经济复杂性系统主体学习算法理论综述

对经济复杂性系统研究文献中常见的主体学习算法进行了一个全面的梳理.将学习算法分为来自智能计算或演化计算的学习算法以及来自心理学和实验经济学的学习算法,并结合文献介绍了各类学习模型在经济学研究中的主要应用.还回顾了文献中对各种算法的分析比较,并对学习算法的发展前景进行了展望.由于学习算法还是一个相对较新的领域,对经济系统中个体学习行为需要进行更加深入的研究和探索.     

团队自主权、报酬差距与团队效率

针对传统的团队锦标赛模型假定过于抽象,不能反映组织结构与管理风格特征的缺陷,引入了团队自主权变量,构建了基于团队自主权的团队锦标赛模型.给出了团队效率最大化的期望效用函数,并采用有约束非线性规划求解方法,探讨了团队自主权、报酬差距与团队效率的关系.发现团队自主权对报酬差距有正向影响,团队自主权与团队效率呈现倒U型关系,而较大的报酬差距能促进团队自主权对团队效率的正向影响.     

带有偏好锥的样本数据包络分析方法研究

针对传统数据包络分析方法无法依据确定参考面提供评价信息的弱点,给出了带有偏好锥的样本数据包络分析模型(PSam-C2WH)和相应的SCDEA有效性及弱有效性概念。分析了(PSam-C2WH)模型的性质以及它与传统DEA模型之间的关系,探讨了(PSam-C2WH)模型刻画的SCDEA有效性及弱有效性与相应的多目标规划非支配解之间的关系。进而,分析了决策单元在样本可能集中的分布特征、投影性质和模型含义等问题。     

浅谈英语教学中的情感教育

英语教学作为语言教学的一个过程,同时也是一个情感交流的过程.本文以英语教学中的情感教育为基础,深入分析了情感教育对于提高英语学习效率的重要性.教学中如果能正确的把握情感教育,既可以塑造学生良好的品行,又能激发学生的学习兴趣,从而提高教学质量。     

止损策略与市场行为级联

为研究止损交易对市场中交易者行为及资产价格的影响,基于多主体模型方法,构建了一个加入止损策略的多主体市场模型。模型模拟结果表明当市场中止损阈值被触及时,极易触发连续的止损交易,造成交易者之间的行为级联。这种交易级联会引致交易者行为的趋同性增强、市场中的买卖订单失衡、市场价格的异常崩跌和流动性黑洞。     

Correcting the January optimism effect

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak-form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.     

产学研协同发展、知识积累与技术创新效率——基于动态面板门限机理实证分析

基于产学研耦合协调度测度模型、数据包络分析模型以及动态面板门限回归模型,运用2013～2017年中国30个省市的面板数据,研究以知识积累为门限变量时,产学研协同发展水平对区域技术创新效率产生的影响.实证结果表明:当以知识积累水平为门限变量时,产学研协同发展水平与技术创新效率间存在明显的双门限效应;随着知识积累水平的提升...     

Can We Predict the Financial Markets Based on Google's Search Queries?

We look into the interaction of Google's search queries and several aspects of international equity markets. Using a novel methodology for selecting words and a vector autoregressive modeling approach, we study whether the search queries of finance‐related words can have an impact on returns, volatility of returns and traded volume in four different English‐speaking countries. We identify several words whose search frequency is associated with changes in the dependent variables. In particular, we find that increases in search queries including the word stock predict increased volatility and decreased index returns over the next week. On top of that, we investigate the performance of a market‐timing strategy based on the search frequency of this word and benchmark it against random words from the Word‐Net database and a naive buy‐and‐hold strategy. The results of this empirical application are positive and particularly stronger during the global crisis of 2009. Copyright © 2016 John Wiley & Sons, Ltd.     

基于MDA机制的跨洋航空网时隙分配方法

考虑到飞机跨洋飞行的限制性条件,提出了一种改进的多单元双向拍卖机制来满足跨洋航空主干网的时隙分配的需要。该机制将VCG双向拍卖和传统的多单元双向拍卖进行结合和改进,并从激励相容、预算平衡、个人理性和机制效率等方面证明了该双向拍卖机制的性质。最后的仿真结果表明,该双向拍卖机制具有较高的效率。     

Overreaction in Survey Exchange Rate Forecasts

We use survey data on five bilateral exchange rates to provide empirical evidence of the fact that professional forecasters of foreign exchange rates behave irrationally, in the specific sense that they respond inaccurately to available information in the market when forming their predictions. In particular, we find systematic biases in the forecasts resulting in the overreaction of analysts to past information contained in the exchange rate dynamics: forecasters change their prediction more than it would be rational on the basis of past realized changes. In addition, forecasters are heterogeneous in their irrationality: low performers in previous periods show a more pronounced overreaction effect. This can be read as an indication of perpetration of past errors and continued inability to learn from the past. In the second part of the paper, we exploit the novel structure of our dataset, which consists of survey data extracted from the Bloomberg platform and readily available to anyone. This feature allows us to consider their own and others' past forecasts as part of the information set that analysts use in making their predictions. By using past forecasts as proxies for relevant macroeconomic variables, we find evidence that analysts fail to correctly process not only the information contained in the spot rate past dynamics but also the information in this broader set. We see this as confirmation of the existence of inefficiency and heterogeneity between low and high performers also when full information is available. Copyright © 2014 John Wiley & Sons, Ltd.