發(fā)布時(shí)間：2018-10-05 20:35

【摘要】：經(jīng)過三十多年的發(fā)展,我國(guó)的金融市場(chǎng)在各個(gè)方面都取得了很大的成就。金融市場(chǎng)的功能日趨完善,覆蓋面也越來越廣,在這樣的金融經(jīng)濟(jì)背景下,怎樣來衡量一個(gè)經(jīng)濟(jì)事件對(duì)企業(yè)產(chǎn)生的影響顯得尤為重要。事件研究法做為一種實(shí)證分析方法已在金融市場(chǎng)研究中得到了廣泛的應(yīng)用。但對(duì)于運(yùn)用事件研究法研究經(jīng)濟(jì)事件或金融事件來說,假設(shè)檢驗(yàn)方法的選擇是至關(guān)重要的。 事件研究中經(jīng)常用到假設(shè)檢驗(yàn)方法有參數(shù)檢驗(yàn)、符號(hào)檢驗(yàn)和秩檢驗(yàn),但這些方法的使用都有各自的約束條件。比如,參數(shù)檢驗(yàn)實(shí)際上已經(jīng)明確地假設(shè)了非正常收益的分布,檢驗(yàn)的可靠性在很大的程度上會(huì)受到分布的限制；符號(hào)檢驗(yàn)要求收益的分布具有對(duì)稱性,但日收益數(shù)據(jù)常顯示出非對(duì)稱性；而秩檢驗(yàn)的缺點(diǎn)是要估計(jì)方差,當(dāng)方差估計(jì)不準(zhǔn)時(shí),秩檢驗(yàn)的檢驗(yàn)功效就會(huì)大打折扣。 相比而言,經(jīng)驗(yàn)似然方法在不依賴于數(shù)據(jù)分布假設(shè)的條件下就可以對(duì)數(shù)據(jù)進(jìn)行分析；當(dāng)數(shù)據(jù)來自非正態(tài)或方差估計(jì)不穩(wěn)定時(shí),經(jīng)驗(yàn)似然法比其他方法通常要精確；經(jīng)驗(yàn)似然法的結(jié)果在很多情況下具有穩(wěn)健性,不易受樣本中異常值的影響；其次,經(jīng)驗(yàn)似然法不需要構(gòu)建軸統(tǒng)計(jì)量,避免了估計(jì)方差的難題。因此,我們將經(jīng)驗(yàn)似然方法引入到事件研究中來,以彌補(bǔ)上述檢驗(yàn)方法的不足。 本文選取了2012年我國(guó)證券市場(chǎng)上兩類事件進(jìn)行實(shí)證研究。一類是上市公司的違規(guī)處理,另一類是上市公司實(shí)施純派現(xiàn)的股利分配政策。對(duì)于這兩類經(jīng)濟(jì)事件我們分別采用了兩種模型(常量均值模型和市場(chǎng)模型)來計(jì)算事件窗內(nèi)的非正常收益,然后分別運(yùn)用參數(shù)和非參數(shù)檢驗(yàn)方法對(duì)非正常收益的顯著性進(jìn)行檢驗(yàn)。最后,從模型層面和事件層面對(duì)參數(shù)檢驗(yàn)和非參數(shù)檢驗(yàn)的結(jié)果進(jìn)行了對(duì)比分析。 通過檢驗(yàn)結(jié)果,我們發(fā)現(xiàn)在上市公司的違規(guī)處理事件中,非參數(shù)與參數(shù)檢驗(yàn)結(jié)果差異較大,很難斷定公告日后公司股票價(jià)格的持續(xù)波動(dòng)是否與違規(guī)處理公告相關(guān)。產(chǎn)生這樣的結(jié)果的原因可能是由于平均累積非正常收益在公告日之前就一直處于幅度較大的波動(dòng)狀態(tài),公告日后波動(dòng)幅度仍然較大,但是由于公告前的大幅波動(dòng)造成了顯著性檢驗(yàn)中CAR顯著趨于0,也就造成了對(duì)原假設(shè)的接受。在上市公司采取純派現(xiàn)股利分配政策的事件中,非參數(shù)檢驗(yàn)結(jié)果和參數(shù)檢驗(yàn)結(jié)果相差不大,我們有理由認(rèn)為上市公司純派現(xiàn)的股利政策確實(shí)對(duì)股票價(jià)格的波動(dòng)產(chǎn)生了較大的影響。
[Abstract]:After more than 30 years of development, China's financial market has made great achievements in all aspects. The function of the financial market is becoming more and more perfect and the coverage is becoming wider and wider. Under such a financial and economic background, how to measure the impact of an economic event on an enterprise is particularly important. As an empirical analysis method, event research has been widely used in financial market research. However, the choice of hypothesis testing method is very important for the study of economic or financial events by event research. Hypothesis testing methods are often used in event research, such as parameter test, symbol test and rank test. However, the use of these methods has its own constraints. For example, the parameter test has in fact clearly assumed the distribution of abnormal income, and the reliability of the test will be limited to a large extent by the distribution; the symbolic test requires that the distribution of income be symmetrical. The disadvantage of rank test is to estimate variance. When variance estimation is not punctual, the efficiency of rank test will be greatly reduced. In contrast, the empirical likelihood method can analyze the data without dependent on the assumption of data distribution, and the empirical likelihood method is usually more accurate than other methods when the data comes from non-normal state or the variance estimation is unstable. The results of empirical likelihood method are robust in many cases and are not easily affected by the outliers in the sample. Secondly the empirical likelihood method does not need to construct axial statistics to avoid the problem of estimating variance. Therefore, we introduce the empirical likelihood method into the event study to make up for the shortcomings of the above test methods. This paper chooses two kinds of events in China's securities market in 2012 to carry on the empirical research. One is the violation of listed companies, the other is the implementation of dividend distribution policy. For these two kinds of economic events, we adopt two models (constant mean model and market model) to calculate the abnormal returns in the event window. Then the significance of abnormal income is tested by parametric and non-parametric test methods. Finally, the results of parameter test and non-parameter test are compared and analyzed from model level and event level. Through the test results, we find that the non-parametric and parametric test results are quite different in the listed companies' irregularities, and it is difficult to determine whether the continuous fluctuation of the company stock prices after the announcement is related to the illegal treatment announcement. The reason for this result may be that the average accumulated abnormal income has been in a state of large volatility before the announcement date, and the volatility after the announcement is still large. However, due to the large fluctuation before announcement, CAR tends to zero significantly in the significance test, which leads to the acceptance of the original hypothesis. In the event that the listed company adopts the policy of pure dividend distribution, the results of non-parametric test and parameter test have little difference. It is reasonable to think that the dividend policy of the listed company has a great influence on the fluctuation of stock price.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

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