Asymmetric information consolidation and price discovery: Inferring bad news from insider sales

Due to the paucity of sources of negative firm‐specific information, US capital markets have more difficulty identifying and incorporating bad news into stock prices than they do good news. Even though insider selling is a potentially important proxy for undisclosed bad news, researchers have difficulty ex ante identifying information‐based sales due to an inability to separate liquidity‐motivated from information‐based insider trades. We hypothesize that when insiders in multiple firms sell shares of one firm in which they are insiders and at the same time buy shares of other insider portfolio firms, the sale is more likely to be information‐based, since the proceeds are reinvested. Conversely, when an insider sells one firm without purchasing others or sells multiple insider firms the sale is likely liquidity‐motivated. We find that insider sales identified as information‐based using this algorithm are followed by significant negative abnormal returns. Information‐based sales are also more likely to be associated with delistings, earnings declines and earnings restatements. Analysts are also more likely to revise their earnings forecasts downwards for these firms. It is thus possible to ex ante identify insider sales with information content. Our results will be of interest to investors and also to regulators designing insider trading rules.     

Muddling through cybersecurity: Insights from the U.S. healthcare industry

The U.S. healthcare sector is inadequately prepared to deal with the reality of cyber threats. The increasing use of smart medical equipment and mobile devices is making healthcare organizations more susceptible to ransomware and other types of malware. The size and complexity of operations, coupled with the presence of numerous legacy and incompatible systems, make it difficult to implement effective cybersecurity measures. The daunting nature of the problem often results in an if-it-ain’t-broke-don’t-fix-it stance among senior healthcare leaders. The preponderance of healthcare-related laws, compliance regulations, and security guidance frameworks serve to complicate the cybersecurity challenge further and too often results in senior leadership assuming a state of blissful ignorance. This study sheds light on the key factors contributing to the chaotic state of affairs and presents a roadmap to a more deliberate and proactive approach to cybersecurity risk management.     

Resource Dependence and Network Relations: A Test of Venture Capital Investment Termination in China

This study examines how venture capital (VC) firms terminate investments in an emerging economy context. We contend that due to the weak institutional environment, it is appropriate to draw on insights from power and social relation perspectives for a better understanding of the phenomenon. Specifically, we argue that a termination decision hinges on not only the dependence relationship between a VC firm and its portfolio companies, but also the social relationships among VC firms. Event history analyses of approximately 12,000 VC deals made in China between 2001 and 2012 reveal that when a VC firm has a greater number of investments in an industry, it is more likely to terminate investments on a portfolio company in that industry. Moreover, such effect on termination is moderated by the focal VC's embeddedness with its syndicate partners and collaboration opportunities with other VC firms outside the immediate access of the syndicate partners. Our study sheds light on research on VC decision making in emerging markets by integrating insights from resource dependence relationships and interorganizational network characteristics.     

Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years

We commemorate the 50th anniversary of Ball and Brown [1968] by chronicling its impact on capital market research in accounting. We trace the evolution of various research paths that post–Ball and Brown [1968] researchers took as they sought to build on the foundation laid by Ball and Brown [1968] to create a body of research on the usefulness, timeliness, and other properties of accounting numbers. We discuss how those paths often link back to the groundwork laid and questions originally posed in Ball and Brown [1968].     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

Predicting the equity market with option-implied variables

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.     

基于冠层光谱和BP神经网络的水稻叶片氮素浓度估算模型

[目的]快速、准确地诊断水稻叶片氮素营养状况,为水稻氮肥精准管理提供依据。[方法]以江西省农科院8种不同施肥处理的晚稻为研究对象,于主要生育期同步测定了水稻冠层光谱反射率及叶片全氮浓度(Leaf Nitrogen Concentration,LNC),系统分析了原始光谱反射率、一阶微分光谱、"三边"参数以及由350～1 350nm两两波段组合的差值(SD (Rλ1,Rλ2))、比值(SR (Rλ1,Rλ2))及归一化(ND(Rλ1,Rλ2))光谱指数与水稻LNC的相关关系,筛选出敏感参数,并以之为自变量构建了水稻LNC的传统预测模型,另外构建不同指标个数的多元线性与BP神经网络模型,并对模型进行验证。[结果](1)水稻LNC与一阶微分光谱在751nm处的相关性最高(r=0. 822);(2)"三边"参数中的红边面积SDr与LNC的相关性较高(r=0. 687);(3) 750nm附近的红边波段与近红外波段差值组合、550nm附近的绿光波段与近红外波段的比值及归一化差值组合与水稻LNC的相关性较高,以SD (R752,R751)、SR (R534,R1 350)和ND (R534,R1 349)表现最好,相关系数分别为0. 827、-0. 790和0. 788;(4)传统回归模型中以SD(R752,R751)构建的一元线性模型最佳(RC2=0. 665、RV2=0. 750、RMSEV=0. 4%、RPD=2. 034);(5)利用5个指标((R'751、SDr、SD (R752,R751)、SR (R534,R1 350)、ND (R534,R1 349))经逐步回归筛选出的2个指标SD ((R752,R751)和SR (R534,R1 350))构建预测水稻氮素的BP神经网络模型,预测效果最佳,其验证参数值分别为R2=0. 859、RMSEV=0. 302%和RPD=2. 669。[结论]基于单指标构建的传统线性模型计算过程简单但精度略低,而基于2个指标(SD (R752,R751)、SR (R534,R1 350))构建的BP神经网络模型预测精度高于该2指标构建的多元线性模型,表明在指标适合的情况下,BP神经网络对氮素具有较好的预测能力,是一种快速准确估算水稻叶片全氮浓度的方法。     

黑龙江旅游网络关注度时空演变特征研究

探究黑龙江旅游网络关注度时空演变特征对黑龙江旅游业良性发展具有重要意义.利用百度指数获取2011—2018年全国和分地区黑龙江旅游网络关注度逐日搜索指数数据.从时间分布、空间分布、时空集聚3个方面研究其演变特征.研究表明:2011—2018年全国黑龙江旅游网络关注度年际变化总体呈现上升趋势,月际变化呈"W"形;2011—2018年31个省区市黑龙江旅游网络关注度空间差异较大、季节性影响显著;东部地区黑龙江旅游网络关注度偏高,西部地区黑龙江旅游网络关注度的季节差异最明显;东部地区内的黑龙江旅游网络关注度差异性最小,西部地区内差异性最大;黑龙江旅游网络关注度具有正空间自相关,部分东部、中部地区表现为高高集聚,多数西部地区表现为低低集聚.     

天地一体化信息网络中天基卫星网络架构设计

为了充分利用各种空间设施资源，提升天基信息网络覆盖与传输能力，分别从天基信息网络体系架构和逻辑功能架构角度进行了系统性设计。与以往天基网络的设计不同，该设计更注重各类卫星网络的深度融合应用，为此将网络虚拟化技术引入天基卫星网络。同时，还提出了解决新架构下卫星组网难题的方法，将电磁涡旋、卫星灵活载荷和边缘计算技术设计到天基网络。仿真表明，所提出的架构能有效提升频谱利用率和减少卫星网络信令开销。     

Sharing versus collaborative economy: how to align ICT developments and the SDGs in tourism?

Great hopes have been placed in the sharing economy to provide a new business model based on peer-to-peer (P2P) exchanges of underutilized assets. As a model, the sharing economy has been expected to make significant contributions to sustainability, providing new opportunities for entrepreneurship, more sustainable use of resources, and consumer co-operation in tight economic networks. However, in recent years, digital platforms have turned into the most important actors in the global sharing economy, turning global corporations, such as AirBnB, Booking, or TripAdvisor into intermediaries controlling and profiting from most transactions. Focused on accommodation, this paper conceptualizes the sharing economy in comparison to the wider collaborative economy, and discusses its social, economic, environmental, and political impacts in comparison to the sustainable development goals. It concludes that the sharing economy has great potential to make very significant contributions to sustainability, though the model is increasingly being replaced by the collaborative economy, which performs as an extension and acceleration of neoliberal economic practices.