Savita Subramanian S&P 500 at 3500 by 2025 : formerly Bogle's 10-Year Forecast

[eta2020]

Bank of America 2016 stock market outlook - Business Insider

I am an optimist and more likely to go with Savita Subramanian then with Bogle.

 

[DEC-1982]

I agree with you. I think Savita's 8% a year over 10 years is a reasonable estimate, even if it is at the high end of 6-8%. I have a lot of faith in America and American ingenuity. Our country has been written off many times but always comes back, better than before.

There was an article I read yesterday, "What makes Warren Buffett so optimistic"?

"When asked what gives him a sense of optimism these days, Buffett named off a long list of modern-day luxuries, including medicine, education, transportation and entertainment."

"In fact, I tell the students, these students in that class today are actually living better than John D. Rockefeller Senior lived when I was born... In all kinds of ways they're living better than the richest man in the world lived at the time of my birth. The luckiest group of babies ever born in the world are the babies being born in the United States today."

The link to that article is provided below:

http://finance.yahoo.com/news/makes...DYmYxBHBvcwMyBHZ0aWQDVklEUUJDS18xBHNlYwNzYw--

 

[Moneygrubber]

Agree, just hope those babies finally grow up and leave the basement before 30,,,


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[NW-Bound]

S&P at 3500 in 10 years is 67% higher than its current value of 2090. That works out to 5.3% annualized. If we add 2% dividend, it becomes 7.3% total return (nominal).

It's higher than Bogle's expectation of 6% nominal. If the market gives it to me, I will gladly accept and be thankful (posting this on Thanksgiving Day).

 

[eta2020]

S&P at 3500 in 10 years is 67% higher than its current value of 2090. That works out to 5.3% annualized. If we add 2% dividend, it becomes 7.3% total return (nominal).

It's higher than Bogle's expectation of 6% nominal. If the market gives it to me, I will gladly accept and be thankful (posting this on Thanksgiving Day).

Bogle: Tough Decade Ahead for Equity Investors

My understanding of Bogle is he expects 4% nominal .... not 6%. That 4% includes 2% dividend.

So that is huge difference from 7.3% expected by Savita. Especially in light of being annualized over 10 years.

 

[NW-Bound]

You are correct. My "superior memory" is getting worn out with age. Bogle used to say 6%, but in more recent interviews, he expressed a concern with P/E contraction which may subtract 2%/yr or more.

One thing we know is that stocks have to compete with bond yields, and if interest rate goes up, stock P/E will drop. The currently high P/E is hanging by a thread, but of course we have to wait to see how it will work out.

 

[Mulligan]

You are correct. My "superior memory" is getting worn out with age. Bogle used to say 6%, but in more recent interviews, he expressed a concern with P/E contraction which may subtract 2%/yr or more.

One thing we know is that stocks have to compete with bond yields, and if interest rate goes up, stock P/E will drop. The currently high P/E is hanging by a thread, but of course we have to wait to see how it will work out.

I had never heard of this until past sunday newspaper. A money manager was quoted as saying a "rough barometer" to determine whether markets current PE ratio is overvalued or not, you should subtract the current 10 year yield from 20 to get a "normal" market PE ratio.


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[Totoro]

I had never heard of this until past sunday newspaper. A money manager was quoted as saying a "rough barometer" to determine whether markets current PE ratio is overvalued or not, you should subtract the current 10 year yield from 20 to get a "normal" market PE ratio.

Benjamin Graham used bonds vs. stocks as a yard stick too.

Bond yields vs. E/P yields. A P/E of 20 = 5% for example. If bonds yield 5% or close to that his view was equities ("junior") aren't worth the added risk.

I keep an eye on it as extra input, together with inflation. If inflation adjusted equity yields touch 0%, big trouble is usually afoot in the very short term.

 

[NW-Bound]

Back when Greenspan was at the helm of the Federal Reserve, I read that the Fed used something like the above. In addition, usually stock E/P (the inverse of P/E) is higher than bond yield for the risk premium.

 

[photoguy]

Eta2020 -- do you have a link to her actual regression analysis? I couldn't find one and the summary article has so little detail as to be useless.

From what I can find, her argument for higher returns seems to be that on most valuation metrics equities are reasonably valued (ShillerPE is the exception). I think this is a reasonable argument as PE1 is only slightly less predictive than PE10 but they lead to very different expectations.

However, one thing that makes me suspicious is she is quoted as saying:

"Our work suggests that valuation is a poor short-term timing indicator, but the single-most important determinant of long-term returns," Bank of America Merrill Lynch's Savita Subramanian said. "Valuations have historically explained 60-90% of subsequent returns over a 10-year horizon. Normalized P/E – our preferred valuation metric – has explained 80-90% of returns over the subsequent 10-11 years."

An R^2 of 90% is astronomically high. This is so high that it's likely the result of overfitting or methodological error or some form of lying with statistics.

(I couldn't find her definition of normalized PE).

 

[CaliforniaMan]

...
An R^2 of 90% is astronomically high. This is so high that it's likely the result of overfitting or methodological error or some form of lying with statistics.
...

You are so right, highly unlikely anyone has a predictor that good. My guess is they are correlating time series, I am surprised so many people do that when it is such a no no. Something I posted previously may be worth repeating:

"A lot of people just don't realize that the observations used in a correlation MUST be independent for the statistical tables for that correlation coefficient to be valid. As the data becomes smoother (as with a time series, or smoothing) the tails of the probability distribution rise so that, depending on the amount of autocorrelation in the series, the tails of the distribution (around +/- one) rise while the middle (around zero) fall. And hence with autocorrelated series you very often get more values near 1 than near zero, even when there is no relationship.

It is a fun exercise to smooth random series and watch as you smooth them as the correlations tend toward 1.0. I think correlations between autocorrelated variables are often used (not that I am saying they are used that way here) to fool people."