Let me take you back to grad school for a few moments, or perhaps your college undergrad. If you’ve studied much finance, you’ve surely studied beta in the context of modern portfolio theory (MPT) and the Capital-Asset Pricing Model (CAPM). If you are a quant like me, you may have been impressed with the elegance of the theory. A theory that explains the value and risk of a security, not in isolation, but in the context of markets and portfolios.
Markowitz‘s MPT book, in the late 50’s, must have come as a clarion call to some investment managers. Published ten years prior, Benjamin Graham’s The Intelligent Investor was, perhaps, the most definitive book of its time. Graham’s book described an intelligent portfolio as a roughly 50/50 stock/bond mix, where each stock or bond had been selected to provide a “margin of safety”. Graham provided a value-oriented model for security analysis; Markowitz provided the tools for portfolio analysis. Markowizt’s concept of beta added another dimension to security analysis.
As I explore new frontiers of portfolio modeling and optimization, I like to occasionally survey the history of the evolving landscape of finance. My survey lead me to put together a spreadsheet to compute β. Here is the beta-computation spreadsheet. The Excel spreadsheet uses three different methods to compute β, and they produce nearly identical results. I used 3 years of weekly adjusted closing-price data for the computations. R2 and α (alpha) are also computed. The “nearly” part of identical gives me a bit of pause — is it simply round off, or are there errors? Please let me know if you see any.
An ancient saying goes “Seek not to follow in the footsteps of men of old; seek what they sought.” The path of “modern” portfolio theory leaves behind many footprints, including β and R-squared. Today, the computation of these numbers is a simple academic exercise. The fact that these numbers represent closed-form solutions (CFS) to some important financial questions has an almost irresistible appeal to many quantitative analysts and finance academics. CFS were just the steps along the path; the goal was building better portfolios.
Markowitz’s tools were mathematics, pencils, paper, a slide rule, and books of financial data. The first handheld digital calculator wasn’t invented until 1967. As someone quipped, “It’s not like he had a Dell computer on his desk.” He used the mathematical tools of statistics developed more than 30 years prior to his birth. A consequence of his environment is Markowitz’s (primary) definition of risk: mean variance. When first learning about mean-variance optimization (MVO), almost every astute learner eventually asks the perplexing question “So upside ‘risk’ counts the same as the risk of loss?” In MTP, the answer is a resounding “Yes!”
The current year is 2012, and most sophisticated investors are still using tools developed during the slide-rule era. The reason the MVO approach to risk feels wrong is because it simply doesn’t match the way clients and investors define risk. Rather than adapt to the clients’ view of risk, most investment advisers, ratings agencies, and money managers ask the client to fill out a “risk tolerance” questionnaire that tries to map investor risk models into a handful of MV boxes.
MPT has been tweaked and incrementally improved by researchers like Sharpe and Fama and French — to name a few. But the mathematically convenient MV definition of risk has lingered like a baseball pitcher’s nagging shoulder injury. Even if this metaphorical “injury” is not career-ending, it can be career-limiting.
There is a better way, though it has a clunky name: Post-Modern Portfolio Theory (PMPT). [Clearly most quants and financial researchers are not good marketers… Next-Gen Portfolio Optimization, instead?] The heart of PMPT can be summed up as “minimizing downside risk as measured by the standard deviation of negative returns. “A good overview of PMPT in this Journal of Financial Planning Article. This quote for that article stands out brilliantly:
Markowitz himself said that “downside semi-variance” would build better portfolios than standard deviation. But as Sharpe notes, “in light of the formidable computational problems…he bases his analysis on the variance and standard deviation.”
“Formidable computational problems” of 1959 are much less so today. Financial companies are replete with processing power, data storage and computer networks. In some cases developing efficient software to use certain PMPT concepts is easy, in other cases it can be extremely challenging. (Please note the emphasis on the word ‘efficient’. An financial algorithm that takes months to complete is unlikely to be of any practical use.) The example Excel spreadsheet could easily be modified to compute a PMPT-inspired beta. [Hint: =IF(C4>0, 0, C4)]
Are you ready step off the beaten path constructed 50 years ago by wise men with archaic tools? To step onto the hidden path they might have blazed, if armed with powerful computer technology? Click the link to start your journey on the one less traveled by.