A Glass Half Empty
Posted on: May 20, 2014 by Martin Curiel, CFA in Investments
When it comes to investing, most people enjoy picturing this rosy scenario. How much wealth will I accumulate? What will my net worth be in 20 years? What nice toys will I buy with my investment gains? No one ever jumps into an investment thinking that it will yield a negative return or even completely wipe out. Putting money to work with stocks, bonds, real estate, entrepreneurial ventures, etc. will ALWAYS involve some level of risk – the possibility of partial or complete loss of one’s original investment. As unpleasant as it may seem, understanding and planning for the worst is a necessary part of risk management.
In this article, I will argue that an effective way to design investment strategies is to forecast the worst and come to terms with that possibility. To illustrate my point, I will discuss the asset allocation decision, which I believe is the most important driver of investment returns over the long term. The good times are not as important as the bad times when it comes to dealing with ending wealth values, so looking at the glass half empty is an appropriate design philosophy.
Asset Allocation and the “80/20 Rule”
In 1906, an Italian economist named Vilfredo Pareto observed that roughly 80% of the land in Italy was owned by 20% of the population. He also observed that about 20% of the pea pods in his garden produced 80% of the peas. In turns out that this “Pareto Principle” can be applied to many real-life scenarios in business and in life: 80% of the results typically stem from about 20% of the causes.
In the investment world, the most powerful application of the Pareto Principle is related to asset allocation, which is a fancy phrase for the mix of investment categories in a portfolio (i.e., stocks, bonds, cash, etc.). Many academic studies have shown that over 90% of the returns can be explained by a portfolio’s asset allocation. Although there is much debate around how much asset allocation is truly responsible for historical returns, it is nevertheless one of the most important “causes” of returns.
Pessimistic Asset AllocationSource: Vanguard.com; data as of 4/30/2014.
A reasonable approach to designing for the “worst” is to start with asset allocation. One could look at a two-factor model that takes into account time horizon and mix of investments categories. For analysis purposes, let’s look at designing a portfolio of stocks and bonds. To simplify things, we will limit the analysis to the U.S. stock market, which represents about 50% of the World Stock Market in terms of capitalization and has ample historical data to work with.
The table below is the output of our analysis (figures are rounded):
| Stocks | Bonds | 1-Year | 3-Year | 5-Year | 10-Year | 20-Year | 30-Year |
| 0% | |||||||
| 10% | |||||||
| 20% | |||||||
| 30% | |||||||
| 40% | |||||||
| 50% | |||||||
| 60% | |||||||
| 70% | |||||||
| 80% | |||||||
| 90% | |||||||
| 100% |
Key Observations:
- An investor could have lost nearly half of her investment over a one-year period if she had invested 100% of the portfolio assets in stocks (which happened around 1931). So, one could argue that by putting 100% of one’s assets in a diversified U.S. stock portfolio, one could expect to be down by nearly 50% in a bad 12-month period.
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The worst consecutive 30-year period is a positive return of 8% per year (which happened in 1957).
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TNo mix of stocks and bonds would have provided a positive return in the worst of one-to-three-year periods. One could argue that to increase chances of a positive return, one needs to have an investment time horizon beyond three years, and preferably of 10 or more years.
Similar charts can be drawn for virtually every category of investments available, including U.S.-listed stocks, financial sector securities, and value stocks. One can slice and dice any collection of stocks and display what the aggregate wishes of the investors with respect to that category.
There are numerous reasons why an investor’s portfolio almost never fully reflects the aggregate wishes of investors in any category. In essence, almost every investor is betting against the market in one or multiple ways. It’s almost unrealistic and impractical to bet with the market in every possible way. What is practical, realistic, and good practice is to understand the bets being made and to answer the simple question: “Did my bets pay off?”
Three major ways investors can deviate from the market are as follows:
- Location Bets
- Factor Bets
- Skill Bets
The return of any investment portfolio can be expressed by the following equation:
Return to Investor =“House” Return
+ / – [Location Bet Returns]
+ / – [Factor Bet Returns]
+ / – [Skill Bet Returns]
Put simply, if the bets increased total return relative to the “House Return,” the bets paid off. Too many times, however, the opposite is true; in that case, the bets destroyed wealth for the investor. Let’s go deeper into how each bet can play out.
Location BetsAt the aggregate level, we see many investors bet against the house by overweighting and underweighting certain geographical areas. For example, many target date funds that exist inside 401(k)s for U.S. investors have a large bias – more than 49% – toward the U.S. Another example is that many investors are big believers in the growth potential of Emerging Markets, which constitute about 10% of the world in terms of market cap. We’ve seen investors allocate as much as 25% to Emerging Markets (a bet of +15%) to express this belief. Yet other investors believe the U.S. is in a permanent decline, and so have very little in U.S. stocks. If one’s portfolio looks different from the World Stock portfolio in terms of geographic location, one is expressing a “location bet.”
Judging Location Bets:
One of the most effective ways to judge location bets is to measure an investment portfolio against a broad market ETF or Index fund that tracks the world portfolio. Two common ones that can be used are the Vanguard Total World Stock Index Fund (Symbol: VT) and the All Country World Index Fund (Symbol: ACWI).
Let’s take, for example, a U.S.-based investor that puts 100% of her portfolio in U.S. Stocks – she made a +50% location bet in favor of the U.S. Let’s assume she bought one fund, the Vanguard Total Stock Market Index Fund (Symbol: VTI), which is one of the most inexpensive funds to track the entire U.S. market.
The table below shows the location bet analysis (returns are annualized):
22.7% |
14.7% |
22.2% |
||
17.0% | 8.9% | 18.1% | ||
+5.7% | +5.8% | +4.1% |
Source: Morningstar.com; returns are annualized through 3/31/2014.
Size (e.g. small-cap, mid-cap, large-cap) Over the last 5 years, the U.S. has done very well compared to the world portfolio, so it is clear that the investor’s location bet against international stocks and in favor of U.S. stocks paid off nicely. Over a 1-year period, for example, there was over a 4% outperformance! Will the location bet pay off in the next 5 years? It’s impossible to know, of course, but it is very unlikely that the U.S. will ALWAYS beat international stocks in the future.
The location bet is analogous to betting against the house by favoring select games such as Roulette or Blackjack. Some nights, the overall house will do better than these selected games, and other nights it will underperform.
A factor can be defined as a security characteristic that drives investment returns. Common factors with stocks include the following:
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Styles (e.g. value, growth, core)
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Sectors (e.g., Financials, Consumer Staples, etc.)
For example, an investor can decide that she strongly believes in value stocks and purchases an ETF that picks all value stocks in the U.S. Let’s assume it is iShares Russell 1,000 Value ETF (Symbol: IWD). In that case, the investor is making a factor bet that U.S. value stocks will outperform all U.S. stocks. Similarly, an investor that purchases a technology sector ETF, say Vanguard Information Technology ETF (Symbol: VGT), is implicitly saying that the technology sector will outperform the average of all other sectors in the U.S.
Judging Factor Bets:We believe the most appropriate methodology to judge the factor bet is at the location level. In the two examples previously mentioned, for example, the appropriate comparison is a product that invests in the entire U.S. stock market, such as VTI.
The tables below display the factor bet analysis (returns are annualized):
22.7% |
14.7% |
22.2% |
||
21.5% | 14.6% | 21.6% | ||
-1.27% | -0.07% | -0.57% |
Source: Morningstar.com; returns are annualized through 3/31/2014.
Source: Morningstar.com; returns are annualized through 3/31/2014.
From the analysis above, it is clear that making a “value” factor bet did not pay off over the last 5 years. The overall U.S. stock market outperformed U.S. value stocks. Betting on the technology factor did pay off over the last 1 year, but did not pay off over the last 3 and 5 years.
The factor bet is analogous to betting against the house using selected casino strategies, such as betting only on black in roulette or only the pass line at the craps table. The risk of loss can be higher than in the location bet.
The most disaggregated bet is the one that bets that a particular investor or investment team has sufficient skill to beat a particular market. Going back to the earlier examples, an investor can decide to pick an actively managed mutual fund that selects the “best value stocks.” For example, one such fund is American Century Value – C (Symbol: ACLCX). Similarly, the investor can decide to only invest in Apple stock as opposed to a technology sector ETF like VGT.
The bet that is being expressed is that an individual can have the knowledge and skill to select the best subset of stocks from a defined universe. In the case of the actively managed mutual fund like ACLCX, the belief is that the fund manager – usually a team of investment professionals – has that capability. In the case of the individual stock position, the belief is that the investor herself has the skill to discern that Apple is indeed the best stock out of a universe of all technology stocks.
Judging Skill BetsWe believe that security selection bets – “skill bets” – should be judged at the factor level. The first step is to identify the most inexpensive and efficient product that is appropriate for the selection bet in question. For example, one could use iShares Russell 1000 Value ETF (Symbol: IWD) to judge ACLCX. Apple could be judged against the performance of Vanguard’s Information Technology ETF (Symbol: VGT).
The table below shows the investor skill analysis (returns are annualized):
Investor Skill within Value Stocks:
Source: Morningstar.com; returns are annualized through 3/31/2014.
Based on the table above, the investor skill bet paid off over a 3-year period, but lost money over the last 1-year and 5-year periods.
Investor Skill within Technology Stocks:
Source: Morningstar.com; returns are annualized through 3/31/2014.
Investing in Apple stock did not pay off over the last year, but did over the last 3 and 5 years.
The skill bet is analogous to betting against the house by picking certain players or playing certain tables. It is the riskiest of all bets against the house, since a lot has to go right for the bet to pay off.
On average and over the long term, the house always wins. This is true inside the casino and in the global stock market. Despite these odds, most investors (including myself) will take the chance to beat the house through location bets, factor bets, and/or skill bets. There is nothing wrong with betting occasionally, but it is important to objectively measure those bets and to be honest about the wealth they are creating or destroying over time.
In my experience, most individual investors do not realize the bets they are making or the ones their advisors are making on their behalf. They also don’t realize the potential wealth destroyed over time.
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