Crowdsourcing Forecasts: Competition For Sell-Side Analysts?
Oklahoma State University
October 25, 2013
Recent research has begun to question the importance of forecasts to sell-side analysts. Prior research established the co-existence of longer horizon optimism and short-term pessimism in sell-side forecasts. These factors motivate us to explore a new phenomenon – crowdsourcing, as an alternative source of forecasts. We obtain revenue and earnings forecasts from estimize, an entity which crowdsources and distributes these forecasts online. We find the estimize forecasts are, on average, as accurate as the sell-side. Further, although our results show the estimize forecasts to be relatively more optimistic, on average, and particularly in short horizons, our analysis suggest it is at least partially explained by the extreme pessimism of the sell-side’s final forecasts. Our market test confirms support for the superiority of estimize’s short-term forecasts. Our results provide support for the value of crowdsourced forecasts.
Crowdsourcing Forecasts: Competition For Sell-Side Analysts? – Introduction
Forecasts are fundamental to markets. Revenues, earnings, and cash flows are common forecast parameters. Sell-side analysts are one publicly available source of such forecasts for market participants and academics. Early research established analyst forecast superiority to time series models (Brown et al., 1987) and linked sell-side analyst career success to forecast accuracy (Stickel, 1992; Mikhail et al., 1999). However, there was also evidence of an optimistic bias (O’Brien, 1988; Hong and Kubik, 2003). An entire stream of literature explored whether analyst incentives were the cause for the forecast bias and, in general, found little support. Subsequent work showed that the optimism had dissipated in later samples, and, instead, longer-term optimism and short-term pessimism co-existed, perhaps to support management’s effort to meet or beat expectations (Richardson et al., 2004).
Recent studies are beginning to question the importance of forecasting to sell-side analysts (Brown et al., 2013). Groysberg et al. (2011) find no relation between forecast accuracy and compensation, providing a possible explanation for its reduced importance. Johnston et al. (2012) show that a simple adjustment to enhance forecast accuracy is overlooked by analysts, thus providing direct evidence of forecasting’s reduced importance. Surveys suggest that industry knowledge is increasingly important to sell-side analysts. If forecasts are becoming less important to sell-side analysts, then it seems plausible that their quality may have declined or may do so in the future, thus begging the question: are there alternative and/or better sources for forecasts? Certainly, many companies now issue their own forecasts, but it is not the norm, and such forecasts may suffer from their own biases. Time-series forecasts are a possibility, Bradshaw et al. (2012) show that in some circumstances, such as smaller or younger firms, they are superior to sell-side analyst annual forecasts. However, neither of these alternatives appear to offer a complete solution. Independent and buy-side analysts are another option, but their forecasts are rarely publicly available, and previous research, which is limited, suggests that both are less accurate than sell-side analysts and, in the case of buy-side, more optimistic as well (Groysberg et al., 2008; Gu and Xue, 2008). However, the samples for both of these studies are limited and fairly dated.
In this paper, we explore a new phenomenon, crowdsourcing of forecasts. We obtain revenue and earnings forecasts from estimize. Estimize is an open platform that collects forecasts from more than 2,000 contributors for approximately 1,400 firms. Many contributors are employed on the buy-side or at independent research firms.1 These forecasts are available on their website as well as recently being added to Bloomberg terminals. Our primary interest is whether these forecasts are comparable to sell-side forecasts in terms of accuracy and whether, perhaps due to differing incentives, reflect less bias.
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