臺灣博碩士論文加值系統

本論文由兩篇與財務分析師推薦有關的主題所構成。分析師因專業分析能力，以及對所分析的公司、產業與市場提供及時的看法而廣受投資人尊重與依賴，在證券市場扮演舉足輕重角色。本論文聚焦於分析師的買進與賣出推薦行為與後續報酬的關係，研究兩個有關財務分析師股票推薦的重要議題，並藉分析師
推薦形成可獲利的投資策略。
第一篇論文嘗試從分析師的推薦與新上市(櫃)承銷價折價水準這兩構面的交叉組合，尋求可行的投資獲利策略。將台灣2007-2011年初次上市上櫃的公司樣本依市場波動大小區分為兩個時期，發現在股市波動幅度很大的2007年至2009年，買入分析師作出偏空推薦且低承銷價折價水準的股票，同時放空分析師作出偏多推薦且高承銷價折價水準的股票，所形成的零投資組合，以Fama–French (1993) 模型分析，可獲得3.34%顯著的超額報酬；而在股市波動幅度較平穩的2010年至2011年， 獲利組合操作策略就有所不同，我們在高承銷價折價水準的族群中，買入分析師作出偏多推薦且同時放空分析師作出偏空推薦的股票組合，可獲得2.13%的超額報酬，雖然未達顯著水準。實證結果隱含著投資人可由分析師推薦與承銷價折價水準之組合，構建有利可圖之投資策略。
第二篇論文研究中國股票市場證券分析師在股票買賣推薦上之從眾行為。透過將LSV方法應用至分析師的投資建議，將分析師推薦行為區分為從眾推薦買進與從眾推薦賣出後作實證分析。實證結果顯示分析師推薦賣出之從眾程度遠大於推薦買進，此現象在多、空頭市場皆然。本論文發現分析師推薦股票的從眾程度與該股未來的股票收益呈現非線性相關。當眾分析師們集中地作出不利的投資建議時，在空頭市場中，投資者接受建議賣出持股可避開重大的投資損失；然而，在多頭市場，此時投資者反向作買入操作則能獲取更高利潤。最後，當進一步探討影響分析師從眾提出買進或賣出建議行為的因素，本研究發現投資績效隨市場情緒不同而變化，可由公司與市場的特質變數之迴歸分析結果得到部分的解釋。

This dissertation is comprised of two topics related to financial analyst recommendations. Financial analysts are professionals who are widely respected and relied upon by investors for their analytical skills and timely views on markets, sectors and individual stocks they cover. This dissertation focuses on analysts’ buy and sell recommendations and subsequent returns to examine several important issues relating to analysts ' stocks advices, and intends to construct profitable investment strategy from those advices.
The first essay is to integrate analysts’ recommendations and IPO underpricing levels to identify feasible profitable investment strategies. By using the analysis of Fama-French model (1993), the empirical results reveal that, during the period of dramatic volatility in the stock market between 2007 and 2009, the investment portfolio of buying stocks classified as unfavorable recommendation with lower underpricing, and short-selling stocks classified as favorable recommendation with higher underpricing, generates significant excess returns of 3.34%. Per relatively stable period between 2010 and 2011, the investment portfolio was different. Among high underpriced stocks, the portfolio of buying stocks with favorable recommendation and short-selling unfavorable recommendation generates positive excess returns of 2.13%, although it fails to be significant. This essay concluded that investors can construct profitable investment strategies through the appropriate integration of two factors: analysts’ recommendations and underpricing levels.
The second essay explores the herding behavior of security analysts, the firm characteristics in attracting herding, and the consequences of the herding recommendations on returns in China’s stock market. By applying the LSV method, the stock recommendations of analysts are partitioned into buy- and sell-recommendations. The results show that the herding level of sell-recommendation is greater than that of buy-recommendation, which is particularly evident in the bear market. Moreover, investors selling the holdings that analysts herd to recommend “sell” might help avoid losses in the bear market. Notably, when in the bull market, if analysts herd to make sell-recommendations, investors might gain more by acting conversely. These performance variations according to market sentiments appear to be partially explained by a firm’s characteristics based on additional regression analyses.

中文摘要………………………………………………………………...........….……I
Abstract……………………………………………………………………….……….II
Acknowledgements…………………………………………………………….……..III
Table of Contents………………………………………………………………......…IV
List of Tables……………………………………………………………………..….VI
List of Figure………………………………………………..…………….………..VII
Chapter 1 Introduction………......…………………………………………………...…1
Chapter 2 Profit from Analysts’ Recommendations and Underpricing of IPOs…….…5
2.1 Introduction…………………………………………………………..…….......…..5
2.2 Literature Review…………………………………………………………...…...….9
2.2.1 Analyst recommendations for IPOs…………………………..……....….…...9
2.2.2 Underpricing of IPOs……………………………………………..…....…....10
2.2.3 IPO stock price effect………………………………..…………………....…13
2.3 Data and Methodology…………………….…………………………....……........14
2.3.1 Data sources………………………………….……………...........…...….....14
2.3.2 Methodology…………………………………….…………………....……..18
2.3.2.1 Favorable and Unfavorable Recommendations………………...…..…18
2.3.2.2 Hot versus Cold Market ………………………………..……..……....19
2.3.2.3 Calculation of the post-IPO return performance…………………....…21
2.4. Empirical Results………………………………………..………..……..…….….23
2.5 Conclusion…………………………………………………..…………….....……39
Chapter 3 Do Security Analysts Herd on Stock Recommendations and Does
It Affect returns?....................................................................................................41
3.1 Introduction…………………………….……………………………..………...…41
3.2 Literature Review……………………………………………………...……....…..43
3.2.1 Analyst herding……………………………………………………….....…..43
3.2.2 Measures of herding……………………………………………………...….46
3.2.3 Analyst recommendation and subsequent stock return…………….……….47
3.3. Data and Methodology……………………………………………………..…….47
3.3.1 Data sources…………………………………………………………….…..47
3.3.2 Herding measure……………………………………………………....……48
3.3.3 Raw and abnormal future returns…………………………………….…….50
3.3.4 Explanatory variables…………………………………………..……....…50
3.3.4.1 Firm fundamental characteristics……………………………..……..51
3.3.4.2 Growth opportunities……………………………………….....……52
3.3.4.3 Market characteristics………………………………………….……52
3.3.4.4 Equity structure characteristics………………………….…………..53
3.3.5 Bull and bear market conditions………………………………….……….54
3.4. Empirical Results………………………………………………………….……54
3.4.1 Do analysts herd on recommendations?........................................................54
3.4.2 Does herding affect returns?..........................................................................57
3.4.3 Firm characteristics for herding……………………………………...……61
3.4.4 Bull and Bear Markets……………………………………………….……67
3.5. Conclusion…………………………………………………………...…..……..69
Chapter 4 Conclusions …………………………………………..….….…....……71

References…………………………………………………………..……….……..74

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