{"id":5445,"date":"2015-09-02T09:46:37","date_gmt":"2015-09-02T13:46:37","guid":{"rendered":"http:\/\/www.jasonapollovoss.local\/?p=5445"},"modified":"2018-09-21T02:03:42","modified_gmt":"2018-09-21T06:03:42","slug":"alpha-wounds-bad-adjunct-methodologies","status":"publish","type":"post","link":"https:\/\/jasonapollovoss.com\/web\/2015\/09\/02\/alpha-wounds-bad-adjunct-methodologies\/","title":{"rendered":"Alpha Wounds: Bad Adjunct Methodologies"},"content":{"rendered":"

Active management has taken a lot of\u00a0body blows\u00a0recently. The principal criticism: Active managers contribute no alpha once their fees are factored in<\/a>. So is it time to write active management’s obituary? Not quite. But active management\u00a0certainly is feeling pain.<\/span><\/p>\n

A few weeks ago, I argued that\u00a0many of the alpha wounds plaguing active management are self-inflicted<\/a>. But this month, I will discuss injuries\u00a0to active managers that are not of their own making. Specifically, I will talk about those wounds resulting from the bad evaluative methodologies employed by the investment industry\u2019s adjuncts: consultants, academics, research institutions, and so forth.<\/span><\/p>\n

Volatility Is Not Risk<\/strong><\/span><\/p>\n

I began my analysis career working as an intern at Portfolio Management Consultants (PMC) in Denver, Colorado. Back then, among other measures, we utilized alpha, beta, Sharpe ratios, and Treynor ratios to quantify whether an investment manager was better than her peers.<\/span><\/p>\n

Over time we started to notice something very interesting for which we could not account: namely, investment managers who beat their benchmarks handily in 17 of 20 quarters and had barely trailed their benchmark in the remaining three quarters. On a graph, this outperformance was especially dramatic. However, we would then look back to our list of quantitative measures \u2014 alpha, beta, and Sharpe and Treynor ratios \u2014 and see that these managers ranked poorly compared to those who had barely beaten the same benchmark over the same time period and who had led during fewer quarters. How can this be? This result\u00a0\u2014 all too common in the investment management industry \u2014\u00a0is due to one simple fact: Volatility is not risk.<\/span><\/p>\n

Yes, I have heard the argument that for investors entering and exiting funds, volatility is<\/em> risk. But why is this the concern of the investment manager? Absent a lock-up period for an investor’s funds, how is the investment manager (active or passive) supposed to account for the anxiety of the end consumer? This is like holding physicians responsible for whether or not their patients smoke, drink, or eat hot dogs covered in trans fats. Or blaming psychologists for whether or not their patients continue to talk to their exes or take cruise ship vacations with the in-laws.<\/span><\/p>\n

A Definition of Risk<\/strong><\/span><\/p>\n

The discussion of whether\u00a0volatility differs from risk also depends on a critical assumption overlooked by most of the industry: Only in finance do we define risk as volatility. Different dictionary definitions of risk all converge on something like the “chance of loss.”\u00a0Here is one such example<\/a>:<\/span><\/p>\n

noun<\/em><\/span><\/p>\n

    \n
  1. exposure to the chance of injury or loss; a hazard or dangerous chance<\/em><\/span><\/li>\n
  2. Insurance<\/span>\n
      \n
    1. the hazard or chance of loss.<\/em><\/span><\/li>\n
    2. the degree of probability of such loss.<\/em><\/span><\/li>\n
    3. the amount that the insurance company may lose.<\/em><\/span><\/li>\n
    4. a person or thing with reference to the hazard involved in insuring him, her, or it.<\/em><\/span><\/li>\n
    5. the type of loss, as life, fire, marine disaster, or earthquake, against which an insurance policy is drawn.<\/em><\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      verb<\/em><\/span><\/p>\n

        \n
      1. to expose to the chance of injury or loss; hazard<\/em><\/span><\/li>\n
      2. to venture upon; take or run the chance of<\/em><\/span><\/li>\n<\/ol>\n

        idiom<\/em><\/span><\/p>\n

          \n
        1. at risk<\/em><\/span>\n
            \n
          1. in a dangerous situation or status; in jeopardy<\/em><\/span><\/li>\n
          2. under financial or legal obligation; held responsible<\/em><\/span><\/li>\n<\/ol>\n<\/li>\n
          3. take\/run a risk: to expose oneself to the chance of injury or loss; put oneself in danger; hazard; venture.<\/em><\/span><\/li>\n<\/ol>\n

            Notice that not a single definition includes volatility as a part of its explanation. Dictionary definitions and popular understandings of risk might differ from a business definition, yet a popular business dictionary describes over a dozen\u00a0different forms of risk, ranging from exchange rate risk<\/a> to unsystematic risk<\/a>, all of which focus on the chance of loss. The insurance business is an industry critically dependent on an understanding of risk, and an insurance licensing tutorial\u00a0says that “Risk means the same thing in insurance that it does in everyday language. Risk is the chance or uncertainty of loss<\/a>.”<\/span><\/p>\n

            Only in finance is risk defined as volatility. Why? In the early days of investment management analysis in the 1950s, academics recognized that average and standard deviation and the entirety of hundreds of years of statistics research thinking could be borrowed to analyze the performance of investment portfolios \u2014 if some of the definitions could be bent to their aims. Also, it was very difficult to run calculations of any sort in an automated fashion when calculators did not even exist and all computations were done by slide rule. So a simple measure was needed. Here, of course, I am talking about standard deviation. With standard deviation transformed into \u201crisk,\u201d the complex work of analyzing portfolios could begin and theories could be developed. Surely the math would take care of itself as the analysis was improved.<\/span><\/p>\n

            Take a look at the calculation of standard deviation and you will see that it is essentially the weighted average variation from a mean. There are two interesting things to note here:<\/span><\/p>\n