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On May 6, 2010, the Dow Jones Industrial Index slumped nearly 1,000 points, losing almost 9% of its value in minutes. What quickly become known as the “Flash Crash” had wiped more than $862 billion off the American stock market.

Who was behind it? Widespread finger-pointing and speculation ensued. Was it caused by a rogue computer program? Some thought it was the work of cyber-terrorists or Wall Street malfeasance. However, the government regulator soon identified the cause: a $4.1 billion sell order placed by Waddell & Reed, a US mutual fund.

The regulator’s report focused on Waddell & Reed’s sale of 75,000 E-mini Standard & Poor’s 500 futures contracts. An E-mini is a futures contract that tracks S&P 500 stock market index trading on the Chicago Mercantile Exchange. It represents a fraction of the value of a corresponding futures contract. The Waddell & Reed mutual fund had used an automated algorithmic (algo) trading strategy to sell E-mini contracts.

The flash crash raises serious questions about the role of high-frequency trading and its impact on wider market stability

They are programmed to execute the trade with respect to price or time. It means that the algorithm will continue to sell even though prices have dropped substantially. As the firm started to sell, high-frequency traders started to buy these E-mini contracts. Some high-frequency traders realized that they had amassed a long position and began to sell aggressively. Some high-frequency traders started buying and then reselling the e-mini contracts, resulting in a “hot potato effect.” Two hundred contracts were bought and sold more than 27,000 times in just 14 seconds. The flash crash raises serious questions about the role of high-frequency trading and its impact on wider market stability.

Algos change market

Algo trading has gained importance in global stock markets in the last decade. Nearly 70% of total trading volumes in developed markets is from algo trading. One of the key advantages algorithmic trading has over common trading is quicker reaction time, which, for example, can be exploited for high-frequency arbitrage. The exponential increase in the performance of computer processing and networking has accelerated the progress. The rise of algorithmic trading poses a new challenge in the form of so-called flash crashes, which result in large price changes in very short periods of time.

There have been hundreds of mini-flash crashes since May 2010, and also more serious incidents. A flash crash on December 5, 2018, saw an abrupt plunge in S&P 500 E-mini contracts. After the day’s opening, these futures plunge 2.5%  in less than three minutes. The biggest concern of today’s market participants is that these crashes could cause a recession in the near future. Volatility is only good if it’s part of the trend and it’s giving you entry points within that trend. But when you are going up and down, but there’s no real trend, you have a nightmare scenario.  

Most of the hedge fund managers today have the biased opinion that algos have rigged the market. The astonishing speed and power of computerized systems has taken the market by storm. Much of the liquidity in the market now comes from high-frequency trading computers. Developers of these algorithmic trading strategies wield a tremendous amount of power over the financial market. There are massive risks associated with using these trading strategies, and the dangers associated with these tactics must be recognized and addressed. High-frequency trading will become the dominant form of algorithmic trading in the near future.

A recent report by Thomson Reuters estimates that algorithmic trading systems are now responsible for 75% of infamousglobal trading volume. The global algorithmic market is expected to grow 10.3% CAGR between 2016 and 2020. In the future, we will see a totally different level of automation of the financial market.

As artificial intelligence (AI) and quantum computing play an increasingly important role in the future, these algorithms will become more complex as algo trading will able to adjust to different patterns. For instance, you can program certain specific rules for trading into your strategy and if the markets do not turn in your favor, the program alters to match the new market changes. As machine learning becomes more sophisticated and gives birth to the next generation of algos, regulators will need to keep pace to prevent major accidental market movements.

Since the global economy is not in good shape, we have already seen two big flash crashes this year and a stern warning from the Bank of England recently to brace for future crashes.

A global regulatory body must be set up to regulate these trades and common standard regulatory measures should be implemented to reduce the risk of flash crashes before something unexpected happens.

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Ravi Kant

Ravi Kant is a financial writer with a deep passion for technology and economics, and also covers international politics and cybersecurity. He has wide experience in the financial world and some of his analysis has been published in leading magazines such as Mogul News and The Indian Economist. He tweets @Rk_humour.

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