This article examines the possibility of an unusual change of the security returns, which is defined as 10% changes, by applying recurrent event data technique in survival analysis. The empirical evidences obtained from S&P 500 firms show that the momentum effect has a significantly positive relation with the probability of the acute fluctuations to occur. And the book-to-market factor, which can be seen as a value/growth indicator, is always negatively related to probability of the events. However, the market factor, the size factor, and the liquidity factor provide no additional information to predict the probability. Based on the estimated hazard rate for the market, we find an interesting result that during the bull market, the stock prices rise gradually over time while collapse acutely, and the converse is true when the market is bad.

Bankruptcy prediction has been of great interest to academics in the fields of accounting and finance for decades. Prior literatures focus mostly on investigating the covariates that lead to bankruptcy. In this thesis, however, we extend the issue of interest to what are the possible covariates that cause significant jumps in bankruptcy probability for a company. We consider the BSM-probability measure examined by Hillegeist, Keating, Cram, and Lundsedt (2004) to help us calculate the variation in bankruptcy probabilities for companies. In addition, recurrent event data analysis is applied to explore these jumps in bankruptcy intensity. By investigating the S&P500 constituents with sample consists of 343 S&P500-listed companies and 17,836 quarter observations starting from 1994 to 2007, we find that, in three of our models, all of these six covariates are negatively related to the recurrences of event that a company will suffer significant jumps in its bankruptcy probability during the next quarter. Additionally, macroeconomic covariates have greater explanatory power as factors affecting the probability of these jumps, while company-specific covariates contribute less to these recurrences of events. In comparison, we conduct another estimation based on the observation of slight increases in bankruptcy probability for companies. Contrary to what we find on the prior dataset, our empirical results suggest the factors that evoke these events are less prominent and their influences on the event recurrence are mixed.

以往重複事件分析法主要用於醫學與科學領域，近來學者逐漸將其應用至經濟、商學、社會科學等領域，本篇論文採用重複事件分析法，分析S&P 500公司股票回購的行為，回顧過去學者對股票回購之研究，我們檢視公司執行股票回購的三大原因，根據我們的實證結果發現公司可能會以股票回購作為影響公司資本結構的工具，再者亦可能用以減低剩餘資金，然而，本篇論文的實證結果較不支持訊號發射理論。 / In this study, we apply Cox proportional hazard model in recurrent event analysis, which usually used in medical and science studies, to analyze the determinants of the stock repurchase events of S&P 500 companies. We investigate three main incentives that companies conduct stock repurchase. The empirical results show that companies employ repurchase as a technique to alter capital structure. In addition, companies conduct stock repurchase to distribute excess capital. In contrast, there are little evidences to support signaling undervaluation.

在公司財務的領域中，探討公司資本結構決策主要有三個主流理論：靜態抵換理論、融資順位理論以及折時理論。本篇文章採用重複事件分析法，首先沿用Baker and Wurgler (2002)中提及之五個因素做為自變數，研究影響公司辦理現金增資危險函數之因子研究，研究結果顯示，公司現金增資之危險函數與財務槓桿成正向關係，此項證據傾向支持融資順位理論，然而本篇論文研究結果，並無顯著證據支持折時理論。本篇論文接著建立另一組變素設定，將價格趨勢納入模型中，取代原來在Baker and Wurgler(2002)中觀察折時現象之因子，結果顯示折時現象是顯著的。因此，本篇論文研究結果並未對是否支持折時理論下定論，值得思考的是，欲觀察公司是否存在折時現象，除了Baker and Wurgler(2002)中提及之變數之外，直接將價格趨勢納入模型或許是另一個可行之道。 / In the field of traditional corporate financing theories, there are three mainstream theories leading the way while talking about the firms’ financing decisions: static trade-off theory, pecking order theory, and market timing theory. In this paper, we apply the recurrent event analysis and follow the independent variables appearing in the Baker and Wurgler (2002) first to examine the factors that affect firms’ hazard rate to offer seasoned equity. The results indicate that higher leverage is in positive relation with the hazard rate of firms’ seasoned equity offering, meaning that firms’ financing decisions follow the pecking order theory to some degree. However, while the recurrent event analysis is adopted, the market timing effect becomes insignificant when considering the independent variables appearing in the Baker and Wurgler(2002). As a result, we proceed to establish another set of covariates in which the price trend factor is involved to examine the market timing effect. While the price trend factor is substituted for the market-to-book ratio to represent the market timing effect, the market timing effect turns out to be significant. Thus, we consider that using the price trend of the market directly may be a suitable way to examine the market timing effect.

This thesis surveys the method of extending Cox proportional hazard models (1972) and the general class of semiparametric model (2004) in the upgrades or downgrades of credit ratings by S&P. The two kinds of models can be used to modify the relationship of covariates to a recurrent event data of upgrades or downgrades. The benchmark credit-scoring model with a quintet of financial ratios which is inspired by the Z-Score model is employed. These financial ratios include measures of short-term liquidity, leverage, sales efficiency, historical profitability and productivity. The evidences of empirical results show that the financial ratios of historical profitability, leverage, and sales efficiency are significant factors on the rating transitions of upgrades. For the downgrades data setting, the financial ratios of short-term liquidity, productivity, and leverage are significant factors in the extending Cox models, whereas only the historical profitability is significant in the general class of semiparametric model. The empirical analysis of S&P credit ratings provide evidence supporting that the transitions of credit ratings are related to some determined financial ratios under these new econometrics methods.

處理多重事件或再發事件之事件發生時間的資料時，常會以Cox模式為基礎而予以延伸，其中較適合再發事件的模式為：A-G模式、GT-UR 模式、PWP-CP 模式及PWP-GT模式。這些模式又可按照是否以發生次數為分層變數，而分為未分層模式(包含A-G模式、 GT-UR 模式)，及分層模式(包含PWP-CP模式、 PWP-GT 模式)。 本論文將以改良的Cox延伸模式，包括對變異數進行修正或加入事故傾向(或隨機效果)，探討公務人員升等的快慢與哪些變數有關。變異數修正方式利用穩健標準誤以解決事件之再發時間之間的相依問題；事故傾向模式則主要是以隨機效果代表無法觀察到的個體間之異質性，且同一個體的各次發生時間共享相同的異質性，並假定異質性服從某種特定分配。對於各種Cox的延伸模式，我們可比較採用穩健變異數與否對估計及推論結果的差異，以及事故傾向加入前後，估計及推論結果與模式配適上的差異。 由本論文對公務人員升等資料的分析可發現，採用變異數修正方法時，未分層的模式有較小的變異數估計值，所以顯著的變數較多，包括性別、官等、教育程度及年齡；分層模式中顯著變數則只有官等及教育程度。若假定事故傾向服從對數Gamma分配，並加入於上述四種模式中，則顯著的變數與未加入事故傾向時一致，且各模式之下均無法拒絕所有人的事故傾向同為0的假設。這種現象或許是因為我們無法取得教育程度與公務人員考試及格種類之歷史資料，也有可能是因為公務人員升等的體制健全，且法規制定嚴謹，運作也有正常的模式可循所致。