Disrupting Wall Street: High Frequency Trading

Group B9 - Case 1 - 1 -

1. Describe the current market structure and trends of US equities trading.

The market structure of US equity trading has experienced various changes over the last couple of decades. In the late 1980’s, the arrival of the digital era and computerised systems dramatically changed the way trades were executed. Instead of paper and open-outcry, exchanges like the London Stock Exchange or the Toronto Stock Exchanges switched to all- electronic trading. Nowadays, the traders are very tech savvy and exchanges need to be connected to several market centres (Polcari, 2012). The new technology, together with two new sets of regulations (Regulation National Market System (NMS) in 2005 and Regulation Systems Compliance and Integrity (RSC) in 2015), put in place by the U.S Securities and Exchange Commission (SEC), helped make the US equity market much more competitive, reliable and thus efficient1. Another distinctive characteristic of the U.S. equity market is its level of complexity, mainly due to its fragmentation (White, 2015). As of January 2014, the five biggest U.S. stock trading venues accounted for 49.4% of U.S. stock orders, other smaller venues accounted for 10.1%, and the remaining 40.7% consisted of orders issued in off-market platforms including dark pools (Bunge, 2014)2.

Despite of a 5-year downward trend in trading volumes from 2008 to 2013 (Bunge, 2014) which can be attributed to the last financial crisis, the value of share trading from 2014 to 2015 in NYSE and NASDAQ has increased (World Federation of Exchanges, 2015), which may mean that the post-crisis decline in trading volumes might be coming to an end. Moreover, another trend in the US equity markets is the participation of more and more non-professional traders as barriers to timely information and trading platform are reduced (by news agencies like Bloomberg) and transactions cost are reduced, making it more profitable to trade smaller orders (Cox, 2014).

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2. Compare the process of traditional trading to electronic trading.

In the past couple of years and with the rise of technology and digitalization, trading has undergone significant changes. Traditionally, trading happened on physical floors, where two traders negotiated a price on behalf of an investor in order to exchange securities. The investor would call a stockbroker to place an order, who would then pass this order onto its floor clerk. Next, the floor clerk would pass the order onto the floor trader, who was responsible for negotiating the price with a trader representing the opposing party. After the negotiation, the final information about the trade moved upstream via the same people back to the investor.

Nowadays most of the trades are executed electronically. This method follows the same process as the traditional trading but in a faster way. The increased speed is achieved because instead of relying on a number of people as intermediaries, electronic trading relies on digital communication. More specifically, when an investor places an order, he or she does so via a trading system, which itself is linked to a so-called smart order router. This router decides to which security exchanges or Alternative Trading Systems (ATS)3 the order should be sent to. Next, buy and sell orders are matched electronically, thus increasing the speed of negotiations.

Over the past few years an extreme form of electronic trading has emerged, called High Frequency Trading (HFT). This form of trading relies on a computer’s speed of executing trades and is not only substituting the intermediaries but redefining the investors’ role in trading: HFT now defines patterns of behaviour (algorithms) for the computer to follow. This allows the computer to execute each trade in milliseconds, increasing the volume of trades (see Q4).

3. What is a flash crash? What caused the flash crash of 2010?

A flash crash is a significant and volatile fall in the prices of securities within a very short period of time. The term usually describes scenarios in which electronic and HFT algorithms lead to

Group B9 - Case 1 - 3 -

interconnected sell orders causing prices to fall rapidly. Although several flash crashes could be observed in the past, the term is most commonly used in relation to the specific flash crash on May 6th, 2010. Back then, within 36 minutes several prominent indices like the S&P 500 and the NASDAQ 100 experienced a strong decrease and then rebounded of the most extreme volatility (Figure 1 in appendix). It was the biggest intraday point decline of the Dow Jones Industrial Average in its entire history (Kirilenko, Kyle, Samadi, & Tuzun, 2014). These events sparked widespread criticism of HFT, which was held responsible for the extraordinary market movements. Moreover, this crash was subject to several investigative reports conducted by the SEC and the Commodity Futures Trading Commission (US Securities & Exchange Commission, 2010). Both authorities concluded that a large sell order of E-Mini Futures4, whose algorithm was linked to volume rather than price, by a mutual fund formed the basis of the flash crash (The Economist Online, 2010). High frequency traders (HFTs) first took pressure off the market by buying the futures and then subsequently increased volume by reselling them shortly after. Those trades triggered once again the sell order of the mutual fund and a repetitive buying and selling circle started. Since the HFTs were net sellers (they sell more than they buy), prices declined with extreme speed and triggered several large automated trading systems to pause the orders, thereby further reducing liquidity (The Economist Online).

Despite these reports, the definite causes of the flash crash remain unclear. In 2015, a single London based trader was found guilty of significantly contributing to market imbalances on this day by placing vast selling orders on E-Mini Futures, which he planned on cancelling later (an illegal practice called “spoofing”). However, many experts are hesitant to believe that a single trader could have led to a flash crash of such enormous extent (Stafford, Fortado, & Croft, 2015).

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