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.     

Data revisions to German national accounts: Are initial releases good nowcasts?

Data revisions to national accounts pose a serious challenge to policy decision making. Well-behaved revisions should be unbiased, small, and unpredictable. This article shows that revisions to German national accounts are biased, large, and predictable. Moreover, with use of filtering techniques designed to process data subject to revisions, the real-time forecasting performance of initial releases can be increased by up to 23%. For total real GDP growth, however, the initial release is an optimal forecast. Yet, given the results for disaggregated variables, the averaging out of biases and inefficiencies at the aggregate GDP level appears to be good luck rather than good forecasting.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

Tests of Normality of Functional Data

The paper is concerned with testing normality in samples of curves and error curves estimated from functional regression models. We propose a general paradigm based on the application of multivariate normality tests to vectors of functional principal components scores. We examine finite sample performance of a number of such tests and select the best performing tests. We apply them to several extensively used functional data sets and determine which can be treated as normal, possibly after a suitable transformation. We also offer practical guidance on software implementations of all tests we study and develop large sample justification for tests based on sample skewness and kurtosis of functional principal component scores.     

Corporates' strategic responses to economic policy uncertainty in China

Using a novel news‐based index of economic policy uncertainty, this paper studies the impact of economic policy uncertainty on corporate strategic positioning and corporate risk in China from 2009 to 2015. The study also investigates the impact of corporate strategic positioning on corporate risk. The results show that corporate strategic positioning and economic policy uncertainty have a significant positive impact on corporate risk. The results also explain that economic policy uncertainty increases the market risk of the firms irrespective of their corporate strategy. However, it increases the business risk of prospector firms and decreases the business risk of defensive firms. The study may help the firms to formulate and improve their strategic positioning while considering economic policy uncertainty. Our results are robust to alternate proxies of economic policy uncertainty and corporate risk.     

新零售视角下贵州农资物流配送模式研究

农业是国民经济的基础,而农业生产资料的支持是农业发展的基础。在“三农”问题愈发重要的今天,农业生产资料的配送问题也随之显得十分重要。贵州省农村物流发展缓慢,传统的农资配送过程中存在着配送环节过多、配送成本过高、企业获得利润过少的问题,同时也造成了农民生产成本过高、收入低的现象。文章在分析了贵州省农资物流供应链发展现状、存在问题以及问题产生的原因基础之上,提出了“新零售”视角下建立贵州农资配送新模式。     

大数据下“互联网+冰雪旅游”产业融合发展对策

十九大报告指出:"要推动互联网、大数据、人工智能和实体经济深度融合"。首先,分析大数据对冰雪旅游产业发展的作用;调查"互联网+冰雪旅游"产业融合现状,分析出现的问题;提出大数据背景下"互联网+冰雪旅游"产业融合发展对策是构建冰雪旅游大数据分析中心,实现精准营销;强化政府主导和支持力度,均衡市场发展;培养复合型旅游专业人才,提升创新能力;创新市场运营模式,增强融合动力。     

云计算中的轻量级查询结果验证机制

存储在云计算服务器上的数据可能被篡改或删除，查询完整性验证的作用是确保查询用户能够验证查询结果中的数据是真实的，且包含所有满足条件的数据。提出了一种基于签名链结构的查询完整性验证机制。方案使用代数签名机制替代数字签名实现校验值的计算，从而有效降低计算和查询验证开销。此外，通过维护一种新的、简单的索引结构实现了仅需下载少量数据即可实现校验值的更新。实验结果表明，所提方案比基于数字签名的签名链方案具有更小的更新开销和更高的验证效率。     

结合新型模糊支持向量机和证据理论的多传感器水质数据融合

在多传感器水质数据融合领域，证据理论是有效的数据融合方法之一，但基本概率分配一般不易确定，从而使数据融合能力难以有效发挥。支持向量机是统计学习理论之上的高级分类算法，具有普适性和全局优化等特点，但输出的基本概率分配有待进一步提高。提出了一种基于证据理论和新型模糊支持向量机相结合的数据融合方法，通过建立基于分类超平面距离的模糊隶属度，训练模糊支持向量机提高传统支持向量机的基本概率分配，并结合证据理论进行海河水质数据融合。通过证据理论分别结合支持向量机和模糊综合评价法与上述方法进行对比实验，经精度、平均绝对百分误差、均方根误差等指标验证，精度提高10.5%，表明所提方法是一种可靠的多传感器的水质融合方法，较其他方法具有更高的融合精度。