Originally published 10/28/2020
Parallax – is a displacement or difference in the apparent position of an object viewed along two different lines of sight.
Billy Beane is the general manager of the Oakland Athletics baseball team, and portrayed by Brad Pitt in the 2011 movie, Moneyball. In the 2001 season, the Oakland A’s had the second lowest payroll in all of baseball. Despite that, they were able to force the team with the highest payroll (The New York Yankees) to an elimination game in the American League Division Series. However, the Yankees did prevail.
The A’s would be losing two key offensive players after the season, since they could no longer afford them. The Yankees would be entering the 2002 season with a payroll over 3 times that of the A’s. Beane knew that in order to remain competitive, he had to see things differently from the traditional thought process.
With the help of assistant Paul Depodesta, Beane began to view players mainly through a quantitative lens which is now referred to as sabermetrics. The thought process was to buy the most runs (since you need runs to win games) with the least amount of money. How could a team create more runs? Get more people on base. For that reason, traditional statistics such as batting average were pushed aside in favor of stats like on-base percentage.
Despite still having the 3rd lowest payroll in 2002 and losing two key players, they still forced their way to game 5 of the division series. Along the way, they did set the Major League Baseball record for longest win streak.
Beane did not possess the financial resources to compete with much larger markets, but he did possess the parallax viewpoint. This allowed him to see the problem from another angle and create a competitive team.
Determining how to invest one’s money requires thoughtful analysis. Outside of economics, sports, and medicine, I’d suspect that no other industry has more armchair experts than finance. This makes sense. The data suggests even professional stock pickers cannot consistently beat the market, nor predict where the market is headed next. The guess from the armchair expert on the next market move may be as accurate as the professional’s. Why is this the case?
Clearly if I were to ask you to try and perform heart surgery as well as the top surgeons in the world, it would end very ugly. But why has it been shown that monkeys throwing darts at the Wall Street Journal stock page can sometimes beat the professionals? The answer is largely attributed to the role of luck in investing.
If you sat down to play chess with Magnus Carlsen, you have a zero percent chance of victory. There is no luck in chess. The outcome is solely determinant on the skill of participants. On the contrary, if you were to play Phil Ivey in a game of heads up poker, you might get lucky and win just from being dealt lucky hands. In the short run luck plays quite a role in poker. If you were to play Phil Ivey everyday for a year, well you’d be broke before the year was over. There’s a reason he is a famous poker player with 10 World Series of Poker bracelets. Over enough trials, the role of skill is more likely to pan out.
If we can eliminate the guesswork entailed in investing, we may improve the odds of a better outcome and relieve ourselves from relying on luck. Further, if we can extract data that has shown to explain portfolio returns down to a formula then we could create a model for decision making that helps remove some of the emotional elements from the investing process.
Some people have probably heard of the term “market beta“. If your portfolio has a market beta of 0.5, and the market declines 10% then you would expect to be down 5%. The same is true for the upside. Market beta is your portfolio’s sensitivity to the market portfolio. The “market risk premium” is the most well known factor, and from 1926 – 2019 it was about 7.1% annualized. This means that stocks have outperformed riskless 1-month treasury bills by that amount. If you had a market beta of 1.0, you would have expected similar performance.
Market beta in itself cannot explain the entirety of a portfolio’s performance, but adding in some of the factors I’ll mention below and we are getting close.
The market risk premium has shown to be pervasive globally as well. From 1966 – 2015 the premium for US stocks was 4.4% annualized, and for the entire world outside of the US it was 4.5% (ranging from a low of 1.40% in Austria, to a high of 6.6% in Sweden).
Notice the word “risk” in “market risk premium”. This gives the hint that the 4.4% annual excess return was not a free lunch, but a reward for bearing market risk (See. Today, other factors have emerged that also have shown to offer long-term premiums. The most widely accepted factors are:
Here is the annualized premium for each of these factors from 01/1964 – 08/2020 (data source: www.portfoliovisualizer.com):
Market: 5.52%
Value: 2.57%
Size: 2.09%
Momentum: 7.02%
Profitability: 2.85%
While some are familiar with determining the market beta of their portfolio, they are likely unfamiliar when it comes to looking at their betas to value, size, momentum, or profitability. A well diversified stock portfolio will generally have a market beta of 1.0 or close to it. In addition to that hopeful market premium, tilting towards the other factors can improve one’s diversification and hope of smoother performance.
Imagine a portfolio with the following sensitivities to each factor (or betas):
Market = 1.0
Value = 0.25
Size = 0.25
Momentum = 0.25
Profitability = 0.10
You would calculate the anticipated return by multiplying the beta times the premium and adding them together. As seen below:
(1.0 *.0552) + (0.25 * .0257) + (0.25 * .0209) + (0.25 * .0702) + (0.10 * .0285) = 8.73%
Diversifying the portfolio across all of the factors listed above would have improved the performance to 8.73% annualized from 5.52%. Once again, these are risk factors and not free lunches. All of the premiums have shown to go through dreadful periods of underperformance (a good example would be small and value stocks right now). However, they have all shown to be pervasive globally and work over varying time periods.
When I have gone through this data with others before, a common question that comes up is along the lines of “If these factors have shown to display premium returns over time, why doesn’t everyone do it?”. It is a fair question, but it comes back to the fact that these are risk premiums. The market premium in itself is very well known, but that has not stopped it from prevailing nor stopped many investors from bailing on it during tough times.
In my opinion, investors should not diversify across these factors solely for the pursuit of excess returns. The total market portfolio is a good option to allocate to and if you are saving prudently, it should help you reach your goals. The benefit of diversifying across the factors mentioned above is to help avoid “lost decade” type of events. In the United States we have experienced three separate extensive periods with very little return from the total market portfolio. They were:
1929 – 1943
1965 – 1974
2000 – 2011
Events like these will continue to happen. Market crashes and lost decades are a feature of investing, not a bug. That’s the risk part.
The question always is, why should the factors mentioned above continue to work if they are now well known? That argument is no different for the value premium than it is for the market factor in itself. We have know for a long time that stocks beat treasury bills over time, and yet they still continue to do so. Stocks involve risks, and their prices are discounted to reflect that. The elimination of risk from the stock market would also eliminate the expectation of a premium return.
Below is a copy of a table from Larry Swedroe’s book Your Complete Guide to Factor-Based Investing. It shows the odds of each factor’s outperformance historically over different rolling periods:
You can see why many advisors are proponents of buy and hold investing. Historically speaking, things tend to get better with time.
We know that past performance is not indicative of future results. We also know that data mining exercises can look enticing and fail to work in real world. These factors have been peer reviewed and withstood the test of academic rigor, and as mentioned earlier are expected to be rewards for bearing risk. Further, they have shown to work around the globe which reduces the odds that the performance was a random outcome.
Real world funds incorporate various factors into creating the portfolio, because a strategy solely reliant on buying cheap stocks has not shown to be terribly effective. A strategy that buys cheap stocks, that have profitability, within the small cap space can change the outcome a bit. Thanks to ETFs and competition within the asset management industry, we are able to implement such portfolios at a low cost with minimal transaction fees.
There is a never ending distraction in financial media that focuses our attention on the wrong questions:
Instead of being roped into viewing these things the same way as everyone else watching CNBC, it’s best to look at the data and what is really impacting your portfolio returns.
Disclosure: Past performance does not predict future results. This article is for informational purposes only and should not be considered a recommendation. Information contained in this article is obtained from third party resources that Meredith Wealth Planning deems to be reliable. Any reference to a market index is included for illustrative purposes only, as it is not possible to directly invest in an index. Indices are unmanaged, hypothetical vehicles that serve as market indicators and do not account for the deduction of management fees or transaction costs generally associated with investable products, which otherwise have the effect of reducing the performance of an actual investment portfolio.
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