Proposed Research in Financial Market Simulation

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With the widely emerging electronic platforms in financial market for the exchange of all types of financial instruments (i.e. securities, derivatives, commodities and futures), the electronic centralized order book has become the standard market mechanism for price discovery in today’s financial markets. As a result, many investors now employ algorithmic trading to automatically make trading decisions, submit orders, and manage those orders after submission. Algorithmic trading is the use of computer software to help make and execute trading decisions based on some pre-programmed computer algorithms [1]. According to a report by Reuters and Bloomberg, algorithmic trading accounts for over 73% of U.S. equity volumes in 2011. The widely use of algorithmic trading has broad impacts on the financial markets. The effects of algorithmic trading especially high-frequency trading (one type of algorithmic trading which is characterized by its short portfolio holding periods [2]) on the market price stability has been widely recognized since the event of ‘Flash Crash’ in the E-Mini S&P futures market which occurred on May 6, 2010. The 2010 Flash Crash was said to be initiated by an unusually large number of E-Mini S&P 500 contracts selling of a large mutual fund in a joint report by the U.S. Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC). The report detailed how the large sell exhausted available buyers and how the high-frequency traders (HFT) started aggressively selling, accelerating the effect of the mutual fund’s selling and contributing to the sharp price drop that day [3]. After this event, a various number of policies have been proposed to create ‘circuit breakers’ and allow markets to r... ... middle of paper ... ...ze based on the strength of the trend. Also, some intraday trading strategies will be developed with the hope that it will further reduce the volatility and increase the Sharpe ratio. Since the quantopian.com provides us with some technical indexes and is compatible with datasets from other sources (i.e. Google trends), we might also consider introducing some technical indexes (i.e. VIX) into our strategies. With more and more sophisticated trading strategies developed, we will try to incorporate those strategies into the agent-based simulation model developed by Prof. Beling’s group. Hopefully, this will help improve the accuracy and reliability of the current zero-intelligence model. Reference [1] http://en.wikipedia.org/wiki/Algorithmic_trading [2] http://en.wikipedia.org/wiki/High-frequency_trading [3] http://en.wikipedia.org/wiki/2010_Flash_Crash

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