High-frequency trading is an increasingly prevalent phenomenon whereby sophisticated traders are using extremely advanced technology to make large trades in a very short amount of time. This process engenders volatility in the stock market, so much so that investors have started placing themselves as close as possible to the physical exchanges to save a few milliseconds from their preprogrammed trades. These investors claim that they are increasing liquidity, which refers to the amount of money circulating on the market. While this may be true, it does not account for the volatility that emerges from flash crashes, placing many vulnerable investors at risk. While the SEC has had difficulty regulating HFT in recent years because High-frequency traders generate and control the majority of the money on the market, certain economists have proposed redesigning the market in radical ways to abolish the imbalance. One noteworthy example includes transforming time to an auction based system instead of a continuous variable when trading on the market. We are still in the midst of the debate as to whether and how regulation will be carried out with regards to HFT— a crucial debate that may change many longstanding patterns within the field of economics.

Dark pools, or Alternative Trading Systems (ATS) is a notable phenomenon fueling HFT. These ominous and heavily guarded spaces decrease transparency within financial markets by obfuscating the list of buyers and sellers within a particular transaction, preserving their anonymity. Dark pools encourage sellers to avoid disclosing the specificities of a particular transaction that they do not want to be shared with other actors in the market. As one can deduce, HFT and dark pools are often intertwined— there is little reason that a High-frequency trader would want to share the exact details of his or her immense operation, especially if it carries a risk of upending the market. This can damage the trust of investors because it increases opaqueness and can produce sudden swings on the market which are difficult to trace since the transactions behind them are hidden. 

Another way in which High-frequency traders can distance themselves from their fellow investors is through arbitrage. Instead of revolving around secrecy like dark pools, arbitrage demands speed and scrutinization. Since there are so many exchanges available for all of the different forms of currency that we have developed, there is bound to be some margin of error. The disparity between the prices of a particular asset can be exploited by High-frequency traders with their lightning-fast technologies. Here’s an example of how such a situation could play out.

Let’s say that a stock is trading for x amount on the New York Stock Exchange (NYSE) but that it is simultaneously being traded for 8 cents more on the London Stock Exchange (LSE). To take advantage of the price difference, an investor could buy a stock on the NYSE and immediately sell it on the LSE before the discrepancy has been detected. This would get him 80 cents of profit. Of course, to actually maximize the gain from this process, one would need to sell an immense amount of shares in an extremely short amount of time. That’s where HFT comes in handy. Almost all traders who successfully execute this act of legerdemain need highly developed technologies that can identify the price difference and act incredibly quickly. HFT and arbitrage are deeply connected and the latter has become more popular due to the growth of the former. Arbitrage exemplifies how HFT can be used to increase economic inequalities because ordinary investors simply cannot act fast enough to exploit the differences between world markets.

For such a process to work, investors use algorithmic trading to facilitate their transactions. HFT and algorithmic trading can merge to form an unassailable investing strategy if planned properly. While HFT in itself requires a human behind the computer to initiate the rapid transaction which buys or sells a considerable amount of assets, algorithmic trading makes it possible to liberate the computer entirely. Automated trading, as it is also called, enables a human user to input criteria into his or her software that a computer will follow to carry out an action. The trader can instruct a computer to memorize a specific price, threshold, or condition for an asset and trust that the software will automatically perform the transaction without requiring anyone to physically be present. If the computer also happens to operate at high technological capacities, it might be executing HFT without any human supervision, potentially leading to a flash crash. Automated trading is once again an example of how HFT can be used in unscrupulous ways. Computers are subject to error and can make massive investments which skyrocket volatility without an investor supervising the transaction.   

Nowadays, an investor can set up a computer based on algorithmic trading which works through Alternative Trading Systems and keeps an eye out for discrepancies in foreign markets to make the most out of HFT— the possibilities for profit are endless. As the market becomes increasingly driven by high-speed algorithms, it is necessary for aspiring investors to gain a better understanding of relevant terms so that they can make successful financial decisions and know how to react when something like a flash crash seems imminent. In this way, investors will be able to protect their savings and not remain dependent on the volatility which HFT entails, contributing to a safer and more organized approach to investing in the long run. And since the stock market impacts families across the country, more responsible and knowledgeable investors can help shape the way that assets are traded to avoid harmful, widespread fluctuations.

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