Are Analysts' Loss Functions Asymmetric?

Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimalforecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal forecast intervals under asymmetric loss

An optimality criterion for forecast intervals under asymmetric loss functions is proposed. A loss optimal forecast interval is obtained by requiring that the expected loss, conditional on a future realization within the desired interval, be minimal. The main difficulty in the context of forecasting under asymmetric loss emerges when there is no knowledge about the distribution of the innovations. For solving this problem, an extension of estimation under the relevant loss function is suggested. In many cases, one also needs to account for the additional variability due to estimation of model parameters. Another solution, based on the bootstrap, works for both problems. Copyright © 2007 John Wiley & Sons, Ltd.     

Expectations,use and judgmental adjustment of external financial and economic forecasts: an empirical investigation

A survey of 124 users of externally produced financial and economic forecasts in Turkey investigated their expectations and perceptions of forecast quality and their reasons for judgmentally adjusting forecasts. Expectations and quality perceptions mainly related to the timeliness of forecasts, the provision of a clear justifiable rationale and accuracy. Cost was less important. Forecasts were frequently adjusted when they lacked a justifiable explanation, when the user felt they could integrate their knowledge into the forecast, or where the user perceived a need to take responsibility for the forecast. Forecasts were less frequently adjusted when they came from a well‐known source and were based on sound explanations and assumptions. The presence of feedback on accuracy reduced the influence of these factors. The seniority and experience of users had little effect on their attitudes or propensity to make adjustments. Copyright © 2008 John Wiley & Sons, Ltd.     

Forecasting the Treasury's balance at the Fed

As part of the Fed's daily operating procedure, the Federal Reserve Bank of New York, the Board of Governors and the Treasury make a forecast of that day's Treasury balance at the Fed. These forecasts are an integral part of the Fed's daily operating procedure. Errors in these forecasts can generate variation in reserve supply and, consequently, the federal funds rate. This paper evaluates the accuracy of these forecasts. The evidence suggests that each agency's forecast contributes to the optimal, i.e., minimum variance, forecast and that the Trading Desk of the Federal Reserve Bank of New York incorporates information from all three of the agency forecasts in conducting daily open market operations. Moreover, these forecasts encompass the forecast of an economic model. Copyright © 2004 John Wiley & Sons, Ltd.     

Are forecasters reluctant to revise their predictions? Some German evidence

People are reluctant to admit mistakes. This could also be true of economic forecasters. If revisions of past forecasts are costly, then it will become optimal for forecasters to only partially adjust a past forecast in the light of new information. The unwillingness to admit to the mistake in the old forecast generates a bias of the new forecast in the direction of the old forecast. We test this hypothesis for the joint predictions of the Association of German Economic Research Institutes over the last 35 years. We find some evidence for such a bias and compute the implied unwillingness to revise forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

Regional,individual and political determinants of FOMC members' key macroeconomic forecasts

We study Federal Open Market Committee members' individual forecasts of inflation and unemployment in the period 1992–2004. Our results imply that Governors and Bank presidents forecast differently, with Governors submitting lower inflation and higher unemployment rate forecasts than bank presidents. For Bank presidents we find a regional bias, with higher district unemployment rates being associated with lower inflation and higher unemployment rate forecasts. Bank presidents' regional bias is more pronounced during the year prior to their elections or for nonvoting bank presidents. Career backgrounds or political affiliations also affect individual forecast behavior.     

Comments on testing for forecast and specification optimality using linear composites

This paper extends the method of using linear composites of forecasts for testing the efficiency of one forecast compared to a finite collection of other forecasts. It also gives necessary and sufficient conditions for forecast optimality in the mean square error sense. Information sets are found to be unnecessary for forecast optimality. Finally, the paper shows that a consistent test of forecast optimality cannot in general be obtained using linear composites. A similar conclusion applies to tests of specification optimality based on linear composites.     

Revealing forecaster's preferences: A Bayesian multivariate loss function approach

Revealing the underlying preferences of a forecaster has always been at the core of much controversy. Herein, we build on the multivariate loss function concept and propose a flexible and multivariate family of likelihoods. This allows examining whether a vector of forecast errors, along with control variables, shapes a forecaster's preferences and, therefore, the underlying multivariate, nonseparable, loss function. We estimate the likelihood function using Bayesian exponentially tilted empirical likelihood, which reveals the shape of the parameter and the power of the multivariate loss function. In the empirical section, the reported evidence reveals that the EU Commission forecasts are predominantly asymmetric, leaning towards optimism in the year ahead, while a correction towards pessimism occurs in the current year forecast. There is some variability of this asymmetry across member states, with forecasts, i.e. gross domestic product growth, for large Member States exhibiting more optimism     

Reducing uncertainty in short-term projections: Linkage of monthly and quarterly models

This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non-recursive Kalman filtering) where a baseline quarterly model forecast is modified through ‘add-factors’ or ‘constant adjustments’. The procedure ‘automatically’ constructs these adjustments in a covariance-minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures.     

The accuracy of IMF and OECD forecasts for G7 countries

This paper analyses the size and nature of the errors in GDP forecasts in the G7 countries from 1971 to 1995. These GDP short‐term forecasts are produced by the Organization for Economic Cooperation and Development and by the International Monetary Fund, and published twice a year in the Economic Outlook and in the World Economic Outlook, respectively. The evaluation of the accuracy of the forecasts is based on the properties of the difference between the realization and the forecast. A forecast is considered to be accurate if it is unbiased and efficient. A forecast is unbiased if its average deviation from the outcome is zero, and it is efficient if it reflects all the information that is available at the time the forecast is made. Finally, we also examine tests of directional accuracy and offer a non‐parametric method of assessment. Copyright © 2000 John Wiley & Sons, Ltd.     

Forecasting the 10‐year US Treasury rate

This study compares the performance of two forecasting models of the 10‐year Treasury rate: a random walk (RW) model and an augmented‐autoregressive (A‐A) model which utilizes the information in the expected inflation rate. For 1993–2008, the RW and A‐A forecasts (with different lead times and forecast horizons) are generally unbiased and accurately predict directional change under symmetric loss. However, the A‐A forecasts outperform the RW, suggesting that the expected inflation rate (as a leading indicator) helps improve forecast accuracy. This finding is important since bond market efficiency implies that the RW forecasts are optimal and cannot be improved. Copyright © 2009 John Wiley & Sons, Ltd.     

Does Disagreement Amongst Forecasters Have Predictive Value?

In the present study we examine the predictive power of disagreement amongst forecasters. In our empirical work, we find that in some situations this variable can signal upcoming structural and temporal changes in an economic process and in the predictive power of the survey forecasts. We examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime‐switching models with constant and with time‐varying transition probabilities are constructed in real time and compared on forecast accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Spreads versus professional forecasters as predictors of future output change

We examine whether real output forecasts obtained from the Survey of Professional Forecasters efficiently embody information in the term structure spread. To this end, we employ revised data as well as real‐time vintage data, and we also allow for the possible impact of asymmetric loss functions. Assuming quadratic loss, our results suggest that the term structure spread does contain information useful for forecasting not reflected in the survey forecasts, at least over the longest forecast horizon. However, if we allow agents' loss functions to become more negatively skewed with the forecast horizon, then we cannot reject the rationality of the survey forecasts. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluating the predictive accuracy of volatility models

Standard statistical loss functions, such as mean‐squared error, are commonly used for evaluating financial volatility forecasts. In this paper, an alternative evaluation framework, based on probability scoring rules that can be more closely tailored to a forecast user's decision problem, is proposed. According to the decision at hand, the user specifies the economic events to be forecast, the scoring rule with which to evaluate these probability forecasts, and the subsets of the forecasts of particular interest. The volatility forecasts from a model are then transformed into probability forecasts of the relevant events and evaluated using the selected scoring rule and calibration tests. An empirical example using exchange rate data illustrates the framework and confirms that the choice of loss function directly affects the forecast evaluation results. Copyright © 2001 John Wiley & Sons, Ltd.     

Forecasting Errors,Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate

We provide a comprehensive study of out‐of‐sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecast accuracy and effort: The case of US inflation rates

This paper investigates the relationship between forecast accuracy and effort, where effort is defined as the number of times the model used to generate forecasts is recursively estimated over the full sample period. More specifically, within a framework of costly effort, optimal effort strategies are derived under the assumption that the dynamics of the variable of interest follow an autoregressive‐type process. Results indicate that the strategies are fairly robust over a wide range of linear and nonlinear processes (including structural break processes), and deliver forecasts of transitory, core and total inflation that require less effort to generate and are as accurate as (that is, are insignificantly different from) those produced with maximum effort. Copyright © 2010 John Wiley & Sons, Ltd.     

Algorithms for explaining forecast revisions

Forecasts are routinely revised, and these revisions are often the subject of informal analysis and discussion. This paper argues (1) that forecast revisions are analyzed because they help forecasters and forecast users to evaluate forecasts and forecasting procedures and (2) that these analyses can be sharpened by using the forecasting model to systematically express its forecast revision as the sum of components identified with specific subsets of new information, such as data revisions and forecast errors. An algorithm for this purpose is explained and illustrated.     

Forecasting Stock Returns: Do Commodity Prices Help?

This paper examines the relationship between stock prices and commodity prices and whether this can be used to forecast stock returns. As both prices are linked to expected future economic performance they should exhibit a long‐run relationship. Moreover, changes in sentiment towards commodity investing may affect the nature of the response to disequilibrium. Results support cointegration between stock and commodity prices, while Bai–Perron tests identify breaks in the forecast regression. Forecasts are computed using a standard fixed (static) in‐sample/out‐of‐sample approach and by both recursive and rolling regressions, which incorporate the effects of changing forecast parameter values. A range of model specifications and forecast metrics are used. The historical mean model outperforms the forecast models in both the static and recursive approaches. However, in the rolling forecasts, those models that incorporate information from the long‐run stock price/commodity price relationship outperform both the historical mean and other forecast models. Of note, the historical mean still performs relatively well compared to standard forecast models that include the dividend yield and short‐term interest rates but not the stock/commodity price ratio. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimation and Prediction Tests of Cash Flow Forecast Accuracy

Recently, analysts' cash flow forecasts have become widely available through financial information services. Cash flow information enables practitioners to better understand the real operating performance and financial stability of a company, particularly when earnings information is noisy and of low quality. However, research suggests that analysts' cash flow forecasts are less accurate and more dispersed than earnings forecasts. We thus investigate factors influencing cash flow forecast accuracy and build a practical model to distinguish more accurate from less accurate cash flow forecasters, using past cash flow forecast accuracy and analyst characteristics. We find significant power in our cash flow forecast accuracy prediction models. We also find that analysts develop cash flow‐specific forecasting expertise and knowhow, which are distinct from those that analysts acquire from forecasting earnings. In particular, cash flow‐specific information is more useful in identifying accurate cash flow forecasters than earnings‐specific information.Copyright © 2011 John Wiley & Sons, Ltd.     

Correcting the January optimism effect

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak-form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.