Does IFRS Adoption Increase the Accuracy of Chinese Analysts’ Forecasts?

This article investigates the impact of International Financial Reporting Standards (IFRS) adoption on the accuracy of Chinese analysts’ earnings forecasts. We find that after IFRS adoption, the accuracy of Chinese analysts’ forecasts decreases rather than increasing as they do in developed countries documented by the extant literature. Further investigation finds that this decrease is associated with a fair value measurement of financial assets held for trading. Our finding provides empirical evidence supporting the argument that the effectiveness of IFRS adoption could be negative in a developing country depending on its setting and fair value measurement brought about by IFRS could contribute to the negative effect in this setting.     

Experts,firms, consumers or even hard data? Forecasting employment in Germany

In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.     

Combining multiple probability predictions using a simple logit model

This paper begins by presenting a simple model of the way in which experts estimate probabilities. The model is then used to construct a likelihood-based aggregation formula for combining multiple probability forecasts. The resulting aggregator has a simple analytical form that depends on a single, easily-interpretable parameter. This makes it computationally simple, attractive for further development, and robust against overfitting. Based on a large-scale dataset in which over 1300 experts tried to predict 69 geopolitical events, our aggregator is found to be superior to several widely-used aggregation algorithms.     

Information and Investor Behavior Surrounding Earnings Announcements

The goal of this paper is to analyze the impact of annual earnings announcements on the market through the order flow data in addition to the usual transaction data. In this respect, examining order flow data can potentially reveal valuable information that is not available from transaction data. In fact, the data allow us to test hypotheses about asymmetric information and investor behavior and to test if the behavior varies with investor sophistication. In addition, the paper tries to identify the determinants of the impact on a firm's value using assumptions about investor behavior.     

Informed trading around earnings and mutual fund alphas

We examine whether informed trading around earnings announcements drives mutual fund performance. The measure is motivated by prior studies arguing that a mutual fund is skilled if it buys stocks with subsequent high earnings announcement returns. We find that this measure predicts future mutual fund returns. On average, after adjusting for Carhart’s four risk factors, the top decile of mutual funds outperforms the bottom decile by 44 basis points per quarter. By decomposing fund alphas into two components in their relations to earnings, we find that this measure is only associated with earnings-related fund alphas. This measure can also be used to predict stock returns at future earnings announcements.     

Five dimensions of the uncertainty–disagreement linkage

We analyze the relationship between forecaster disagreement and macroeconomic uncertainty in the Euro area using data from the European Central Bank’s Survey of Professional Forecasters for the period 1999Q1–2018Q4 and find that disagreement is generally a poor proxy for uncertainty. However, the strength of this link varies with the dispersion statistic employed, the choice of either the point forecasts or the histogram means for calculating disagreement, the outcome variable considered and the forecast horizon. In contrast, distributional assumptions do not appear to be very influential. The relationship is weaker in subsamples before and after the outbreak of the Great Recession. Accounting for the forecasters’ entry to and exit from the survey has little impact on the results. We also show that survey-based uncertainty is associated with overall policy uncertainty, whereas forecaster disagreement is related more closely to the expected fluctuations on financial markets.     

Evaluating quantile forecasts in the M5 uncertainty competition

Probabilistic forecasts are necessary for robust decisions in the face of uncertainty. The M5 Uncertainty competition required participating teams to forecast nine quantiles for unit sales of various products at various aggregation levels and for different time horizons. This paper evaluates the forecasting performance of the quantile forecasts at different aggregation levels and at different quantile levels. We contrast this with some theoretical predictions, and discuss potential implications and promising future research directions for the practice of probabilistic forecasting.     

Does Social Media Get Your Attention?

The author investigates how social media affects stock prices and post–earnings announcement drift in response to companies’ quarterly earnings announcements. Using quarterly earnings data as well Twitter and StockTwits data, the author utilizes Twitter volume and a residual methodology to generate an attention proxy that is orthogonal to the growth of Twitter accounts. The author finds that the new attention brought by social media after the earnings announcements positively affects the cumulative abnormal returns. Further, even companies reporting bad news can still have positive immediate cumulative abnormal returns if they attract enough attention from investors after an earnings announcement. The new attention effects are different in both magnitudes and statistical significance between social media popular and unpopular industries.     

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.