UPDATE: Why the S&P 500 should climb even higher by March
By Mark Hulbert, MarketWatch
Sam Eisenstadt's latest forecast for the U.S. market
Do you really believe Wall Street cares who in Washington is calling someone else a moron? Of course not.
So why was the story that Secretary of State Rex Tillerson may have called President Donald Trump a "moron" (http://www.marketwatch.com/story/tillerson-called-trump-a-moron-and-had-to-be-talked-out-of-resigning-nbc-reports-2017-10-04) one of the most popular on the MarketWatch site earlier this week?
The reason I ask: I want to make a point about how hard it is for us to focus on what really matters. We let our attention be diverted by myriad distractions dominating each day's headlines, without stopping to ask whether any of them make any real or lasting difference to the profitability of the companies we own. Indeed, evidence shows that trading behavior (http://www.marketwatch.com/story/wall-street-fear-hits-a-historic-low-as-stock-market-scores-rip-roaring-records-2017-10-05) is influenced by factors as extraneous as whether the sun is shining (https://ssrn.com/abstract=265674) or whether our favorite sports teams are winning or losing (https://ssrn.com/abstract=677103).
One adviser who's made a career of keeping his eyes on the prize is Sam Eisenstadt. For those of you who don't know him, he is the former research director at Value Line Inc. (VALU). Though he retired in 2009 after 63 years at that firm, Eisenstadt continues to update and refine a complex econometric model that generates six-month forecasts (http://www.marketwatch.com/story/why-stocks-may-be-on-verge-of-a-melt-up-2017-10-04) for the broader U.S. market.
That model's latest projection is that the S&P 500 will be between 2,620 and 2,640 on March 31 of next year. That's between 2.7% and 3.5% higher than where the U.S. market benchmark is trading currently.
The reason to take this projection seriously is Eisenstadt's track record. Consider a statistic known as the r-squared, which measures the degree to which one data series predicts or explains another. If the first series perfectly predicted the second, the r-squared would be 1.0; if the first series had absolutely no predictive ability the r-squared would be zero.