Trading & Dealing in Security Markets - Essay Example

? Trading and Dealing in Security Markets October 14 Introduction High Frequency Trading – HFT makes use of powerful computers that automatethe trading process and where trading software takes the decisions to buy, sell or hold stock. The practice is also called as Algorithmic trading, Algo trading, automated trading or black box trading. Since the commands are executed by software systems, positions on various stocks are held for durations that vary from a few seconds to a few minutes. HFT has been blamed for ‘flash’ price variations where the prices changes very rapidly for no apparent reason (Lattemann, et al,, 2006). With high profits almost assured and reduction of losses to a minimum, many traders have started using HFT. In a way, HFT represents the inevitable move to high technology. Like many other areas of business processes such as procurement, automatic ordering in supply change management, ERP systems, humans are removed from decision-making and power is vested in machines. However, the process must be regulated and flash price increases must be controlled (Ye, 2011). This paper examines the pros and cons of HFT and makes recommendations on how regulators can address the concerns that market practitioners have over HFT. 2. Literature Review HFT was allowed from 1999 when electronic exchanges were authorised. In those days, the trading execution time was of many seconds while in 2011, the time is reduced to microseconds. In USA, 70% of all stock trade in 2007 was due to HFT while UK had 60% of HFT. Other markets such as China, Japan and other markets have a proportion of 80%. Conservative estimates are that in 2007, HFT strategies used by 300 traders made profits of 21 billion USD (Keehner, 2007). HFT is used for trading in futures options, hedge funds, equity and volatile scrip’s from sectors such as IT, biotechnology, currency and other stocks that see quick variations. HFT may not be very useful for stable instruments such as bonds, stocks of infrastructure firms, public sectors firms that see very little variation (Aldridge, 2009). 2.1. Unwanted effects of HFT HFT has been blamed for Flash price changes in a number of stock exchanges. On 6 May 2010, the US Stock Exchange and the Dow index sharply fell by 1000 points or 9% and then recovered in a few minutes (Scannel, 2 October 2010). Investigations showed that the crash occurred due to a single mutual firm fund that sold a large number of contracts worth 4.1 billion USD and this triggered a reaction from HFT machines that quickly dumped their positions thus pulling the Dow index down. The HFT machines then quickly bought stock at the lower prices and this made the stocks rise then dumped the stock and this ‘hot potato’ trading continued until equilibrium was brought in. All this happened in a few minutes due to the fragile and interconnected nature of the market (SEC, 30 September 2010). There are fears that since algorithms used in the software decision making are hidden, it is difficult to asses if HFT amplifies or dampens the market equilibrium prices. HFT also encourages Flash Trading where certain traders can see incoming buy or sell orders for securities about 30 milliseconds before others do. This lead called 'front running' allows HFT traders to execute their trading strategy ahead of others (NYT, 8 October 2012). HFT traders also have the advantage that they can turn their machines off when the risks and trends are against them and thus they can cut their losses. Overall, trading is thus reduced to a war of technology and speed of connectivity and the financial software and hardware market was worth 25.4 billion USD in 2011. Terms such as scalping, shorting have become common and HFT run transactions in less than 10 milliseconds (Gsell, 2009). 2.2. Advantages of HFT HFT brings equality in the market and a small trader with a single terminal running powerful HFT software can overcome billion dollar traders. HFT is also credited with bringing liquidity in the market, reducing risk of frauds perpetrated by large traders and reducing market volatility. It helps development and deployment of advanced technology and the use of advanced heuristic solutions in the financial sector (Goldfarb, 1 October 2010). 3. Analysis HFT uses powerful algorithms or code that analyse market information from different inputs, process it to understand the trends and then take the programmed investment decision. Profits are very narrow, in some cases, just a penny per share. However, since the trading is done at very fast speeds, the volume is very high and the reward per unit of risk - the Sharpe ratio is hundreds of times greater than the normal buy and hold practice of human traders. This creates instability and market regulators cannot control the market. Such an environment is not good for the economy. However, this is due to advancement and injection of new technology and it cannot be prevented. The Flash crash and profits some HFT firms are not doing anything illegal. Some regulatory measures are needed that will curb the side effects. However, too much regulation can drive the HFT firms to other nations from where they can launch their operations with impunity. 4. Conclusion Since HFT yields high profits, many more firms will take up this practice. HFT certainly has some ill effects on the stock movement and these needs to be constrained and regulated. The regulations must not place HFT under disadvantage and new technology must not be punished. 4.1. Recommendations Some recommendations are made for regulation of HFT. These are as given below (ICSCO, July 2011). SEC should use software that regulates and restricts the number of transactions executed for a particular stock by a trader per minute. As an example, traders should not be able to buy/ sell stock certain firms for more than say three times per minute. Traders should not be allowed to switch off their machines when stocks are falling. This will ensure HFTs remain operational even when they stand to make a loss, just as any day trader. When the Dow index sees a steep and rapid fall or rise in a few minutes frame, trading should be automatically shut off. This will ensure HFTs do not upset the market equilibrium. References Aldridge, I., 2009. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. Wiley Publications: London Goldfarb, Z., 1 October 2010. Report examines May's 'flash crash,' expresses concern over high-speed trading. Washington Post. Retrieved 14 October 2012 from http://www.washingtonpost.com/wp-dyn/content/article/2010/10/01/AR2010100103969.html Gsell. M., 2009. Algorithmic activity on Xetra. Journal of Trading, 4(3), pp. 74-86 ICSCO, July 2011. Regulatory Issues Raised by the Impact of Technological Changes on Market Integrity and Efficiency. International Organisation of Securities Commissions. Spain: Madrid Keehner, J., 2007. Milliseconds are focus in algorithmic trades. Reuters. Retrieved 14 October 2012 from http://www.reuters.com/article/2007/05/11/us-exchanges-summit-algorithm-idUSN1046529820070511 Lattemann, C., Loos. P., Gomolka. H., and Burgho. H. P., 2006. High Frequency Trading Costs and Benefits in Securities Trading and its Necessity of Regulations. Business & Information Systems Engineering, 2, pp. 93-110 NYT, 8 October 2012. High-Frequency Trading. New York Times. Retrieved 14 October 2012 from http://topics.nytimes.com/topics/reference/timestopics/subjects/h/high_frequency_algorithmic_trading/index.html Scannel. K., 2 October 2010. Flash Crash is pinned on one trade. Wall Street Journal. Retrieved 14 October 2012 from http://online.wsj.com/article/SB10001424052748703859204575525973854203534.html SEC, 30 September 2010. Findings regarding the market events of May 6, 2010: U.S. Securities and Exchange Commission. Retrieved 14 October 2012 from http://www.sec.gov/news/studies/2010/marketevents-report.pdf Ye. G., 2011. High frequency trading models. Wiley Publications: NY Read More