重庆市农业科技创新资源配置效率及影响因素研究

[目的]通过分析重庆市农业科技资源配置效率及影响因素,以期对重庆市的农业科技资源的合理配置提出参考建议。[方法]构建评价指标体系,以农业科技创新资源配置环境、农业科技资源经费投入、农业科技人才储备和农业科技创新成果4个方面构成综合层,向下分16个具体指标,运用层次分析法和模糊评判综合评价重庆市农业科技资源配置效率及影响因素。[结果]重庆市农业科技创新资源的配置效率综合得分为81.08,为2级水平,其中农业科技人员流失比例较大,投入产出效率较低成为主要制约因素。[结论]重庆市农业科技创新资源的配置效率处于中等水平,合理配置农业科技人员和经费投入以及提高资源配置环境是提高重庆市农业科技协同创新资源的配置效率的关键;农业科技资金投入浪费严重和农业科技活动成果的转化率较低、农业科技人才结构和农业科技创新资源配置方式的不合理以及农业科技市场机制尚不完善是影响农业科技资源配置效率的主要因素。     

Models of static and dynamic technical efficiency of municipal libraries in the Czech Republic

This article deals with the modelling of the static and dynamic technical efficiency under conditions of municipal libraries of municipalities with 1000–5000 inhabitants. The aim of this article is to determine the level of the technical efficiency and the factors that influence the results of modelling of the static and dynamic technical efficiency of 34 selected municipal libraries for the years of 2011 and 2015. The first model tests the technical efficiency of conventional services of public libraries. The second model tests the technical efficiency of municipal libraries’ operation. The third model tests the technical efficiency of the key revenues and expenditures. The results in the static models estimate the average technical efficiency of municipal libraries in the interval (0.691–0.759) for the input-oriented models, and in the interval (1.413–2.005) for the output-oriented models. In the dynamic models, the majority of municipal libraries in 2015 showed lower technical efficiency and productivity in comparison with the year of 2011. The factors influencing the level of efficiency and its course include the inputs and the outputs, and their combinations within individual models.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

基于DEA模型的建筑业环境行为效率评价研究

对建筑企业进行环境行为效率评价是促进我国建筑行业健康可持续发展的重要手段。根据建筑行业特点及我国现有的企业环境信用标准,构建了建筑企业环境行为评价的投入产出指标体系,将改进的数据包络分析方法模型运用到企业环境行为效率评价领域,解决了现有企业评价研究主观性突出的问题,得出建筑企业评价分析方法。在此基础上对某代表性建筑企业2010—2017年环境行为数据进行了评价分析。分析结果表明该企业环境行为总体效率良好,但鉴于出现了奇数年和偶数年的效率波动情况,仍需提高环境行为效率。     

Settlement procedures and stock market efficiency

This study examines why most derivatives markets that settle on the day following expiration choose the opening rather than the closing price as the final settlement price (FSP), whereas most markets that settle on the expiration day select an average rather than a single price as the FSP. Four exogenous changes in the Taiwan Futures Exchange settlement procedures provide an experimental basis for studying the settlement procedures’ impact on underlying assets. Greatest market efficiency is observed when the FSP is determined by a single rather than an average price and hypothesize that manipulation is prevented at the expense of market quality.     

Street-level new public governances in integrated services-as-a-system

The paper explores the emergence of street-level public service integration in eight cities, in five countries using a new framework: services-as-a-system to explore new public governances in health and social care integration. Data is analysed from eight cities in six countries (Australia, Canada, The Netherlands, Spain, UK, and USA) gathered in over 100 semi-structured interviews with key agents. We show that whilst culture and context shape the form and processes of governances there are underlying processual drivers of new public governances, in particular where users are involved in the co-design and co-production of integrated services.     

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.     

面源污染视角下江西省耕地利用效率研究

[目的]在面源污染视角下考察耕地利用效率,可以为推动耕地可持续利用提供理论和参考依据。[方法]文章基于2000—2014年我国13个粮食主产省(区)耕地投入产出的面板数据,运用SBM方向性距离函数将耕地面源污染因素纳入到传统的效率分析框架,从省际比较的维度对江西省耕地利用效率进行了测度与分析。[结果]考虑面源污染因素对耕地利用效率测算产生了一定的影响;江西省耕地利用效率值呈现阶段性特征,耕地利用效率排名比较稳定,近年来排名有上升趋势;江西省耕地利用绩效一般,且与最佳实践者存在较大的差距。[结论]忽视面源污染因素的耕地利用效率评估是失真和不符合实际的,宜将面源污染因素纳入到耕地利用效率的测度框架,以此来更科学地量化评估耕地利用绩效;设计和完善相关政策,以有效控制耕地面源污染;加强与先进省(区)的交流与合作,学习其农业可持续耕作技术与管理经验,进一步推动江西省耕地产出与资源、环境协调发展。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

公司为什么放弃债务的收益呢？

经典资本结构理论认为债务具有税盾效应和财务杠杆效应，因此公司会选择适当的负债以获得债务的税收收益。但是，现实中一些公司的负债很低，甚至为零。公司为什么放弃债务的税收收益呢？本文以2007～2011年中国A股上市公司为研究样本，从行为公司金融的视角分析管理者能力是否导致公司采用“低杠杆”策略。结果表明，中国上市公司存在普遍的低杠杆现象，管理者能力的差异是导致公司采用“低杠杆”策略的重要原因。