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"Recency bias" is giving undue weight to the most recent information.  In financial planning, it often stems from headlines reporting the most recent volatility in the market and, by implication, investment returns.  Recency bias is one of over 50 different types of cognitive biases listed in Wikipedia, which are defined as "…systematic patterns of deviation from norm and/or rationality in judgment."

Recent information appeals to our natural instincts, likely a leftover from when we lived in caves and needed to know what was going on near us to survive (avoiding saber tooth tigers, looking for food, etc.).  Even today, we need these instincts to drive a car.

As finance professionals, however, we all have enough training to know that short-term fluctuations are best perceived as random noise around long-term trends, and lifetime financial plans should be based on those long-term trends.  Even the national journalists who report the latest fluctuations probably know this, but they don’t care – their job is to capture readers or listeners to sell ads. 

And they must be doing that job well because advisors in a 2021 survey ranked recency bias as the number one cognitive or behavioral bias they experienced with clients.  So, what is the best way to fight – or at least mitigate – recency bias? 

Correlations for the short-term:  Days, weeks, months, and quarters

According to the survey, explaining (or re-explaining) the long-term record ranked as the most effective technique for quelling the stress caused by recency bias.  For those who are comfortable with numbers, determining how well movements in one factor correlate to the movements in another factor – like recent returns in the market with future returns - is captured by what statisticians aptly call the "correlation coefficient." 

The correlation coefficient ("r") measures both the direction (positive or negative – that is, moving in the same or opposite directions) and strength (+100% is the strongest possible positive correlation, -100% the strongest possible negative, and 0% is the weakest possible correlation).  Exhibit 1 shows the correlation coefficients for daily returns of the S&P 500 back to 1928.  In this case, all correlations for daily movements are very weak (near zero), as expected.  This suggests independent behavior from one day (or days) to the next.  The strongest negative correlation was for the average return for two trading days with the following two trading days, -2.4%.  The average correlation was -1.5%, and the strongest positive turned out to be least negative, -0.9%. 

Exhibit 2 summarizes the results equivalent to Exhibit 1 but includes other short-term time periods – weeks, months, and quarters.  The primary conclusion from Exhibit 2 is that all correlations are very weak.  These are the facts that back up what every financial professional should be telling their clients:  "There is no predictive power in short-term market action.  It is like trying to predict the outcome of the next flip of a coin when you know the last flip was heads – that information doesn't help predict the outcome of the next flip.  They are independent."

Exhibit 2. Summary of Correlations for Daily, Weekly, Monthly, and Quarterly S&P 500 Returns, 1928-2022

Correlations for longer terms:  Years and decades

Exhibit 3 continues the analysis for years and decades, the longest terms examined.  Correlations for 1-5 years for the next 1-5 years were all below 20%.  But for spans of 10 years or longer, the correlations begin to become respectable.  The strongest here is for 15 years versus 20 years – a correlation of -91.2%.  Note that it is strong and negative.

Exhibit 4 provides a further visualization of the 15- to 20-year relationship.  It clearly shows that when 15-year returns go up, the following 20-year returns tend to go down. 

Exhibit 5 displays yet another visual of the data over time.  It further illustrates what statisticians call “reversion to the mean.”  The smooth line shows the average long-term compounded annual return of 9.8% per year.  The choppy blue line shows the actual return.  It is obvious that it sometimes rises above the long-term average, then falls below in repeating cycles.  Hence, the negative correlation.

What do all these correlations tell us? 

The only statistically significant correlations in returns involve decades, at least for the S&P 500.  For any time period shorter than decades, there are simply no useful correlations, at least for the S&P 500, regardless of what journalists imply every day. 

This does not mean that the use of other variables for prediction would reach the same conclusion, of course.  A study by Vanguard (2012) looked at a number of other factors and concluded none provided very good predictions of the S&P 500.  But other researchers pursuing the never-ending quest to predict the market or discover evidence of momentum have made some progress.  The CAPE ratio may provide some predictive capabilities (see Michael Finke and Larry Swedro). 

But even if it is true only for the S&P 500 (which has a correlation of about 95% with the overall market), these charts and tables will hopefully help a little by presenting some facts that refute recency bias, especially regarding short-term market behavior. 

But will these facts make a big dent in recency bias?  Probably not.  It is simply too ingrained within us as human beings to ever go away regardless of the evidence.  As advisors, we must simply learn to manage it.

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(Note:  A full version of this article appears in Advisor Perspectives, Dec. 5, 2023.)

Brent Burns is president and co-founder of Asset Dedication (a turnkey investment platform for financial planners) and a frequent speaker and author on retirement income, investments, and technology. As an adjunct professor, Brent periodically teaches Personal Financial Planning, Data Analytics, and Introduction to Finance. He co-authored Asset Dedication (McGraw-Hill, 2005) with Stephen Huxley. Reach him at burnsb@assetdedication.com.

Jeremy Fletcher, MBA, CFA (since 2000), is managing director of investments and has been in fixed income management since 1991. Previously, Jeremy co-managed the $3.5 billion fixed income assets at the City and County of San Francisco and managed $2 billion at American Century Investments, launching a top-performing U.S. fixed income fund. Reach him at fletcherj@assetdedication.com. 

Stephen J. Huxley, PhD, is director of research and co-founder of Asset Dedication, a professor of Business Analytics, and former Associate Dean, University of San Francisco. He is a recipient of teaching, research, and service awards at USF and in international competitions with numerous publications. Stephen co-authored Asset Dedication (McGraw-Hill, 2005) with Brent Burns.  Reach him at huxleys@assetdedication.com.