Systematic Relative Strength

Risk is fundamental to investing, but no one can agree what it is.  Modern Portfolio Theory defines it as standard deviation.  Tom Howard of AthenaInvest thinks investment risk is something completely different.  In an article at Advisor Perspectives, he explains how he believes investment risk should be defined, and why the MPT definition is completely wrong.  I think his point is a strong one.  I don’t know how investment risk should be defined—there’s a lot of disagreement within the industry—but I think he makes, at the very least, a very clear case for why volatility is not the correct definition.

Here’s how he lays out his argument:

The measures currently used within the investment industry to capture investment risk are really mostly measures of emotion. In order to deal with what is really important, let’s redefine investment risk as the chance of underperformance. As Buffett  suggests, focus on the final outcome and not on the path travelled to get there.

The suggestion that investment risk be measured as the chance of underperformance is intuitively  appealing to many investors. In fact, this measure of risk is widely used in a number of industries. For example, in industrial applications, the risk of  underperformance is measured by the probability that a component, unit or service will fail. Natural and manmade disasters use such a measure of risk. In each situation, the focus is on the chances that various final outcomes might occur. In general, the path to the outcome is less important and has little influence on the measure of risk.

I added the bold to highlight his preferred definition.  Next he takes on the common MPT measurement of risk as volatility and spells out why he thinks it is incorrect:

In an earlier article I reviewed the evidence regarding stock market volatility and showed that most volatility stems from crowds overreacting to information. Indeed, almost no volatility can be explained by changes in underlying economic fundamentals at the market and individual stock levels.  Volatility measures emotions, not necessarily investment risk. This is also true of other measures of risk, such as downside standard deviation, maximum drawdown and downside capture.

But unfortunately, the investment industry has adopted this same volatility as a risk measure that, rather than focusing on the final outcome, focuses on the bumpiness of the ride. A less bumpy ride is thought to be less risky, regardless of the final outcome. This leads to the unintended consequence of building portfolios that result in lower terminal wealth and, surprisingly, higher risk.

This happens because the industry mistakenly builds portfolios that minimize short-term volatility relative to long-term returns, placing emotion at the very heart of the long-horizon portfolio  construction process. This approach is popular because it legitimizes the  emotional reaction of investors to short-term volatility.

Thus risk and volatility are frequently thought of as being interchangeable. However, focusing on short-term volatility when building long horizon portfolios can have the unintended consequence of actually increasing investment risk. Since risk is the chance of underperformance, focusing on short-term volatility will often lead to investing in lower expected return markets with little impact on long-term volatility.¹ Lowering expected portfolio return in an effort to reduce short-term volatility actually increases the chance of underperformance, which means increasing risk.

A clear example of this is the comparison of long-term stock and bond returns. Stocks dramatically outperform bonds over the long run. By investing in bonds rather than stocks, short-term volatility is reduced at the expense of decreasing long-term wealth. Equating short-term volatility with risk leads to inferior long-horizon portfolios.

The cost of equating risk and emotional volatility can be seen in other areas as well. Many investors pull out of the stock market when faced with heightened volatility. But research shows this is exactly when they should remain in the market and even increase their stock holdings, as subsequent returns are higher on average.²  It is also the case that many investors exit after market declines only to miss the subsequent rebounds. Following the 2008 market crash, investors withdrew billions of dollars from equity mutual funds during a period in which the stock market more than doubled.

The end result is that investors frequently suffer the pain of losses without capturing the subsequent gains. Several studies confirm that the typical equity mutual fund investor earns a return substantially less than the fund return because of poorly timed movements in and out of the fund. Again, these are the dangers of not carefully distinguishing emotions from risk and thus allowing emotions to drive investment decisions.

I added the bold here as well.  I apologize for such a big excerpt, but I think it’s important to get the full flavor here.  The implication, which he makes explicit later in the article, is that current risk measures are largely an agency issue.  The advisor is the “agent” for the client, and thus the advisor is likely to pander to the client’s emotions—because it results in less business risk (i.e., the client leaving) for the advisor.  Of course, as he points out in the excerpt above, letting emotions drive the bus results in poor investment results.

Tom Howard has hit the nail on the head.  Advisors often have the choice of a) pandering to the client’s emotions at the cost of substantial long-term return or b) losing the client.  Since investment firms are businesses, the normal decision is to retain the client—which, paradoxically, leads to more risk for the client.  While “the customer is always right” may be a fine motto for a retail business, it’s usually the other way around in the investment business!

There’s another wrinkle to investment risk too.  Regardless of how investment risk is defined, it’s unlikely that human nature is going to change.  No matter how much data and logic are thrown at clients, their emotions are still going to be prone to overwhelm them at inopportune times.   It’s here, I think, that advisors can really earn their keep, in two important ways, through both behavior and portfolio construction.

1. Advisor Behavior: The advisor can stay calm under pressure.  Hand-holding, as it is called in the industry, is really, really important.  Almost no one gets good training on this subject.  They learn on the job, for better or worse.  If the advisor is calm, the client will usually calm down too.  A panicked advisor is unlikely to promote the mental stability of clients.
2. Portfolio Construction: The portfolio can explicitly be built with volatility buckets.  The size of the low-volatility bucket may turn out to be more a function of the client’s level of emotional volatility than anything else.  A client with a long-horizon and a thick skin may not need that portfolio piece, but high-beta Nervous Nellies might require a bigger percentage than their actual portfolio objectives or balance sheet necessitate—because it’s their emotional balance sheet we’re dealing with, not their financial one.  Yes, this is sub-optimal from a return perspective, but not as sub-optimal as exceeding their emotional tolerance and having the client pull out at the bottom.  Emotional blowouts are financially expensive at the time they occur, but usually have big financial costs in the future as well in terms of client reluctance to re-engage.  Psychic damage can impact financial returns for multiple market cycles.

Tom Howard has laid out a very useful framework for thinking about investment risk.  He’s clearly right that volatility isn’t risk, but advisors still have to figure out a way to deal with the volatility that drives client emotions.  The better we deal with client emotions, the more we reduce their long-term risk.

Note: This argument and others are found in full form in Tom Howard’s paper on Behavioral Portfolio Management.  Of course I’m coming at things from a background in psychology, but I  think his framework is excellent.  Behavioral finance has been crying out for an underlying theory for years.  Maybe this is it.  It’s required reading for all advisors, in my opinion.