Introduction
The technique of high-frequency trading has been around for years, but especially close attention and a new surge of interest in it arose after the sensational release in May 2010 of the book by Michael Lewis “Flash Boys”. In fact, it is hard to call high-frequency trading methodology a revolutionary or even new. Although now, talking about it, everyone remembers high-performance computers and automatic programs with elements of artificial intelligence, but if to leave these technological “tricks” and the terms out of the brackets, everything becomes much easier. Actually, we are dealing with a business model, when using a variety of technical solutions, players quickly gather information and based on it can act before the rest of the market (Durden, 2014). However, high-frequency trading has managed to radically change the principles of exchange trading and destroy many of the stereotypes. For business, this concept is alien, but for the exchange of modern technologies, HFT market became an integral part of a powerful structure (Cartea, Jaimungal and Penalva, 2015).
Thus, high-frequency trading (HFT) represents one of the types of automated trading, in which the modern algorithms are used actively, processed with high-performance computers. At the same time, the most difficult trade operations are processed by machine in the shortest possible time and with the highest accuracy. After its occurrence, the system literally covered the entire market, taking almost half of the world’s trading. High-frequency algorithms have become really popular, respected and improved (Aldridge, 2013).
Historical Retrospective of High Frequency Trading Concept
It is believed that the basic implementation of this method in the trading took place in 1960 with the foundation of NASDAQ (National Association of Securities Dealers Automated Quotation), the first substantially automated stock trading system. Paradoxically, to a large extent, the core of high-frequency trading for the first time was revealed in October 1987, when the first instant stock market crashed. These events are called Black Monday. This market collapse was caused by excessive proliferation of the so-called program trading (when trading of securities is based on the software display) (Aldridge, 2013).
The maximum peak of the popularity of HFT was somewhere within 2005-2009. During several years, the leading traders of the planet picked up a promising and profitable direction, making it their main strategy. Already in 2010, the share of high-frequency trading accounted for more than 60% of all US stocks. However, this figure fell slightly in two years (Durden, 2014).
It is worth stating that the founder of this direction is a computer genius Steven Swanson. He was the person, whom in 1989 a thought to use high-performance computers for exchange trading came in mind. Regular guy managed to develop algorithms that allowed looking into the future of almost one minute. The program prompted the trader how stock prices would behave in the near future (Capgemini, 2012). At the same time, trade execution was assigned to the program. Two Steven’s mates, David Whitcomb and Jim Hawkes, participated in the creation of the first HFT Company. Together, they created the first office that worked as the Automated Trading Desk. They made literally impossible at that time – the processing of the order in a second. It was a crazy speed, because most of the applications were implemented on paper. Specialized “robots” brought huge profits, which led to the emergence of many followers (Aldridge, 2013).
In general, first automated trading systems (ATS) have appeared in the 90s of the twentieth century and were concentrated in the hands of large institutional investors. More large-scale distribution of trading robots began with the advent of the technical feasibility of trading on the stock exchange through the Internet and the development of brokerage trading terminals (Capgemini, 2012). Figure 1 shows how the evolution of algorithmic trading securities (which is a kind of high-frequency trading) proceeded over the past half-century (Durden, 2014).
Figure 1. Evolution of Algorithmic Trading Securities, including HFT (Durden, 2014)
In 2012, IT organizations invested millions dollars in HFTtechnology. There was one new computer chip developed specially for HFT arrangement of trades in 0.000000074 seconds; a suggested intercontinental cable with $300 million price is being constructed just to cut 0.006 seconds off transaction periods between NYC and London. The next 2013 year was characterized by continuance of discussions by economists on HFT treats after Flash Crash. The opinion of the respective Nobel Prize winner in Economics Mr. Spence concerns the necessity to forbid HFT (Cartea, Jaimungal and Penalva, 2015).
Thus, algorithmic trading has become popular among institutional investors in hedge funds, which carry out a large turnover of capital in order to get speculative profit.
High Frequency Trading’s Characteristics and Peculiarities
Over the past decade, the stock market has undergone considerable fragmentation. In many, a number of legislative initiatives implemented, which actually should have been to promote competition between trading platforms, contributes to such changes. Unplanned result of the introduction of new rules and regulations appeared in the dispersal of liquidity across multiple platforms (such as public exchanges and hidden pools). Coupled with the computerization of the marketplaces, it created favorable prospects for the technologically advanced players (Shorter and Miller, 2014).
Despite absence of formal meaning of high frequency trading, the U.S. Securities and Exchanges Commission points out several particular features, namely (Capgemini, 2012):
The application of very well thought-out and high-speed technological systems for generating, direction-finding, and accomplishing orders.
The application of personal information feeds from exchanges in addition to co-found servers with the intention of minimization of network and other forms of expectancies.
Sustaining very limit deadlines for starting and liquidating positions, causing the regular turnover of numerous minor positions in one or more economic tools.
Submitting many orders that are invalid soon after provision.
Keeping a small number, if any, of overnight positions.
The principle of HFT operation is simple. The securities are traded on the market in large quantities. At the same time, the administration of applications is carried out extremely quickly, in one millisecond or even less. The main objectives of the companies, which work on the algorithm, is to close all positions in securities by the end of the day. The main income in this case is a small margin on transactions with shares, which volumes are achieved due to the high speed and the amount of transactions. The main difficulty in the implementation of such a system is to achieve a minimum delay. For this, it is vital to optimize the structure of the entire trading and perform the installation of HFT servers in close proximity to the exchange gateways. In addition, in order to accelerate the processing of the incoming data, the work on the development of specific hardware units based on gate arrays with the possibility of reprogramming is performed. In many companies, special paid methods of elimination of are used. Exchange member may, if desired, make a certain payment and get the opportunity to see applications earlier than the other participants. The reserve of just thirty milliseconds for high-speed machines is enough to process received data and almost instantly place their orders. The remaining traders must be content with only “scraps” (Aldridge, 2013). Figure 2 shows the use of HFT by different traders (Ladd, 2014).
Figure 2. Application of HFT by Market Participants (Ladd, 2014)
Today, HFT-traders have a high rate of execution of transactions. They act quickly on a large scale and are always based on the maximum result. This work, in turn, gives the maximum market liquidity and reduces volatility. The main high-frequency trading success is an efficient algorithm and minimum delays in obtaining market data and execution of transactions. However, there are traders, who think that HFT-trading is too risky direction, which in the future could greatly affect the stability of the market. There is even a proposal to conduct strict control of companies that are engaged in such activities (Cartea, Jaimungal and Penalva, 2015).
Positive and Negative Effects of HFT
Therefore, trading robots today form a significant part of the turnover on the world stock exchanges, thus providing significant impact on the entire exchange infrastructure. In particular, this is reflected in the increase in trading volume, liquidity and raising short-term volatility and activity, with which trading robots expose applications, leads to a significant reduction in spreads. First of all, HFT companies make contribution of more than 50% of the equity turnover through capacity in certain important markets, and have a crucial impact on ensuring order flow, growing the liquidity rate. Usual liquidity suppliers, for example, market makers, currently earn refund fees through leveraging HFT approaches to comprise for the loss of proceeds affected by reduced spreads. Also, the application of algorithms and processors in trading has caused the values of securities being rationalized more often and more precisely. In more effective businesses, values reveal market data more rapidly and precisely. HFT facilitates this to take place through providing precise pricing at lesser time periods. Moreover, HFT has facilitated reduced spreads and lesser trading expenses, and such benefits are transferred to separate consumers, who invest ultimately in the markets by mutual and pension financial institutions. Finally, HFT has caused a noteworthy growth in the trading capacities of both, exchanges and Electronic Communications Networks (Capgemini, 2012).
Nevertheless, algorithmic trading has negative aspects of the impact on the stock market. One of the most important of these aspects is the over-reliance of markets on the activity of hyperactive trading robots, which arose in connection with the large-scale spread of ATS. Failure of a single robot system can lead to extremely negative consequences for the entire stock market. An example of such an impact is the case, which occurred March 16, 2009, on FORTS. During the evening session, the RTS Index futures fell in half an hour by 9% to 582 points (afternoon session closed at 647 points), by the end of the evening session, it played back a large part of the fall and closed at 612.5 points. According to the specialists of the RTS Stock Exchange, the tenderer had a failure in its trading robot, which for half an hour performed deliberately losing trades, thereby causing undue market fluctuations (Cartea, Jaimungal and Penalva, 2015).
Certain institutional financiers claim that some HFT approaches seek repetitive trading configurations and front run the establishment by noticing an entering order flow, subsequently which the HFT mechanism purchases the similar security and after that changes and trades it to the establishment at a somewhat higher price. Mentioned approaches from HFT members may unfavorably affect the approach and market effect expenses for such institutional financiers. Additionally, as HFT contains prompt intraday trading, including positions normally detained just for minutes, sometimes even only couple seconds, it may cause the increase in price variations and temporary volatility. Taking into account that HFT capacities are usually a comparatively high proportion of total trading, the price variations affected by specified approach can cause total market volatility. Moreover, the practice of performing trading operations and immediately terminating them just to generate automated procurement from other companies is an ethical matter that has been examined by various experts. Reduced effectiveness of traditional approaches to forecasting, along with the emergence of periods of inappropriately high and unpredictable market volatility can lead to the fact that over time, the stock market will be unable to perform one of its most important functions – estimation, since the dynamics of prices of market assets will be too mechanistic, detached from economic reality and economically unjustified. Finally, HFT companies influence distinctive services, namely co-position facilities and initial information feeds, which are usually not manageable for minor companies and retail financiers due to the fact that they cannot provide with the essential investments. Thus, smaller companies and investors face shortcoming. Furthermore, certain HFT companies frequently start trading operations only for the liquidity refund; however this does not enhance value to the retail or durable financier (Capgemini, 2012).
Thus, trading robots today have a significant impact on the exchange infrastructure. Further spread of algorithmic trading systems can carry significant risks for the normal functioning of the stock market. While such risks are unlikely to be the cause for rejection of algorithmic trading as a whole, its influence on the stock market requires attention on the part of regulators.
It is worth mentioning that in order to prevent possible negative effects of algorithmic systems on the market infrastructure, exchanges and regulators are attempting to control certain trading robots. Specific concerns of exchanges induce technological risks associated with the increasing flow of applications from algorithmic systems, most of which does not lead to actual transactions (Aldridge, 2013). Many Western regulators (US Commission on the Securities and Exchange Commission, the British Financial Services Authority, etc.) raise the question of restricting the activities of trading algorithms. The first steps in this direction have already been made. In 2014, in Italy the Tobin tax, which applies to high-frequency trading and transactions with derivatives on stocks, became effective (Cartea, Jaimungal and Penalva, 2015). In the USA, Commodity Futures contract Trading Committee (CFTC) fined Panther Energy Trading in the amount of $2.8 million for the “spoofing”, namely the misleading of other market participants about the real ratio of demand and supply through the high-nomination and removal of applications. One of the world’s largest currency pairs’ electronic trading platforms EBS ICAP for trading a pair of Australian dollar-US dollar introduced an innovation, when the traditional algorithm of applications’ processing FIFO is replaced by a random selection of applications. This measure is designed to balance the chances of high-frequency algorithmic systems and traders (Shorter and Miller, 2014).
Thus, the widespread and increased efficiency of algorithmic systems raise the question of the need for human intervention in the process of trade in general. At the moment, some of the largest investment banks have reoriented to ensure that hiring professional developers of trading robots, not traders. If these trends continue in the future, only robots can remain at the stock exchange, which will compete with each other, while ordinary investors are not able to compete with them. In this case, market pricing will be completely mechanistic and will be based on the desire to beat each other’s robots (Cartea, Jaimungal and Penalva, 2015).
Conclusion
Trade automation allows effectively using trends in the price movements of financial assets for gaining market returns that has played a particularly important role in the background of strengthening the mutual influence of the world markets, where for ordinary traders it has become hard enough to keep track of a large number of indicators, when making investment decisions. With a distinct advantage over the traditional way of committing stock transactions, algorithmic trading allows neutralizing many of the shortcomings inherent in the stock trading. In a short time, this led to such a rapid spread of algorithmic systems that today are an important factor in the development of stock markets. The main advantages of HFT include an efficient algorithm, minimum delays in obtaining market data and execution of transactions, the increase in trading volume, liquidity and raising short-term volatility and activity, with which trading robots expose applications, leads to a significant reduction in spreads (Capgemini, 2012).
However, the analysis of the impact of the spread of algorithmic trading on the stock market and measures, taken against it by exchanges and regulators, shows that to date, due to the significant positive impact of algorithmic trading on the market, severe restrictive measures have not been applied yet. Exchanges and regulators now refrain from using such measures as they may lead to painful for exchanges reduction of trade volumes, as well as cause significant outflow of liquidity.
Therefore, many experts have serious doubts regarding the “bright future” of high-frequency trading. Further development needs huge costs that are no longer paid off. Even large companies refuse from further developments, not to mention the small market representatives. Additionally, high trading is incompletely protected from a variety of programming errors that result in huge losses (Aldridge, 2013; Shorter and Miller, 2014).
It should be concluded that it is very difficult to predict the direction the market will go next. Some participants are betting on new types of trading, with a longer forecasting period. Others continue to believe in the power of HFT and try to get the maximum of it. It is still difficult to say how long it will last. In any case, we can say that in this type HFT will not live long. Perhaps, there will be a powerful breakthrough, but prospects are not visible yet.
References
Aldridge, I. (2013). High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. Hoboken: Wiley.
Capgemini (2012). High Frequency Trading: Evolution and the Future. [online]. Available from: <https://www.capgemini.com/resource-file-access/resource/pdf/High_Frequency_Trading__Evolution_and_the_Future.pdf> [Accessed: 30 March 2016]
Cartea, A., Jaimungal, S. and Penalva, J. (2015). Algorithmic and High-Frequency Trading (Mathematics, Finance and Risk). Cambridge: Cambridge University Press.
Durden, T. (2014). High Frequency Trading: All You Need To Know. [online]. Available April 7, 2014, from: <http://www.zerohedge.com/news/2014-04-06/high-frequency-trading-all-you-need-know> [Accessed: 30 March 2016]
Ladd, J. (2014). High Frequency Trading or High Frequency Technology. [online]. Available December 15, 2014, from: <https://epta.fia.org/articles/high-frequency-trading-or-high-frequency-technology> [Accessed: 30 March 2016]
Shorter, G. and Miller, R. S. (2014). High-Frequency Trading: Background, Concerns, and Regulatory Developments. [online]. Available from: <https://www.fas.org/sgp/crs/misc/R43608.pdf> [Accessed: 30 March 2016]
