The long-run reversal in the long run: Insights from two centuries of international equity returns

We perform the most comprehensive test of long-term reversal in national equity indices ever done. Having examined data from 71 countries for the years 1830 through 2019, we demonstrate a strong reversal pattern: the past long-term return negatively predicts future performance. The phenomenon is not subsumed by other established cross-sectional return patterns, including the value effect. The long-term reversal is robust to many considerations but highly unstable through time. Finally, our findings support the overreaction explanation of this anomaly.     

大数据对法院司法统计的积极功效及边界

最高人民法院于2016年应用人民法院大数据管理和服务平台以后,全国法院司法统计完全实现计算机自动采集。从发展脉络来看法院司法统计正处于智能化转型阶段,仍然存在一些问题。随着大数据及新技术的引入,司法统计“大数据”得以成为现实,包括了数据采集的全面化、统计分析的综合化、统计平台的社会化,进而提出了大数据助力司法统计精准化的具体路径,包括了提高基础数据的量与质、加强对司法统计数据的深度分析和可视化分析、推进基础设施建设。然而,二者并不具有正相关性。因此,推进司法统计大数据化需要注意有两个限度。     

基于西门子PLC除尘器粉尘防爆危险源信号检测及预警系统的研究

研发适用于滤袋式除尘器粉尘爆炸危险源信号的实时检测及预警系统。该系统综合应用工业控制、PLC控制、智能测控终端无线传输、云平台数据管理等技术。采用传感器分别监测除尘器运行时的进出风口压差、除尘器运行状态、高压脉冲喷吹压力和箱体温度。当监测信号不在系统预设的合理范围时,PLC输出就地预警信号,监测信号也可通过无线传输模块上传到云平台。运用云平台的数据分析功能,实现除尘器运行状态系统化监测和综合管理。     

大数据与监视型资本主义

本文首先介绍了监视型资本主义的概念,描述信息时代的资本是如何通过收集个人数据,无视私隐,筛选用户,在不受法律监控的情况下进行帝国式扩展的。进而,本文提出数据的附身性和数码资本错位这两个特征,用以分析技术如何在数据收集和应用上激活数据的私人财产属性,以及资本如何利用价值错位和技术优势去诱使用户参与这一资本增值的进程。数据的附身性揭示了个体如何被卷入大数据时代的生产关系,数码资本错位揭示了资本具体实现监视型资本主义的技术。这两个概念将完善监视型资本主义这一理论体系,进一步展现这个时代隐蔽的不平等状况。     

Neighbors and friends: How do European political parties respond to globalization?

This paper explores the relationship between globalization and party positions accounting for potential differences between left- and right-wing parties. The analysis is based on a panel model of 36 political parties in 18 Western European countries between 1970 and 2015. We find that right-wing parties move leftward in response to globalization, while left-wing parties do not alter their position. Additionally, we find that ideological party positions are affected by foreign parties’ positions of the same ideological bloc. These findings indicate that there is a convergence towards the left of the political spectrum due to right-wing party moderation.     

On the measurement of investment types: Heterogeneity in corporate tax elasticities

This paper contributes to debates about the appropriate characterisation of heterogeneous investment types and to what extent different investment motives affect the responsiveness to corporate taxation. In particular, we employ and refine a methodology to better evaluate the tax elasticity of investment types. Using a combination of both firm‐specific information and sector‐specific information from input–output tables, we discuss how to classify investment as non‐related, horizontal, vertical and complex types. First, we point out to what extent the resulting classification depends on assumptions made by the researcher. Second, we employ an ample set of classifications and find that non‐related investment reacts stronger to corporate taxation, whereas horizontal investment is less responsive, though, significant negative tax semi‐elasticities turn out for the subset of manufacturing industries. To address inherent characteristics of vertical and complex investment, we extend the methodology and find that, by and large, stronger business motives reduce the tax responsiveness of investment to a larger extent. Given the current debates about substantial corporate tax reforms, it is all the more important to recognise that corporate tax effects can vary fundamentally between countries, driven by country‐specific differences in their composition of industries and investment types.     

Are firms ready to use big data analytics to create value? The role of structural and psychological readiness

In this study, we leverage Information Technology (IT) readiness literature and resource-based view (RBV) to investigate the impact of firm structural and psychological readiness on firm value creation, as mediated by big data analytics usage. The proposed research model is empirically validated using survey data from 179 senior IT managers. The findings demonstrate the importance of both structural (i.e. IT infrastructure capability, tools functionality, employee analytical capability, and bigness of data) and psychological readiness (i.e. IT proactive climate) in enhancing firm value creation through big data analytics usage. These results provide interesting theoretical and practical insights.     

The development trinity: How regional integration impacts growth,inequality and poverty

Developing countries today have become more active participants in regional trade agreements. This raises questions about how the benefits of integration are distributed, and the extent to which lower‐income countries are able to capture development gains. Historically, such impacts have been difficult to identify with precision. This paper seeks to address this gap by empirically analysing the impact of regional integration on development, particularly the effects on growth and welfare. Using both bilateral and regional integration measures, we show that the ability to capture gains from integration varies across developing country regional groups, with developing Asia benefiting on par with developed countries. The findings in the paper indicate that trade and trade policy play an important role in reducing inequality and poverty in developing countries. It also shows that regionalism can function as a channel to make multilateralism a more adept way of addressing national challenges.     

Defining Supply Chain Management: In the Past,Present, and Future

The article titled “Defining Supply Chain Management” published in 2001 in the Journal of Business Logistics has been cited over 4,900 times in the last 17 years. In this paper, we first provide a historical review of how the article originated and the contributions the article made to both the theory and practice of supply chain management (SCM). Next, we highlight the key market and technological changes that have emerged in SCM followed by how the theory proposed in the 2001 article can still be relevant to support SCM research and practice going forward. We also propose ways of configuring a supply chain and partnering across companies to serve customers in an optimal way. We conclude with a call for research on developing new frameworks to better describe, explain, predict, and shed light on the evolving nature of SCM.     

M5 competition uncertainty: Overdispersion,distributional forecasting,GAMLSS, and beyond

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data face strong overdispersion and sporadic demand, especially zero demand. We discuss modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fail to address the data characteristics, due to the considered objective functions. Distributional forecasting provides a suitable modeling approach to overcome those problems. The GAMLSS framework allows for flexible probabilistic forecasting using low-dimensional distributions. We illustrate how the GAMLSS approach can be applied to M5 competition data by modeling the location and scale parameters of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawbacks, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.