Reginald Smith of the Bouchet-Franklin Institute (not a brand name institution, by any measure) has written a paper titled Is high-frequency trading inducing changes in market microstructure and dynamics?:
Using high-frequency time series of stock prices and share volumes sizes from January 2002-May 2009, this paper investigates whether the effects of the onset of high-frequency trading, most prominent since 2005, are apparent in the dynamics of the dollar traded volume. Indeed it is found in almost all of 14 heavily traded stocks, that there has been an increase in the Hurst exponent of dollar traded volume from Gaussian noise in the earlier years to more self-similar dynamics in later years. This shift is linked both temporally to the Reg NMS reforms allowing high-frequency trading to flourish as well as to the declining average size of trades with smaller trades showing markedly higher degrees of self-similarity.
The abstract immediately suggested the title of this post. If large stocks are correlated with each other (rather than, you know, with how their business is doing) then deviations from fair value will be more frequent, offering value traders more entry and exit points.
In addition, the HFT strategy of taking advantage of pricing signals from large orders has forced many orders off exchanges into proprietary trading networks called ‘dark pools’ which get their name from the fact they are private networks which only report the prices of transactions after the transaction has occurred and typically anonymously match large orders without price advertisements.
I can assure you that this is correct; I can assure you that the size of an order required to move the market is smaller than you might think; and I can assure you that there are many, many institutional PMs who will grin at you condescendingly when you tell them this. This comes from personal experience with S&P 500 stocks, not the preferred share backwater, by the way.
Given the relative burstiness of signals with H > 0.5 we can also determine that volatility in trading patterns is no longer due to just adverse events but is becoming an increasingly intrinsic part of trading activity. Like internet traffic Leland et. al. (1994), if HFT trades are self-similar with H > 0.5, more participants in the market generate more volatility, not more predictable behavior.
…
Traded value, and by extension trading volume, fluctuations are starting to show self-similarity at increasingly shorter timescales. Values which were once only present on the orders of several hours or days are now commonplace in the timescale of seconds or minutes. It is important that the trading algorithms of HFT traders, as well as those who seek to understand, improve, or regulate HFT realize that the overall structure of trading is influenced in a measurable manner by HFT and that Gaussian noise models of short term trading volume fluctuations likely are increasingly inapplicable.