Key Points

  • Sound investing comes down to focusing on what you can control, while still maintaining a solid understanding the risks associated with what you cannot.
  • Risk Management involves understanding and controlling for the uncertainties related to the financial markets (systematic risk) or a particular company (idiosyncratic risk).
  • Risk-adjusted return can be quantified by the ratio of expected return divided by risk.
  • Volatility is a common, although imperfect measure of risk.


The RAND (Research ANd Development) Corporation has been around since the 1940’s, and was originally incorporated as a non-profit with the goal of providing the U.S. Armed Forces with cutting edge research and development. Over its impressive history, the firm has been influential and far-reaching in shaping public policy and decision making in areas, “including the space race, the U.S.-Soviet nuclear arms confrontation, the creation of the Great Society social welfare programs, the digital revolution, and national health care.[Page 10]

As an example, during the Cold War, RAND was highly influential in the drafting of the doctrine of nuclear deterrence by mutually assured destruction (MAD). “The strategy is a form of Nash equilibrium in which, once armed, neither side has any incentive to initiate a conflict or to disarm.” (Wikipedia) Yes, Nobel Laureate John Nash, was also a former RAND employee. These days, the firm is a global policy think tank, providing research and advice to domestic and international governments, foundations, and private companies.

Over its esteemed history, the breadth and depth of individuals that have passed through the company’s doors have been formidable, to say the least.

Thirty-two recipients of the Nobel Prize, primarily in the fields of economics and physics, have been associated with RAND at some point in their career.[15][16]Wikipedia

To put this into perspective, only four nations have more than this number of Nobel Laureates associated with them; the United States, United Kingdom, Germany, and France.

When it comes the topic of this particular series, we can’t get too far into a discussion of managing risk as it pertains to the financial markets without introducing the serendipitous connection between two particular RAND employees, both highly influential in modern financial theory: Harry Markowitz and William Sharpe. Markowitz is best known for his pioneering work on portfolio management and optimization (which we’ll be sure to cover later in this series); while Sharpe is best known for his advancement in quantifying risk-adjusted returns (which we’ll also cover in this series).

Harry Markowitz (Left) & William F. Sharpe (Right)
Photographs by Institutional Investor (Left); and by Larry D. Moore CC BY-SA 3.0 (Right)

Markowitz was a true pioneer. He joined RAND in 1952, where he pushed forward various optimization techniques involving the managing of portfolios comprised of financial securities, with the goal of enhancing return, while also reducing risk; i.e., he was looking for ways to enhance risk-adjusted return. To further his research at RAND, Markowitz helped to develop the first simulation programming language, which was later released to the public domain.

After joining RAND in 1956, Sharpe was introduced to Markowitz. Markowitz became Sharpe’s unofficial thesis advisor, and the two began to collaborate on various security pricing models; i.e., quantitative ways of estimating a security’s return based upon an understanding of its risk. Needless to say, when it comes to finance, it’s all about the risk.

Sound investing comes down to being able to differentiate between what you can control and what you can’t, and then doing your best to focus on the former while still maintaining a solid understanding the risks associated with the latter.

Ultimately, it was this original pioneering work on financial risk that set the stage for both individuals to receive the 1990 Nobel Memorial Prize in Economic Sciences.

Risk, Everywhere

Let’s begin with risk, which we can define as the concerns relating to bad things that could potentially impact the financial markets or a particular company. When a risk pertains to the market as a whole, we call this systematic risk (or market risk). For instance, these types of risks can pertain to the chance of a war breaking out, which can cause lasting economic impact, or the federal government changing interest rates, which can impact borrowing and lending between firms. When a risk pertains to a particular company, we call this idiosyncratic risk. For instance, these types of risk can be associated the chance of a company not being able to pay its bills, the chance that a company’s products will go obsolete, and even the chance that a company’s CEO might have a heart-attack. Thus, when we talk about estimating risk, we are essentially talking about estimating the impact of these types of uncertain events.

Therefore, we can describe risk-adjusted returns as better way to compare the expected future returns of two stocks. For example, if two stocks are expected to return the same amount over the next ten years, it behooves an investor to invest in the stock with the lower expected risk. More formally, we can quantify this with a ratio: expected future return divided by expected future risk (i.e., expected risk-adjusted return = expected future return divided by expected future risk).

But just how does one measure risk? For example, how do we measure the risk of IBM versus the risk of Microsoft? Turns out estimating expected future risk is no easy task, as we must consider all bad things that can potentially impact an investment and somehow quantify all this meaningfully. To make things especially challenging, there are some types of risk that are everyday occurrences (such as the random movement of a stock over the course of a day, and its associated risk of falling in price as a result of this random movement). Then there are risks that can happen under certain low probability situations (such as an earthquake, or a terrorist attack, or a changing of political power in government). These occurrences, although rare, can lead to significant turbulence in the financial markets.

In finance there are dozens measures that help quantify and estimate these uncertainties, but ultimately, these measures are only as good as the assumptions that make them up, given that we are talking about the future. To make things more complicated to measure, the future is full of a multitude of unknowns that includes both known-unknowns and unknown-unknowns. A known-unknown is a risk that we can foresee, but don’t know the outcome to (such as an upcoming election, or an earnings announcement). An unkown-unkown is a risk that we can’t foresee, and thus it’s hard to even consider an outcome to such a risk (such as a terrorist attack, or the unexpected death of a CEO).

The grand problem with risk is that even if you could sufficiently determine and quantify all relevant known-unknowns, you would still be left with all of the unknown-unknowns, and unfortunately, there is an infinite number of these latter types of risks, so one cannot possibly account for and react to them all.

Except possibly, placing all of your money in cash under your mattress; but then again, if the U.S. Government fails, then so may your cash as well. Of course, there’s gold; but there’s no guarantee that gold will preserve its value in the event of global thermonuclear war (should Nash’s theories prove to incorrectly underestimate human nature). Afterall, during nuclear fall out, you might not be able to leverage that bar of precious metal at your local, now radioactive Taco Bell.

When it comes to investing, given the uncertain nature of the financial markets, and humans in general, it’s likely that the risk that you expect the least is the one that ends up hurting you the most.

Nevertheless, as investors we try to do our best based upon the information and estimation techniques that we have at our disposal. Accordingly, the first and foremost technique that investors study in quantifying risk is volatility. So let’s dig into this measure as a first step in estimating risk in Part 2 of this series.