Algorithmic trading, according to Wikipedia, is defined as a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, volume and price. In this article I will provide details about how this field emerged, some of the significant contributions to its development, and what it means for emerging markets going forward. Let us begin.
Early Developments
In the 1950s, Harry Markowitz, the father of Modern Portfolio Theory which states that risk-averse investors can construct portfolios to optimize or maximize expected return based on a given level of market risk, introduced computational finance to solve the portfolio selection problem. The portfolio selection problem is one of the key components of Modern Portfolio Theory. One of the most significant impediments to solving this issue in the 1950s was the lack of computing power. Markowitz would later go on and collaborate with hedge fund managers Ed Thorp and Michael Goodkin and they would be the first to use computers for the purposes of arbitrage trading. This was a giant leap for the world of finance, and portfolio management in particular.
Another significant contribution to the field of Algorithmic Trading would be that during the 1970s and 80s, the personal computer became available to greater parts of the populations of citizens in developed countries, and this in turn encouraged research into the application of various techniques in computational finance. An interesting fact about this particular exploration into computational finance during the 1980s would be that a lot of the strategies that emerged came from the field of signal processing, rather than the field of computational economics, as one might expect. This was a result of the end of the Cold War, which resulted in physicists and applied mathematicians moving into the field of finance. Even though, up until this time, algorithmic trading had not yet become a significant field in the world of finance, important foundational layers had been placed for what algorithmic trading would later become.
Technological Building Blocks
In the 1970s the New York Stock Exchange introduced a Designated Order Turnaround System, or DOT for short. DOT is a" computerized order system that allows orders to buy or sell large baskets of stock to be transmitted immediately to the specialist on the exchange, where execution will occur quickly, depending on the basket size”.This system played crucial role in the development of algorithmic trading; for the first time, individuals and institutions could bypass the broker when interacting on the exchange.
Another pivotal happening for the field of algorithmic trading would be the development of the Terminal by Michael Bloomberg. This not only changed trading as a field, but the world of finance as well. Michael Bloomberg built the first computer system that made use of real-time market data to quote stock prices and relay information. Bringing an unprecedented evolutionary shift to finance almost 40 years ago, the Terminal continues to build advanced technologies that tracks, monitors and compiles huge amounts of data for clients of Bloomberg Professional Services.
Algorithmic Trading in Emerging Markets
From at least the 19th century, the emerging world has followed the developed world with everything from diet, sports, fashion, business structures, and political systems but to name a few. According to this trend, which ranges from insignificant products and services to how agents in an economy interact with one another, it should become apparent that algorithmic trading will one day become the norm in emerging markets like South Africa.
In Asia, which is predominantly still an emerging continent, algorithmic trading is becoming the norm. According the Aite Group, this particular form of trading accounted for 32% of market volume in 2015, the greatest proportion in any developing market. The main players in this market would be, probably to know one's surprise, India and China. Another significant player in the market would be Russia, where algorithmic trading accounted for 36% in the equities market, and 40% of the derivatives market. In Brazil, 21% percent of the trading volume in the equities market can be attributed to algorithmic trading, and 18% percent in the derivatives market.
If you are familiar with political associations, then BRICS should pop into your head whenever at least Russia, China, or India are mentioned together, just as they are in the preceding paragraph. And if the emerging economies in this association are all moving towards algorithmic trading, it seems likely that South Africa will eventually do the same, although no one can say for sure.
Concluding Remarks
The field of algorithmic trading has grown at a rapid pace since its formal introduction in the 1970s. According to some estimates, 80% percent of all trades in the US stock markets occurs through this medium. There are of course, advantages and disadvantages to this particular form of trading. It will be interesting to see how this field continues to develop going forward.
References
Beginnings of Algorithmic Trading, Rialto-Ai
History of Algorithmic Trading, AlgorthimicTrading.net
Michael Bloomberg, Wikipedia.com
A brave new world: the 1980s home computer boom , historyextra.com
Harry Markowitz, Wikipedia.com
BRICS, infobrics.org
Designated Order Turnaround, Investopedia.com
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