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.     

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.     

管理层盈余预测与不对称及时性实证研究

为了分析管理层盈余预测对盈余不对称及时性之间的关系,即发布消息的时间点和内容分类对盈余不对称及时性的影响,用Basu模型分段实证检验盈余预测对盈余不对称及时性的影响、正(负)向盈余预测与不对称及时性的关系,以及当期发布的盈余预警对盈余不对称及时性的影响。结果表明,不对称及时性对公司发布的未来盈余的影响不显著,公司发布预期盈余的时间及时性主要集中在负向盈余意外,这与价格引导盈余引起更多的未来盈余预测的向下有偏的不对称及时性系数相一致,当期发布盈余预警会降低盈余与收益的不对称及时性。管理层发布内部盈余预测,有助于缓解与外部信息使用者之间的信息不对称,满足利益相关者的决策需求,进而有助于促进证券市场向半强式有效市场转化。     

The Economic Relevance of Environmental Disclosure and its Impact on Corporate Legitimacy: An Empirical Investigation

In determining its environmental disclosure strategy, a firm's management faces a tension between responding to the information needs of financial markets and maintaining its legitimacy within the community. In this paper, relying on information economics and legitimacy theory, we explore how firms resolve this tension. Results show that a firm's environmental disclosure enhances the quality of analysts' information context, which ultimately allows them to make better forecasts. Moreover, financial analysts seem to be able to decipher environmental information, discounting discourses that are inconsistent with a firm's underlying environmental performance. We find also that a firm's environmental disclosure serves another purpose, as it influences how its other stakeholders (beyond financial ones) perceive its legitimacy. Such enhanced legitimacy reduces the information uncertainty faced by financial analysts. Our results suggest also that both economic‐based environmental disclosure and sustainable development and environmental disclosure are useful to analysts in making their forecasts and enhance a firm's legitimacy. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.     

Forecasting and forecast narratives: The Bank of England Inflation Reports

We analyze the narratives that accompany the numerical forecasts in the Bank of England’s Quarterly Inflation Reports, 1997–2018. We focus on whether the narratives contain useful information about the future course of key macro variables over and above the point predictions, in terms of whether the narratives can be used to enhance the accuracy of the numerical forecasts. We also consider whether the narratives are able to predict future changes in the numerical forecasts. We find that a measure of sentiment derived from the narratives can predict the errors in the numerical forecasts of output growth, but not of inflation. We find no evidence that past changes in sentiment predict subsequent changes in the point forecasts of output growth or of inflation, but do find that the adjustments to the numerical output growth forecasts have a systematic element.     

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.     

非洲铁路发展需求分析与市场开发策略

中资企业面向非洲铁路市场,多以"广覆盖"的形式投入开发,一定程度上造成了资源浪费。为优化中资企业在非洲铁路市场布局以及资源配置,在分析非洲铁路发展现状、铁路网发展影响因素基础上,基于铁路系统需求度、铁路发展需求度分析非洲铁路发展需求,提出了非洲大陆49个国家高、较高、中、低4个等级的铁路发展需求度,同时提出非洲铁路市场开发策略建议,即:优化非洲铁路市场开发资源配置,分区域实施铁路市场开发及项目建设,创新合作模式、拓宽资金来源。     

Oil price shocks and economic growth: The volatility link

This paper shows that oil shocks impact economic growth primarily through the conditional variance of growth. Our comparison of models focuses on density forecasts. Over a range of dynamic models, oil shock measures and data, we find a robust link between oil shocks and the volatility of economic growth. We then develop a new measure of oil shocks and show that it is superior to existing measures; it indicates that the conditional variance of growth increases in response to an indicator of the local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of the growth volatility to oil price changes. The uncertainty about future growth is considerably lower than with a benchmark AR(1) model when no oil shocks are present.     

Are betting returns a useful measure of accuracy in (sports) forecasting?

In an economic context, forecasting models are judged in terms not only of accuracy, but also of profitability. The present paper analyses the counterintuitive relationship between accuracy and profitability in probabilistic (sports) forecasts in relation to betting markets. By making use of theoretical considerations, a simulation model, and real-world datasets from three different sports, we demonstrate the possibility of systematically or randomly generating positive betting returns in the absence of a superior model accuracy. The results have methodological implications for sports forecasting and other domains related to betting markets. Betting returns should not be treated as a valid measure of model accuracy, even though they can be regarded as an adequate measure of profitability. Hence, an improved predictive performance might be achieved by carefully considering the roles of both accuracy and profitability when designing models, or, more specifically, when assessing the in-sample fit of data and evaluating out-of-sample forecasting performances.     

Linking words in economic discourse: Implications for macroeconomic forecasts

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.