The Impact of Algorithmic Trading on Mutual Funds Performance - Research Proposal Example

Algorithmic trading Name: Course: Tutor: Date: Algorithmic trading Literature Review Section Algorithmic trading, automated trading, or electronic financial markets is the use of computer programs and systems to enter trading orders. This is possible with the computer algorithm that is used to decide on all aspects of orders such as quantity, price, and timing (Chan, 2008, p. 167). This will allow initiation of orders without human intervention or interference. Algorithm trading has been widely used in mutual funds, pension funds, and other investor driven (buyer side) institutional traders (Pole, 2007, p. 43). According to Aldridge (2009), mutual funds performance has improved in the recent years because of the introduction of algorithmic trading with high frequency trading (HFT). The high-frequency trading is common where computers are set to make elaborate decisions that initiate orders based on the data and information received electronically (Aldridge, 2009, p. 289). Mutual funds are managed professionally because it is a collective investment. Money is pooled from many investors to buy short-term money market, bonds, stocks, or other securities that may be available in the market. In US and EU, a third of the stocks that were traded in 2006 were transacted using algorithms or automatic programs. This was a survey conducted by Boston-based consultant firm Aite Group and financial services industry research (Rieves & Wagner, 2009, p. 94). It found out that 73% of all US and EU equity trading volumes were transacted through HFT in 2009. In 2006, over 40% of the orders and transactions in the London Stock Exchange were entered through algorithmic trading and 70% was expected in 2007 (Rishi, 2009, p. 195). European markets and US markets have the highest proportion in the world for managing mutual funds effectively than any other market. The 90% mark and forecast of 2009 was arrived at after 80% achievement in 2008. The foreign exchange markets have adopted the new market trading system that is active in trading and having a positive impact on the performance and productivity of the organization. Mutual markets have been fairly and easily integrated into the US mutual fund market by 2010; all options in the mutual funds were expected to be computer generated by December 2010 according to Aldridge (2009). Most of the data existing in the literature focus on the study of American and European Union yet there are other stock exchanges in the world in China, Japan, Middle East countries, Africa, and Asia. According to Abbink (2010), most investors in the Middle East believe they can introduce and adopt new methods of trading without much research compared to those in America and European Union (Cralle & Leshik, 2011, p. 200). Essvale Corporation Limited (2007) believe that mutual funds management in US will improve efficiency and performance in the firms; hence, enhancing competition. It is manage by mutual fund manager or sponsor. The manager buys and sells the fund’s investment according to the objectives of the investment. In addition, mutual funds are safe in US because they are registered under Securities and Exchange Commission (SEC) and are overseen by the board of directors. Research and training should extend to all countries in the world especially in Japan where they have the new technology (Rishi, 2009, p. 199). Kendall (2007) has emphasized this because transacting business online for instance buying and selling shares will cut on the cost of transportation, transaction, and employing many people to keep records (Essvale Corporation Limited. 2007, p. 123). In America and European Union, mutual funds transaction through algorithmic trading has taken place, there is great improvement and stakeholders are gaining a lot because it reduces time wasted and reduces on the cases of fraud because all transactions are computerized. According to Fabozzi, Anson, CFA. & Jones (2010) American and European Union investors on stock exchange should maintain and introduce in other markets not only in mutual funds but also on bond and Treasury bill markets in others that have not computerized their operations (Abbink, 2010, p. 376). Other stock markets that have the highest potential are the Shenzhen Stock Exchange and Shanghai Stock Exchange that have attracted foreign investors who want to benefit from algorithmic trading especially in the mutual fund market. Radu (2010) is for the view that the best models to be used are the volume weighted average price (VWAP) and implementation shortfall (IS) (Vancas, 2010, p. 55). Chan (2008) is for the view that mutual funds returns and transactions can be enhanced by sharing information in all markets globally so that economic development and growth can be realized globally. He further criticized Americans for being mean in sharing information on the new methods of operating businesses yet it could improve business partnership and expanding its business operations, expanding customers base, and increasing income in the country (Vancas, 2010, p. 56). Methodology The model used in the algorithmic trading is the one for New York Stock Exchange. The mutual fund performance evaluation and application of system BCC Model by Yu-chuan is important in evaluating mutual fund performance. In addition, it will ease comparison of results between system BCC model and BCC model (Chan, 2008, p. 169). It is ideal because it is in the market that algorithmic trading stated and has the required information. Expense ratio according to Vancas (2010) is an important metric used when comparing funds performance of different investors in different markets. Shenzhen Stock Exchange and Shanghai Stock Exchange differ with how NYSE operates; hence, expense ratio is used to determine the profits earned after deducting the expenses. Better management is not proportional with high expenses according to Cralle & Leshik (2011). To improve management of mutual fund and to solve problems facing the market, mutual funds should be subjected to low charges because expense funds usually underperform index and average funds (Lajoux, et al. 2010, p. 525). Mutual Fund Expense Ratio = Total Operating Expenses Average Net Asset Value The typical range accepted is between 0.18% and 2%. This is possible if comparison is required between different market products because it will help determine whether expenses are high or low so that it does not affect profitability (Cralle & Leshik, 2011, p. 207). According to Rieves & Wagner (2009) rate of return on investment is used in the New York Stock Exchange market to determine the returns or profits the firm or investor gain from the investment (Fabozzi, 2008, p. 167). Rate of Return = (Return – Capital)/ Capital) * 100% Rates of return vary depending on average annual rate of returned determined by the company. The percentage is accurate because algorithmic trading is computerized. There is no manipulation of information as it has been in the past (Essvale Corporation Limited. 2007, p. 127). Comparison is easy in different stock exchange and sharing of information is faster. Computer systems are set on geometric or compound rates; hence, the model used by the New York Stock Exchange is the best yardstick to measure investment performance in the long run. This is according to Pole (2007) who came up with this model used in America to solve the arithmetic and manipulation of information in the past (Radu, 2010, p. 312). According to Rishi (2009), BCC model adopted by the New York Stock Exchange has improved mutual fund performance because average score of the stock funds is less than average score of balanced fund. Furthermore, this model can be introduced in other mutual markets through their stock exchange i.e. the Shenzhen Stock Exchange and Shanghai Stock Exchange (Longo, 2009, p. 69). Information can be shared freely using this model because it can accommodate changes in the business environment. It is not easy to be manipulated because errors can easily be detected. It will avoid distinction between stock funds and balanced funds; hence, easing performance assessment (Kendall, 2007, p. 76). Risk adjustment ratio S = (R – Rf / d) R – Rate of return Rf – risk free rate D – Standard deviation Or RAR = (trade / days) * (average profit / STDV) Where STDV is the standard deviation of the trades within the period provided Days are the number of days in the period of operation This risk adjustment ratio meets all the requirements; hence, bring out the desired outcomes. HFT – High Frequency Trading High frequency trading has positive implications on algorithm because it relies on the modern technology. It makes transactions faster; hence, temporary price disparities ensure profits are gained. It is used to detect and approve large orders because it can split them into smaller lots that are manageable. In addition, it increases high liquidity in the market; hence, investors with money in mutual funds benefit a lot because money can transact large volumes faster within the shortest time possible (Rishi, 2009, p. 195). This model can be adopted by other markets globally because its application is not complex and can give accurate information to all stakeholders without biasness. According to Lajoux, A; Lajoux & Monks (2010), the mutual fund performance can be built well using the capital asset pricing model (CAPM) because the model adopted accommodate all assets of different individual investors and companies (Rieves & Wagner, 2009, p. 99). The most famous Hong Kong Stock Exchange can operate without complications because of the number of investors who are increasing significantly. This is the only model accepted and reliable to most investors. Risk can easily be measured and detected in the mutual fund using the model because of its clear procedures and processes (Abbink, 2010, p. 379). Conclusion In conclusion, algorithmic trading has improved performance of many stock exchanges especially in America and European Union because they were the first markets to adopt the system of managing mutual funds (Pole, 2007, p. 53). Algorithmic trading is important and should be adopted in all countries to manage mutual funds because it has been found to improve performance in the market. Algorithmic trading utilizes computer in its operations because it is faster and efficient. Technology is utilized and the BCC model suits the method of managing mutual funds (Vancas, 2010, p. 58). References: Abbink, J. B. 2010. Alternative Assets and Strategic Allocation: Rethinking the Institutional Approach. New York: John Wiley and Sons. Pp. 376-379. Aldridge, I. 2009. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. New York: John Wiley and Sons. Pp. 283-287 Chan, E. P. 2008. Quantitative Trading: How to Build Your Own Algorithmic Trading Business. New York: John Wiley and Sons. Pp. 167-169 Cralle, J. & Leshik, E. 2011. An Introduction to Algorithmic Trading: Basic to Advanced Strategies. New York: John Wiley and Sons. Pp. 200-207. Essvale Corporation Limited. 2007. Business Knowledge for IT in Investment Management: The Complete Handbook for IT Professionals. Dubai: Essvale Corporation Limited. Pp. 123- 127. Fabozzi, F. J. 2008. Handbook of Finance: Investment management and financial management. New York: Wiley. Pp. 165-167. Fabozzi, F. J; Anson, M. J; CFA. & Jones, F. J. 2010. The Handbook of Traditional and Alternative Investment Vehicles: Investment Characteristics and Strategies. New York: John Wiley and Sons. Pp. 433-437. Kendall, K. 2007. Electronic and algorithmic trading technology: the complete guide. Salt Lake City: Academic Press. Pp. 70-76 Lajoux, A; Lajoux, R. & Monks, R. A. G. 2010. Corporate Valuation for Portfolio Investment: Analyzing Assets, Earnings, Cash Flow, Stock Price, Governance, and Special Situations. New York: Bloomberg Press. Pp. 522-525. Longo, J. M. 2009. Hedge fund alpha: a framework for generating and understanding investment performance. New York: World Scientific. Pp. 64-69. Pole, A. 2007. Statistical arbitrage: algorithmic trading insights and techniques. New York: John Wiley and Sons. Pp. 43-53. Radu, S. 2010. Financial Cryptography and Data Security: 14th International Conference, FC 2010, Tenerife, Canary Islands, January 25-28, 2010, Revised Selected Papers. New York: Springer. Pp. 302-312. Rieves, R. A. & Wagner, W. H. 2009. Investment Management: Meeting the Noble Challenges of Funding Pensions, Deficits, and Growth. New York: John Wiley and Sons. Pp. 94-99. Rishi, K. N. 2009. Inside the black box: the simple truth about quantitative trading. New York: John Wiley and Sons. Pp. 195-198. Vancas, I. 2010. Due Diligence and Risk Assessment of an Alternative Investment Fund. Olympic: Diplomica Verlag. Pp. 55-58. Read More