Implied Risk and Three of Our Favorite Books

Implied Risk data works -- helps people make more money and lose less -- because the theories surrounding the Normal Curve are perfect for investors who like to buy issues low and sell them high.

The best book on risk and the stock market is Against the Gods: The Remarkable Story of Risk ('96), by Peter Bernstein. The author traces the development of probability theory, which is based on the Normal Curve, to the observation of everyday life occurrences. Now probability theory has developed to the point that it's fair to say most of science is based on, or incorporates, some form of probability theory. The first reason Implied Risk data works is because it's based on the Normal Curve and probability theory.

Probability theory was introduced to the stock market by Harry Markowitz, who defined the concepts that William Sharpe turned into a risk measurement called "beta." (Both won a Nobel Prize for their efforts.) Beta measures an issue's risk relative to that of a constant, such as the historical volatility of the S&P 500 (SPX) or the rate of return from a three-month Treasury bill. In the absence of a market to provide true risk estimates, these relative risk estimates were a vast improvement on no risk estimates at all.

The listed options market debuted in 1973, first with calls only. Puts were added in 1974, paving the way for the theoretical option pricing model developed by Fischer Black and Myron Scholes in 1976. The Black/Scholes model introduced the world to the idea of option implied volatility, or the expected volatility of the underlying issue as implied by the price of the option. The key to this approach was the creation of the listed options market before Black and Scholes wrote their paper. Implied volatility is a product of the forward-looking listed options market, while beta is a product of the backward-looking calculator. That's the second reason Implied Risk data works:
Option implied volatility is produced in the marketplace by stock owners expressing risk concerns about the issues they own.

Another reason Implied Risk data works originated in Beat the Dealer ('66), a book about blackjack and card counting by Edward Thorp. He demonstrated how a seemingly random game could become predictable based upon immediately prior events (observations). The goal of card counting is to determine when the odds turn in your favor. Example: If a high number of face cards have appeared, you can accept new cards more readily because the chances of busting have dropped. Same with stock prices: If the stock has moved to the bottom of the market's estimated range for that issue, the chances of the stock dropping much further are minimal -- as long as you've chosen a fundamentally worthy issue. That's the third reason the Implied Risk data works: The imposition of a scale, along with rules attendant to the scale, permits the data to display an opinion concerning the expectations for further movement.

The key here again is the use of a scale, combined with the observation and analysis of immediately prior events. In blackjack, the scale is 52 cards X #of decks and the observations are which cards have appeared this time around the shoe. In stocks, the scale is three standard deviations from the current mean price and the observations are the stock's current price in relation to that current mean price.

Thorpe demonstrated that the imposition of a scale coupled with observations of events from the immediate past can increase the odds of certain actions occurring, turning a seemingly random game into one that can be somewhat predictable. Another work that continues the thought, The Winner's Curse ('92) by University of Chicago finance professor Richard Thaler, elucidates why opportunities -- he calls them anomalies -- should be expected to occur at either end of the Normal Curve. The anomaly with which we’re most concerned is called reversion to the mean.

Thaler and his colleagues in the burgeoning field of behavioral finance cite the prevalence of financial anomalies as proof that the theory of the fully rational investor is full of holes. One colleague, Yale economics professor Robert Shiller, has demonstrated statistically that markets "overreact" to events, an anomaly Shiller traces to behavioral factors. (Shiller coined the phrase "irrational exuberance" for Federal Reserve Chairman Alan Greeenspan.) Indeed, issues move to three standard deviations away from their 90-day means with greater frequency than the Normal Curve expects. But since Implied Risk data attempts to identify anomalies as interesting trading opportunities, the more the merrier.

One portion of Thaler's book examines the data supporting the conclusion that the phenomenon of reversion to the mean affects the stock market. "Indeed, stock prices do appear to be somewhat predictable," Thaler writes. "In particular, if one takes a long-term perspective (three-seven years) or examines individual securities that have experienced extreme price movements, then stock returns display significant negative serial correlation, in other words, prices are mean reverting." Implied Risk customers believe that stock price movements are mean-reverting over periods as short as three months, and they further believe that the options market provides a very reliable estimate of standard deviation from the mean. Maybe one day Thaler will study Implied Risk customers. Until then, that's the fourth reason Implied Risk data works: Reversion to mean is a fact of life among listed issues.

If reversion to the mean can be measured on a long-term basis of more than 100 years, we should be able to measure it on a shorter term basis such as three months. And if mean reversion works for the overall market, it must work for individual issues. Implied Risk data takes the theory of mean reversion and combines it with option market assumptions to produce useable information about the direction stocks will take in the immediate future.

The ultimate reason Implied Risk data works is because it's built upon sound theoretical assumptions. Two of the assumptions cited -- standard deviation as scale, and reversion to the mean as likely -- spring from the first assumption, that Normal Curve and probability theory apply to the stock market. The final assumption, market volatility as expressed through the options market, is what allows those other three theoretical assumptions to be fully realized.