全流通格局下沪市主并上市公司并购绩效研究

以全流通格局下2007年发生并购的56家上市公司为样本,应用超常收益方法对沪市主并上市公司并购的绩效进行实证研究.结果表明:主并上市公司并购后绩效呈上升趋势;在横向并购、纵向并购和混合并购三种方式中,纵向并购的效果优于横向并购和混合并购,而横向并购与混合并购对绩效的影响区别不明显.     

中国股市和债市波动溢出效应的MV-GARCH分析

股市和债市的波动溢出效应是研究金融市场信息流动、风险传递的重要内容。在估计了股市和债市候选MV-GARCH模型参数基础上,通过AIC准则等拟合优度方法选择了t分布型BEKK为最优模型,因为它更好的捕捉到了金融时序尖峰、厚尾的特征.结果显示,中国股市和债市波动溢出具有明显时变特征,波动影响不对称,股市对债市影响大于债市对股市影响。动态相关系数偏弱说明两个市场在资源配置能力、信息流动等方面存在显著的缺陷.     

Neural spike renormalization. Part I — Universal number 1

For a class of circuit models for neurons, it has been shown that the transmembrane electrical potentials in spike bursts have an inverse correlation with the intra-cellular energy conversion: the fewer spikes per burst the more energetic each spike is. Here we demonstrate that as the per-spike energy goes down to zero, a universal constant to the bifurcation of spike-bursts emerges in a similar way as Feigenbaum's constant does to the period-doubling bifurcation to chaos generation, and the new universal constant is the first natural number 1.     

浅析相关系数的显著性检验

本文用最小二乘法作直线拟合,给出相关系数显著性检验的判断方法,指出了用概率理论判断线性相关的必要性。     

A股上市公司内部人股票买卖行为及获利能力分析

系统地分析了2006年至2011年我国A股市场上市公司的内部人在二级市场的交易行为及其收益预测.结论表明:尽管在我国证券市场中存在针对内部人交易的法律法规,但总体上内部人在交易中仍然表现出良好的时机把握能力.不论是买入还是卖出,均能获得显著的超额收益.内部人交易超额收益的大小受到公司所有权性质、内部人类型等因素的影响.一方面,较于董事、监事等内部人,高管获利能力普遍较强,而其中董事长和总经理的获利能力最强;另一方面,由于我国特殊的公司所有权性质与晋升机制,使得国有上市公司的董事长和总经理利用内部人交易获利的动机小于非国有企业;规模越大的国有上市公司,其董事长和总经理通过内部人交易获取超额收益的程度也越小.     

Uniform asymptotics for a multi-dimensional time-dependent risk model with multivariate regularly varying claims and stochastic return

This paper is devoted to asymptotic analysis for a multi-dimensional risk model with a general dependence structure and stochastic return driven by a geometric Lévy process. We take into account both the dependence among the claim sizes from different lines of businesses and that between the claim sizes and their common claim-number process. Under certain mild technical conditions, we obtain for two types of ruin probabilities precise asymptotic expansions which hold uniformly for the whole time horizon.     

A probability distribution for precipitation data analysis

Hydrologic design is often based on assessments of large return interval measures; it is vital to be able to conclude them as precisely as possible. Henceforth, the selection of a probability distribution is very crucial for such cases. In view of this scenario, we propose and study a pliant probability distribution for precipitation data analysis. Some mathematical and statistical properties are analyzed. In order to make stronger predictions and judge the realistic return period, we have also characterized the model via Laplace transformation. We have estimated its parameters via the maximum likelihood estimation and constructed its information matrix for developing the confidence belt of population parameters. Moreover, a real-life setup is also considered by applying the model over precipitation data of diverse regions, including Jacksonville, Florida (USA), Barkhan (Pakistan), British Columbia (Canada), and Alexandria (Egypt). This investigated study is based on various statistical parametric and nonparametric tests, which indicates that the proposed model is one of the better strategies for precipitation data analysis when compared with the famous three-parameter Kappa model.     

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).     

Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

Politically-themed stocks mainly refer to stocks that benefit from the policies of politicians. This study gave the empirical analysis of the politically-themed stocks in the Republic of Korea and constructed politically-themed stock networks based on the Republic of Korea’s politically-themed stocks, derived mainly from politicians. To select politically-themed stocks, we calculated the daily politician sentiment index (PSI), which means politicians’ daily reputation using politicians’ search volume data and sentiment analysis results from politician-related text data. Additionally, we selected politically-themed stock candidates from politician-related search volume data. To measure causal relationships, we adopted entropy-based measures. We determined politically-themed stocks based on causal relationships from the rates of change of the PSI to their abnormal returns. To illustrate causal relationships between politically-themed stocks, we constructed politically-themed stock networks based on causal relationships using entropy-based approaches. Moreover, we experimented using politically-themed stocks in real-world situations from the schematized networks, focusing on politically-themed stock networks’ dynamic changes. We verified that the investment strategy using the PSI and politically-themed stocks that we selected could benchmark the main stock market indices such as the KOSPI and KOSDAQ around political events.