High frequency trading - page 6

High-frequency trading (HFT) is algorithmic trading that utilizes sophisticated low latency technologies to conduct automated market making, spread trading, arbitrage and macrotrading. HFT currently generates approximately 35% and 70% of UK and U.S. equity trades, respectively, and thereby exerts significant influence over the global financial markets. The ubiquitous diffusion of HFT systems proceeds in contrast to the discord and controversy engendered by those systems. Financial regulators, market participants, academics and members of the media express diverging, and sometimes exaggerated, views with respect to HFT benefits and risks. The May 6, 2010 Flash Crash, which temporarily erased $1 trillion in market value, highlights the potential for amplification of financial anomalies by HFT systems. We argue for an informed dialogue between stakeholders to realize a common domain of discourse, and an appropriate and resilient regulatory framework. To this end, we have conducted a survey that collates the opinions of academics, market participants and regulators, with respect to HFT benefits and risks. Our findings are presented in this report.

We analyze the impact of high frequency trading in financial markets based on a model with three types of traders: liquidity traders, market makers, and high frequency traders. Our four main fi ndings are: i) The price impact of the liquidity trades is higher in the presence of the high frequency trader and is increasing with the size of the trade. In particular, we show that the high frequency trader reduces (increases) the prices that liquidity traders receive when selling (buying) their equity holdings. ii) Although market makers also lose revenue to the high frequency trader in every trade, they are compensated for these losses by a higher liquidity discount. iii) High frequency trading increases the volatility of prices. iv) The volume of trades doubles as the high frequency trader intermediates all trades between the liquidity traders and market makers. This additional volume is a consequence of trades which are carefully tailored for surplus extraction and are neither driven by fundamentals nor is it noise trading. In equilibrium, high frequency trading and traditional market making coexist as competition drives down the profi ts for new high frequency traders while the presence of high frequency traders does not drive out traditional market makers.

What does “bad” HFT look like, how often does it happen, and how do we detect it?

Focussing on the Negative Aspects of HFT

In our previous report High Frequency Trading – The Good, The Bad, and The Regulation, we identified and grouped a variety of High Frequency Trading strategies. We concluded that classifying all HFT as “bad” was too broad a generalisation, as we found evidence of strategies that improved market quality alongside those that did not.

We think it is important to highlight liquidity-enhancing strategies such as market making or statistical arbitrage, which seek to correct short term mispricing. However, this report will focus specifically on strategies which seek to create short term mispricing, and how to respond accordingly to this “bad” HFT.

Concrete Examples and Detection Techniques

In this piece we highlight a subset of negative high frequency trading, examining strategies such as: Quote Stuffing, Layering/Order Book Fade and Momentum Ignition. We analyse a number of different aspects of these strategies, providing examples to help demonstrate what they “look” like, as well as broader data statistics on how often they occur and how we detect them.

Exhibits 1 and 2 below provide examples of Quote Stuffing, which is one of the most visually obvious forms of HFT. We will delve into Quote Stuffing in more detail in the next section. We then focus our analysis on Layering/Order Book Fade and Momentum Ignition in subsequent sections. Finally, we highlight the ways in which AES responds to “bad” HFT, protecting our clients and enhancing our strategies.

The Problem

• Accurate and efficient short term prediction of one change in the price of an asset.

• A number of strategies developed over time, from simple and fast to sophisticated models. These include methods based on time series analysis, support vector machines, hidden Markov models, nearest neighbor classifiers, etc.

This text in form of a pdf-document is provided for convenience purposes only. Only the original text of the FAQs as published under the heading “Frequently asked questions relating to the High-frequency Trading Act (Hochfrequenzhandelsgesetz)” on the BaFin website is authentic.

1) When does the authorisation requirement for highfrequency trading as defined in the High-frequency Trading Act (hereinafter High-frequency Trading) take effect?

The High-frequency Trading Act entered into effect on 15 May 2013. However, with respect to the authorisation requirement, the Act stipulates transitional periods for applying for an authorisation (see section 64p of the Banking Act (Kreditwesengesetz – KWG). A transitional period of six months, i.e. until 14 November 2013, for submitting an application for authorisation is stipulated. For enterprises that are not domiciled in Germany and that are not enterprises within the meaning of section 53b (1) sentences 1 and 2 of the KWG, a transitional period of nine months until 14 February 2014 is stipulated

Capital markets are sensitive to quick changes in sentiments. Sometimes those changes are related to real changes in the economy, or more accurately, to new information about potential changes in the economy in the future—the discovery of large amounts of gas, unanticipated changes in monetary policy, the break-out of war, the breaking of a bubble. In many cases, these events or the new news had been partially anticipated. It was possible that war would break out. But the break-out of a war changes what was a possibility, or even something that had a high likelihood of occurring, into a certainty.

But often, those changes in sentiments are related to perceptions, to beliefs, which can spread across the investment community, with little relationship to underlying fundamentals. Rob Shiller and others have documented that much of the variation in stock market prices cannot be explained by changes in fundamentals, or news about changes in fundamentals2. There was nothing so dramatic unveiled in October 1987 that could come any way near accounting for the wiping out of a quarter of the value of the stock market. There were no changes in the fundamentals that could account for the wiping out of a trillion dollars of stock market value in the flash crash of May, 2010.

Keynes talked about these changes in sentiments in terms of animal spirits. Using a context that today would be viewed as politically incorrect, he focused on the market as a beauty contest, where the objective was not to ascertain the “fundamentals,” but rather to assess what others were thinking. Modern research in psychology, sociology, and social psychology has begun to explore how each of our beliefs are shaped by those around us; there can be contagion—spirits of optimism and pessimism can spread. (There can also be rational expectations contagion—actions of individuals can convey information; but the observed patterns entail more than this.)

A medieval architect named Marcus Vitruvius Pollio determined the ideal proportions of a man's body around the birth of Christianity: a man should be eight heads high. Vitruvius went to great lengths at describing how body parts should be related to each other, much like mechanistic parts in architecture or civil engineering. Later, around 1490, Leonardo da Vinci famously pictured the ideal proportions of a man in an illustration now known as the “Vitruvian Man.” Da Vinci also coined an analogy between the workings of the human body and the universe. The symmetry of the man's posture in da Vinci's illustration is striking, representing the over-ruling systematicity everywhere. Whether ideal proportions of a high-frequency trader exist is the key question we ask in this article. What should an ideal high-frequency trader look like? To what extent should he be a human and to what extent a machine? If systematicity rules over trading, mechanistic parts may be expected to take control in the long run. Applying the ideology of Vitruvius and da Vinci, the ideal trader could be one hundred percent machine. And if he were a hybrid, as might be reasonably assumed in financial markets today, what are the proportions currently? Further, how fast should the hybrid trader converge to the proportions of the “ultimate trader”? It appears plausible that sometime in the future, a machine is able to think like a man with distinctive “Vitruvian symmetry and proportions.”