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研究生:蔡欣達
研究生(外文):Hsin-Ta Tsai
論文名稱:投資人恐慌與分析師評等修正
論文名稱(外文):Investors‟ Fears and Analysts‟ Recommendation Revisions
指導教授:林月能林月能引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:財務金融系所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:43
中文關鍵詞:波動度指數VIX期限結構評等修正從眾反從眾投資人情緒評等後續趨勢Hazard模型
外文關鍵詞:VIXVIX term structureRecommendation revisionHerdingAnti-herdingInvestor sentimentPost-recommendation driftHazard model
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我們使用CBOE的波動度指數(VIX)來當作投資人情緒指標,本研究發現,在不同的VIX期間下,分析師所做的評等修正對於累積超額報酬與修正後趨勢的影響,也會有所不同。

分析師在高VIX期間下,給予公司升評,所造成的一年累積超額報酬達到15.4%較高於,分析師在低VIX期間下,給予公司升評,所造成的一年累積超額報酬3.36%。另外,高VIX期間下,給予公司升評的後續上升趨勢,一直延長至一年後,也比較低VIX期間下,給予公司升評的後續上升趨勢延長至兩個月後來的長。反之,在低VIX期間下,給予降評的反應亦同。

我們將我們的實證發現,分成兩個部分來分析,第一個部份就是評等後續趨勢的不同,我們將測試,是否因為投資人在市場不確定時,所造成的遞延效果。第二部分就是,由於Jegadeesh and Kim (2010)研究顯示,當分析師反從眾時,投資人將會有較大的反應,因此我們假設,在高VIX期間下大部分的分析師會給降評,而在低VIX下大部分的分析師會給升評,因此我們可以推論,在高VIX期間升評以及低VIX期間降評,是反從眾的行為,因此市場反應會較大,因此我們延用了Jegadeesh and Kim (2010)的模型並加入變數來做測試。

然而VIX最多只能預測未來三十天的市場波動程度,而我們評等後續趨勢將有到一年以後的期間,因此我們加入了VIX期限結構,並且融入Cox (1972) 的Hazard模型來預測評等後續趨勢的結束機率,而我們觀察到中期的因子預測能力較好,因此我們推測投資人情緒將會影響評等修正的後續趨勢兩個月至六個月之久。


The study takes CBOE Volatility Index (VIX) as investors’ sentiment, and we discover that investors’ fears could change the effect of analysts’ recommendation revision on the cumulative abnormal return (CAR) of stocks and the post-recommendation drift. When analysts revise their recommendation at different levels of VIX periods, the CAR of stocks would have different value and the influenced period of stock reaction drift would persist in different lengths. When analysts upgrade at a high VIX period, an average CAR of +15.4% over the holding period [0, 252 days] is greater than an average CARs of 3.36% at the time that analysts upgrade at a low VIX period. When analysts upgrade at a high VIX period, the post-recommendation drift that extends from the event date till one year is longer than the drift at a low VIX period, which extends from the event date till two months. Conversely, when analysts downgrade at a low VIX period, an average CAR of 13.9% over the holding period [0,252 days] is greater than the average CARs of 3.6% at the time that analysts downgrade at a high VIX period. In other words, when analysts upgrade at a low VIX period, the post-recommendation drift which extends from the event date till one year is longer than the drift at a high VIX period which extends from the event date till six months.

We perform two parts of analyses to find out the situation we mention before through empirical findings in this study. First, since the upgraded (downgraded) post-recommendation drift at high VIX period is longer (shorter) than the post-recommendation drift at low VIX period, we examine that whether investor will fear for buying stock at high VIX period or not. Second, shown as Jegadeesh (2010), stock markets will react more dramatically with respect to anti-herding analysts’ recommendation revisions. Therefore, we argue the evidence that the upgraded CAR at a high VIX period is greater than the upgraded CAR at a low VIX period. Instead, downgraded CAR at a low VIX period is greater than the upgraded CAR at a low VIX period is happened when investors discovered analysts’ anti-herding. We extend Jegadeesh’s (2010) model to test whether stock price reaction following recommendation revisions will change at different VIX level periods or not. However, the VIX only can forecast one month fear at most. Comparatively, the post-recommendation revision drift could be six months. Therefore, we use VIX term structure to observe the relationship between forward VIX and the revision drift. Otherwise, we combine Cox’s (1972) hazard model to estimate probability of the drift end. We observe that midterm factor has well predictability to forecast probability of the drift end. According to our results, we indicate that investors’ sentiment would influence the revisions’ reaction from two months to six months.


Table of Contents

Table of Contents vii
Lists of Table viii
List of Figure viii
Chapter 1 Introduction 1
Chapter 2 Methodology 12
2.1. Regression Model 12
2.2. Hazard Model 16
2.3. Data and Sample 22
Chapter3 Empirical Tests 24
3.1. Price Reaction to Recommendation Revisions 24
3.2. Herding Regression 27
3.3. Relation between VIX and Global Consensus 32
3.4. Hazard Model 35
Chapter4 Conclusion 39
Reference 41



Reference

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Cox, D. R., 1972, Regression models and life-tables, Journal of the Royal Statistical Society135, 187-220.

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