Learning to let go: Social influence,learning, and the abandonment of corporate venture capital practices

Research summary: This study examines the abandonment of organizational practices. We argue that firm choices in implementing practices affect how firms experience a practice and their subsequent likelihood of abandonment. We focus on utilization of the practice and staffing (i.e. career backgrounds of managers), as two important implementation choices that firms make. The findings demonstrate that practice utilization and staffing choices not only affect abandonment likelihood directly but also condition firms' susceptibility to pressures to abandon when social referents do. Our study contributes to diffusion research by examining practice abandonment—a relatively unexplored area in diffusion research—and by incorporating specific aspects of firms' post‐adoption choices into diffusion theory. Managerial summary: When do firms shut down practices? Prior research has shown that firms learn from the actions of other firms, both adopting and abandoning practices when their peers do. But unlike adoption decisions, abandonment decisions need to account for firms' own experiences with the practice. We study the abandonment of corporate venture capital (CVC) practices in the U.S. IT industry, which has experienced waves of adoption and abandonment. We find that firms that make more CVC investments are less likely to abandon the practice, and are less likely to learn vicariously from other firms' abandonment decisions, such that they are less likely to exit CVC when other firms do. Staffing choices also matter: hiring former venture capitalists makes firms less likely to abandon CVC practices, while hiring internally makes abandonment more likely. Plus, staffing choices affect how firms learn from the environment, as CVC managers pay attention to and learn more from the actions of firms that match their work backgrounds; i.e., firms that staff CVC units with former venture capitalists are more likely to follow exit decisions of VC firms, while those that staff with internal hires are more likely to follow their industry peers. Our results suggest that firms wanting to retain CVC practices should think carefully about the implementation choices they make, as they may be inadvertently sowing seeds of abandonment. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning by hiring and change to organizational knowledge: Countering obsolescence as organizations age

Research summary: This paper investigates the relationship between hiring and the ability of organizations to evolve their capabilities as they age. While prior research establishes that organizations become rigid to change as they age, it underemphasizes measures that they may take to renew their adaptive potential. I address this gap by investigating whether hiring stimulates change to the knowledge organizations possess. Learning by hiring, I argue, helps organizations to evolve their knowledge as they age by disrupting routine, introducing distant knowledge, and facilitating socialization. I test the effectiveness of these mechanisms using 38 years (1970–2007) of data from the U.S. biotechnology industry, and find that hiring stimulates more change as organizations age, enabling them to renew their knowledge and counter the effects of obsolescence. Managerial summary: As organizations age, they become less responsive to the needs of their environment, resulting in a trend for them to become technologically obsolete. Little is known as to how they may reverse this trend and counter obsolescence. I provide evidence that hiring may be used to stimulate change to organizational knowledge and capabilities as they age by disrupting routine activity, introducing new‐to‐the‐firm knowledge, and inducing incumbent members to learn. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning and the Yield Curve

Two central implications of Expectations Hypothesis under rational expectations are inconsistent with yield curve data: (i) future expected long yields fall, instead of rising, when yield spread rises; (ii) long yields are excessively volatile relative to short yields. I propose an optimization framework in which boundedly rational agents use adaptive learning to form expectations. The belief structure rationalizes pattern of yields observed in the data so that the first puzzle does not arise with subjective expectations: intertemporal income and substitution effects are amplified relative to rational expectations. The second puzzle is partly accounted for by extra volatility due to parameter uncertainty.     

Modeling catastrophic deaths using EVT with a microsimulation approach to reinsurance pricing

Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in Ekheden & Hössjer (2014). We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model comparisons. A case study examining accidental deaths in the Finnish population is provided. We further extend the applicability of the catastrophe risk model by considering small and big accidents separately; the resulting combined MPP model can flexibly capture the whole range of accidental death counts. Using the proposed model, we present a simulation framework for pricing (life) catastrophe reinsurance, based on modeling the underlying policies at individual contract level. The accidents are first simulated at population level, and their effect on a specific insurance company is then determined by explicitly simulating the resulting insured deaths. The proposed microsimulation approach can potentially lead to more accurate results than the traditional methods, and to a better view of risk, as it can make use of all the information available to the re/insurer and can explicitly accommodate even complex re/insurance terms and product features. As an example, we price several excess reinsurance contracts. The proposed simulation model is also suitable for solvency assessment.     

微信教学在西方经济学课程中的运用初探

近年来,以“微课”和“翻转课堂”为代表,各个高校开展了大量的课程教学改革实践,一定程度上激发了学生的学习兴趣,增强了教学效果。然而,由于课程特点不同,不同的课程需要不同的教学模式。微信在西方经济学课程教学中的运用,可以利用“互联网+”改变传统的教学模式,引导激励学生课下自主学习,积极实践,提高学生对于教学活动的参与度,培养学生的自主学习能力和实践能力。     

金融安全背景下的证券市场稳定测度新方法——基于大数据支持向量机的市场预测与套利价值度量研究

我国证券市场经过数十年的发展,在不断的探索中渐渐成熟,但与发达国家的股票市场相比,还有不完善的地方。如我国证券市场的市值,并没有较好地与经济增长同比增长及契合实体经济的发展。通过研究2002-2017年的历史数据,使用支持向量机(SVM)预测证券市场的价格变化,通过误差统计分析,得出我国市场（上证交易所）与美国证券市场（纳斯达克综合指数）相比,更具不稳定性的结论。因此,可以利用套利价值（VaP）作为一种直观度量市场成熟度的参考指标。     

大学生运动员学习动机的培养与激发

大学生运动员的学习动机对于其学习、锻炼、竞赛等都起着推动作用。影响学习动机的因素是多方面的。教育者应根据社会和学生自身需求,创设教学情境,培养和激发学生的学习动机,促进其全面发展。     

On moment condition failure in German stock returns: an application of recent advances in extreme value statistics

This note reconsiders divergent results on the extremal behaviour of German stock returns that have been published recently. In particular, investigations of this issue have arrived at different conclusions regarding the finiteness of the second moment of the return distributions. Here we apply some newly developed, improved techniques for the estimation of the so-called tail index to the time series of returns on various German stocks. We find evidence indicating that in the vast majority of cases the tails are not fat enough to conform with an infinite-variance distribution. Conflicting results in previous studies are shown to be due to different a priori choices of the size of the tail region. First version received: Dec. 1998/Final version received: April 2000     

Surrogate modelling of a detailed farm-level model using deep learning

Technological change co-determines agri-environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm-level models that are rich in technology details and environmental indicators, integrated with agent-based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade-offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi-directional Long Short Term Memory.     

论领导干部的学习力和领导力

学习力同领导力的关系,学习力是根本,没有好的学习力就不能切实实行领导力。黑龙江由于特殊的历史原因,领导力起着关键和决定性因素。黑龙江要实现新的飞跃,需要继续加强领导力。各级领导干部要有使命感,发扬传统,不断提高自身修养,努力学习,真正贯彻和执行好党和人民交给与期盼的各项任务。