The intraday change from the low to the high of the S&P 500 index on Friday, May 26, was only o.19%, which is the lowest in about 23 years and the fourth lowest in 31 years. I list some of the possible reasons for this volatility crash.
The daily S&P 500 chart below shows when the intraday low to high range was less than 0.19%.
In a total of 7917 trading days since 1986, there have been only four days with an intraday low to high change of less than 0.19%. Therefore, this is extremely rare.
Obviously, an explanation for this rare event would be more interesting. Many hypotheses can be made but it is hard or impossible to verify them. In my opinion a cause of this is a shift of attention to other markets that offer high return potential, such as bitcoin and cryptocurrencies for example. Then, a major shift to algo trading taking place at the institutional level and until the models are forwarded tested, risk parameters have been set to low levels and volatility is low.
As a result of the shift to algo trading and machine learning, the impact of emotional human trading that caused higher volatility has decreased. However, I believe that at some point the risk parameters of the models will have to be adjusted to increase profitability and volatility will return to the market. I have no idea how long this will take; it may be anywhere from three months to a year.
Institutions should realize that although algo trading, machine learning and related applications have an impact on market structure they cannot alter its basic foundation, for example that for every buyer there must be a seller. Therefore, at the end of the day, just the level of complication is raised by new technology but the game remains zero-sum. I hope the ramifications of this are understood but by reading articles in magazines and blogosphere I believe some fund managers do not get it and think that gold can be found where there is little or none. Some of these fund managers may be disappointed, especially if they are not familiar with the perils of these new technologies and they believe the unrealistic expectations of people with good programming skills but with little or no market experience and no understanding of data-mining bias, curve-fitting and the limitations of these models in general.
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