“汶川地震”的证券市场板块效应研究

本文采用事件研究法分别研究了汶川地震对我国金融板块和四川板块的影响。本文根据汶川地震的特点确定了估计窗和事件窗,选择市场模型作为正常收益的估计模型,得到了各个板块在事件窗内的累积非正常收益并对其进行了显著性检验。本文研究结果表明:汶川地震对我国金融板块没有显著性影响;汶川地震对我国四川板块的影响是四川板块组合的累积非正常收益由显著为正到显著为负的变化过程。     

事件研究在证券欺诈案中的司法应用

事件研究法 事件研究法是一种财务经验研究方法,是了解股票市场证券价格与特定事件(如公司股利宣告或盈余发布)间关联性的实证分析,其研究的逻辑线索简单明了,即用非正常收益(abnormal return)来判断某事件的发生是否影响了时序性价格数据的产生.若此事件显著,使该公司股价波动状况异于无此事件时的表现,则会产生非正常收益.而事件研究主要的目的在于利用统计方法检验非正常收益状况,借以明确该事件是否对公司股价造成影响.非正常收益是一个很重要的指标,用来度量股价对事件发生或信息披露异常反应的程度.实施事件研究可分成三步:     

上市公司收购信息披露的市场效应比较

在上市公司的收购过程中,提示公告和正式公告是两个最重要的信息披露.以我国在 2002年 12月 1日至 2003年 10月 31日期间提交证监会审核无异议的上市公司收购案例为样本,利用市场模型考察提示公告与正式公告事件日周围的超额收益率、平均超额收益率 ,以及狭小窗口的累积超额收益与累积平均超额收益情况.结果表明,在上市公司收购事件中,提示公告的市场反应要显著高于正式公告的市场反应.     

我国上市公司高管涉嫌犯罪对股价波动的影响研究

本文以2013年沪深两市的涉事公司为样本,采用事件研究法分析了国有上市公司和民营上市公司高管涉嫌犯罪等事件的市场反应。通过分析事件窗内个股的累积异常收益及显著性检验发现,在事件期[-10,10]共21个交易日内,国有上市公司样本通过了显著性检验,呈现出显著的负反应;而民营上市公司样本则未通过显著性检验。     

高管变更对会计盈余影响——基本不同所有制结构上市公司的实证分析

本文研究了2010-2012年中国A股上市公司总经理变更前后,不同所有制结构上市公司的实际会计业绩在总经理变更前后的变化.民营上市公司显著的业绩变化与市场对总经理变更显著的消极反应一致,而国有控股上市公司不明显的业绩下滑也与市场表现出的不显著为负的累计非正常收益相一致;在证券市场拥有负累计非正常收益的公司表现出较正累计非正常收益公司更差的会计业绩.     

基于上海A股市场的市场有效性检验

本文就上海A股市场,是否具有弱式有效及半强式有效进行实证检验及分析判断。实证分析对象是上证A股市场中上证180中采样期间无除息停牌等重要事件发生的126只个股作为研究样本,分别采用序列相关性检验与事件研究法对市场有效性进行检验。其中,事件研究法以银行发出"双降"通知的日期2015年10月23日为事件日,对信息公布前后运用市场模式和残差分析法进行计算,计算出事件窗口的每日超额收益——残差,最后得出AARt和CAARt趋势图,对图形进行分析。实证结果表示,中国上海A股票市场目前已经实现弱式有效,但离半强式有效仍有一定差距。     

利率调整对股票市场的影响——基于非参数秩检验的事件研究

从累积平均异常收益的秩检验来看,在事件日后,利率调整对股票市场的影响也较大,说明股票市场对利率调整事件存在事后反应。货币政策作为货币当局调控经济的手段必然会对股票市场产生广泛而深远的影响,在各种货币政策工具中,以利率调整对股票市场的影响最为显著。央行利率调整主要是针对固定资产投资、信贷规模、物价水平这三方面的,对股票市场间接产生调控作用,一方面,必要的利率调整可以     

新三板做市商制度、股票流动性与证券价值北大核心CSSCISSCI

本文研究了新三板挂牌公司的股票转让方式由协议转让变更为做市转让对股票流动性和证券价值的影响。研究发现,一是与对照组公司的均值比较和基于倍差法模型的回归结果表明,实施了做市转让方式的处理组公司的股票流动性更高;二是处理组公司在事件日前后存在显著为正的平均累积异常收益,而这一现象在对照组公司中不存在;三是交易机制变更的个股的累积异常收益率与股票流动性变化正相关,控制样本选择偏差后的结论基本不变;四是交易机制变更的个股的股票流动性变化与做市商数量正相关,控制样本选择偏差后的结论基本不变。研究结果表明,做市商制度对股票流动性和证券价值有正面影响,我们不能因新三板市场整体换手率的波动而否定做市商制度的作用。     

中央银行利率调整对股票市场影响的实证分析

利率调整会对股票市场造成很大影响,如何度量这种政策效应?考虑到我国存款利率受管制及股票收益不服从正态分布的特性.与传统事件研究模型不同,运用GARCH模型对超额收益进行预测,通过对超额收益的显著性检验和信息泄露检验,度量了中国股票市场对历次存款利率调整的反应,得到股票市场的利率政策效应与市场对利率调整的预期有很大的关系等结论.     

上证指数:移动平均线有效性的技术交易规则检验

本文采用程序交易的方法检验了1990年12月至2008年6月上证指数各交易日收盘指数据,检验结果表明, "指数-10日均线交叉"的交易规则能够超越买入并持有策略.具体表现为,该交易规则收益率的均值高于市场的收益率均值,收益率的标准差低于市场收益率的标准差."指数-10日均线交叉"的交易规则对时间变动、交易成本变动和样本变动具有很好的稳健性. "指数-10日均线交叉"的交易规则主要特点是能够有效地避免市场的大幅度下跌.     

Event-Induced Volatility and Tests for Abnormal Performance

I analyze a simple test statistic for mean abnormal returns in the presence of stochastic volatility during both event and nonevent windows and in the presence of event‐induced variance increases. Unlike previous tests, the parametric test evaluated here does not require that the volatility effect of the event be the same across all securities. Simulations show that the test exhibits nontrivial gains in power over previously developed parametric and nonparametric tests, and the true null hypothesis is rejected at appropriate levels.     

Conducting Event Studies on a Small Stock Exchange

Abstract

This paper analyses whether it is possible to perform an event study on a small stock exchange with thinly trade stocks. The main conclusion is that event studies can be performed provided that certain adjustments are made. First, a minimum of 25 events appears necessary to obtain acceptable size and power in statistical tests. Second, trade to trade returns should be used. Third, one should not expect to consistently detect abnormal performance of less than about 1% (or perhaps even 2%), unless the sample contains primarily thickly traded stocks. Fourth, nonparametric tests are generally preferable to parametric tests of abnormal performance. Fifth, researchers should present separate results for thickly and thinly traded stock groups. Finally, when nonnormality, event induced variance, unknown event day, and problems of very thin trading are all considered simultaneously, no one test statistic or type of test statistic dominates the others.     

Conducting event studies with Asia-Pacific security market data

We investigate the effectiveness of several well-known parametric and non-parametric event study test statistics with security price data from the major Asia-Pacific security markets. Extensive Monte Carlo simulation experiments with actual daily security returns data reveal that the parametric test statistics are prone to misspecification with Asia-Pacific returns data. Two non-parametric tests, a rank test [Corrado and Zivney (Corrado, C.J., Zivney, T.L., 1992, The specification and power of the sign test in event study hypothesis tests using daily stock returns, Journal of Financial and Quantitative Analysis 27(3), 465-478)] and a sign test [Cowan (Cowan, A.R., 1992, Non-parametric event study tests, Review of Quantitative Finance and Accounting 1(4), 343–358)] were the best performers overall with market model excess returns computed using an equal weight index.     

Bond market event study methods

The procedures used in corporate bond event studies to date fail to control for heteroskedasticity due to differences in return volatility by term-to-maturity, rating, and other factors resulting in low test power. Bond return standardization yields considerably more powerful tests. Also, due to infrequent trading, use of bond transaction price observations over several days before and after an event, while giving more weight to returns calculated from transactions closer to the event, yields considerably more powerful tests than returns based solely on transactions the day before and the day after the event. Exploring the test bias caused by overlapping event dates, we find that, adjusted for rating and maturity, the correlation among standardized abnormal bond returns is small but that even fairly small correlations can result in biased test statistics. A bond market modification of the Kolari and Pynnönen (2010) procedure corrects this bias.     

DETECTING ABNORMAL RETURNS USING THE MARKET MODEL WITH PRE-TESTED DATA

The current literature suggests various alternative procedures for increasing the power of tests to detect abnormal returns in event studies. Using randomly constructed portfolios, we simulate events and compare the results of tests using three alternative procedures: traditional, cross-sectional, and cross-sectional with standardized residuals. For each test, we compare results when all observations are included with results when the observations with high trading volume are omitted from the estimation period. The simulation results indicate that both the traditional approach with omitted observations and the cross-sectional approach using standardized residuals with all observations yield approximately the correct test sizes and significantly improve the power of tests to detect abnormal returns. However, the cross-sectional approach using standardized residuals is clearly dominant among the three procedures.     

A Complete Nonparametric Event Study Approach

Event studies have been used to examine the direction, magnitude, and speed of security price reactions to various phenomenon. Concerns over the lack of normality in stock return distributions motivated the introduction of nonparametric test statistics in the event study literature. A parametric procedure (OLS), however, has been extensively employed in the estimation of parameters for the market model. This paper, in contrast, applies Theil's nonparametric regression in the estimation of abnormal returns; an approach which is distribution free and provides a complete nonparametric approach for the detection of abnormal performance. Simulation results indicate Theil's estimation procedure offers a slight improvement in power in the detection of abnormal performance over the traditionally employed methodology. The results suggest employing Theil's nonparametric estimation procedure combined with the rank statistic. This complete nonparametric combination offers similar power with fewer underlying assumptions.     

上市公司季报公布对股票收益率的影响

本文拟对沪深两市36家上市公司季报公布前后股票收益率变动情况进行研究。基于FFJR在1969年的文献中所提及的事件研究方法。发现每天的异常收益率在事件窗口中表现比较平稳，而每天累积的异常收益则具有明显的趋势，投资者在季报公布前后能获得显著的异常收益。反映出我国资本市场尚未达到半强有效，文章在最后指出一国应加强资本市场独立性的建设，有利于增强资本市场对宏观经济周期的超前预示作用。     

Improved Methods for Tests of Long-Run Abnormal Stock Returns

We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness-adjusted t -statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series t -statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous.     

Nonparametric rank tests for event studies

Because stock prices are not normally distributed, the power of nonparametric rank tests dominate parametric tests in event study analyses of abnormal returns on a single day. However, problems arise in the application of nonparametric tests to multiple day analyses of cumulative abnormal returns (CARs) that have caused researchers to normally rely upon parametric tests. In an effort to overcome this shortfall, this paper proposes a generalized rank (GRANK) testing procedure that can be used on both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived, and their empirical properties are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to popular parametric tests.     

The Information Value of the Stress Test

We investigate whether the “stress test,” the extraordinary examination of the 19 largest U.S. bank holding companies conducted by federal bank supervisors in 2009, produced useful information for the market. Using standard event study techniques, we find that the market had largely deciphered on its own which banks would have capital gaps before the stress test results were revealed, but that the market was informed by the size of the gap; given our proxy for the expected gap, banks with larger capital gaps experienced more negative abnormal returns. Our findings are consistent with the view that the stress tests produced valuable information about banks.