分布式创新、区域创新体系与区域分工

分布式创新以主导公司内部知识主体与外部知识主体共同构建知识网络,在保证人力资本的专用性和节省交易费用的前提下,对区域创新体系构建中微观主体发挥比较优势、进行有效知识资源配置有深远影响,对解决区域自主创新和区域分工演进的问题有借鉴意义。     

一种电力线载波通信系统的设计与实现

介绍一种以电力线作为载波信道的分布式单片机系统通信方式以及该系统的设计方法。     

非战争军事行动装备保障资源调配模型

本文基于对非战争军事行动装备保障问题的研究,对非战争军事行动装备保障资源调配中存在问题和建模的影响因素进行了分析和描述,采用DSCP方法对保障资源调配决策进行建模,并给出了案例分析。     

Bitcoin mining: converting computing power into cash flow

Bitcoin is the world’s leading cryptocurrency, with a market capitalization briefly exceeding $300 billion. This hints at Bitcoin’s amorphous nature: Is this a monetary or a corporate measure? Hard values become explicit in the processing of transactions and the digital mining of Bitcoins. Electricity is a primary input cost. Bitcoins earned are often used to circumvent local currency controls and acquire US dollars. For the period August 2010 to February 2018, we examine the financial components of Bitcoin mining revenues, their statistical contribution to daily changes, and to its variance. We provide empirical evidence that Bitcoin transaction processing is capacity constrained.     

ARDL Investigation of Macroeconomic Mechanisms on ASEAN-5 Stock Earnings Equation

The objective of Autoregressive Distributed Lag （ARDL） investigation of macroeconomic mechanisms on ASEAN-5 stock earnings equation can contribute to analyzing and demonstrating macroeconomic forces acting a statistically and economically significant effect on rate of return of securities for a given time through the application of the ARDL approach to cointegration when the variables are mixed degrees of integration in cointegrated time series, i.e. I（0） and I（1）. This results in a long-run or a short-run relationship between macroeconomic force acting for a given time affecting 24 quarterly rates of return for listed companies in Information and Communication Technologies OCT） sector of Stock Exchange of Thailand （SET）, the Kuala Lumpur Composite Index （KLSE） index （Malaysia）, Financial Times Share Index （FTSI）, Philippine Stock Exchange （PSE）, and Jakarta Composite Index （JKSE） （Indonesia）. The impulses of three ASEAN macroeconomic forces---GDPt, EXt, and INTt acting for a given time influence the earnings of selected 23 1CT returns for listed companies on both domestic investments and outsider investments in the same period. This paper has investigated how other-concerning macroeconomic force acting might interrelate with rate of return of securities in the ICT sector, debt and financial innovations, in line with some significant formalized facts. The funding of capital inflow in part of ICT securities was statistically globally significant to recognize significant achievement in ICT specific pathways to distinction as the science of accomplishment. The public sector performing as a key purchaser of ICT security solutions related to integrate the ASEAN exchange members into modern capitalization can lead to adverse effects, such as risky investment-hub interventions due to the mixing financial systems in three ASEAN regional integrations through investments.     

闲暇效应与内生增长——基于中国和瑞典数据的分析研究

本文综合利用社会学与心理学的研究成果对闲暇的经济含义进行了扩展,认为闲暇对生产具有积极的互补效应,并通过将该类效应引入到带人力资本的卢卡斯内生增长模型,利用中国和瑞典的相关数据进行分析。本文的计量分析显示,瑞典的闲暇增加对经济增长具有正作用,但中国与此相反,因而瑞典的技术进步、人口状态等因素对增长的作用优于中国。最后,本文建议推动休闲经济发展、适时推进弹性工作制,实现"工作—闲暇平衡"。     

Distributed ARIMA models for ultra-long time series

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle the challenges of forecasting ultra-long time series using the industry-standard MapReduce framework. The proposed model combination approach retains the local time dependency. It utilizes a straightforward splitting across samples to facilitate distributed forecasting by combining the local estimators of time series models delivered from worker nodes and minimizing a global loss function. Instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we only make assumptions on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed approach with AutoRegressive Integrated Moving Average (ARIMA) models using the real data application as well as numerical simulations. Our approach improves forecasting accuracy and computational efficiency in point forecasts and prediction intervals, especially for longer forecast horizons, compared to directly fitting the whole data with ARIMA models. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.     

Climate reputation risk and abnormal returns in the stock markets: A focus on large emitters

Reputation risk is among the possible climate transition risks companies face, especially in emission-intensive industries. Failing to meet stakeholders' expectations about the contribution to climate goals might influence investors' strategies and produce financial damages. We look at the climate-related social media talk in a sample of highly polluting companies. For these companies, reputation risk materialises if their climate talk is perceived as not coherent with their action-taking. We then assess the impact of climate talk on short-term stock market performance, as measured by abnormal returns, and find a positive association between climate-related social media talks and abnormal returns. The strength of this association lowers during peak days of social media attention on climate-related topics.     

Aggregate accounting research and development expenditures and the prediction of real gross domestic product

The role of accounting information for public policy making has received increased attention in recent years. Konchitchki and Patatoukas, 2014a, Konchitchki and Patatoukas, 2014b demonstrate that growth in aggregate accounting earnings can predict future growth in nominal and real Gross Domestic Product (GDP). We extend the micro to macro literature by decomposing earnings into the R&D and pre-R&D components. Using the Almon (1965) finite distributed lag model, we find that both components can predict future real GDP growth with different lead-lag structures. Importantly, this decomposition significantly increases the explanatory power of the predictive model using accounting information. Aggregate accounting R&D can predict real GDP through the personal consumption, business investment, and net export channels of GDP. Our study extends prior research on the forecasting usefulness of accounting information at the aggregate level and has practical implications for macro forecasting and for public policy making regarding innovative activities of publicly listed firms.     

Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing

Changes to the General Ledger (GL) represent a link between transactional business events from Journal Entries and prepared financial statements. Errors in these very large datasets can result in material misstatements or account misbalance. Unfortunately, a plethora of conditions renders traditional statistical and non-statistical sampling less effective. As a full-population examination procedure, Multidimensional Audit Data Sampling (MADS) mitigates these issues. In conjunction with top practitioners, we utilize a design science approach in applying the full-population MADS methodology to a real dataset of GL account balance changes. Issues such as the effectiveness of internal controls, detection of low-frequency high-risk errors, and earnings management concerns are addressed. This paper demonstrates how vital insights can be gained using MADS. More importantly, this approach also highlights the exact portion of the population that is error-free with respect to the auditors' tests.