“The short-term volatility of price will be greater than the short-term volatility of value.” –Fisher Black
In 1986, Fisher Black attempted to explain the concept of “noise” in financial markets. He said that “noise is what makes our observations imperfect. […] It is information that hasn’t yet arrived.” He claimed that if noise trading didn’t exist, there would be very little trading in individual assets. Put another way, noise trading results in liquidity.
Twenty years later, in a piece in The Wall Street Journal, Jeremy Siegel contrasted the “noisy market hypothesis” with the idea of market efficiency. He stated that stock prices are not always the best estimate of value because “prices can be influenced by speculators and momentum traders.”
While noise in prices has been cited in numerous academic articles examining value and size premiums, it’s not a term with a singular definition in finance, nor is it well understood. Noise and volatility are often mistaken for one another. As counter-trend traders, we attempt to take advantage of noise in financial markets. To do so successfully, we must have a precise understanding of the differences between noise and volatility, and the ability to measure those distinct phenomena.
We believe that “noise” measures the choppiness of the market’s price path, while “volatility” measures the magnitude of the market’s price changes. One way to quantify noise is by measuring the smoothness of a price series’ path; this is done by examining the ratio of the net price movement over a time period relative to the total up and down movement. This ratio quantifies the percentage of an asset’s movements that were congruent with its underlying trend, or the percentage of net directional movement.
The higher the percentage of net directional movement to total movement, the smoother the price series and the less noise there is.
How much time does the market spend in high-volatility environments? How much time does it spend in high noise environments? To answer these questions we examined the return data for the S&P 500 from Jan. 1, 1997 to Dec. 31, 2013, and classified each month based on its volatility and noise characteristics. Additionally, we measured the performance of the S&P 500 and a simple short-term counter-trend strategy within different environments for noise and volatility. The results are summarized in the chart above (see Figure 1).
Upon reflection, readers may wonder whether noise and volatility levels are predictable. We found volatility has some predictability (significant positive autocorrelation), but noise does not. The numbers in Figure 2 highlight the probability of transitioning from one quadrant to another. In short, regardless of your current environment, it’s roughly the same odds as a coin flip as to whether next month will be a high or low noise environment.
As demonstrated, both noise and volatility impact returns to equities as well as to short-term counter-trend trading strategies. Importantly, the return profiles are distinctly different, i.e., they are uncorrelated over time, and the worst periods for equities are often the best for counter-trend strategies. Investors looking for ways to truly diversify their portfolios may want to become more familiar with “noise” and with strategies that thrive on this market phenomenon.