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High Frequency Trading Hbs Case Study

Essay by   •  January 31, 2019  •  Case Study  •  2,753 Words (12 Pages)  •  15 Views

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Question 1 :

Over 200 years ago, in 1790, the Philadelphia Stock Exchange (PHLX) was created.  PHLX was the first equity market created in the United States, and was soon followed by the creation of the New York Stock Exchange (NYSE), founded in 1817. Equity markets since, have played a crucial role in the growth and development of publicly traded corporations and in the creation of wealth for investors. Through equity markets companies can easily access capital and investors can acquire ownership in firms with the aim of realizing future gains.

It was not until 181 years (1971) after the creation of PHLX that NASDAQ, the world’s first electronic stock market was created in the US. The birth of NASDAQ meant the beginning of a new era, in which the physical or floor trading would gradually be replaced for electronic or online trading. Currently,  the US equity market is considered a “hybrid market”, trading takes place both on the exchange floor and electronically.

In January 2007 the New York Stock Exchange (NYSE) introduced its “hybrid” electronic platform. Three months later, 82 per cent of the stock exchange’s trading volume was executed electronically (Evans, 2014). Thus, reducing the mount of human traders at NYSE by the thousands in a bit over 30 years (Evans, 2014).  Since its introduction by NASDAQ in 1971 and its implementation by the NYSE in 2007, electronic trading has rapidly increase, currently accounting for over 70% of  U.S. trading volumes (Evans, 2014). Also, nowadays most markets only offer electronic trading.

The NYSE is, however, one of the few markets that currently still offer floor trading. Many critics have evolved around this question debating the reasons behind it. They argue that the fact the NYSE still offers floor trading is a mere marketing reason. Others argue that human presence is really needed on the trading floors and so NYSE has a competitive edge, employing such “hybrid” model. Indeed, Stacey Cunningham, chief operating officer at NYSE Group stated: “While the market can be entirely automated, you lose value when you no longer allow for human interaction. We believe that combination is the gold standard (Detrixhe, 2017)”. “If we look at the companies´ sides, for some of companies, human presence and human interactions between traders of company shares aren't a priority for them”, according to Chris Westfall, a vice president at Financial Executives International, also said, “however, a lot of firms value this “human touch”.

Question 2:

Traditionally, floor-based trading was supported by designated market intermediaries who arranged trades between different market participants. An investor would call his stockbroker to place an order. He would, in turn, place the order through to the floor clerk, who at last would put it through to the floor trader.  In the last decades, securities trading experienced significant changes and more and more stages in the trading process were automated by the incorporation of electronic systems. In 1987 the SEC pushed for electronic trading. Electronic trading enabled market participants to transmit orders electronically rather than via phone or in person, granting remote access to order books. Since trade orders were now matched according to price-time priority, uniform rules were applied to all market participants and fairness was ensured. Nowadays, IT implementations have grown in complexity and the securities trading landscape is characterized by a significant market share of automated trading technologies like algorithmic trading (AT) and high-frequency trading (HFT).


Algorithmic trading is an evolution of electronic trading. Computer algorithms automatically generate orders for trading individual instruments when predefined market conditions are met, without any human intervention. These conditions relate to a wide range of trading strategies, such as arbitrage and trend following. Algorithms determine the timing, price, quantity, and routing of orders, dynamically monitoring market conditions across different securities and trading venues, reducing market impact by optimally breaking large orders into smaller ones. Due to the high trading volume constant technological innovation is a must. Algorithmic trading has reduced the overall trading costs for investors, as fewer human traders are involved. The lower transactions costs, together with the higher liquidity resulting from higher trade volumes, increase the operational efficiency of financial markets, supposedly benefitting all market participants (Goldenberg, 2018).  

High-frequency trading has emerged quite recently, after gaining significant attention due to the flash crash in the U.S. on May 6, 2010. At this date the Dow Jones lost 998.5 points in a fraction of a time; erasing $ 1 trillion of market capitalization (Evans, 2014). With HFT, a large number of orders – usually fairly small in size – are sent into the market at high speed, with the objective to exploit trading opportunities arising from market liquidity imbalances or other short-term pricing inefficiencies. These opportunities may open up for milliseconds and, so reducing latency and increasing speed is key. When the market situation at the arrival of an order differs significantly from the market situation which the trading decision was based, there is the risk that the order is no longer appropriate. To minimize that risk, reducing the delay of data communication with the market‘s backend is of utmost importance, so high-frequency traders physically locate trading machines directly adjacent to the market operator’s infrastructure. Indeed, the faster the algorithms can be executed, the greater the investor’s edge (Evans, 2014).

As demonstrated above, the stock exchange has changed severely. Human intervention has been mostly eliminated, and as such, trading has become a matter of milliseconds. What has prevailed are: the need of companies to be listed and the serving role of the stock exchange.

Question 3:

A flash crash is a sudden market plummet, in a very short period of time (i.e. seconds to minutes) that then rebounds. Different things can set it off, but computer trading programs make any crash worse. Through the use of  algorithms that recognize irregularities within the market, such as sell orders, these “bots” react automatically by selling their holdings to avoid further losses. These chain reactions can then lead to flash crashes.



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