Mixed-frequency approaches to nowcasting GDP: An application to Japan

In this paper, we discuss the approaches to nowcasting Japan’s GDP quarterly growth rates, comparing a variety of mixed frequency approaches including a bridge equation approach, Mixed-Data Sampling (MIDAS) and factor-augmented version of these approaches. In doing so, we examine the usefulness of a novel sparse principal component analysis (SPCA) approach in extracting factors from the dataset. We also discuss the usefulness of forecast combination, considering various ways to combine forecasts from models and surveys. Our findings are summarized as follows. First, some of the mixed frequency models discussed in this paper record out-of-sample performance superior to a naïve constant growth model. Second, albeit small, the SPCA approach of extracting factors improves predictive power compared with traditional principal component approach. Furthermore, we find that there is a gain from combining model forecasts and professional survey forecasts.     

Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.     

残差灰色预测模型在物流需求预测中的应用

为了提高预测精度,在灰色预测模型的基础上建立残差灰色预测模型,对物流需求进行预测,并以实际铁路货运量算例为基础,作分析比较。结果表明,该方法具有预测精度高、理论可靠、计算简单等优点,具有良好的实用性。     

基于灰色预测模型的铁路客运量预测研究

在介绍灰色预测基本原理和算法的基础上,应用灰色预测理论开发了基于MATLAB的铁路客运量预测程序,主要功能是以交互的方式输入数据,动态输出显示模型曲线图形和方程。通过建立株洲站旅客发送量的灰色预测模型,说明利用灰色模型预测铁路客运量具有良好的精度,可以为客流组织提供依据。     

Analyst Firm Coverage and Forecast Accuracy: The Effect of Regulation Fair Disclosure

In this study, we revisit the relationship between analyst firm coverage and forecast accuracy. In contrast to the proposed negative association in Clement (1999) owing to the portfolio complexity effect, we hypothesize an ‘economy‐of‐scale effect’ that is likely to dominate when analysts rely mostly on public information. In support of the latter effect, we find a positive association between firm coverage and forecast accuracy after the enactment of Regulation Fair Disclosure (Reg FD), which substantially reduces the flow of material private information to analysts. Such a result survives a battery of robustness analyses. We further show that, in the post‐Reg FD period, covering more firms increases an analyst's probability of being selected as a star analyst in the subsequent year. Overall, our findings highlight the importance of the information environment in shaping the economic link between an analyst's firm coverage and forecast accuracy.     

On the joint Fourier–ESTAR testing of PPP

The purchasing power parity (PPP) is the hypothesis that the real exchange rate series are stationary. This study briefly reviews and applies six competing unit root test procedures to test PPP. Reflecting the existing literature, the results are mixed. The Kiliç test is the most favourable while the Kapetanios, Shin, and Snell (KSS) test is the least favourable to PPP and the standard ADF test lies in between. The same conclusion applies to the Fourier extensions of those three tests. The results support a recently suggested F-test for the significance of Fourier terms in unit root test equations.     

科技创新人才区域性需求趋势研究

科技创新人才是结构转型和经济社会高质量发展的重要支撑力量。运用灰色系统GM（1,1）模型,对四川、陕西、上海三省市2017-2021年科技创新人才进行需求预测和比较分析。R语言求解结果表明,三地及高技术产业对科技创新人才的需求数量均呈稳步增长态势,但增速存在区域性差异,新的科技创新人才发展区域性非均衡态势将逐渐形成,区域性人才竞争将进一步加剧。加大科技创新人才培育力度,合理规范人才竞争应成为国家层面的人才政策;加快产业转型升级、优化人才生态环境、提高人才与产业匹配度既是优化人才资源配置效率的关键,也是区域性人才管理工作的重点。     

Does foreign investor demand for information affect forecast accuracy? Evidence from the Chinese stock markets

Prior international accounting studies have examined mainly the supply side of analyst research. Specific trading rights and financial reporting systems in China provide a unique opportunity to test both demand and supply factors of analyst information. For shares traded only by less-informed foreign investors, the increased information demand may create incentives for analysts to exert greater efforts than for shares traded by local investors. This study provides evidence that analyst effort (proxied by analyst coverage) and expertise (proxied by consensus cross-analyst forecast variability) affect forecast accuracy more significantly for shares traded by foreign investors than for shares traded by local investors. Additionally, I find that the relation between analyst characteristics and relative forecast accuracy is stronger for shares traded by the foreign investors. The findings are consistent with analysts responding to demands for information by less-informed investors.     

分析师的现金流预测有用吗——基于信息供给的视角

文章考察了证券分析师现金流预测行为对盈余预测质量的影响。研究发现,相对于没有现金流预测的公司,有预测的公司其盈余预测质量更高;现金流预测的次数越多,盈余预测质量越高;以上关系在盈余波动大、经营活动现金流波动大的公司更加明显。这说明我国分析师提供现金流预测信息的确有用,能够帮助分析师改善其盈余预测质量。文章的发现具有重要的实践价值,能够帮助投资者了解分析师现金流预测对盈余预测的影响,从而更有效地利用证券分析师报告进行决策。     

股市对业绩预告修正一视同仁吗？

本文搜集我国A股上市公司2002年至2011年间发布的所有业绩预告修正公告中董事会披露的业绩修正原因说明,首次系统地分析了我国独特的业绩预告制度下的业绩修正原因如何影响股市投资者对业绩修正的判断。研究结果显示,公司解释业绩预告变动原因时带有倾向性,公司对利好消息和利空消息的解释会倾向于不同的原因,而且业绩变动原因对股市投资人理解业绩预告有重大的影响。如果公司将原因归于公司不可控的宏观原因、不可控的会计因素和会计记账错误,股市对利好的业绩修正反应更强烈,也能削弱利空消息对股市的打击程度。如果是其它原因或公司不说明原因,股市的反应会更加糟糕。本文为资本市场投资者了解业绩修正原因说明的经济后果提供了实证证据。