If I tell you that the price of a stock is going to increase and you believe me enough to rush out and buy that stock, the price of that stock will increase because, of course, buying stocks causes their price to rise. But since I am a nobody, and you are probably not much of anybody (sorry), the quantity of stock you buy based on my advice won’t change the price much.
But if I am a major, respected, bow-tie-wearing analyst from a large brokerage and I make the same forecast, people of importance with lots of money are likely to believe me. They will buy the stock—which will cause the price will increase!
At some later date, these investors will look at their purchase and consider my forecast. I said the stock price would rise and it did. My forecast was seemingly correct. But if these folks are not careful, they will attribute to me a predictive skill I don’t deserve. This will make it easier for me to sell my services to them in the future.
My act of making the forecast changed the behavior of the people who buy and sell stock. Of course, the stock might have still have risen had nobody heard my forecast, or it might have fallen. So how can we tell how good my forecast is in the presence of influence? Not easily.
Two headlines from the Wall Street Journal illustrate the difficulty.
The first is “Bearish Calls: Two Analysts Get Shunned Over Their Views.” Seems Forest Laboratories (FL) were miffed at Oppenheimer’s John Newman and CLSA Asia-Pacific Markets’ David Maris because these two gentlemen had the temerity to say that they believe FL stock would decrease. In other words, people should sell.
A look at the stock price after Maris advised “sell” shows that the stock started falling, from about $33 to roughly $29, a whopping decrease for anybody with a large holding of FL.
It is true that the stock price recovered after Maris’s prediction, but not by as much as the executives at FL thought it would had Newman not later agreed with Maris’s dim view of FL. The executive were so angry over the predictions that they decided to cut Maris and Newman off, to “shun them” as the paper said. No more would FL whisper sweet somethings into the ears of these analysts!
The next example: “Mortgage Rates Seen Rising to 5.1% in 2011: Industry Group Pegs Levels Going From Record Lows to Merely Historic Lows.” I’m not sure what the difference between “record lows” and “historic lows” is; but there you have it. The industry group is the Mortgage Bankers Association (MBA).
The folks at the MBA are well known to financial insiders, but they are not a household name among house buyers. But suppose the ordinary house buyer was aware of the MBA and the importance of its forecasts. Then he might decide to buy a house now before the interest rate rises. And if enough other buyers follow suit, then the subsequent run on bank money will cause interest rates to rise.
That scenario is unlikely. But it is true that the behavior of the financial insiders who follow the MBA can cause the mortgage interest rate to rise or fall. But that rate is much more a function of the economy as a whole, the activity of millions of house buyers and sellers, and to some extent the behavior of people in other countries as those folks influence our economy. The chance that the MBA will influence their forecast of increasing mortgage rates is thus low, but not zero.
Finally, a third kind of forecast comes from your weatherman. No matter what he says about tomorrow’s high temperature, nothing anybody does is going to change it (barring any sci-fi scenarios, of course). Predictions of any physical event outside of human control can never be influential.
If “important” stock forecasters only make one forecast, there is no way to tell just how much influence they had. That is, no data exists that will answer the counterfactual question “Would this stock have risen had the forecast not been issued?” But if they make lots of forecasts, we might be able to gauge the level of influence. Even then, we still have to posit the kind of relationship the forecast has to the actual event; and if we get that wrong, we’ll miss-estimate the influence.
Plus, forecasts and events are seldom linear. Short, unwelcome summary: telling how good people are at forecasting human events is difficult.