Research Review | 12.28.2011 | Forecasting: What Have We Learned?

Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations
Chikako Baba and Turgut Kisinbay (IMF) | October 2011
This study proposes a data-based algorithm to select a subset of indicators from a large data set with a focus on forecasting recessions. The algorithm selects leading indicators of recessions based on the forecast encompassing principle and combines the forecasts. An application to U.S. data shows that forecasts obtained from the algorithm are consistently among the best in a large comparative forecasting exercise at various forecasting horizons. In addition, the selected indicators are reasonable and consistent with the standard leading indicators followed by many observers of business cycles. The suggested algorithm has several advantages, including wide applicability and objective variable selection.

Predicting the Small Stock Premium Over Different Horizons: What Do We Learn About its Source?
Valeri Zakamouline (University of Agder) | October 2011
In this paper we present the evidence that the small stock premium is predictable both in-sample and out-of-sample using a set of lagged macroeconomic variables. It is possible to forecast the size premium over horizons from one month to one year. We demonstrate that the predictability of the size premium allows a portfolio manager to generate an economically and statistically significant active alpha. The results obtained in this paper support the view that the small stock premium tends to appear in economic bad times. Yet the size premium seems to be not a risk premium, but a behavioral phenomenon. Our findings suggest that the small stock premium appears mainly as the result of a delayed and strong reaction of small stocks to good news after a period of prolonged bad times.
Forecasting Bond Risk Premia Using Technical Indicators
Jeremy Goh (Singapore Management University), et al. | November 2011
While economic variables have been used extensively to forecast the U.S. bond risk premia, little attention has been paid to the use of technical indicators which are widely employed by practitioners. In this paper, we fill this gap by studying the predictive ability of using a variety of technical indicators vis-a-vis the economic variables. We find that the technical indicators have statistically and economically significant in- and out-of-sample forecasting power. Moreover, we find that utilizing information from both technical indicators and economic variables substantially increases the forecasting performances relative to using just economic variables.
Analysts’ Earnings Forecast, Recommendation and Target Price Revisions
Ronen Feldman (Hebrew University of Jerusalem) | June 2011
This study examines the immediate and delayed market responses to revisions in analyst forecasts of earnings, target prices, and recommendations. Consistent with prior literature, revisions in earnings forecasts are positively and significantly associated with short-term market returns around the revisions. However, we show that short-term market returns around target price revisions and recommendation changes are even stronger. We also find superior future performance (return drift) for portfolios that use information from all three types of revisions to those using information from only one of the three types of revisions.
Do Stock Prices Influence Analysts’ Earnings Forecasts?
Lisa Sedor (DePaul University and Jeffrey Miller (Notre Dame) | September 2011
We examine whether current-period stock prices influence analysts’ earnings forecasts. Using an experiment with financial analysts, we find that analysts updating their earnings forecasts in response to a management earnings forecast provide different forecasts depending on the stock price reaction to management’s forecast. Lower (higher) stock price leads to lower (higher) analysts’ forecasts. Further, we demonstrate that the influence of stock price on analysts’ forecasts is moderated by uncertainty about future earnings: higher earnings uncertainty increases the influence of stock price on analysts’ forecasts. Additional analyses indicate that this effect is mediated partially by analysts’ confidence in their forecasts and appears to be unintentional. Evidence from a follow-up survey, however, indicates that some professional analysts intentionally incorporate stock price information into their earnings forecasts. Overall, our evidence suggests that the association between prior security returns and analysts’ earnings forecasts documented in prior research is due, at least in part, to professional analysts incorporating stock price information, intentionally and unintentionally, into their earnings forecasts.
How Does the FOMC Learn About Economic Revolutions? Evidence from the New Economy Era, 1994-2001
Richard Anderson and Kevin Kliesen (St. Louis Fed) | December 2011
Forecasting is a daunting challenge for business economists and policymakers, often made more difficult by pervasive uncertainty. No such uncertainty is more difficult than projecting the reaction of policymakers to major shifts in the economy. We explore the process by which the FOMC came to recognize, and react to, the productivity acceleration of the 1990s. Initial impressions were formed importantly by anecdotal evidence. Then, policymakers—and chiefly Alan Greenspan—came to mistrust the data and the forecasts. Eventually, revisions to published data confirmed initial impressions. Our main conclusion is that the productivity-driven positive supply side shocks of the 1990s were initially viewed favorably. However, over time they came to be viewed as posing a threat to the economy, chiefly through unsustainable increases in aggregate demand growth that threatened to increase inflation pressures. Perhaps nothing so complicates business planning and forecasting as policymakers who initially embrace an unanticipated shift and, later, come to abhor the same shift.
The Wisdom of Competitive Crowds
Kenneth Lichtendahl Jr. (University of Virginia), et al. | November 2011
When several individuals are asked to forecast an uncertain quantity, they often face implicit or explicit incentives to be the most accurate. Despite the desire to elicit honest forecasts, such competition induces forecasters to report strategically and non-truthfully. The question we address is whether the competitive crowd’s forecast (the average of strategic forecasts) is more accurate than the average of truthful forecasts. We analyze a forecasting competition in which a prize is awarded to the forecaster whose point forecast is closest to the actual outcome. Before reporting a forecast, we assume each forecaster receives two signals: one common and one private. These signals represent the forecasters’ past shared and personal experiences relevant for forecasting the uncertain quantity of interest. In a set of equilibrium results, we characterize the nature of the strategic forecasts in this game. As the correlation among the forecasters’ private signals increases, the forecasters switch from using a pure to a mixed strategy. In both cases, we find that the competitive crowd’s forecast is more accurate and measure the improvement. These findings suggest that forecasting competitions may be attractive alternatives to prediction markets because they are easy to implement and may be more accurate.
Do Commodity Futures Help Forecast Spot Prices?
Shaun Roache and David Reichsfeld (IMF) | November 2011
We assess the spot price forecasting performance of 10 commodity futures at various horizons up to two years and test whether this performance is affected by market conditions. We reject efficient markets based on in-sample tests but, out-of-sample, we find that the forecast from the futures market is hard to beat. We find that the forecasting performance of futures does not depend on the slope of the futures curve, in contrast to the predictions of well-known models of commodity markets. We also find futures’ forecasting performance to be invariant to whether prices are in an upswing or downswing, casting doubt on aspersions that uninformed investors participating during bull markets impede the price discovery process.
Forecasting the Price of Oil
Ron Alquist (Bank of Canada), et al. | July 2011
We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real or nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures markets in forecasting the price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?