Social media bots and stock markets

This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.     

一种基于自适应相似矩阵的谱聚类算法

为了消除在构建谱聚类算法的相似矩阵时,高斯核函数中尺度参数的波动影响,构建了一种自适应相似矩阵,并应用到谱聚类算法中。自适应相似矩阵中数据点间的距离度量采用测地距离算法,相距较近的两点间的距离近似于欧氏距离,相距较远的两点则先根据欧氏距离得到每个数据点的k个近邻点,然后累加近邻点的测地距离,由此得到每对数据点间的最短距离。两点间的局部密度用共享近邻的定义来表示,更好地刻画了数据集的本征结构。在5个人工数据集和国际通用UCI数据库中的5个真实数据集上进行实验。实验结果表明,所提算法的聚类准确率高于对比算法的准确率,对复杂分布数据有很强的自适应能力。研究成果为数据挖掘及机器学习提供了思路和方法。     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

基于SARIMA-SVR组合模型的 空气质量指数预测

空气质量指数(AQI)在波动中既具有整体的时间序列线性特征和明显的季节性波动周期,又具有多种因素影响的不确定性,为了提高AQI的预测精度,基于Ri386 3.3.3和Matlab R2014a两种编程软件,提出了一种同时具有线性和非线性的复合特征的时间序列预测模型——SARIMA-SVR组合模型。以太原市2014年1月—2019年7月的AQI月均值数据为基础,利用SARIMA时间序列模型进行线性预测,利用SVR模型对残差进行非线性预测,加和得到组合预测模型的预测结果,分析比较SARIMA,SVR和SARIMA-SVR这3种模型的预测结果和平均绝对百分比误差。结果表明,组合预测模型发挥了2种模型各自的优势,相较于单一预测模型的预测结果而言,其预测精度更高,稳定性更好。通过此模型得到的空气质量预测结果不仅可为人们的日常生活提供指导,而且可为大气污染的防治工作提供科学依据和借鉴意义。     

The Complementary Relationship Between Institutional and Complexity Economics: The Example of Deep Mechanismic Explanations

Analyzing economic systems from an evolutionary-institutional or a complexity perspective are two complementary approaches to economic inquiry. I discuss three arguments in favor of this hypothesis: (i) eminent institutional economists have examined the economy as what today could be considered a complex system; (ii) complexity economists lack meta-theoretical foundations which could be provided by institutionalist theory; and (iii) institutional economists could benefit from using methods of complexity economics. In this context, I argue that scholars considering the economy to be complex should seek to explain it by discovering social mechanisms instead of focusing on prediction. In order to distinguish between alternative explanations, scholars should refer to the deepness of an explanation, rather than to Occam’s razor.     

应用型本科大学数学课程考试模式研究

文章针对应用本科培养模式的特点,分析了大学数学课程教学及考试模式的不足,提出了应用型本科大学数学课程网络考试的改革设想。并且对应用型本科大学数学课程网络考试系统的架构和功能给出了框架式的设计,不但节约资源,而且可以较大程度上实现教考分离,将一考定结局的模式调整为分阶段分过程的考查。网络考试的模式不但可以更加真实合理地考核及评价学生,而且还能改变功利性教学的现状,客观地实现以考促学、以考促教的效果,更符合教育规律。从而印证了应用型本科大学数学课程网络考试模式改革的合理性。     

将数学建模引入高等数学教学中的典型案例

首先阐述了将数学建模思想融入高等数学教学内容中的意义,接着从高等数学中的基本概念和基本定理出发,通过具体案例说明如何将数学建模案例融入在高等数学教学中。最后给出了根据高等数学内容选编的典型建模案例。     

新时期高职院校数学教学改革探讨

中国的高等职业教育已经进入一个崭新的阶段,无论是招生数量,还是师资力量都呈现出一个全新的面貌。但相对于本科教育还是存在很多不足之处。本文就高职院校的数学教学提出一些个人的看法,以期与各位朋友共同探讨。     

浅谈新课程标准下数学课堂教学的优化

新课标重在培养学生的自学能力、动手能力及实践探索能力,对教师的教和学生的学都提出了新的要求。为了较好的贯彻新课标的要求,要在课堂教学中下功夫,进行课堂教学优化设计。文章结合具体的教学经验,提出了数学课堂进行教学优化设计的具体方法和措施,以及提高学生主动性和创新性的方法。