我国上市公司首次披露股权激励计划的市场反应分析

本文首次以《上市公司股权激励管理办法》颁布以来沪深两市首次披露股权激励计划的上市公司作为研究对象,运用事件研究法计算超额收益的方式,检验了股权激励计划披露前后超额收益率的变动情况.研究发现,在股权激励计划披露后,证券市场有显著的正面反应;在股权激励计划披露前34天的短时窗内证券市场有不显著的正面反应,在披露前60天到90天的长时窗内,证券市场有负面反应.     

上市公司高管人员薪酬激励方式的探讨

长期以来,我国上市公司高管人员薪酬制度不合理,其人力资本不能按照生产要素分配原则参与分配,导致高管人员缺乏工作积极性和创造性,经营行为短期化,造成部分上市公司的财务状况及经营情况日趋恶化.对上市公司高管人员薪酬制度的研究对于理顺上市公司的利益分配关系,实现上市公司乃至证券市场的可持续发展具有重要的现实意义.     

上市公司“高利贷”

证券市场的功能原本是实现资源的优化配置,其目的是让有发展前途的企业通过证券市场融资而发展壮大,如果越来越多的上市公司将闲置资金甚至募集资金挪作他用,而不是安心发展主业,那就违背了资本市场资源配置的初衷。     

上市公司财务危机动态预测模型研究

预测上市公司财务危机是投资者、债权人及证券市场监管机构所广泛关注的课题.运用现代资本结构理论和期权模型,以企业“资不抵债”作为上市公司陷入财务危机的标志,利用资本市场的信息指标——股价建立起上市公司动态财务危机预测模型,克服了统计预测方法利用财务报表信息预测的时期性和滞后性的缺陷.     

上市公司自愿性披露与证券信用关系的实证研究

目前，证券市场对上市公司自愿性披露和信用建设日益重视，因此，对我国上市公司自愿性披露的状况及与公司证券信用的关系的研究，十分具有现实意义。本文抽取沪市60家上市公司作为研究对象，选取20个自愿信息披露评价指标，利用其2004年年报，计算出上市公司自愿性披露指数，并对上市公司自愿披露程度与公司证券信用之间关系进行实证分析。实证结果对证券信用评级参考指标有些启示。     

经营业绩与市场评价:基于上市公司行业特征的实证研究

从行业角度对中国上市公司 2 0 0 2年的经营业绩以及市场对公司的评价进行了实证研究 .结果显示 :不同行业上市公司的经营业绩以及市场评价都存在显著差异 ;总体上市场评价与公司业绩正相关 ,但绩优公司和绩差公司的市场评价差距不大 ,且部分行业的市场评价与公司业绩无关 .这说明中国证券市场具有较强投机性 ,许多投资者购买股票并不考虑上市公司的经营业绩 .     

有效证券市场上证券价格长期波动控制系统的反馈控制

探讨有效证券市场上证券价格长期波动控制系统的反馈控制问题,建立了有效市场条件下证券价格长期波动的控制系统模型.根据系统完全能达的条件,讨论了以股市政策为单控制输入配置极点的问题和以上市公司政策为单控制输入配置极点的问题;根据系统非完全能达的条件,探讨了上市公司不发展时的镇定问题和股市宏观上不调控时的镇定问题.设计了一个降维状态观测器,用以估计证券的内在价值.选择使上市公司均衡增长时对应的证券平均内在价值作为目标值,设计线性多变量调节器,使所得到的闭环系统渐近稳定,且系统的输出跟踪该目标值.通过反馈控制以改善控制系统的内部结构特征和性能,达到人们对证券市场进行调控的预期目的.     

基于PLS通径分析的上市公司财务指标综合评价

为了对上市公司财务报表中的大量财务指标进行综合认识和评价,本文提出利用偏最小二乘(PLS)通径分析方法建立综合评价指标,该指标可以最大程度的综合各原始指标的信息,并且还可以利用其对各组原始指标所反映的不同隐变量信息进行研究.本文以证券市场上钢铁板块的22个上市公司公布的财务数据为例,利用PLS通径分析模型建立了一个综合评价指标,并用其对各上市公司进行了排名,得到了与实际情况十分吻合的结果.     

相对有效性分析在上市公司投资价值评价中的应用

我国股票市场成立十年来炉深两地上市公司数已经超过千家，以上述公司投资价值的分析与判断一直是市场争论的热点。1997年万青先生在中国证券报上发表了一篇关于系统分析上市公司投资价值的章，当时就引来了众多证券研究机构和分析师的深入讨论，迄今为止有关上市公司投资价值定量化分析的方法仍然是证券市场里一个崭新的课题。本尝试采用DEAW分析方法为定量地评价上市公司的投资价值，建立相对有效性评价模型和评价指标体系，期望通过对模型的演算和结果的分析，最终能实现指导证券交易的目标。     

从股民素质分析中国股市

从股民素质的角度，利用博弈理论分析证监会、上市公司、股民之间的关系，得出股民素质的高低极大地制约了中国证券市场的健康发展的结论。提出在完善制度建设的同时，还应不断提高广大股民，特别是普通股民的素质。     

基于自组织映射和模糊隶属度的财务危机预警研究

良好的财务危机预警模型能够有效监控企业运营情况,避免企业倒闭或被重组的悲剧发生。本文结合自组织映射模型和模糊C均值的模糊隶属度,构造模糊自组织映射模型,并应用到财务危机预警中。该模型将模糊隶属度带入到学习率函数中,在计算过程中自动更新获胜节点邻域范围,并在迭代过程中采用批学习算法,提高预测精度、稳定输出结果。对沪深两市上市公司的财务指标进行实证研究,通过与传统预警模型对比,得出模糊自组织映射模型在财务危机预警方面具有更优越的预测性能。     

考虑背景风险的高阶矩三角模糊随机投资组合模型

在已有的大部分投资组合模型中,证券的收益服从随机分布或者模糊分布。然而,在实际的市场中存在大量的不确定性,市场不仅具有内在的风险,也存在由投资者个体差异产生的背景风险。本文推导随机模糊数的高阶矩性质,构建一个考虑背景风险的高矩三角模糊随机投资组合风险模型,采用沪深股市的数据分析背景风险对投资组合的影响。     

金融演化网络建模

本文基于收集的股票日交易数据,通过对时间窗口进行划分和股票收益间的相关性,将股票市场构建为一个金融演化网络,通过分析金融演化网络的拓扑特征,进而可以研究金融市场演化的内在机制．     

Prediction of financial distress: An empirical study of listed Chinese companies using data mining

The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial crisis for better corporate governance. This paper studies the phenomenon of financial distress for 107 Chinese companies that received the label ‘special treatment’ from 2001 to 2008 by the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We use data mining techniques to build financial distress warning models based on 31 financial indicators and three different time windows by comparing these 107 firms to a control group of firms. We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using majority voting. An important contribution of the paper is to discover that financial indicators, such as net profit margin of total assets, return on total assets, earnings per share, and cash flow per share, play an important role in prediction of deterioration in profitability. This paper provides a suitable method for prediction of financial distress for listed companies in China.     

上海股票市场和国际主要股票市场之间波动溢出效应的实证研究

使用BEKK—二元GARCH(1,1)模型,对于我国股票市场和国际主要股票市场之间的波动溢出效应进行了实证研究.分析结果表明,上证综指和标准普尔500指数、日经225指数之间存在单向波动溢出效应,而上证综指和香港恒生指数之间存在双向波动溢出效应,上证综指和新加坡海峡时报指数之间不存在波动溢出效应.     

A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

GARCH models are commonly used for describing, estimating and predicting the dynamics of financial returns. Here, we relax the usual parametric distributional assumptions of GARCH models and develop a Bayesian semiparametric approach based on modeling the innovations using the class of scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution. The proposed specification allows for greater flexibility in capturing the usual patterns observed in financial returns. It is also shown how to undertake Bayesian prediction of the Value at Risk (VaR). The performance of the proposed semiparametric method is illustrated using simulated and real data from the Hang Seng Index (HSI) and Bombay Stock Exchange index (BSE30).     

ACD模型在沪市中的实证研究

在证券交易中,交易持续期反映了市场交易的重要信息,因此对交易者行为具有重要的影响,并且影响到证券市场的流动性.为了检验在交易中交易持续期对交易的影响,本文选择了沪市A股的四只股票,利用由Engle和Russell提出ACD模型对其交易持续期进行了实证研究,讨论了交易持续期的相关性质,表明交易持续期具有明显的日内模式,并检验log-WACD模型与中国证券市场的吻合程度.     

Non-Parametric Regression Methods

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).     

Portfolio rebalancing model with transaction costs based on fuzzy decision theory

The fuzzy set is one of the powerful tools used to describe an uncertain environment. As well as quantifying any potential return and risk, portfolio liquidity is taken into account and a linear programming model for portfolio rebalancing with transaction costs is proposed. The level of return that an investor might aspire to, the risk and the liquidity of portfolio are vague in an uncertain financial environment. Considering them as fuzzy numbers, we propose a portfolio rebalancing model with transaction costs based on fuzzy decision theory. An example is given to illustrate the behavior of the proposed model using real data from the Shanghai Stock Exchange.     

The three-factor model and artificial neural networks: predicting stock price movement in China

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China’s stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is significant, given the volatility of the Chinese stock markets and the added risk that arises from the Chinese legal and regulatory environment. In this paper we address this research gap by examining the predictive ability of several well-established forecasting models, including dynamic versions of a single-factor CAPM-based model and Fama and French’s three-factor model. In addition, we compare the forecasting ability of each of these models with that of an artificial neural network (ANN) model that contains the same predictor variables but relaxes the assumption of model linearity. Surprisingly, we find no statistical differences in the forecasting accuracy of the CAPM and three-factor model, a result that may reflect the emerging nature of the Chinese stock markets. We also find that each ANN model outperforms the corresponding linear model, indicating that neural networks may be a useful tool for stock price prediction in emerging markets.