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High Frequency Trading and Risk Management
Introduction
In the recent past decades, a significant transformation in the financial market as a result
of improvement in information technology has been witnessed. The development in information
technology, across the world, has led to the establishment of new opportunities and ideas that
individuals use to make money in the market. Since 1990's, computer-aided trading has been
used by investors to make money. However, the markets experienced an increase in the trading
capacity for the algorithmic trading from 2006 to 2007. This move has been precipitated by the
implementation of the network management systems (NMS), and the Market in Financial
Instruments Directive (MiFID) regulations in the United States and Europe, respectively. The
implementation of the two regulations ensured that brokers made their trades based on the best
prices regardless of the basis of the exchange.
Background and Development of High Frequency Trading (HFT)
The High Frequency Trading (HFT) is a modern method of financial market trading that
has stirred public attention. The HFT is an algorithmic trading that utilizes refined technological
tools and complex algorithmic in analyzing and executing orders from multiple markets based on
the prevailing market condition. The HFT is based on the speed of executing orders as the fastest
investors acquire more profits as compared to slow executors. There are some divided ideologies
about the HFT and its impacts on the financial markets. This paper will focus on explaining HFT
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and some strategies used by firms employing HFT practices. An analysis of the HFT effects and
risks on the market will also be evaluated. The High Frequency Trading was in place from 1999,
after the authorization of electronic exchanges in 1998 by the United States Securities and
Exchange Commission (SEC). Since its inception, HFT has undergone numerous
transformations with the aim of ensuring efficiency within the system (Aldridge 54).
Algorithmic Trading (AT)
HFT is recognized as a subset of Algorithmic Trading (AT). Algorithmic trading is
described as a method of participating in the trade financial markets through the use of computer-
aided algorithms. Algorithms are programmed in order to analyze a single financial asset, such as
stock, which figures out the proper method of identifying the best trading strategy. These
programmed algorithms are, usually, based on practices, which were used prior to the "IT-era."
For instance, such practices could be utilized to figure out arbitrage opportunities or to evaluate
the best method of making a vast investment using a long-term approach. One of the advantages
of using algorithms in analyzing these financial assets is that they are faster as compared to
human beings. In 2000, HFT in the United States amounted to 10 percent of equity orders and
the practice grew rapidly in the following years. As technological advancements continue to take
place, traders have become more aware of the HFT strategies, which have increased the
percentage of traders engaging in trading operations (Ye 67).
High Frequency Trading
In order to distinguish HFT from AT, it is imperative to look at some HFT features that
distinct from AT. For instance, an HFT approach entails numerous amounts of orders, and placed
orders can be cancelled. Investors are supposed to hold their positions within a short span of time
and for a low latency. In High Frequency Trading, time is an essential factor, since it is measured
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in microseconds. According to statistics, the average stock in the United States can be held in the
market for 22 seconds. In the United States, 73 percent of the trading volume in the equity
market is as a result of HFT. However, there are only 2 percent of 20,000 trading firms using the
HFT strategy (Halsey 55).
There have been numerous definitions in regard to the HFT and the theory of how it
operates in the market. HFT is characterized by a full automated mechanism that uses a very low
latency and entails a constant high messaging frequency. According to the HFT operations, high
frequency traders use a range of strategies that could involve statistical arbitrage strategies that
tend to facilitate pricing efficiency. According to a consultancy Tabb Group, HFT was estimated
to have made up to 51 percent of equity trades in 2012, in the United States. In Europe, on the
other hand, it made up to 39 percent of the cash market trade value. The improvements in
technology have increased HFT efficiency in the developed countries, limiting chances of illicit
trading practices. The developing countries have also witnessed a relative growth in HFT
although, on a small trading base. In the modern society, HFT operations have become more
transparent and efficient compared with the former market structures (Aldridge 26).
Common HFT Strategies
Strategies embraced in the HFT strategy are simple, although the actual implementation
of the respective strategies remains to be a secret for the company. In the current world, the
financial market for HFT and algorithms is gradually transforming with recent technological
development and adoption. One of the simple HFT strategies to understand is the Statistical
Arbitrage. This strategy is utilized through the provision of imbalances in financial asset prices in
various markets and the difference in profits. Statistical arbitrage can also entail related assets
such as derivative and the subsequent underlying asset. As a result, an increase in the asset will
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have a positive effect on the derivative. Therefore, the strategy becomes effective and profitable
when an investor takes advantage of the variation before the derivative price has settled (Malz
87).
The Filter Trading is another strategy used in HFT that analyses the financial asset by
focusing on current events and new and their impact on the public. The algorithm evaluates the
impact of an asset, given market information, and provides a profitable move based on the public
reaction after the announcement. Like other strategies, filter trading is effective when an investor
undertakes a quick decision based on the market profitable signs. This method also focuses on
stocks behavior, such as bulk trading volumes and takes advantage of an opportunity in the
market (Ye 76).
The final HFT strategy is referred to as Rebate Trading and it is based on rebates issued
