If the above lines are thought of as measuring cups, the pain index is the volume of liquid required to fill the drawdown space. The pain index essentially measures the “volume” between the break-even line and the drawdown line. These three results are exactly what the pain index measures. If one were to fill in the entire area between the drawdown line and the break-even line, it would encapsulate three things: the depth of losses, the duration of losses, and the frequency of losses. The red line represents the peak-to-trough losses associated with the S&P 500. In the graph below, we look at a 20-year period with two full-market cycles (both a bull and bear market), from July 1997 to July 2018. Specifically, it measures the depth, duration, and frequency of losses. Where it differs, however, is in its definition of risk.Īs a capital preservation metric, the pain index measures risk in losses. Thomas Becker and Aaron Moore of Zephyr Associates, t he Pain Index is similar to other measures of risk like standard deviation, beta, tracking error, etc. Standard deviation is a classroom concept capital preservation is a real-world issue.ĭeveloped by Dr. And yet standard deviation treats those months as independent observations, each one distinct from the next. In the case of the S&P 500, compounding month after month of epic losses resulted in a maximum drawdown of over 50% between August 2007 and February 2009. A crisis will play out independent of a calendar, taking however long it will take. In the midst of a crisis, the markets don’t hit a “reset button” and start afresh just because everyone flips the calendar ahead to a new month. A further twelve of the worst months of the last 25 years occurred during the dot-com bubble and the subsequent bear market at the start of the new millennium. Should the investor care about this flaw in standard deviation? Yes.īetween 19, seven of the worst months in the entire 25 year range of the S&P 500 happened within July 2007 and February 2009-less than two year timeframe. If, for example, a decade has half a dozen exceptionally bad months, standard deviation cannot differentiate whether or not these bad observations were randomly scattered throughout the decade or if they were all concentrated within a narrow time frame. The more significant failing of standard deviation is that it does not account for the timing of the negative returns. The observations are viewed as independent when they clearly are not. Most investors would not punish a manager with a high standard deviation if a good portion of the volatility was upside volatility.ģ. Unfortunately, standard deviation makes no distinction between the “good” observations that fall above the mean and the “bad” returns that fall below the mean. It fails to distinguish between upside and downside risk.īy definition, standard deviation measures the volatility of individual returns around a mean return. It’s unlikely that many financial advisors field calls from angry clients asking, “What was my volatility last month?” It’s more likely most angry calls are phrased, “How much money did I lose?”Ģ. Most investors think of risk in terms of capital preservation-how much money they could potentially lose. Investors don’t think of risk in terms of standard deviation.
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