Key findings:
• Alternative data sources improve short-term stock forecasts but worsen long-term projections.
• Analysts face information overload, diverting focus from long-term forecasting to short-term analysis.
• Poor long-term forecasts can misalign stock valuations and discourage long-term corporate investments.
Since the beginning of the century, the number of satellites orbiting Earth has increased by more than 800 percent, from less than 1,000 to more than 9,000 currently. The profusion of satellites littering our skies has had a number of strange and disturbing repercussions.
One of them is that there are companies selling data from satellite images of parking lots to financial analysts. Analysts then use this information to help them gauge a store’s foot traffic, compare a retailer to competitors, and estimate revenue.
This is just one small sample of the new information, or “alternative data,” that is now available to stock analysts to help them make their predictions. (Traditionally, analysts would use earnings reports and other information made public by firms themselves to issue their assessments.)
In a paper on the effect of alternative data on financial forecasting, with my collaborators, we counted more than 500 companies that sold alternative data in 2017, which had ballooned from less than 50 in 1996.
Tweets, Twits and Credit Card Data
Besides satellite images, more obvious sources of new information include Google, credit card statistics and social media, like X (formerly Twitter) or Stocktwits, a popular Twitter-like platform where investors share ideas about the market.
There has been more and more data available like that. And people in the financial industry are using those data sets.
We found, as others had in the past, that the availability of more data explains why stock analysts have become progressively better at making short-term projections.
We went further, however, by asking how this alternative data affected long-term projections. Because of its nature, capturing information about firms in the moment, alternative data is useful mostly for short-term forecasts. Longer-term analysis — of a year or up to five years into the future — is a much more important judgment.
However, over the same period that saw the rise in accuracy of short-term projections, the validity of analysts’ long-term forecasts fell.
Information Overload Impacts Analysts' Long-term Forecasting Accuracy
If they put more effort in one area, they have less bandwidth for the second one.
Because analysts had easy access to information for short-term analysis, they directed their energy there. Indeed, previous papers have proved the common-sense proposition that analysts have a limited amount of attention. If analysts have a large portfolio of firms to cover, for example, their scattered concentration begins to yield diminishing returns.
Similarly, if financial professionals have (too) many data points for their short-term forecasting, they will have limited time to invest in researching long-term results.
To investigate this proposition, we burrowed down into the Stocktwits platform. As might be expected, certain stocks like Apple, Google, or Walmart generate much more discussion than small companies that aren’t even listed on the NASDAQ.
We conjectured that analysts who followed stocks that are heavily discussed on Stocktwits — who were exposed to a lot of alternative data — would experience a larger decline in the quality of their long-term forecasts than those whose stocks were little discussed.
After controlling for other factors, that’s exactly what we found.
Broader Repercussions for Poor Long-term Forecasting
The consequences of this inundation of alternative data may be profound.
When assessing a stock’s value, investors must take into account both the short and long term.
If the quality of the long-term forecasts deteriorates, there is a good chance that stock prices will not be a good assessment of a firm’s value.
Moreover, from a firm’s point of view, it would like to see the value of its decisions reflected in its stock price. But if the firm is making long-range decisions that are not being correctly viewed by analysts, it might be less willing to make investments that will only pay off years away.
The Cases of Mining Companies and Carbon Reduction
In the mining industry, for instance, it takes time to build a new mine. It’s going to take maybe nine, 10 years for an investment to start producing cashflows.
Companies might be less willing to make such investments if their stocks are being undervalued because market participants have less accurate forecasts of the impact of these investments on firms' cash flows — the subject of another paper I am working on.
Even more alarming is the example of investment in carbon reduction.
That investment, again, tends to pay off in the long run, when global warming will be an even bigger issue. Firms may have less incentive to [invest] if the worth of that investment is not quickly reflected in their valuation.
Methodology
Using data on equity analysts’ forecasts from 1983 to 2017, a total of 65 million forecasts, we measured the quality of the forecasts. (The data they used was from the Institutional Brokers’ Estimate System Explained, or IBES, a database of stock analysts’ estimates of the future earnings of publicly traded American companies.) They compared the analysts’ predictions to the actual earnings per share of companies’ stock. They then proposed that the evolution they observed — increased accuracy of short-term forecasts and declining accuracy of long-term predictions — was due to a concomitant proliferation of alternative sources for financial information. To validate this hypothesis, they studied the relative accuracy of analysts’ long-term forecasts according to how much data they were exposed to on the social media platform Stocktwits.
Practical Applications
The results of this research suggest that it might be wise for financial firms to separate teams that research short-term results and those that make long-term forecasts. This would alleviate the problem of one person or team being flooded with data relevant to short-term forecasting, but being expected to also research long-term results. Another application of the research is for investors looking for bargains. Though there are downsides to poor long-term forecasting, it could present an opportunity for those who are able to recognize firms that the market is underestimating.
