Besides the publicity angle, there's also the possibility that a company could use these predictions for more direct profit. Consider the following:
- Buy up lots of stock at $50/share
- Publish that you predict that stock will get to $70/share
- Wait for people following your advice to start pushing the stock price up
- Sell the stock when it gets to $70/share. (or even $60/share)
In your particular case, they're not dealing with a single stock, but there might still be some manipulation -- trying to get people to vote for a specific candidate, without having to directly endorse that candidate.
In the case of this article, there's bias by omission -- to quote the article, highlighting two portions:
A victory for the Republican candidate could push the S&P 500 to as high as 3,900 at year-end under the most optimistic case laid out by Dubravko Lakos-Bujas, the bank’s chief U.S. equity strategist. ... While a number of traders have come to consider a Democratic sweep followed by a prompt fiscal deal among bullish scenarios for the equity market, Lakos-Bujas disagrees, seeing Trump’s victory as the most favorable outcome.
So, by only showing the "most optimistic" of all predictions, they may be cherry picking the data.
Assume we have a some scenario that we want to influence the outcome. Estimates are that Outcome A will result in an S&P increase of 8-12%, normally distributed. But for Outcome B, we have a wider range of predictions, and it's 3-13%, also normally distributed. But we can pick the outlier for B, and publicize that. So instead of comparing the means of 10% vs. 8%, we report on "most optimistic case", and compare 12% vs. 13%.