High Frequency Trading and Risk Management

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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