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研究生:羅文昌
研究生(外文):Luo Wen-Chang
論文名稱:美國證券分析師推薦網際網路類股績效與影響因素之研究
論文名稱(外文):The Performance of "Internet Stocks" Recommendations by Security Analysts of major U.S Brokerage Firms and Infulential Factors of Recommendations Decision Process
指導教授:林修葳林修葳引用關係
指導教授(外文):Lin Hsiou-Wei
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:國際企業學研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2000
畢業學年度:88
語文別:中文
論文頁數:50
中文關鍵詞:分析師推薦網際網路類股基本變數網路評價
外文關鍵詞:analysts recommendationsInternet Stocksfudamental variablesvaluation of internet stocks
相關次數:
  • 被引用被引用:13
  • 點閱點閱:207
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  • 下載下載:52
  • 收藏至我的研究室書目清單書目收藏:4
本研究為國內外首篇針對證券分析師推薦網際網路類股(Internet Stocks)績效進行探討,所探討主題為:(1)分析師所對股票的推薦評級是否有訊息價值?不同時期、經營模式(Business Model)其訊息價值是否有所不同?(2)基本財務報表變數是否對於分析師推薦決策產生影響?(3)透過專業分析師的選股組合,基本變數的解釋力是否會因市場多、空頭或不同的經營模式而不同?
本文研究設計特色有三點:(1)針對網際網路類股進行分析,並進一步依其經營模式分類觀察;(2)以往的學者並未對分析師『無推薦』(0:No Recommendation)的資料進行探討,但『無推薦』有可能只是因為分析師想作負面推薦卻又怕得罪企業所得的結果,因此本研究進一步觀察『無推薦』所代表的訊息意涵,並以其作為對照組;(3)利用較不受分配限制的Ordered Probit Model捕捉基本變數對分析師推薦決策的影響。
本研究實證發現:(1)在市場模式下,分析師所推薦的股票整體而言不具有訊息價值,甚至較『無推薦』組的表現為差,但就不同的推薦評級而言,『薦售』及『強力薦售』較具有訊息意涵。(2)『無推薦』組表現較『強力薦購』及『薦購』組為佳,顯示分析師無法作出推薦決策的股票組合表現卻優於其經過研究而建議買進之股票,更可看出網際網路類股的評價難度!(3)以原始報酬進行衡量,發現『強力薦購』及『薦購』組股價表現亮麗,代表分析師雖不具擇股能力,卻有擇時能力;同時也發現大牌分析師或研究機構其預測準確度較高或影響投資者行為的能力較大。(4)針對不同經營模式發現,分析師在推薦非網路零售公司的股價表現平均而言較零售公司為佳,顯示對不同經營模式的公司而言,分析師的推薦績效有所不同,且分析師與投資者的看法亦有所差異。而在不同的年度比較上,發現1998年的表現最佳,1997年以前表現次之,而以1999年的表現最不理想,因此分析師似乎並不具有學習效果。(5)『經營模式』對於基本變數的影響力並不大,反而是『多空頭趨勢』會造成基本變數影響力的變化,空頭時期影響較顯著者,如企業規模、淨值市價比、系統風險貝他值等變數的重要性皆大於多頭時期,這代表在空頭時期,分析師較易回歸基本面,而其他基本變數的不顯著代表者財務變數在捕捉分析師心目中的決策準則上稍嫌薄弱,亦再度證明基本分析有其明顯不足之處。
This study examines the performance of "Internet Stocks" recommendations by security analysts of major U.S brokerage firms. The study provides insights into three research questions:
(1) Whether and to what extent different levels of analyst recommendations have the same information content? Do recommendation period and business model serve to explain the differential performance of analysts’ price forecasts?
(2) Do fundamental variables affect the decision process of analysts'' recommendations?
(3) Does market condition or business model affect the explanatory power of fundamental variables for the decision process of analysts'' recommendations?
Our study has three special features: (1) It focuses on "Internet Stocks" and catalyze the Internet firms into "E-tailers" and "Non-E-tailers." (2) It analyzes the information content of "No Recommendations." (3) It to capture the relationship between analysts'' recommendation magnitudes and fundamental variables via "Ordered Probit".
We conclude that, taken as a whole, the securities that analysts recommend do not outperform the benchmark portfolios. In contrast, "Sell" and "Strong Sell" recommendations appear to have information content. In addition, "No Recommendations" group outperforms "Strong Buy" and "Buy" groups. Our data also provide modest evidence that the securities analysts perform well in ranking the stocks when performance is measured by raw returns. This finding suggests that the analysts appear to have the "Market Timing" instead of "Stock Picking" abilities. We also find some "Famous Analysts" get the power to influence the investors'' behaviors and their performances are superior than the average. For different business models, we find the performance of "Non-E-tailers" groups is better than "E-tailers" group. This suggests that analysts seem to have difficulties on predicting the "E-tailers" another explanation is probably that the investors do not look the perspective of "E-tailers" as the same way as the analysts. Further more, for different recommendation periods, the performance in "Year 1998" is the most sparkling but in "Year 1999" the performance is not as well as expected. This result demonstrates the analysts'' recommendations on "Internet Stocks" do not have the learning effect. Finally, we find evidence that company size (LS), book value to market value ratio (BM) and system risk beta (BETA) have significant impact on analysts'' recommendations. More importantly, when the market is bearish, those financial fundamental variables are more likely to become statistically significant.
目錄
第一章 緒論………………………………………………………….…………….1
第二章 文獻探討………………………………………………………………….6
第一節 分析師推薦相關文獻…………………………………………………..…6
第二節 網際網路類股評價相關文獻…………………………………………..…8
第三章 研究方法………………………………………………………………...11
第一節 樣本與資料處理…………………………………………………………11
第二節 資料來源…………………………………………………………………13
第三節 分析師推薦訊息效果……………………………………………………13
第四節 基本變數對分析師推薦之影響…………………………………………14
第四章 實證結果與分析……………………………………………………….20
第一節 分析師推薦評級訊息效果………………………………………………20
第二節 基本變數的對分析師推薦決策的影響…………………………………35
第五章 結論與建議……………………………………………………………..43
第一節 結論…………………………………………………………………..…43
第二節 後續研究………………………………………………………………..45
附錄………………………………………………………………………………….46
參考文獻………………………………………………………..………………….49
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