Conditional Mandates on Management Earnings Forecasts: The Impact on the Cost of Debt

Exploiting a unique conditional disclosure mandate on management earnings forecasts (MEFs) in China, we examine the differential effects of voluntary and mandatory MEFs on the cost of debt. We find that firms providing voluntary MEFs have lower cost of debt than do mandatory forecasters and nonforecasters. The results of the channel analyses reveal that voluntary forecasters have greater commitment to voluntary MEFs in future periods than do mandatory forecasters and nonforecasters, and the precision, accuracy, and timeliness of MEFs are higher for voluntary forecasters than for mandatory forecasters. Additional analyses show that the differential effects of voluntary and mandatory MEFs on cost of debt are stronger for voluntary forecasters operating in opaque information environments, issuing high-quality and confirming forecasts, controlled by private shareholders, and operating in highly competitive product markets. Overall, our results indicate that, compared with mandatory MEFs, voluntary MEFs are more informative for credit investors, particularly for firms facing greater information risk and operating uncertainty.     

Forecast-error-based estimation of forecast uncertainty when the horizon is increased

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.     

How (Over) Confident Are Financial Analysts?

Extensive research has been devoted to the quality of analysts' earnings forecasts. The common finding is that analysts' forecasts are not very accurate. Prior studies have tended to focus on the mean of forecasts and measure accuracy using various summaries of forecast errors. The present study sheds new light on the accuracy of analysts' forecasts, by measuring how well calibrated these forecasts are. The authors follow the tradition of calibration studies in psychological literature and measure the degree of calibration by the hit rate. They analyze a year's worth of data from the Institutional Brokers Estimate System database, which includes over 200,000 annual earnings forecasts made by over 6,000 analysts for over 5,000 companies. By using different ways to convert analysts' point estimates of earnings into a range of values, the authors establish the bounds that are necessary to determine the hit rates, and examine to what extent the actual earnings announced by the companies are bracketed by these intervals. These hit rates provide a more complete picture of the accuracy of the forecasts.     

Analyst earnings forecast precision and local advantage: evidence from American depositary receipt firms

Prior research has documented a finding that local analysts provide more accurate earnings forecasts than nonlocal analysts in many settings. However, little is known about local and nonlocal analysts’ relative earnings forecast abilities for ADR stocks. In this study, we find that the local advantage disappears for ADR stocks and that nonlocal analysts in fact outperform local ones in this case. We investigate the source of this ‘local disadvantage’ and find evidence against hypotheses based on accounting standards and exchange rates. We document that the local advantage decreases with increased investor interest in foreign firms, consistent with our nonlocal investors’ interest hypothesis.     

DSGE forecasts of the lost recovery

The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.     

How efficient is the market for Australian firms’ earnings information?

We construct a measure of the speed with which forecasts issued by sell-side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price-based measures of information efficiency. We document large variation in EIFT across firm-years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings-related information is recognised. We also find that EIFT is higher for firm-years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non-GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non-GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.     

CEO internal experience and voluntary disclosure quality: Evidence from management forecasts

Internally‐promoted CEOs should have a deep understanding of their firm's products, supply chain, operations, business climate, corporate culture, and how to navigate among employees to get the information they need. Thus, we argue that internally‐promoted CEOs are likely to produce higher quality disclosure than outsider CEOs. Using a sample of US firms from the S&P1500 index from 2001 to 2011, we hand‐collect whether a CEO is hired from inside the firm and, if so, the number of years they worked at the firm before becoming CEO. We then examine whether managers with more internal experience issue higher quality disclosures and offer three main findings. First, CEOs with more internal experience are more likely to issue voluntary earnings forecasts than those managers with less internal experience as well as those managers hired from outside the firm. Second, CEOs with more internal experience issue more accurate earnings forecasts than those managers with less internal experience as well as those managers hired from outside the firm. Finally, investors react more strongly to forecasts issued by insider CEOs than to those issued by outsider CEOs. In additional analysis, we find no evidence that these results extend to mandatory reporting quality (i.e., accruals quality, restatements, or internal control weaknesses), perhaps because mandatory disclosure is subjected to heavy oversight by the board of directors, auditors, and regulators. Overall, our findings suggest that when managers have work experience with the firm prior to becoming the CEO, the firm's voluntary disclosure is of higher quality.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Political forecast cycles

A moral hazard model is used to show why overly optimistic revenue forecasts prior to elections can be optimal: Opportunistic governments can increase spending and appear more competent; ex post deficits emerge in election years, thereby producing political forecast cycles – as also found for US states in the empirical literature. Additionally, we obtain three theoretical results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up.     

Combining forecasts for universally optimal performance

There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter.