The majority of investor portfolios are rife with risk, even when they’re heavily “diversified.” Is your portfolio one of them?
Addressing the unknown
No one can ever truly understand all risks in the financial system, regardless of academic pedigree or professional experience. I’ll repeat that, no one can ever truly understand all risks in the financial system, regardless of academic pedigree or professional experience. Acknowledge this fact, and you’ve already begun addressing the problem. Humans commonly suffer from biases that cause them to feel over-confident in their understanding and decision-making abilities. In a system with ever-present hidden risks, over-confidence in financial markets can be extremely dangerous. Many market experts failed to prepare for the events that unfolded in the 2008 stock market crash, proving that even the brightest minds make mistakes. Everyone makes mistakes; but you must learn from your mistakes, and adhere to the fact you can not possibly predict future events or know all potential risks. Risk management is the most essential part of any investment strategy. The best asset managers always place importance on process over outcome.
"A manager that has become overconfident by using a bad process is like somebody who plays Russian roulette three times in a row without the gun going off, and thinks they’re great at Russian roulette. The fourth time, they blow their brains out." —Daniel Loeb, Alpha Masters
1. The industry standard is downright dangerous
When choosing an advisor to manage your assets, one of the single most important factors should be to discover how they view “risk” and what they do to address it. In the simplest of terms, financial risk can be loosely defined as the potential for loss of capital. Industry standard (Modern Portfolio Theory, for example) does not, however, define risk as a likelihood of loss, but as volatility. The higher the standard deviation (volatility), the higher the risk, according to standard approaches. Simply put, the standard deviation of a market or stock is the percentage amount it is expected to move on average over the course of a given time period (i.e. a day, or year). If a stock has a daily standard deviation of 1%, then according to a normal distribution, 68% of the time it is expected to move within +1% or -1% a day, and 95% of the time it is expected to move within +2% or -2% a day. This statistical method fails to take into account many other risks, and as stated prior, has no permanent correlation to risk (i.e. loss of capital). This fact alone should cause one to be concerned by anyone who takes this approach.
There are countless problems with this mainstream concept of risk. Most importantly, there isn't any permanent correlation between risk (when defined as volatility) and return. High volatility does not necessarily give better results, nor does lower volatility necessarily give lesser results. That fact, based on empirical evidence, alone goes against much of what most financial advisors have preached to clients for decades. This assumption is the platform upon which modern portfolio theorists are building “optimal portfolios” using intricate mathematical models. These models are intended to maximize returns at a given level of risk or minimize risk at an expected level of return, but this approach is rife with dangers.
Take a look at the Efficient Frontier below (Figure 1), based off Modern Portfolio Theory--the industry standard approach to "risk to reward allocation" in a financial portfolio. Unfortunately, this method relies on many flawed assumptions and can be dangerously misleading as a representation of risk.
"Despite its theoretical importance, critics of Modern Portfolio Theory (MPT) question whether it is an ideal investing strategy, because its model of financial markets does not match the real world in many ways."
-Mahdavi Damghani B., Wilmott Magazine
Figure 1: The Efficient Frontier was designed help asset managers construct a set of optimal portfolios that offers the highest expected return for a defined level of risk or the lowest risk for a given level of expected return. Empirical research shows this model takes on many dangerous assumptions that do not properly address the issue of risk.
For more information on the criticisms and assumptions of the Efficient Frontier and MPT visit this link.
How misleading can this practice be? Let’s illustrate it. Suppose the price of a stock goes up 15% in one month, 5% percent the next, and 10% percent in the third month. The standard deviation would be 5% with a return of 32.8%. Compare this to a stock that declines 15% three months in a row. The standard deviation would be 0% with a loss of 38.6%. An investor holding the falling stock might find solace knowing that the loss was incurred completely “risk-free.”
What do some market experts have to say about this approach? James Mai of hedge fund Cornwall Capital was one of the most successful investment managers during the 2008 crash, which he largely attributes to his views and focus on risk management:
“One of the great misconceptions of the investing public is equating risk with volatility, which is wrongheaded on multiple grounds. Frequently, the most important risks don’t show up in the track record and hence are not reflected by volatility.” - James Mai, founder & CIO of Cornwall Capital
By accepting that risk is not the same as volatility, we must also question any portfolio strategies that rely on this view. Portfolio Selection Theory (developed by Nobel Prize winning Markowitz) and CAPM (Capital Asset Pricing Model, developed by Nobel Prize winning Sharpe) both assume a positive correlation between risk (defined as volatility) and return. Using this logic, higher expected returns can only occur with correspondingly higher risk; and investors who seek to lower their risk levels must reduce their return expectations, accordingly. This approach is not only wrong, it dangerously places the focus on volatility as a means of evaluating risk of a portfolio or asset.
Wise investors should seek to avoid the investment models that rely heavily upon the assumptions discussed above. The most common investment models guilty of this flawed “optimal portfolio” logic are:
- Mean Variance Optimization (MVO)
- Resampled Mean Variance Optimization (and other variations of MVO)
- Black-Litterman Model (BLM)
- Capital Asset Pricing Model (CAPM)
- Other models based off the Efficient Market Hypothesis/Modern Portfolio Theory (EMH/MPT)
Without an effective risk management strategy in place, such portfolios are exposed to “behavioral risk” of a financial disaster, which is the true risk, not volatility. Ultimately, what matters is not how often you are right, but how large your cumulative errors are. This is an issue that the common allocation models simply can’t be optimized for.
2. How to address “risk.”
Start simply by viewing the problem from two angles. Broadly speaking, there are two types of dangers in the market: known unknowns and unknown unknowns. The difference between these two is the difference between risk and uncertainty, respectively.
To further clarify:
- Risk is not knowing what is going to happen next, but knowing with some certainty what the statistical distribution looks like.
- Uncertainty is not knowing what is going to happen next, and not knowing what the possible statistical distribution looks like (commonly referred to as a “Black Swan” event) (Source: Risk versus Uncertainty).
Our world experiences unknown unknowns more often than we admit or realize (due to hindsight bias), and they can have severe negative impacts on financial markets and investment portfolios.
While volatility can function as a reasonable proxy for risk, it should never be relied as heavily upon as it so often is. When markets behave normally, standard distributions of returns can help represent the realistic “risk of loss” in a position. However, history has shown markets don’t always behave normally, which is why using normal probability distributions for risk evaluation is so dangerous (see Figure 2).
Figure 2: The blue line represents a fitted normal probability distribution—a display of market returns that most portfolio models use for the basis of risk measurement. The red columns display the frequency of monthly returns in the S&P500 since 1985.
In one instance in Figure 2 above, the market returned -20% one month (far left black arrow). According to the above fitted normal distribution this is a 17.8 standard-deviation event, meaning a 1 in 1.5x10-69 chance of occurring. In this case, such returns are deemed by statistics as virtually impossible. The real-life occurrences show, however, that these extreme events (months with large market crashes) occur much more often than portrayed by a standard distribution. This is another flawed assumption most investment models take that simply add hidden risks to a client's portfolio.
Should you look at volatility or use it in any investment models? Yes, of course, but more strictly as a performance metric (i.e. Sharpe ratio) for comparing strategies or portfolios. Specifically at our investment firm, to avoid the known pitfalls, we do not use volatility to optimize portfolio allocations nor do we use it as a risk metric in terms of potential future loss.
Risk management must come before all else. In an attempt to mitigate potential future losses, asset managers should take a multi-level approach that goes deeper than addressing risk at just the portfolio volatility level. A few examples of specific risk measures include, but are not limited to: portfolio hedging, stop-losses, cash positions, individual asset analysis, daily monitoring of all positions, intraday liquidity provisions, etc.
Ray Dalio of Bridgewater Associates, a $150 billion hedge fund, discusses their approach to risk mitigation through true diversification:
People think that a thing called correlation exists. That’s wrong... If you try to represent the stock/bond relationship with one correlation statistic, it denies the causality of the correlation. Correlation is just the word people use to take an average of how two prices have behaved together. When I am setting up my trading bets, I am not looking at correlation; I am looking at whether the drivers are different... I am talking about the causation, not the measure. - Hedge Fund Market Wizards
What is he getting at? Ray Dalio is making the point that by simply placing a large number of stocks and bonds in a portfolio does not properly address the true risks. It is more effective, risk-wise, to analyze the individual portfolio assets based on causation, or the "drivers", instead of the correlation among the assets in a portfolio--as correlation is constantly changing, especially during a market crash.
3. Avoiding ruin
"Rule No.1: Never lose money. Rule No.2: Never forget rule No.1." -Warren Buffett
Figure 3 Breakdown of a drawdown illstrated with S&P500 market data from December 2009 to July 2011.
How bad are the negative effects of portfolio drawdowns? They can have more damaging effects than just loss of capital. Drawdowns affect investors emotionally too, which often leads to irrational decisions. Most advisors are coached to "talk clients off the edge" or keep them steadfast in the market, even if everything around seems to be crashing down. While market timing is a very difficult practice, there are other methods as discussed prior that can drastically help protect an investor's portfolio from large drawdowns. If the only value your advisor provides to you is talking you in to remaining steadfast through major losses (sometimes greater than 30%) during a crash (i.e. 2008), then what risk-mitigation value are they really providing? In this instance, it took most investors over 3 years to get back to even from the highs prior to the crash. Some people don't have the luxury of time; they need proactive and constant portfolio risk-management measures in place, and the standard portfolio allocation approach simply doesn't cut it.
Market drawdowns are a very real thing. On a long enough time-line markets do rise, but it is important not to forget how relatively frequent and large some drawdowns can be. Every drawdown is a chance to lose hard-earned capital in one's investment portfolio. Often times, the best offense is a good defense.
Figure 4 Illustrates the market drawdowns (underwater chart) from 1975 to 2011.
Figure 5 below illustrates the returns required to get back to even at a given drawdown level. If a portfolio sustains a 40% drawdown, like many did in 2008, the investor needed to gain 67% just to get back to even. A 60% drawdown requires a 150% gain to get back to even.
Figure 5 Gain needed in order to recover from previous portfolio highs.
What is most important is understanding that all investors have one "commodity" that once lost can never be regained—time. Eventually one can recover from a market crash, such as the 13 years it took for the S&P 500 to get back to even from 2000. We can not, however, regain the time lost to save and grow our investments to fund our retirement. It is critical to remember that what the "index" does from one year to the next is far less important than understanding what the ramifications will be to your long-term investment goals if you don’t aim to protect your downside. Risk management is not something that should ever take a back seat to performance or convenience.
What's the take home message? Going back to Warrent Buffet's number 1 rule in investing, Mark Sellers summarizes it quite well:
"Focus on the downside and the upside will take care of itself." -Mark Sellers, Managing Partner at Sellers Capital