ESG Controversies,ESG Disclosure and Analyst Forecast Accuracy

We investigate whether an environmental social governance (ESG) disclosure moderates the relation between ESG controversies and analyst forecast accuracy. The previous literature has shown that ESG controversies increase uncertainty about a firm's future prospects, while ESG disclosure decreases this uncertainty. We therefore take the next step and integrate ESG controversies, ESG disclosure and uncertainty into one model. Our study is based on 8,369 firm-year observations across 51 countries from 2008 to 2017, containing data from RepRisk, Bloomberg and the Institutional Brokers' Estimate System. We find that analyst forecast errors are generally higher for firms with higher exposure to ESG controversies. More importantly, we establish ESG disclosure as a moderator that mitigates the strength of the relation between ESG controversies and analyst forecast errors. Additionally, we identify that the most important pillar for the relation derives from social controversies and disclosure.     

Forecasting crude oil futures market returns: A principal component analysis combination approach

To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.     

Evaluation of the best M4 competition methods for small area population forecasting

The ‘M4’ forecasting competition results were featured recently in a special issue of the International Journal of Forecasting and included projections for demographic time series. We sought to investigate whether the best M4 methods could improve the accuracy of small area population forecasts, which generally suffer from much higher forecast errors than regions with larger populations. The aim of this study was to apply the top ten M4 forecasting methods to produce 5- and 10-year forecasts of small area total populations using historical datasets from Australia and New Zealand. Forecasts were compared against the actual population numbers and forecasts from two simple benchmark models. The M4 methods were found to perform relatively well compared to our benchmarks. In the light of these findings, we discuss possible future directions for small area population forecasting research.     

Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models

Many static and dynamic models exist to forecast Value-at-Risk and other quantile-related metrics used in financial risk management. Industry practice favours simpler, static models such as historical simulation or its variants. Most academic research focuses on dynamic models in the GARCH family. While numerous studies examine the accuracy of multivariate models for forecasting risk metrics, there is little research on accurately predicting the entire multivariate distribution. However, this is an essential element of asset pricing or portfolio optimization problems having non-analytic solutions. We approach this highly complex problem using various proper multivariate scoring rules to evaluate forecasts of eight-dimensional multivariate distributions: exchange rates, interest rates and commodity futures. This way, we test the performance of static models, namely, empirical distribution functions and a new factor-quantile model with commonly used dynamic models in the asymmetric multivariate GARCH class.     

Identifying the true nature of price discovery and cross-market informational flow in the investment grade CDS and equity markets

We examine the comparative efficiency of systematic investment grade credit default swap (CDS) and equity markets using a time-varying coefficient vector autoregression. This modeling framework enables a view of cross-market informational flow along each point in the time-period under investigation by taking into account parameter instability. We obtain smoothing estimates of parameters capturing such flow between CDS and equity markets using daily data from 2004 to 2019, and measure the strength of flow via relative predictive gains. In contrast to prior studies, we find a two-way interactive effect in which certain types of information are captured more efficiently in prices by each market. We also find that the time-varying coefficient vector autoregression results in superior forecasting gains relative to models not accounting for price discovery. These results have implications for systematic investors, arbitrageurs and stakeholders who monitor systematic markets for their informational content.     

人工智能技术对就业的影响及趋势预测:基于北京市工业机器人安装量的数据

本文基于德国国际机器人联盟(IFR)统计的中国工业机器人安装量数据,利用回归模型分析检验了人工智能(AI)技术对北京市就业总量、从业人员工资水平、从业人员素质结构的影响。结果显示,北京市机器人安装量对于北京市的就业总量具有替代效应,对于从业人员的工资收入具有补偿效应,对于从业人员的素质提升则具有推动效应,而北京市三次产业增加值的增长会调节机器人安装量对就业总量的影响。运用灰色预测模型法,文章有效预测了2019-2030年北京市机器人安装量变化所引起的就业变化,并提出了相应的政策建议。     

Why is the forecast error of quarterly GDP in Japan so large? – From an international comparison of quarterly GDP forecast situation

We examined the accuracy of prediction of Canada, Japan, United Kingdom, and United States from the viewpoint of forecast errors. Compared with the forecast error of each country at the around same time, the forecast error of Japan is about 2 times larger. In case of Japan, even immediately before release of quarterly GDP, the forecast error is over 1 %, which is the same level of forecast error as 94 days before in the United States and 135 days before in Canada.Evaluating the characteristics of forecast errors, it can be pointed out that Japan's forecasts are as efficient as those of other countries, and the addition of major economic statistics is unlikely to improve forecast errors. The reason for Japan's large forecast errors is the fluctuations in the GDP growth rate. These results provide evidence that volatile GDP may make the outlook worse.Large fluctuations in Japan's quarterly GDP have already been pointed out. It is necessary to examine the factors behind the large fluctuations in the rate of change in Japan's quarterly GDP.