If an investment strategy seems too good to be true, look under the hood

Can investing 51% in a portfolio S&P 500 index give the same return as investing 100%?

In mid-March 2020, a financial adviser told a writer that a recent analysis published by a large investment management firm showed that this could be the case.

In other words, the strategy provides the same portfolio return in only half the volatility.

At a time when market volatility exceeds the level of the 2008-2009 financial crisis, such strategies have an understandable application. In terms of potential impact, we have looked closely at the study.

According to the analysis, investing 100% in the $ 100,000 S&P 500 Index will become ক্র 310,570 as of 31 December 2019 according to a buy-and-hold strategy on March 24, 2000. Alternative Strategy Invested 51% of $ 100,000 Index 24 March 2000. The study did not indicate what happened to the remaining 49%, but we found that if we put what they report under the mattress of 49 49,000, it takes a 0% return. The portfolio was rearranged to 51% market weight in October 2002, October 2002, and March March 200. The value of the portfolio fluctuates freely between those dates.

The 51% market-weighted strategy raised the 100 100,000 investment to 31 311,560 on December 31, 2019. So the 51% market-weighted strategy outperformed the 100% market-weighted strategy by about half the portfolio risk, as the financial adviser said.

What jumped between us was the brutality of the three rearrangement dates. 2007 and 2009 have similarities with the top and bottom of the market, respectively.

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Recent Covid-1 –- shows volatility, making it difficult to determine the optimal time to buy or sell. For example, after the S&P 500 index fell 4.9% we probably bought on March 11, 2020, only the next day it dropped 9.5%. Therefore, investors are unlikely to rebalance on specific dates.

So how will different dates affect the 51% market-weight strategy? Although we could not completely replicate the results, we were able to create something similar using the total returns from the SPDR S&P 500 Index ETF (SPY), which is considered a proxy because it is investable and closely tracks the S&P 500.

In our study, the 100% market-weight strategy received 30 304,122 as of December 31, 2019. To lose some of that portfolio, we need to invest 51% in the market on each rearrangement date. This resulted in a portfolio value of 6 306,311 towards the end of 2019.

Using 51% market weight and our data, we tested how sensitive the portfolio is to rearrange a little early or a little late. First, we rearranged the portfolio one calendar week before the original recycling dates. This gave us a portfolio value at the end of the measurement period of $ 292,772, which is 3.7% lower than the 100% market-weight strategy. Next, we rebuilt the portfolio one calendar week later than the original dates. It received a portfolio value of 27 278,587 at the end of 2019, 8.4% lower than the 100% market-weighted strategy.

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Adjusting rearrangement dates between two calendar weeks and worse results. Two weeks ago and $ 100,000 $ 281,559-7.4% less than the 100% market-weight strategy. Two weeks later and that’s $ 262,884, or 13.6% less than the 100% market-weight strategy. More generally, the result is for virtually all (~ 98%) possible one- and two-week shift dates.

So what’s the takeaway?

The market needs extreme time to pull the 51% market-weight strategy: specific re-balancing dates must be chosen accurately. If that fails, we better invest in a 100% market with a buy-and-hold strategy.

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The opinions expressed herein do not merely reflect the views of the author and those of Compass Lexacon or its other staff.

All posts are the author’s opinion. As such, they should not be construed as investment advice, or the opinions expressed must not reflect the views of the CFA Institute or the author’s employer.

Photo Credit: © Getty Images / Georges

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Clifford S. Ang, CFA

Clifford S. He specializes in business valuation and hard-to-value assets in the context of litigation. He has worked in hundreds of engagements involving firms across a wide range of industries. Ang Data Camp teaches equity and bond valuation courses, an interactive data science learning platform. He has published and presented a number of evaluation-related topics. Ang is the author of a textbook on financial modeling, data analysis, and data visualization that analyzes financial data and implements financial models using R, published by Springer. The second edition of the book is scheduled to be published in the spring of 2021. He is a CFA Charterholder and MS in Finance. He was a member of the CFA Institute, a member of the CFA Society in San Francisco, an abstract of the CFA Digest, and a volunteer in support of the CFA program. He is a member of the Big Data Advisory Board and the Olin Business School Alumni Board at Rutgers University in Washington, DC. Her website is and her e-mail is

Merit Lion

Merit Lion is a PhD student in statistics at George Washington University. His research interests are in random structures, applicable possibilities, and statistical education. His professional experience involves creating and implementing machine learning models, most recently for use in the residential mortgage market.

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