A Better Way to Manage Risk

Risk management took on a renewed sense of importance during the Global Financial Crisis of 2008-09.  As Fannie Mae and Freddie Mac were placed into conservatorship, Lehman Brothers filed for bankruptcy, and AIG became the definitive case study in “Too Big to Fail”, investors learned that effectively managing risk had quietly — and quickly — become very complex. The realization of systemic risks became the rationale for massive government intervention because, as we learned, recent financial theory had failed to account for the capital market equivalent of a domino chain. That is, not only had financial institutions become dependent on one another as trading counter-parties, but the securities themselves were intertwined. How could risk be managed if all financial assets were losing value simultaneously?

This question exposes a critical flaw in the professional practice of investment management, a flaw which affects most investors and which is propagated by financial advisors who, much of the time, do not understand the implications of their own faulty assumptions about risk. To be fair, thoughtful advisors aren’t acting irrationally or recklessly. They have been simply relying on failed academic models — models which tend to treat risk as a commodity.

In this paper, we will briefly explain generally accepted concepts of risk management, illustrate the critical flaws therein, specifically how risk has become commoditized, and point toward a more economically prudent method of managing investment risk. We should note that this approach is not new. Its assumptions are basic and inject tones of common sense from bygone eras. And, it’s how Peloton has been managing risk for decades.

A Brief History of Financial Risk

Financial risk is a very old concept. Historians believe that forward contracts were used in ancient Rome and India as means of hedging against fluctuations in the prices of grain for future delivery.1 Up through the late Medieval and early Renaissance periods, the sources of risk remained mysterious and seemingly capricious, even among the merchant classes. At the dawn of the Enlightenment, however, Pierre de Fermat and Blaise Pascal developed probability theory, which provided the mathematical foundation for the field of statistics. Throughout the 19th and 20th centuries, as our ability to collect and measure ever-increasing amounts of data grew, so did our reliance upon statistics as a tool for measuring and managing risk.

In 1952, Harry Markowitz, then a doctoral student at the University of Chicago, developed what was to become known as Modern Portfolio Theory (MPT).2 MPT has become the dominant framework among financial advisors in managing investment portfolio risk, but when Markowitz presented the idea to his dissertation committee, the concepts were so novel that Milton Friedman is purported to have exclaimed that the research was not even economics. The key idea of MPT is the proposition that individually risky assets can be combined in a portfolio to produce risk-reducing synergies. That is, if an investor begins with a risky asset and adds to it a second asset with equal or higher risk, the resulting portfolio might become, counter-intuitively, less risky. In this sense, risk is understood as the potential variance, or volatility, of the investment portfolio’s future returns.

Peloton WealthKey to understanding how this happens is the statistical concept of correlation. Correlation uses series of previously observed data to express a theoretical future relationship between independent variables. Two independent items or processes may range from being perfectly negatively correlated with one another (measured as -1.0) to perfectly positively correlated (1.0). To illustrate, imagine a classroom of children reciting a poem aloud with their teacher. The children’s words, one might say, are perfectly positively correlated with the teacher’s — they all “move” together in exactly the same way. On the other hand, perfect negative correlation is illustrated beautifully in the Beatles song “Hello, Goodbye” — no matter what Paul says, his girl says the exact opposite.

In portfolio management, if asset A and asset B are perfectly negatively correlated, owning them both in identical proportions can theoretically completely offset the volatility experienced by owning each asset individually. As this concept became widely understood, academics began to use historical returns data to study the effects of combining various risky assets in differing proportions in order to achieve “efficient” portfolios, that is, portfolios with the lowest level of volatility for a given expected return. Buttressed by such intellectual heft, financial advisors ushered in the age of asset allocation as the supreme means of controlling investment risk. The message advisors carried to their clients was: what matters most is not the riskiness of the assets themselves but the way in which they are combined to lower portfolio risk.

Flawed Assumptions

MPT relies on several assumptions which are at best incomplete. Two of those assumptions are particularly important for this discussion. First, consider the jump from early probability theory to modern statistical-based forecasting. Fermat and Pascal were principally working in the physical world and in pure mathematics when constructing probability theory. Their original concern was to better understand games of chance, such as the roll of dice or the toss of a coin, and thus to quantify the likelihood of a future event given certain past events.3

But laws in the social sciences, like financial economics, function differently than the laws of nature. Economics studies the behaviors of individuals and groups of individuals, making choices with limited resources. Thanks to technological development, goods and services are evolving, and thus, economic reality is constantly changing, too. This dynamic nature is wholly unlike the physical world or mathematics, both of which are governed by constants — or absolute truths. The outcome of the roll of dice is explained by physics and simple probabilities. Each roll will produce one of a finite number of outcomes, limited by the ways in which two six-sided dice can be combined. The predicted results of economic policies — or portfolio allocation decisions, for that matter — are essentially conjectures that mechanisms and participants will behave like they did in the past. In other words, relying heavily on historical economic data to predict future outcomes confuses the descriptive power of statistics with its predictive power. Economic history is a guide, not a road map.

MPT also assumes that an asset’s risk is most aptly defined by the volatility of its returns:  the more volatile the asset’s price fluctuations, the more risky it is. Usually this is expressed in a mean variance analysis measure, like standard deviation. Thus, the financial advisor who successfully combines risky assets to lower the overall standard deviation has produced a less risky portfolio.

But do investors feel that they are in less financial danger when their portfolios’ standard deviations are lower? While investment price swings can be disconcerting, it is our experience that investors are fearful not of the volatility per se, but because of their common perception that greater volatility leads to loss of future financial security.

What Went Wrong

During the Global Financial Crisis of 2008-09, financial assets across the risk spectrum experienced severe declines simultaneously, with the singular exception of U.S. Government bonds. In statistical parlance, “correlations became increasingly positive in a rather severe manner.” In street parlance, ”there was nowhere to hide” from broadly declining asset prices. As the system broke down, portfolios of risky assets became much more volatile than expected. There is now little question that the massive amount of leverage in the financial system at the time contributed to this phenomenon:  risky assets, however distinct they might be apart from the leverage effect, all came under heavy selling pressure during the panic as institutional investors attempted to de-lever their balance sheets.

But even adjusting for the leverage effect, this domino chain was never supposed to happen. Under the MPT paradigm, risk — defined solely as standard deviation of returns — should have been thoroughly mitigated in portfolios which combined dissimilarly correlated assets. The obvious question was: why had it become so commonplace to define risk by only a narrow statistical measure?

The academic study of finance is heavily mathematics-based. It also deals in generalities, drawing inferences from large amounts of observed data. This approach works well when studying purely objective financial phenomena: the average bid-ask spread for stock transactions, or the probability of bond defaults for various credit ratings, for example. However, since investment risk is a profoundly subjective concept, defined quantitatively and qualitatively at a personal level, it doesn’t lend itself to the tools of traditional academic financial study. In order to objectify risk for the sake of research, academia identified volatility (standard deviation) as a convenient proxy for truer, yet academically more cumbersome, measures of risk.

Financial advisors then began to utilize this academic definition of risk in their perceived management of risk. In so doing, their advisory services gained credibility. More significantly, as far as the firms employing financial advisors were concerned, treating risk as an abstraction represented a huge business opportunity. If risk could be commoditized, growing a financial advisory firm required only a small team of experts to construct “model portfolios” together with an army of financial advisors to gather assets and distribute the models. The recipe for growing the business offered virtually limitless potential: hire natural-born sellers, equip them with a choice of several “customized” risk-minimizing model portfolios, and provide adequate incentives for the advisors to sell.

However, despite the convenience offered by defining risk in easily researchable terms, and despite the way in which abstracting risk allowed for massive growth in the retail financial advisory business, this definition of risk failed miserably for the most important constituent: the individual investor.

A New, Old Way of Managing Investment Risk

At Peloton, we believe that future returns are influenced by past returns, not determined by them. History never perfectly repeats, but it definitely rhymes. We do not contemplate risk simply in terms of an asset’s or portfolio’s price volatility. Instead, we strive to be forward-looking, considering risk from a client’s perspective and asking the question, “What’s the likelihood that my investments will meet my future financial needs?”  We try to answer this question on two fronts: with the individual security and with the composition of the portfolio.

In our selection process, we mitigate risk through fundamental analysis of each security in which we invest. We begin by identifying key macro themes which we believe provide opportunities for faster growing corporate profits over the ensuing 12 to 36 months. Some themes are demographic (e.g., the spending patterns of the Baby Boom generation), some are global (e.g., the emerging middle class), and some are economic (e.g., a recovery in corporate technology spending).

We then consider the merits of different companies which fit those themes. Where is the firm positioned within its industry? Is the management team credible? What are the main sources of its revenue? Do we see likelihood of improving operating efficiencies? Can we identify one or more potential catalysts for price improvement?

Finally, we value each security to be certain the price we pay aligns with our sense of expected economic benefit over a reasonable holding period. Our valuation discipline places high importance on free cash flow (FCF).4 FCF generation is a sound indicator of a firm’s health because, at a very basic level, the whole point of being in business is to produce more cash. FCF is cash that is generated by the core operations of a business, after deducting expenses required to maintain the company’s production capacity. Thus, FCF approximates the true unencumbered cash generated for the benefit of the company’s shareholders by the business itself. For bond investors, FCF is an important measure for assessing the security of timely interest and principal payments.

At the portfolio level, we work with our clients to achieve a detailed understanding of their future financial requirements. We distinguish among capital gains, dividend income, interest income and principal from maturities as we structure portfolios specifically designed to satisfy their individual cash flow needs. Segmenting the sources of return this way affords us the luxury of patience as we wait for opportune market conditions to effect purchases and sales of securities. In the short run, prices are very unpredictable, but over a market cycle, security prices reflect fundamentals — operating performance and ultimately cash generation capability. This understanding allows us to devote our analytical resources to identifying fundamental business investment opportunities, capitalizing on them when we observe short-term disconnects in security pricing. In a sense, we mitigate the “risk” of short-term price volatility by structuring portfolios so that all decisions can be based solely on the expected future returns of each security — not simply in reaction to price volatility.

Despite the fact that correlation is an imperfect science, diversification and asset allocation play important roles in our portfolio construction. We realize that our investment themes will succeed to varying degrees and at different times. And because we recognize our processes and disciplines are imperfect, we pursue several themes at the same time. We also agree with research which suggests that the variability of portfolio returns is determined in large measure by asset allocation.5

Risk management has forever changed as conventional wisdom was proven flawed. Risk is an inherently individual measure, never a commodity. The practice of relying exclusively on quantitative measures rooted in historical data assumes that history will repeat itself. We are hopeful for a return to time-tested methods of controlling risk: rigorous fundamental analysis and sound portfolio construction, focused on the needs of the individual investor.

1 Robert W. Kolb, Understanding Futures Markets (Massachusetts: Blackwell Publishers, 1997), 2.

2 Harry Markowitz, “Portfolio Selection,” The Journal of Finance, 7:1 (1952): 77-91.  First published in the Journal of Finance, Markowitz further developed the material for his doctoral dissertation, which he successfully defended in 1955.

3 Peter L. Bernstein, Against the Gods: The Remarkable Story of Risk (New York: John Wiley & Sons, 1996), 60.

4 For a more detailed description of the discounted cash flow method of equity valuation, see Alfred Rappaport, Creating Shareholder Value (New York, The Free Press, 1986), 50-65.

5 Gary P. Brinson, L. Randolph Hood, and Gilbert L. Beebower, “Determinants of Portfolio Performance,” Financial Analysts Journal, July – August (1986): 39-44.  This paper is the seminal study in the effects of asset allocation decisions on the variability of portfolio returns.  The central conclusion of the study is often misunderstood to be that asset allocation determines portfolio performance, rather than the variability of portfolio returns.