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研究生:吳文臨
研究生(外文):Wen-Lin Wu
論文名稱:最適國際投資組合選擇:偏向本國資產投資迷惑研究
論文名稱(外文):Optimal International Portfolio Choice: Research in Home Bias Puzzle
指導教授:滑明曙滑明曙引用關係
指導教授(外文):Mingshu Hua
學位類別:博士
校院名稱:國立暨南國際大學
系所名稱:國際企業學系
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:2010
畢業學年度:98
語文別:英文
論文頁數:141
中文關鍵詞:濾波偏向本國資產投資不完全資訊資訊品質國際資產配置卡爾曼—布希濾波器學習
外文關鍵詞:FiltrationHome biasIncomplete informationInformation qualityInternational asset allocationKalman-Bucy filteringLearning
相關次數:
  • 被引用被引用:1
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  • 下載下載:22
  • 收藏至我的研究室書目清單書目收藏:0
本博士論文企圖利用不完全資訊模型來解釋為何本國人在進行國際投資時會有偏向本國資產現象(home bias)。本論文由兩篇研究—「不完全獲知投資人的國際資產配置」及「資訊品質對全球投資組合的影響」所組成並對偏向本國資產研究學者提供一些經濟與實務意涵。
本論文第一篇研究之目的在於藉由檢驗資訊品質對於不完全獲知之小型投資人其資產配置決策的影響來解釋偏向本國資產困惑。本研究之動機在於下列事實的出現:小型投資人僅用自身所擁有的公開資訊來預測本國與外國股票報酬的預期參數值,以及某些實證證據顯示偏向本國資產現象似乎由資訊不對稱和行為偏差所共同來解釋。作者利用標準濾波(filtering)理論來模擬這群小型投資人的先驗信念之更新過程,並且根據他們所理解的投資機會來計算他們的最適投資組合權重。
根據每月MSCI 指數資料,我們的數值結果顯示為了規避資訊之變動品質,當他們在更新他們對外國資產之預期報酬的估計時,具不完全獲知以及相對上比較保守的這些小型投資人會比平均數-變異數最適化所建議的最適權重更趨向持有較少外國資產。這意謂者投資人具有偏向本國資產投資的現象。然而這些小型投資人的偏向本國資產之投資組合選擇將會隨他們的估計準確度以及本國與外國資產報酬相關度等的增加而降低。
本論文之第二篇研究旨在以理論模型檢驗在不完全資訊下,對於不完全獲知專業投資人其權益證券之預期報酬的估計誤差對全球投資組合選擇的影響。他們利用可用之公開資訊及其私有訊號來更新有關本國與外國資產預期報酬之先驗信念。為了解決他們的濾波問題,作者利用多維濾波理論來分析其更新過程。並從他們所理解的投資機會來推斷他們的全球投資組合選擇決策。
根據數值結果指出,存在於較大預期報酬估計誤差下,專業投資人會持有較多本國資產當他們對外國新資訊呈現低度反應時,亦或會持有較多外國資產當他們對外國私有訊號過度反應時。我們同時也觀察到當投資人從動態學習效果中改善他們的先驗信念時,則降低其偏向本國資產或外國資產的現象會出現在他們的資產配置策略中。
根據這兩篇理論分析和校正測驗中,作者證實了資訊品質越佳,則其投資組合選擇會有較少偏差的現象。該證實使作者對過去數十年來偏向本國資產現象提供可能解釋。作者同時也延伸一個國家的不完全資訊模型到兩個國家不完全資訊模型來解釋投資人的投資組合策略。本論文指出偏向本國資產現象可以成功地被不完全資訊與行為偏差所聯合解釋。因此,研究人員在解釋為何投資人會過度加重本國資產持有時應以多解釋因子來解釋,絕非單一解釋因子來解釋。
The objective of this doctoral dissertation is to examine the effect of the incomplete information model on home bias. This dissertation is composed of two studies that may provide some implications for the home bias studies. These studies are “International asset allocation for incompletely-informed investors,” and “The impact of information quality on global portfolio choices.”
The first studies of my dissertation aims to explain the home bias puzzle by examining the effect of information quality on partially-informed smaller investors’ asset allocation decisions. This study is motivated by the evidence that smaller investors only use public available information what they have to predict the expected returns on the home and foreign assets and some empirical suggestions that the home bias is likely explained by a mixture of information asymmetries and behavioral biases. I use standard filtering theory to model these smaller investors’ updating process and calculate their optimal portfolio weights based on their perceived investment opportunities.
My calibration results based on monthly MSCI indexes indicate that in order to hedge for the changing quality of the information, as updating their estimates of expected returns of foreign assets, those smaller investors who are partially informed and relatively more conservative are inclined to hold less foreign assets than the mean-variance optimization. Besides, the magnitude of home bias in the portfolio of partially-informed smaller investors decreases with the precision of their estimate and the correlation between the returns on the home and foreign assets.
The second study of my dissertation intends to theoretically examine the effect of estimation errors in expected returns on equities under incomplete information on global portfolio choices for professional investors. These investors use both public available information and private signals to update their prior beliefs regarding expected returns on the home and foreign assets. To solve their filtering problems, I use multidimensional filtering theory to analyze their updating process. Then, I infer their global portfolio choice decisions from their perceived investment opportunity.
Our numerical results indicate that, given larger estimation errors in true expected returns, professional investors either hold more home assets as they under-react to new foreign information, or substantial foreign assets as they over-react to their foreign private signals. We also observe that a decline in home or foreign bias in their asset allocation strategies as they improve their prior beliefs from the dynamic learning effect.
Based on my two theoretical analyses and calibrating exercises, I confirm that the better information quality is, the less bias their portfolio choices are. This makes me provide a potential explanation for a reduction in home bias over decades. I also extend one-country incomplete information model into two-country incomplete information to explain investors’ portfolio strategies. My dissertation show that the home bias successfully can be explained a mixture of incomplete information and behavioral biases. Therefore, researchers explain why investors outweight their domestic assets from multiple explanation factors, not single explanation factor.
Table of Contents
Acknowledgement in Chinese …....………………………………………….i
Abstract in Chinese ………………………………………………………...iii
Abstract …………...………………………………………………………v
Table of Contents ………………………………………...………………..vii
List of Tables ………………………………………………………………ix
List of Figures ……………...…………………………………………………………x

Chapter 1 Introduction ………………………………………………....…………...1
1.1 Research motivations and objectives ……………………………………...3
1.2 Research methodologies and empirical results ……………………..……..5
1.3 Research innovations and contributions …………………………………..6
1.4 Research organization …………………………. …………………………7

Chapter 2 Literature review ………………………………. ……………………….8
2.1 The equity home bias: Facts and effects ……………………………. ……8
2.2 Explanations for the equity home bias ……………………………………13
2.2.1 Institutional barriers ………………………………………………….13
2.2.1.1 Explicit barriers …………………………………………...…...14
2.2.1.2 Implicit barriers …….......……………………………………...15
2.2.2 Hedging motives …………………………………….……………….19
2.2.3 Information asymmetries …………………………...…. ……………21
2.2.4 Behavioral bias …………..………………………. ………………….26
2.3 Discussions and future research ………….…………………...………….34

Chapter 3 International asset allocation for incompletely-informed investors ……….37
3.1 Introduction ……………………………………………………………….37
3.2 The two-country model …………………………………………………...47
3.2.1 The economy …………………………………………………………47
3.2.2 The filtering problems of partially-informed agents …………………51
3.2.3 Optimal portfolio choices …………………………………………….57
3.2.3.1 Portfolio choices of fully-informed agents …………...……….57
3.2.3.2 Portfolio choices of partially-informed agents ………...………59
3.3 Calibration results …………………………………………..……………64
3.4 Conclusions ……………………………………...……………………….79
3.5 Appendix: Proofs of Equations (3.5) and (3.6) …..…………...………….82

Chapter 4 The impact of information quality on global portfolio choices ……..….86
4.1 Introduction ………………………………………………………..……...86
4.2 Literature review ……………………………………………………….....89
4.3 Theoretical analysis ……………………………..………………………...94
4.3.1 The economy …………………………………..………………..……94
4.3.2 The filtering problems ………………………………………………..97
4.3.3 Optimal portfolio choices …………………………..……………….102
4.3.3.1 Portfolio choices of fully informed agents …….....…………..102
4.3.3.2 Portfolio choices of partially informed agents …...…..………103
4.4 Calibration analysis ………………………………………………..…….107
4.5 Conclusions …………………………………………………………..….118

Chapter 5 Conclusions and future research .………………...……………………121
5.1 Conclusions …………………………………..……………………….…121
5.2 Limitations and avenues for future research ………..………………..…123

References ………..…………………………...……………………………………125

List of Tables
Table 2.1 Home bias in equity portfolio from Year 2003 to Year 2008 ……………...9
Table 3.1 International home bias in equity portfolios, December 2005 …………....38
Table 3.2 Summary statistics for equity returns ……………………………………..66
Table 4.1 International home bias in equity portfolios, December 2006 ……………90
Table 4.2 Summary statistics for equity returns ……………………………..……..108

List of Figures
Figure 2.1 Information environments for global portfolio choices ……………….22
Figure 3.1 The 95% confidence intervals of the partially informed agent’s estimates of the true value of means ……………………………………………………….…69
Figure 3.2 Agents' optimal portfolio weights ………………………………….....….73
Figure 4.1 The 95% confidence intervals of the estimates of the true value of means launched in January 1988 ………………………………………………...……112
Figure 4.2 Agents’ optimal portfolio weights with estimation period in month (K).. 114
Figure 4.3 Agents’ optimal portfolio weights with time horizon in month (t) ……...117
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