exchanges, in particular the high frequency trading activities and algorithm strategies have an impact on these correlations.

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Financial black swans driven by ultrafast machine ecology

Abstract

Society’s drive toward ever faster socio-technical systems means that there is an urgent need to understand the threat from ‘black swan’ extreme events that might emerge. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly. Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system’s internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast ‘fractures’ and the slow ‘breaking’ of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.

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Federal Market Information Technology in the Post Flash Crash Era: Roles for Supercomputing

Abstract

This paper describes collaborative work between active traders, regulators, economists, and supercomputing researchers to replicate and extend investigations of the Flash Crash and other market anomalies in a National Laboratory HPC environment. Our work suggests that supercomputing tools and methods will be valuable to market regulators in achieving the goal of market safety, stability, and security. Research results using high frequency data and analytics are described, and directions for future development are discussed. Currently the key mechanism for preventing catastrophic market action are “circuit breakers.” We believe a more graduated approach, similar to the “yellow light” approach in motorsports to slow down traf?c, might be a better way to achieve the same goal. To enable this objective, we study a number of indicators that could foresee hazards in market conditions and explore options to con?rm such predictions Our tests con?rm that Volume Synchronized Probability of Informed Trading (VPIN) and a version of volume Her?ndahl-Hirschman Index (HHI) for measuring market fragmentation can indeed give strong signals ahead of the Flash Crash event on May 6 2010. This is a preliminary step toward a full-?edged early-warning system for unusual market conditions.

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High Frequency Lead/lag Relationships Empirical facts

Abstract

Lead/lag relationships are an important stylized fact at high frequency. Some assets follow the path of others with a small time lag. We provide indicators to measure this phenomenon using tick-by-tick data. Strongly asymmetric cross-correlation functions are empirically observed, especially in the future/stock case. We confirm the intuition that the most liquid assets (short intertrade duration, narrow bid/ask spread, small volatility, high turnover) tend to lead smaller stocks. However, the most correlated stocks are those with similar levels of liquidity. This lead/lag phenomenon is not constant throughout the day, it shows an intraday seasonality with changes of behaviour at very specific times such as the announcement of macroeconomic figures and the US market opening. These lead/lag relationships become more and more pronounced as we zoom on significant events. We reach 60% of accuracy when forecasting the next midquote variation of the lagger using only the past information of the leader, which is significantly better than using the information of the lagger only. However, a naive strategy based on market orders cannot make any profit of this effect because of the bid/ask spread.

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The race to zero

1.  Introduction

Stock prices can go down as well as up.  Never in financial history has this adage been more apt than on 6 May 2010.  Then, the so-called “Flash Crash” sent shocks waves through global equity markets.  The Dow Jones experienced its largest ever intraday point fall, losing $1 trillion of market value in the space of half an hour.  History is full of such fat-tailed falls in stocks.  Was this just another to add to the list, perhaps compressed into a smaller time window?

No.  This one was different.  For a time, equity prices of some of the world’s biggest companies were in freefall.  They appeared to be in a race to zero.  Peak to trough, Accenture shares fell by over 99%, from $40 to $0.01.  At precisely the same time, shares in Sotheby’s rose three thousand-fold, from $34 to $99,999.99.  These tails were not just fatter and faster.  They wagged up as well as down.

The Flash Crash left market participants, regulators and academics agog.  More than one year on, they remain agog.  There has been no shortage of potential explanations.  These are as varied as they are many:  from fat fingers to fat tails; from block trades to blocked lines; from high-speed traders to low-level abuse.  From this mixed bag, only one clear explanation emerges:  that there is no clear explanation.  To a first approximation, we remain unsure quite what caused the Flash Crash or whether it could recur.

That conclusion sits uneasily on the shoulders.  Asset markets rely on accurate pricing of risk.  And financial regulation relies on an accurate reading of markets.  Whether trading assets or regulating exchanges, ignorance is rarely bliss.  It is this uncertainty, rather than the Flash Crash itself, which makes this an issue of potential systemic importance.

 In many respects, this uncertainty should come as no surprise.  Driven by a potent cocktail of technology and regulation, trading in financial markets has evolved dramatically during the course of this century.  Platforms for trading equities have proliferated and fragmented.  And the speed limit for trading has gone through the roof.  Technologists now believe the sky is the limit.

This rapidly-changing topology of trading raises some big questions for risk management.  There are good reasons, theoretically and empirically, to believe that while this evolution in trading may have brought benefits such as a reduction in transaction costs, it may also have increased abnormalities in the distribution of risk and return in the financial system.  Such abnormalities hallmarked the Flash Crash.  This paper considers some of the evidence on these abnormalities and their impact on systemic risk.

Regulation has thin-sliced trading.  And technology has thin-sliced time.  Among traders, as among stocks on 6 May, there is a race to zero.  Yet it is unclear that this race will have a winner.  If it raises systemic risk, it is possible capital markets could be the loser.  To avoid that, a redesign of mechanisms for securing capital  market stability may be needed.

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