High-frequency trading (HFT) has been gaining popularity since the 2010 market crash in the United States of America. It was on the 6th of May 2010 when massive sell orders crashed Dow Jones Industrial Average (DJIA) and wiped off trillions of dollars in a matter of few minutes. Studies conducted later-on were of the opinion that HFT was largely responsible for this crash. HFT is a subset of Algorithmic Trading, where the information is received, processed, analyzed, and executed at an enormously faster rate with the help of supercomputers and a highly efficient data transfer mechanism. It is correct that retail traders cannot compete with the HF traders as far as their technology and accessibility is concerned but is it true that HFT is here to harm the retail traders’ interest, is still a valid point for discussion.

Ever-growing human needs and aspirations to exploit natural and man-made resources have been pressing demand for technological advancements so as to discover new resources or make efficient use of existing resources. The trading business has also been a part of this transformation.

Physical securities have been dematerialized by the exchanges and the electronic means of communication in trading has made the trading process fast. Today it is not only the stock’s value that is more important but also the ‘velocity of the data’ carrying that value to the receiving end.

Trading firms have been investing millions to access space within or around the exchange premises to place their servers as close to the exchange servers as possible so that they can receive ticks faster than the competition around.

The race has all been about getting right information at the right time and for that more advanced technologies like fiber optics and micro wave technology (radio waves) are being used for data transfer.  Some firms spend up to 1million dollars to achieve a difference of just 1 millisecond for faster data transfer. In this way, HFT firms are able to receive data much quicker than the retail trader or investor because of their location advantage.

The second most important factor for HFT is to reduce ‘latency’ which is nothing but the ‘reaction time’ to grab trading opportunities. The trading firms have been hunting for ultra-low latency of less than 1 millisecond. For that, they use very high-end computer systems and highly paid programmers to make highly efficient algorithms that not only can process the data at a much faster pace but also execute trades on the basis of that analysis.

As an example, an algorithm can trade a stock 25-30 times, depending upon the opportunities, while the ticks are being transferred from the exchange to the retail trader’s monitor. According to a study, almost 10% of the total orders in the exchange stay in the system for less than 1 millisecond, which can only be done by HFT. So the retail trader would never be able to notice those price movements, leave aside grabbing those opportunities.

So the question arises if HFT is detrimental to retail traders’ interest or not. It is true that there is no match between the two in any aspect, except that both have been buying and selling in the market. However, an unbiased testimony does reflect some positive aspects. There is a widely accepted view that HFT does provide ‘liquidity’ to the market. Liquidity is a feature with which an individual or group of individuals can buy or sell an instrument in the stock market without causing a drastic change in the price of that instrument. HFT provides sufficient and consistent liquidity relevant to the market participants for frictionless functioning of the stock market. This liquidity helps in reducing the bid-ask spread in stocks and other trading instruments.

In the absence of a sufficient number of shares in the market, it would become practically impossible for retail traders to trade due to wider spreads. HFT firms may sometimes have to suffer losses in the interest of the overall market, which they might cover through their edge over the competition. They can arbitrage and exploit temporary divergences in stock price among different exchanges due to their faster data communication. They can also use their other subjective valuation hedging techniques to mitigate their losses or to generate profits.

In contrast, there is another opinion that the liquidity provided by the HFTs is very volatile or short lived. It is so volatile that retail traders can’t even take advantage of the available opportunities. Also, there is no specific answer to the events like the 2010 crash in DJIA. On the 6th of May 2010, the market actually opened under pressure due to bad global economic news.

HF Algorithms considered the decline as a short term opportunity and started buying. But when the market kept on slipping, the algorithms also started dumping and booking losses. As everybody started selling, there were no buy orders left in the market which led to a flash crash. DJIA had a sharp decline of 600 points in just 5 minutes. However, it covered almost all of it in the next 20 minutes. Later reports concluded that HFT was not responsible for causing the flash crash but they contributed to it due to their urgency to stay ahead of other participants.

In the final remarks, we can say that any technological development brings with itself both the brighter and the darker side. As an example, nuclear technology can be exploited as an efficient source of energy but can also be used for making nuclear bombs. HFTs provide liquidity to the market but at the same time, it may harm the interest of other market participants in one way or the other. An algorithm is just a set of instructions given to a computer, so that it grabs buy and sell opportunities. It does not have emotions to protect or hurt retail traders. In fact, retail orders are too small to fill their baskets. The reality is that, the competition is among the algorithms itself as they have been trying to predict each other’s next moves.