S&P 500 10-Year Nominal Return Forecasts

/S&P 500 10-Year Nominal Return Forecasts

S&P 500 10-Year Nominal Return Forecasts

By | 2017-08-18T16:57:16+00:00 April 17th, 2013|Tactical Asset Allocation Research|2 Comments
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(Last Updated On: August 18, 2017)

We’ve outlined the fundamentals of long-term return projection models in a recent post:

https://alphaarchitect.com//2012/12/projected-15-year-sp-500-returns/

Butler|Philbrick|Gordillo and Associates have a great post that focuses on the “Shiller” or “Hussman” models for return forecasting

http://gestaltu.blogspot.com/2013/04/valuation-based-equity-market-forecasts.html

Empiritrage has a detailed report outlining a more sophisticated way of forecasting long-term returns:

HERE IS A LINK TO THE PIECE

Here is some chart porn:

First, a look at the “Hussman” model for long-term returns. This model assumes a peak-to-peak earnings growth, inputs the current P/E and div yield, and then generates low and high return bounds based on future P/Es.

hussman1

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Another approach is to model the dynamics of revenue growth, profit margins, and valuations, and then simulate what the economy might look like under certain assumptions. The benefit of this approach is the incorporation of mean-reverting profit margins and valuation ratios directly into the model.

basics

Here are some baseline results:

Empiritrage_Forecasts_2013.Q2

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.


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About the Author:

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.
  • Robert T. Samuel III

    Looking over the data from the supporting research, there appears to be strong auto-correlation with the dependent series (10yr Average S&P Returns), so why not use an AR(p) model? In addition, if you think that Inverse_CAPE has explanatory power, why not a Vector Autoregressive (VAR) model? Since that dependent series also exhibits strong auto-correlation. Lastly, wouldn’t confidence interval estimates on the predictions have as much, if not more, informational content than just point estimates?

    All in all, I enjoyed the piece as I thought it generated some very important questions and have enjoyed a lot of your past research. I apologize if I seem to be overly caustic.

    Robert

  • Robert,
    Your points are exceptional and all of the things you mentioned could be integrated into a return prediction model…and probably make it better! Great insights. We focused on adding a bit more complication (dynamic margins and valuations), but still wanted to keep things relatively simple. Thanks, Wes