Could a portfolio
invested 51% in the S&P 500 Index yield the same returns as one that’s 100%
2020, a financial adviser told one of the authors that recent analysis
published by a large investment management firm found that it could.
In other words, the strategy delivers the same portfolio return at only half the volatility.
At a time when
market volatility exceeds 2008–2009 financial crisis levels, such a strategy
has an understandable appeal. Given the potential implications, we gave the
research a closer look.
According to the
analysis, $100,000 invested 100% in the S&P 500 Index under a buy-and-hold
strategy on 24 March 2000 would have grown to $310,570 as of 31 December 2019.
The alternative strategy invested 51% of the $100,000 in the S&P 500 Index
on 24 March 2000. The research didn’t indicate what happened to the remaining
49%, but we found that the returns they report are generated if we put the
$49,000 under a mattress, so it assumes a 0% return. The portfolio was rebalanced
to 51% market weight on 9 October 2002, 9 October 2007, and 9 March 2009. In
between those dates, the portfolio value fluctuated freely.
market-weight strategy grew the $100,000 investment to $311,560 on 31 December
2019. So the 51% market-weight strategy slightly outpaced the 100%
market-weight strategy with about half the portfolio risk, just as the
financial adviser said.
What jumped out at us was the serendipity of the three rebalancing dates. Those from 2007 and 2009 correspond to a market top and bottom, respectively.
As recent COVID-19–related volatility demonstrates, determining the optimal time to buy or sell is difficult. For example, we may have bought on 11 March 2020 after the S&P 500 Index fell 4.9%, only to see it drop 9.5% the next day. Investors, therefore, are not likely to rebalance on the precise dates specified.
So how would
different dates impact the 51% market-weight strategy? While we could not
perfectly replicate the results, we managed to produce something similar using total
returns from the SPDR S&P 500 Index ETF (SPY), which made sense as a proxy
since it is investable and closely tracks the S&P 500.
In our study, the
100% market-weight strategy yielded $304,122
as of 31 December 2019. To slightly beat that portfolio, we also needed to
invest 51% in the market on each rebalancing date. That resulted in a portfolio
value of $306,311 at the close of 2019.
Using the 51% market weight and our data, we tested how sensitive the portfolio is to rebalancing a little early or a little late. First, we rebalanced the portfolio one calendar week before each of the original rebalancing dates. That gave us a portfolio value at the end of the measuring period of $292,772, 3.7% lower than the 100% market-weight strategy. Next, we rebalanced the portfolio one calendar week later than the original dates. This yielded a portfolio value at the end of 2019 of $278,587, which is 8.4% lower than the 100% market-weight strategy.
Adjusting the rebalancing dates by two calendar weeks and the results are even worse. Two weeks early and the $100,000 becomes $281,559 — 7.4% less than the 100% market-weight strategy. Two weeks later and it equals $262,884, or 13.6% less than the 100% market-weight strategy. More generally, the result holds for virtually all (~98%) possible one- and two-week shifts of the rebalance dates.
So what’s the
To pull off the 51% market-weight strategy requires extreme market timing: the specific rebalancing dates must be chosen perfectly. Failing that, we’re better off with a buy-and-hold strategy that is 100% invested in the market.
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Opinions expressed herein are solely those of the author and do not reflect the views and opinions of Compass Lexecon or its other employees.
All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Image credit: ©Getty Images / Giorez
Professional Learning for CFA Institute Members
Clifford S. Ang, CFA, is a Senior Vice President at Compass Lexecon, an economic consulting firm. He specializes in the valuation of businesses and hard-to-value assets primarily in the context of litigation. He has worked on hundreds of engagements involving firms across a broad spectrum of industries. Ang teaches the equity and bond valuation courses at DataCamp, an interactive data science learning platform. He has published and presented on numerous valuation-related issues. Ang is the author of the financial modeling, data analysis, and data visualization textbook Analyzing Financial Data and Implementing Financial Models Using R, which is published by Springer. The second edition of the book is scheduled to be released in Spring 2021. He is a CFA charterholder and holds an M.S. in finance. He is a member of the CFA Institute, a member of the CFA Society San Francisco, an abstractor for the CFA Digest, and has been a volunteer in support of the CFA program. He is also a member of the Rutgers University Big Data Advisory Board and Olin Business School Alumni Board at Washington University in St. Louis. His website is www.cliffordang.com and his e-mail is firstname.lastname@example.org.
Merritt Lyon is a PhD student in statistics at the George Washington University. His research interests are in random structures, applied probability, and statistical learning. His professional experience involves developing and applying machine learning models, most recently for use in the residential mortgage market.