The intention is to minimise transaction costs and to receive a good price — if a large order were submitted in one go it might adversely move the entire market.
Human market makers used to provide quotes to buy or sell a given stock and were responsible for maintaining an orderly market. They have been replaced by algorithms that automatically post and adjust quotes in response to changing market conditions. Algorithms drove the human market makers out of business by being smarter and faster.
Further concerns about algorithmic trading are focused on another kind — proprietary trading algorithms. Hedge funds, investment banks and trading firms use these to profit from momentary price differentials, by trading on statistical patterns or exploiting speed advantages. Rather than merely optimising a buy or sell decision of a human trader to minimise transaction costs, proprietary algorithms themselves are responsible for the choice of what to buy or sell, seeking to profit from their decisions.
These algorithms have the potential to trigger flash crashes.
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Many traditional portfolio managers use mathematical models to inform their trading. Nowadays such strategies are often implemented using algorithms, drawing on large datasets. High-frequency algorithmic trading HFT is on the other end of the spectrum, where speed is fundamental to the strategy.
Flash Crashes, Algo Manipulation & Demystifying Market Abuse Regulation
These algorithms operate at the microsecond scale, making decisions and racing each other to the market using an array of different strategies. Winning this race can be highly profitable — fast traders can exploit slower traders that are yet to receive, digest or act on new information. Proponents of HFT argue that they increase efficiency and liquidity because market prices are faster to reflect new information and fast market makers are better at managing risks.
Many institutional investors , on the other hand, argue that HFTs are predatory and parasitic in nature. According to these detractors, HFTs actually reduce the effective liquidity of the stock market and increase transaction costs, profiting at the expense of institutional investors such as superannuation funds. While some algorithms are harmful to institutional investors, causing higher transaction costs, others have the opposite effect.
This increases the risk of low liquidity during a market crash. The biggest criticism of HFT is that it can actually cause market crashes and increased volatility. Various flash crashes in recent years have been attributed to HFT. Increased instability in markets is bad for everyone. We have all seen what happens when markets become extremely volatile.
On the whole, there is no consensus as to whether HFT has positive social value? Many regulators increasingly believe that HFT is bad for markets, and are taking steps to discourage it. Partly, this is due to the perception that HFT firms have an unfair advantage over other market participants. Unfairness is a bigger problem than it seems. If other market participants believe that the market is rigged against them, then they will not trade. And in the long run, social value is destroyed.
Time will tell whether HFT is good or bad for markets. But for now, it is here, and we need to be aware of it. As a trader, what is the best way to deal with the existence of HFT? As I mentioned, HFT creates winners and losers. Next time, I'll tell you how to be one of the winners. Do you think HFT is good or bad for markets? Share your views in the Club or share your comments here. Aug 01, What do they mean when they say something is so many light years away Aug 01, Is the concept of "wave function collapse" obsolete?
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