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研究生:吳珍如
研究生(外文):Wu, Chen Ju
論文名稱:散戶投資者行為偏誤之研究 - 以非信息事件為例
論文名稱(外文):The Reactions of Individual Investors toward Non-Informative Events
指導教授:林靖庭林靖庭引用關係陳威光陳威光引用關係
學位類別:碩士
校院名稱:國立政治大學
系所名稱:金融研究所
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
畢業學年度:103
語文別:英文
論文頁數:51
中文關鍵詞:行為財務學羊群效應
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  由於有越來越多金融市場上的異常現象浮現,故本文目的在於探究效率學派對於金融市場之理性假設是否完美。本文利用最小交易量股票佔市場之比例來代表散戶投資者佔市場上之比例,並藉由檢視散戶投資者佔市場之比例與台股加權指數跨越千點事件之間的關係,來觀察台灣股市中散戶投資者的交易行為是否理性反應。由於「台股加權指數跨越千點事件」為一種不含實質信息但卻能吸引投資者注意力的事件,故若散戶投資者顯著對該類事件產生反應,則動搖效率市場假設的可能存在。而本文發現了三個現象:首先,平均而言,散戶投資者會對台股加權指數跨越千點事件產生顯著反應,而這顯示散戶投資者的交易行為存在不理性的現象。第二,散戶投資者彼此間的交易行為會不理性地相互影響且產生羊群效應,顯示其交易行為並非隨機無相關地分佈。最後,從當天短期角度或以一星期相對長期角度檢視,發現散戶投資者平均承受負報酬。
Due to the discovering of market anomalies, this paper is aimed to test the validity of the Efficient Market Hypothesis. I use the percentage of the smallest trade size in shares as the proxy for the percentage of appearance of individual investors. I examine the relationship between individual investors’ trading behaviors and the crossings of multiples of 1,000s of TAIEX index. The crossings of multiples of 1,000s of TAIEX index are non-informative events but can actually grab people’s attention and affect investors’ behaviors. Thus, if the behaviors of individual investors are significantly affected by the non-informative events, the possibility of the misassumption of the EMH exists. The results show that 1) individual investors react to the crossings of multiples of 1,000 of TAIEX index; 2) their trading strategies are irrational in similar ways, which causes the phenomenon of the herding effect; and 3) individual investors perform poorly from either short-term or long-term perspective.
TABLE OF CONTENTS

ABSTRACT....................................................III
LIST OF TABLES................................................V
LIST OF FIGURES...............................................V
1. INTRODUCTION...............................................1
2. LITERATURE REVIEW..........................................6
2.1 Introduction to Behavioral Finance....................6
2.2 Individual Investors Behaviors........................8
2.3 Attention-grabbing Events............................10
2.4 Herding Behavior.....................................13
3. DATA DESCRIPTION..........................................16
4. EMPIRICAL EVIDENCE........................................22
4.1 Autocorrelation of the past trade size...............22
4.2 Main regression model................................26
4.2.1 Methodology....................................26
4.2.2 Evidence.......................................28
5. ROBUSTNESS TESTS..........................................37
5.1 Combine two regressions into one regression..........37
5.2 Percentage of trade size V.S. Average trading volume.42
5.3 Reduce the lag terms from ten to five days...........45
6. CONCLUSION................................................48
7. REFERENCE.................................................49

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