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After years in the investment world as an institutional manager, an academic researcher and lecturer, and an independent advisor to individual investors, I’ve learned that there are three main objectives we’re trying to achieve when we design client portfolios:
1. Constructing a portfolio that offers the highest probability of success at providing for the client’s spending needs for their entire lifespan;
2. Designing the portfolio in such a way that the expected drawdowns will not frighten the investor into abandoning the recommended strategy (i.e., dealing effectively with drawdown risk); and
3. Providing for the investor’s wish to have assets to pass along to heirs or charitable causes upon their death (maximizing expected ending assets).
Obviously, the initial conversations we have with clients are aimed at drawing out their priorities around these three issues. By learning all we can about their available resources, spending levels, and income, we are in a position to run various simulations using a variety of assumptions in order to determine the clients’ probability of success, expected ending assets and expected drawdowns under different market and economic scenarios.
So far, so good. But a problem can occur when certain fundamental assumptions are made. The Monte Carlo simulations used by most advisors are modeled using the Random Walk methodology incorporated from Modern Portfolio Theory. As most of us know, Random Walk assumes that the markets have no “memory”; the results for any given period are assumed to be completely unrelated to the results for any other period.
However, I posit that market rates of return are less “random” than the Random Walk hypothesis asserts. Historical return data shows that equity markets do exhibit mean reversion. From this assumption, it follows that by incorporating the mean-reversion principle into our simulations, we can help clients improve their portfolio design in meaningful ways.
Consider these four scenarios:
1. A risk-averse client wants a portfolio with a 100% probability of success. She has a 4% spending rate and has a 20-year horizon. If we presented simulations built on the Random Walk hypothesis, the client would be advised to have a 25% stock / 75% bond portfolio. However, by using a Monte Carlo analysis incorporating mean reversion, the portfolio can be improved by using an 85% stock and 15% bond portfolio, providing the client with a 100% chance of success and substantially higher expected ending assets to pass on to her heirs or favorite charities. While this portfolio would have a higher expected drawdown than a 75% bond/25% stock portfolio, knowing that there is still a 100% chance of success, the client is likely to find this level of drawdown acceptable.
2. For an investor willing to take a small risk of running out of money in exchange for an increased level of spending, an advisor using a Random Walk Monte Carlo analysis might typically recommend that the client increase spending to a 5% rate and accept a 96% probability of success. This would be predicated on a 60% stock and 40% bond portfolio. However, by incorporating mean reversion assumptions, the advisor could recommend that the client employ a 5% spending rate while improving his probability of success to 98% by recommending an 85% stock and 15% bond portfolio. The portfolio would also offer substantially higher expected ending assets with a slightly higher expected portfolio drawdown.
3. For an investor willing to take a moderate amount of risk of running out of money (92% probability of success) but wants a substantially higher spending rate, an advisor using a Random Walk Monte Carlo simulation might recommend a 100% stock portfolio and a 5% spending rate. However, an advisor using mean reversion can recommend the client increase their spending to 6%, and the 100% stock portfolio will give the same probability of success.
4. Portfolios can also be improved by relaxing the constraints from an initial risk tolerance questionnaire. For example, a typical risk tolerance questionnaire may lead the advisor to conclude that the client can’t tolerate the risks of a portfolio that has more than 60% in equities, thereby limiting the range of possibilities the advisor is able to suggest. Ironically, however, the probability of success for a client with a 5% spending level in a 60–40 portfolio over a 30-year period is 82% using Random Walk assumptions, while using a mean reverting Monte Carlo analysis would allow an advisor to recommend a 90–10 portfolio with a higher probability of success (90%) than the 60-40 portfolio, along with substantially higher expected ending assets. However, the expected drawdown will be higher than that of a 60-40 portfolio. Thus, the benefit of a lower chance of going bust and higher expected ending assets may indicate that the 90–10 portfolio could be better for the client.
It has been my experience that once clients understand the concept of mean reversion, they are also able to grasp the implications for their portfolio design. At the very least, presenting these foundational concepts allows us to have meaningful conversations with clients that often result in better, more intuitive understandings of fluctuations in market pricing. These understandings can also offer the side benefit of helping clients remain more objective and patient in the face of market volatility. The bottom line is that by discussing mean reversion and its implications for asset returns, we are able to help clients make more informed decisions that can lead to improved long-term portfolio performance.
Scott Bondurant is the CEO and founder of Bondurant Investment Advisory, based in Evanston, IL. His prior roles include managing director and global head of long/short strategies at UBS Global Asset Management, from 2005-2014. He was responsible for leading the development, implementation, and marketing of the UBS Global Asset Management’s $2.5 billion equity long/short platform. In addition, he has authored a series of white papers addressing topical issues related to long/short strategies. Scott was also a member of the equity management team, equity investment committee, and equity business committee at UBS. He is currently an adjunct professor at Northwestern University and has taught the course titled “The History of Investing” for 10 years. The Northwestern Business Review named this course as “one of the five coolest business-related classes.”
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.
