The May 6, 2010 Flash Crash - Essay Example

? 6 MAY, FLASH CRASH Introduction The Flash Crash of 6 May, caused titillations even amongst economic scholars. It was characterized by sharp drop and recovery of the prices of securities. In less than half an hour, the asset value of several securities collapsed. However, they also recovered in almost a similar period. Many questions on what could have caused such an occurrence in a financial market that is considered efficient arose. The financial market enables fair exchange of assets. The efficiency of a market is measured by its stability where the demand is equivalent to supply. On the contrary, the contemporary market is characterized with higher demand as compared to the supply. Financial innovations enable changes in the financial market by introducing new ways of trading assets. One of the newest financial innovations entails trading from computer to computer through use of complex mathematical algorithms that are hard for humans to comprehend. The recent financial crisis resulted in increased unemployment, which is an indicator of the increased inefficiency of the stock market. This paper agrees with Stiglitz opinion that that Flash Crash will lead to less investment in information, which is harmful to the markets price discovery function hence the financial market. The paper will oppose the opinion that Flash Crash could be a positive feedback loop of the trading environment. Computer trading has become a common phenomenon, which has increased the speed of trading making it impossible for humans to intervene in times of occurrences such as flash crash. Additionally, the explosive trading speed results in undermined efficiency since the market becomes incapable of allocating resources efficiently. Flash Crash entail trading from computer to computer through use of pre-programmed algorithms. On 6 May 2010, the worst flash crash occurred and resulted in Dow Jones Industrial Average decreasing by 9 percent in about twenty minutes. What was dramatic was that most of the fall occurred in about seven minutes. Other U.S.-based equity products also dropped and recovered in a similar speed. In a period of twenty minutes, Shares in Accenture had declined in value from the price of $40 per share to $0.01. During the Flash Crash, a contract could be traded for more than 27,000 in a period of about 14 seconds (U.S. Commodity Futures Trading Commission & U.S. Securities & Exchange Commission, 2010; Stiglitz, 2012). According to the SEC report, computer generated algorithms, which are used for high frequency trading comprise more than 70% of trading in U.S. equities. On the SEC joint report on the flash crash of 6 May 2010, the American shares fell by 10% within a few minutes, which resulted in many questions regarding the credibility of nanosecond trading, which characterizes computer to computer trading. The computer based trading does not make use of price discovery but uses algorithms that makes it possible for dealers to extract information regarding expected price of securities through observing patterns of prices and trades. The dealers are thus incapable of making sound decisions. High frequency trading undermines the stability of the market. During the flash clash, high frequency trading firms started by absorbing sell pressure but eventually started forceful selling, which resulted in increased orders in the market and creation of feedback loop. Eventually, the high frequency trading firms began to buy and resell to each other e-mini contracts resulting in decreased net buying irrespective of the increased volume of e-mini contracts. Buyers using traditional trading methodologies refused to buy the extra E-minis resulting in the fall in trading funds. This was because dealers could not comprehend the transactions and feared taking risks. Therefore, computer based trading is inapplicable since there are no clear models on how it operates (Stiglitz, 2012; Mackenzie, 2006). High frequency trading resulted in liquidity crisis when automated trading systems paused due to unpredictable prices. Market makers as well as other liquidity providers responded differently. Some widened their spreads of buying and selling while others withdrew from the trade. Others began to trade manually but were incapable of competing with the high frequency traders who continued to trade even during the clash. The varying decisions were due to lack of knowledge on how to deal with inefficiencies resulting from computer generated algorithms (Stiglitz, 2012). Like Stiglitz concludes, automate orders threatens the efficiency of the market since they trigger price swings. The swings can be extreme if the algorithms used fails to account for prices. Additionally, computer to computer trading, which makes use of high frequency trading, results in erosion of liquidity reducing market efficiency (Stiglitz, 2012; Mackenzie, 2006). The joint report by U.S. Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) showed that automated trading is inefficient in that it causes fragility in the market (U.S. Commodity Futures Trading Commission & U.S. Securities & Exchange Commission, 2010). The flash crash resulted due to ineffectiveness of a single though large trade transaction but affected the entire securities market. While automated traders detected the sharp rise and falling of the process decided to withdraw from trading, trader not using the automated trading failed to detect the inefficiency, and continued trading resulting in great loses. Automated trading therefore results in biasness since not all traders are able to participate equally (Stiglitz, 2012) For a market to be efficient, the trading information must be made available to the participants. Additionally, the participants must be given sufficient time to process the information to enable them make sound decisions. However, in high frequency trading, transactions are carried out in nanoseconds. During the flash crash, order fillers were able to dual trade-fill whereby they could fill orders and trade at the same time. The markets become less transparent and it becomes hard to determine the real price of the securities. This enables diffusion of information resulting in market inefficiency (Stiglitz, 2012; (U.S. Commodity Futures Trading Commission & U.S. Securities & Exchange Commission, 2010). Computerized or automated trading increases fragility of the market since it does not apply the usual trading rule. Instead, computer based algorithms which cannot be understood by humans are used. Computer based algorithms are programed to trade large volumes of stock and other financial instruments in a period of milliseconds, a period within which humans cannot respond. Stiglitz (2012) suggests that automated trading is widening the gap between economic theories and economic reality. This has resulted in a financial market that humans cannot comprehend. This is because human trading is entirely different from computer trading. Computer based trading depends on convergence of different algorithms, which are vulnerable resulting in market inefficiency. The computer driven trading makes it impossible for SEC to regulate the market since it is impossible for humans to follow the rise and fall of stock prices. According to Stiglitz, a market requires government regulation for it to be free and competitive. Without such regulation, firms dominating the market are likely to use their leverage to increase their profits at the expense of other traders. This was the case in the 2010 clash where high frequency traders dominated the market while other trader halted their operations (Stiglitz, 2012; Mackenzie, 2006). Efficient trading entails gathering of information before making a decision on whether to buy securities under offer. Computer based trading operates in second, which makes it impossible for a buyer to process the information before making a decision. Although electronic trading has increased competition and led to decrease in transaction costs, the computer generated algorithmic trading has led to elimination of rule-based trading. This has resulted in monitory and liquidity problems, which are hard to address since the theoretical knowledge regarding such problems is limited. Unless a market is capable of there is liquidity in the market, computer based trading will remain inefficient (Stiglitz, 2012). As Stiglitz (2012) argues, security markets can only be efficient if they operate under pre-designed regulations, which ensure that traders have access to vital information prior to making decisions. Such information allows for transparent price quotes. However, high frequency trading reduces market liquidity resulting in fluctuations in security prices (Stiglitz, 2012; Mackenzie, 2006; (U.S. Commodity Futures Trading Commission & U.S. Securities & Exchange Commission, 2010). Conclusion The Flash Clash that occurred on May 6, 2010, is one of the strangest economic occurrences in history. In a matter of seven minutes, Dow Jones stock index dropped by 600 points. This resulted in similar occurrences in other major securities. However, it was stranger when the securities recovered as first as they had fallen. Automated trading, which results in high frequency trading is impugned causing this clash. This is because it results in lack of transparency due to the high speed in which transactions are conducted. Under such speed, it becomes hard for humans to access the information and react, which results in inefficiency in trading. Computer based trading results in increased volatility resulting in feedback loops. Additionally, computer based trading is to fast resulting in instabilities that cannot be monitored by market regulators. High frequency trading results in uncertainties. In the 2010 flash crash, the high frequency trading resulted in increased uncertainty amongst traders. Those who could not withstand the uncertainty withdrew from the trade resulting in increased uncertainty which resulted in an almost clash of the security market. References Mackenzie, D. (2006). An Engine, Not a Camera: How Financial Models Shape Markets. New York: MIT Press. Stiglitz, J. (2012). The Price of Inequality: How Today's Divided Society Endangers Our Future. New York: W. W. Norton. U.S. Commodity Futures Trading Commission, & U.S. Securities & Exchange Commission. (2010). Findings Regarding the Market Events of May 6, 2010. Washington, D.C.: Joint Advisory Committee on Emerging Regulatory Issues. Read More