There has been no shortage of attacks against the notion that investors can rely upon the “long term average return of equities” being 7%, a thesis that seems to originate from Jeremy Siegel’s Stocks for the Long Run.  Given that the figure sits at the heart of most variable life insurance illustrations, portfolio growth projections, and financial planning projections, it is an assumption that deserves to be under constant scrutiny.

While long-term equity returns may have averaged 7% during the last century, even the most diligent investors rarely have a work horizon of more than 40 years to create their retirement nest egg – and none invest 100% up front.  Therefore, when projecting future wealth and retirement planning, it is prudent to explore the distribution of how investors have historically fared depending on when they were born and the market cycle they invested over.

In order to realistically simulate these past portfolios, we must accurately reflect the behavior of investors.  Specifically, we have to model income growth and savings habits, which together define the size of the annual contribution to the retirement portfolio, making the order of market returns much more relevant to long-term portfolio growth.

For simplicity, we will assume 3% income growth per year; for savings rates, we used average savings rates by age statistics:

AgeSavings Rate

Source: How America Saves, Vanguard, 2008.

We assume the investor begins at age 25 and retires at 65.  Every year, we assume the investor achieves the market return (specifically, the total return of the S&P 500) and then adds that year’s savings to their investment portfolio.  We do this over rolling 40-year periods to simulate how market participants would have fared depending on when they were born and the market cycle they invested over.


Those fortunate to invest from 1960-2000 received an extra $5.20 for every $1 they put into the market; those less fortunate, who invested from 1915-1955, only received an extra $0.69.  By comparison, an investor had received 7% a year using our income growth assumptions and savings scheme, and investor would have $2.03 for every $1 invested.

The “luck of the market cycle” becomes more apparent when we consider investors who began investing only 5 years apart but had considerably different results.

Time FramePortfolio Growth

Being fortunate to be born 5 years later can mean the difference between an extra $1.80 for each $1 invested or an extra $5.20 for each dollar invested.

But how do allocation choices play into final portfolio values?  Common market advice is to de-risk as we approach retirement.  To model this concept, we used the following allocation scheme changes based on age:

AgePortfolio Allocation
25-3480% equities / 20% bonds
35-4460% equities / 40% bonds
45-5440% equities / 60% bonds
55-6420% equities / 80% bonds

For bonds returns, we transformed 10-year U.S. Treasury rates into a constant-maturity index and used the annual returns of that index.


Time FramePortfolio Growth

While the disparity in results is dampened to some extent, considerable differences still remain.  Those who started investing pre-1940 never saw more than $1 return for their $1 invested; those who invested after rarely saw below $2.

While many of these assumptions are the baseline projections for a variety of different financial products and scenarios, we see the potential importance of needing to effectively manage risk to more actively account for the long-term impact that a mere five-year difference can make.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter.