Consensus Forecasts Research Articles

Range Has it: Decoding the Information Content of Forecast Ranges

Given that most management forecasts in recent years are issued in range form and that most firms are followed by multiple analysts issuing dispersed forecasts, the conventional measure of management forecast news based on the midpoint of management forecast and the consensus analyst forecast does not fully capture the information content conveyed by range forecasts. This paper seeks to partially uncover such information by investigating an unexamined dimension unique to range forecasts –– overlapping between management forecast ranges and the ranges formed by individual analyst forecasts, which occurs in nearly 84% of range forecasts. Controlling for the width of management forecasts and analyst forecast dispersion, we find that compared with “overlap” management forecasts, “non-overlap” forecasts have greater impact on analyst revisions and stock market reaction to per unit forecast news. The non-overlap forecasts also turn out to have high relative forecast accuracy over analyst consensus forecasts. Moreover, individual analysts whose prior forecasts are outside of management forecast ranges respond more strongly to “non-overlap” forecasts than to “overlap” forecasts, and this difference is significant only when management forecasts convey bad news. Overall, our findings suggest that range forecasts convey more information beyond just the midpoint and analysts’ consensus.

European Commission's Forecasts Accuracy Revisited: Statistical Properties and Possible Causes of Forecast Errors

This paper updates a previous assessment of the European Commission's track record for forecasting key economic variables (Gonzalez Cabanillas and Terzi 2012) by extending the observation period to 2014. It also examines the accuracy of the Commission's forecasts over a shorter and more recent period (2000-2014) so that a comparison can be made between the performance of forecasts made before and after the Great Recession of 2008-2009. Going beyond the 2012 approach, this paper also examines the extent to which forecast errors can be explained by external or technical assumptions that prove incorrect ex post. It also updates the comparison of the Commission’s performance vis a vis the OECD, the IMF, a consensus forecast of market economists, and the ECB.Inclusion of the 2012-2014 period lowers the forecasting error for some key variables or leads to no change in others. Focusing on the years since the turn of the century, current-year and year-ahead forecasting errors for the three main variables examined (GDP growth, inflation and general government balances) have been larger in the crisis and post-crisis period (2008-2014) than in the pre-crisis period (2000-2007) for a large majority of Member States. This appears mainly to be the result of an anomalously large error in 2009, a year which confounded many forecasters. The country-by-country analysis confirms the finding of earlier studies which show that the Commission's forecasts are largely unbiased. The newly-introduced panel data approach also confirms the absence of bias in current-year GDP forecasts across EU Member States but shows that year-ahead forecasts for GDP growth tend to be slightly over optimistic across the whole sample. The analysis also shows that auto correlation of forecast errors is not a major issue in the Commission's forecasts. Other advanced tests shed more light on the performance of the Commission’s forecasts, demonstrating that they are directionally accurate and generally beat a naive forecast but that they are not always efficient in terms of their use of all available data.The decomposition of forecast errors shows that unexpected changes in external assumptions seem to have only a limited impact on current-year GDP growth forecasts. However, more than half of the variance in year-ahead forecast errors appears to come from external assumptions that prove to be incorrect ex post. Finally, the Commission’s economic forecasts come out as being more accurate than those of the market and comparable to those of the other international institutions considered.

Improving Model-Based Near-Term GDP Forecasts by Subjective Forecasts: A Real-Time Exercise for the G7 Countries

We investigate to what extent it is feasible to improve model-based near-term GDP forecasts by combining them with judgmental (quarterly) forecasts by professional analysts (Consensus survey) in a real-time setting. Our analysis covers the G7 countries over the years 1999-2013. We consider as combination schemes the weighted average and the linear combination. Incorporating subjective information delivers sizable gains in forecasting ability of statistical models for all countries except Japan in 1999-2013, even when subjective forecasts are somewhat dated. Accuracy gains are much more pronounced in the volatile period after 2008 due to a marked improvement in predictive power of Consensus forecasts. Since 2008, Consensus forecasts are superior at the moment of publication for most countries. For some countries Consensus forecasts can be enhanced by model-based forecasts in between the quarterly release dates of the Consensus survey, as the latter embody more recent monthly information.

Equity Value as a Function of (eps1, eps2, dps1, bvps, beta): Concepts and Realities

This paper examines three basic equity valuation concepts: (1) residual income valuation (RIV); (2) in the spirit of Miller‐Modigliani, the irrelevance of a firm's dividend payout policy; (3) betas/CAPM, to quantify risk and capitalization factors. As a first cut, results show that RIV, concept (1), lacks empirical support while in contrast concepts (2) and (3) hold up reasonably well. To address (2) and (3) we develop a model where earnings and earnings growth determine value. This model supplants RIV because of its greater intuitive appeal and empirical support. A linear function of eps1, eps2, dps1 maps into stock prices and theory specifies the coefficients’ admissible magnitudes. Both the concepts of dividend payout irrelevance and risk (cost‐of‐equity per CAPM) restrict the coefficients. Bvps is value‐irrelevant, in both the analytical and empirical analyses. The latter can be viewed as a case study; it considers S&P500 firms at two points in time, and the data was hand‐collected in real time from a public website (Yahoo!Finance). This scheme ensures perfectly synchronized data and it usefully provides not only consensus forecasts but also real time betas. Overall, the paper contributes by developing valuation concepts and by showing how these can be evaluated empirically.

Harnessing the Wisdom of Crowds

We examine the negative information externality associated with herding on a crowd-based earnings forecast platform (Estimize.com). By tracking user viewing activities, we monitor the amount of information a user viewed before she makes an earnings forecast. We find the more public information viewed, the less weight she will put on her private information. While this improves the accuracy of each individual forecast, it reduces the accuracy of the consensus forecast since useful private information is prevented from entering the consensus. Predictable errors made by influential users early persist in the consensus forecast and result in return predictability at the earnings announcements. To address the endogeneity concerning the information acquisition choice, we collaborate with Estimize.com to run experiments where we restrict the information set on randomly selected stocks and users. The experiments confirm that forecasts lead to more accurate consensus and convince Estimize.com to switch to a blind platform from November 2015. Overall, our findings suggest that wisdom of crowds can be better harnessed by encouraging independent voice from the participants.

News Sentiment and Cross-Country Fluctuations

What is the information content of news-based measures of sentiment? How are they related to economic fluctuations? I construct a sentiment index by measuring the net amount of positive expressions in the full corpus of Economic news articles produced by Reuters covering 12 countries over the period 1987-2013. The index successfully tracks fluctuations in GDP at the country level, is a leading indicator of GDP growth and contains information on future GDP growth which is not captured by consensus forecasts. This suggests that forecasters do not appropriately incorporate available information in predicting future states of the economy.

Tests of investor learning models using earnings innovations and implied volatilities

This paper investigates alternative models of learning to explain changes in uncertainty surrounding earnings innovations. As a proxy for investor uncertainty, we use model-free implied volatilities; as a proxy for earnings innovations, representing signals of firm performance likely to drive investor perceptions of uncertainty, we use quarterly unexpected earnings benchmarked to the consensus forecast. We document that uncertainty declines on average after the release of quarterly earnings announcements and this decline is attenuated by the magnitude of the earnings innovation. This latter result is consistent with models that incorporate signal magnitude as a factor driving changes in uncertainty. Most important, we document that signals deviating sufficiently from expectations lead to net increases in uncertainty. Critically, this result suggests that models allowing for posterior variance to be greater than prior variance even after signal revelation [e.g., regime shifts in Pastor and Veronesi (Annu Rev Financ Econ 1:361–381, 2009)] better describe how investors incorporate new information.

Output spillovers from changes in sovereign credit ratings

This research examines how a sovereign rating revision of one country influences the economic growth rates of other countries. Rating revisions have significant output spillover effects: A one-notch upgrade (downgrade) prompts on average a significant downward revision of about 0.03% (0.07%) in the consensus forecast of annual economic growth rates of other countries in the two-month period after the event. The spillovers are transmitted through direct and indirect trade and financial linkages between event and non-event countries. The evidence indicates that a predominance of differential (common) spillovers leads upgrades (downgrades) to produce adverse output effects for other countries.

Net Neutrality: A Fast Lane to Understanding the Trade-offs

The last decade has seen a strident public debate about the principle of “net neutrality.” The economic literature has focused on two definitions of net neutrality. The most basic definition of net neutrality is to prohibit payments from content providers to internet service providers; this situation we refer to as a one-sided pricing model, in contrast with a two-sided pricing model in which such payments are permitted. Net neutrality may also be defined as prohibiting prioritization of traffic, with or without compensation. The research program then is to explore how a net neutrality rule would alter the distribution of rents and the efficiency of outcomes. After describing the features of the modern internet and introducing the key players, (internet service providers, content providers, and customers), we summarize insights from some models of the treatment of internet traffic, framing issues in terms of the positive economic factors at work. Our survey provides little support for the bold and simplistic claims of the most vociferous supporters and detractors of net neutrality. The economic consequences of such policies depend crucially on the precise policy choice and how it is implemented. The consequences further depend on how long-run economic trade-offs play out; for some of them, there is relevant experience in other industries to draw upon, but for others there is no experience and no consensus forecast.

Duchenne muscular dystrophy drugs face tough path to approval.

Highly anticipated as new disease-modifying treatments for Duchenne muscular dystrophy (DMD), therapeutics by BioMarin Pharmaceutical (Kyndrisa™; drisapersen) and Sarepta Therapeutics (eteplirsen; AVI-4658) both recently received negative FDA reviews and are now facing battles for approval in the U.S. At present, BioMarin is committed to working with the FDA to forge a pathway to approval following the failure of its NDA, while Sarepta awaits the formal decision on its NDA, which is expected by late May 2016. Despite the critical nature of both reviews, analysts consider that there is still a narrow possibility of approval of both drugs. According to Consensus forecasts from Thomson Reuters Cortellis for Competitive Intelligence, Kyndrisa is forecast to achieve sales of USD 533.71 million in 2021.

A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth

Analyzing the performance of the economy in real time is a challenge for those who must forecast macroeconomic variables such as inflation or employment. A key aspect of this challenge is evaluating the incoming flow of information contained in economic announcements. In this article, the authors develop a simple-to-read index of these announcements that they then use to construct nowcasts. The index tracks whether key economic data have come in stronger, weaker, or as expected during the current quarter relative to a baseline consensus forecast. Specifically, the data releases are weighted relative to their importance in updating forecasts of real gross domestic product growth during the current quarter.

Assessing macroeconomic forecasts for Japan under an asymmetric loss function

This paper examines the asymmetry of the loss functions of the Japanese government, the International Monetary Fund (IMF), and private forecasters for Japanese output growth and inflation forecasts. It tests the rationality of the forecasts, assuming a possibly asymmetric loss function. The results indicate considerable evidence of asymmetry. The 15-month forecasts are overpredicted, irrespective of forecaster identity or the target variable. However, the biases in the three-month forecasts vary among forecasters: the IMF provides prudent short-term forecasts for output growth and inflation, while private forecasters provide unbiased inflation forecasts. The government uses the information provided in the IMF and consensus forecasts efficiently when making its own forecasts. A comparison with the projections for the German economy indicates that the biases of the Japanese government may be attributable to its debt-to-GDP ratio, which is the highest among advanced economies.

Os analistas sell-side fazem boas previsões no Brasil?

Analisamos o desempenho do consenso de previsões e recomendações de analistas sell-side no Brasil a partir de três medidas - viés, informatividade e acurácia. A amostra compreendeu 195 ações listadas na BM&FBovespa, 75 corretoras e 569 analistas. Aplicamos MQO com dados em painel para um total de 8477 observações e encontramos: i. viés otimista e concentração em recomendações de compra, mesmo quando os preços-alvo sugerem pessimismo; ii. analistas aprendem com os erros, reduzindo seus vieses; iii. otimismo nas previsões tem relação positiva com variação do dólar e negativa com variação de taxa de juros.

A Consensus Forecast for Tropical Cyclone Gale Wind Radii

Abstract The National Hurricane Center (NHC) has been forecasting gale force wind radii for many years, and more recently (starting in 2004) began routine postanalysis or “best tracking” of the maximum radial extent of gale [34 knots (kt; 1 kt = 0.514 m s−1)] force winds in compass quadrants surrounding the tropical cyclone (wind radii). At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that wind radii forecasts could be evaluated for skill. If the best-track gale radii are used as ground truth (even accounting for random errors in the analyses), the skill of the NHC forecasts appears to be improving at 2- and 3-day lead times, suggesting that the guidance has also improved. In this paper several NWP models are evaluated for their skill, an equally weighted average or “consensus” of the model forecasts is constructed, and finally the consensus skill is evaluated. The results are similar to what is found with tropical cyclone track and intensity in that the consensus skill is comparable to or better than that of the individual models. Furthermore, the consensus skill is high enough to be of potential use as forecast guidance or as a proxy for official gale force wind radii forecasts at the longer lead times.

Tests of Investor Learning Models Using Earnings Innovations and Implied Volatilities

This paper investigates alternative models of learning to explain changes in uncertainty surrounding earnings innovations. As a proxy for investor uncertainty, we use model-free implied volatilities; as a proxy for earnings innovations, representing signals of firm performance likely to drive investor perceptions of uncertainty, we use quarterly unexpected earnings benchmarked to the consensus forecast. First, we document ― consistent with most Bayesian models of learning and prior research ― that uncertainty declines on average after the release of quarterly earnings announcements. Second, we show that this decline is attenuated by the size of the signal ― that is, by the magnitude of the earnings innovation. This latter result is inconsistent with some models of Bayesian learning, and consistent with more sophisticated models, which incorporate signal magnitude as a factor driving changes in uncertainty. Third and most important, we document that signals deviating sufficiently from expectations ― that is, sufficiently large earnings innovations ― lead to net increases in uncertainty. Critically, this latter result suggests that models allowing for posterior variance to be greater than prior variance even after signal revelation (such as “regime shifts” in Pastor and Veronesi 2009) appear more descriptive of how investors incorporate new information.

Russian devaluation in 2014–2015: Falling into the abyss or a window of opportunity?

Abstract Falling oil prices are leading to a reduction in domestic demand and lowering of the ruble exchange rate, thus enhancing the price competitiveness of Russian producers and stimulating the supply side of the economy (especially in foreign markets unaffected by the recession). Indeed, all of this create the possibility of offsetting the decline in domestic demand to a varying degree through increased net exports. However, the present study shows that, taking into account all of the structural problems of the Russian economy, the devaluation of the ruble may lead to a more severe recession than anticipated by most experts in their estimates, judging by average consensus forecasts (as of the end of September 2015).

Learning, Dispersion of Beliefs, and Risk Premiums in an Arbitrage-Free Term Structure Model

This paper studies an arbitrage-free dynamic term structure model of a Bayesian learner who presumes that dispersion of beliefs is priced in Treasury markets and that measured dispersion has forecasting power for future Treasury yields. The estimated learning rule reveals which aspects of risk and the state of the economy agents learn about and which aspects they (evidently) know from information embedded in the current yield curve. Moreover, we quantify the effect of learning on measured risk premiums in Treasury markets, as well as how an agent pricing with the fitted stochastic discount factor would have adjusted the parameters of her learning rule over business cycles and as the measured degree of disagreement changed over time. Our {\em real-time} model-based learning rule substantially outperforms the consensus forecasts of market professionals over the past twenty-five years, particularly during the years following recessions in the U.S. economy. Moreover, we find that the informativeness of dispersion in beliefs about future yields is distinct from information about the macroeconomy.

Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

AbstractIn the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth‐surface processes and hazards. In some cases, we have the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth‐surface scientists and Earth‐system modelers. This paper assesses the state‐of‐the‐art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross‐cutting issues, including the importance of nonlinear/threshold‐dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth‐surface response to C&LUC. Five supplements delve into different scales or process zones (global‐scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.

Retraction: Financial Reporting Transparency and Earnings Management

Prior research indicates that greater transparency in reporting formats facilitates the detection of earnings management. The current study hypothesizes and demonstrates that greater transparency in comprehensive income reporting also reduces the likelihood that managers will engage in earnings management in the area of increased transparency. In our experiment, 62 financial executives and chief executive officers decide which available‐for‐sale security to sell from a portfolio. We manipulate the transparency of comprehensive income reporting and the relationship of projected earnings to the consensus forecast in a 2×2 between‐subjects design. When projected earnings are below (above) the consensus forecast, participants sell securities that increase (decrease) earnings. However, the rarely used, more transparent format for reporting comprehensive income significantly reduces both income‐increasing and income‐decreasing earnings management. Participants in the less transparent setting indicate that earni...

Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

AbstractThis study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.