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題名 流動性與定價錯誤:解構意見分歧
Liquidity and Mispricing: Decomposing Disagreement
作者 華濰儒
Hua, Wei-Ru
貢獻者 陳鴻毅<br>郭維裕
Chen, Hong-Yi<br>Kuo, Wei-Yu
華濰儒
Hua, Wei-Ru
關鍵詞 市場微結構
定價錯誤
異質信念
Market microstructure
Mispricing
Heterogeneous beliefs
日期 2019
上傳時間 1-Feb-2021 14:00:10 (UTC+8)
摘要 本研究以理論模型及實證證據,分析資產的報酬率及流動性如何受到投資人的資訊環境差異所影響,其中資訊環境中的差異包含:投資人之間的不對稱的資訊(asymmetric information, AI)、不同的雜訊(different information, DI)、不同的意見(different opinion, DO)。基於市場微結構之理論模型,本研究提供一個實證工具,能透過低頻率資料(年、月資料),衡量AI、DI與DO。研究樣本期間涵蓋1987-2016年。具體而言,本文基於一內生資訊交易模型,整合了AI、DI、DO與分析師報告等驅動資訊交易人下單行為的變數,並說明上述變數對流動性與價格的影響。此模型不僅提供了理論基礎,更能透過數學證明,進一步將分析師報告的意見分歧(analyst disagreement)解構成三個不同元素:資訊、雜訊與意見元素;上述三元素分別與投資人之間的AI、DI與DO程度相關。此理論模型說明:AI降低了資產的流動性並提高定價錯誤、DI同時增加了資產的流動性與定價錯誤、DO提升了資產的流動性並降低了定價錯誤。實證結果支持上述理論的預測。此外,實證亦發現平均而言,當股票具有較高的AI或DI時,其價格傾向被投資人高估;而股票具有較低的DO時,其價格傾向被投資人低估。
We investigate the information environment among investors, including asymmetric information (AI), different information (DI), and different opinion (DO), in affecting asset returns and liquidity. Using a market microstructure model, we provide an empirical device to measure AI, DI, and DO, and we estimate these measures using monthly and annual data for 1987–2016. Specifically, we incorporate AI, DI, DO, and analyst forecasts into a model of endogenous informed trading. This model allows us to decompose analyst disagreement into three components, information, noise, and opinion components, and then identify the level of AI, DI, and DO, respectively. Our model shows that AI increases illiquidity and pricing errors, while DI reduces illiquidity and increases pricing errors, and DO reduces illiquidity and pricing errors. The empirical results support the theoretical model. Moreover, we find that stocks with high AI or high DI tend to be overpriced, and stocks with low DO tend to be underpriced.
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Akins, Brian K, Jeffrey Ng, and Rodrigo S Verdi, 2012, Investor Competition over Information and the Pricing of Information Asymmetry, The Accounting Review 87, 35–58.
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Amihud, Yakov, Allaudeen Hameed, Wenjin Kang, and Huiping Zhang, 2015, The Illiquidity Premium: International Evidence, Journal of Financial Economics 117, 350–368.
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Baker, Malcolm, and Jeffrey Wurgler, 2006, Investor Sentiment and the Cross-Section of Stock Returns, The Journal of Finance 61, 1645–1680.
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Barron, Orie E, Oliver Kim, Steve C Lim, and Douglas E Stevens, 1998, Using Analysts’ Forecasts to Measure Properties of Analysts’ Information Environment, The Accounting Review 73, 421– 433.
Barron, Orie E, and Pamela S Stuerke, 1998, Dispersion in Analysts’ Earnings Forecasts as a Measure of Uncertainty, Journal of Accounting, Auditing & Finance 13, 245–270.
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描述 博士
國立政治大學
財務管理學系
102357502
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102357502
資料類型 thesis
dc.contributor.advisor 陳鴻毅<br>郭維裕zh_TW
dc.contributor.advisor Chen, Hong-Yi<br>Kuo, Wei-Yuen_US
dc.contributor.author (Authors) 華濰儒zh_TW
dc.contributor.author (Authors) Hua, Wei-Ruen_US
dc.creator (作者) 華濰儒zh_TW
dc.creator (作者) Hua, Wei-Ruen_US
dc.date (日期) 2019en_US
dc.date.accessioned 1-Feb-2021 14:00:10 (UTC+8)-
dc.date.available 1-Feb-2021 14:00:10 (UTC+8)-
dc.date.issued (上傳時間) 1-Feb-2021 14:00:10 (UTC+8)-
dc.identifier (Other Identifiers) G0102357502en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/133845-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理學系zh_TW
dc.description (描述) 102357502zh_TW
dc.description.abstract (摘要) 本研究以理論模型及實證證據,分析資產的報酬率及流動性如何受到投資人的資訊環境差異所影響,其中資訊環境中的差異包含:投資人之間的不對稱的資訊(asymmetric information, AI)、不同的雜訊(different information, DI)、不同的意見(different opinion, DO)。基於市場微結構之理論模型,本研究提供一個實證工具,能透過低頻率資料(年、月資料),衡量AI、DI與DO。研究樣本期間涵蓋1987-2016年。具體而言,本文基於一內生資訊交易模型,整合了AI、DI、DO與分析師報告等驅動資訊交易人下單行為的變數,並說明上述變數對流動性與價格的影響。此模型不僅提供了理論基礎,更能透過數學證明,進一步將分析師報告的意見分歧(analyst disagreement)解構成三個不同元素:資訊、雜訊與意見元素;上述三元素分別與投資人之間的AI、DI與DO程度相關。此理論模型說明:AI降低了資產的流動性並提高定價錯誤、DI同時增加了資產的流動性與定價錯誤、DO提升了資產的流動性並降低了定價錯誤。實證結果支持上述理論的預測。此外,實證亦發現平均而言,當股票具有較高的AI或DI時,其價格傾向被投資人高估;而股票具有較低的DO時,其價格傾向被投資人低估。zh_TW
dc.description.abstract (摘要) We investigate the information environment among investors, including asymmetric information (AI), different information (DI), and different opinion (DO), in affecting asset returns and liquidity. Using a market microstructure model, we provide an empirical device to measure AI, DI, and DO, and we estimate these measures using monthly and annual data for 1987–2016. Specifically, we incorporate AI, DI, DO, and analyst forecasts into a model of endogenous informed trading. This model allows us to decompose analyst disagreement into three components, information, noise, and opinion components, and then identify the level of AI, DI, and DO, respectively. Our model shows that AI increases illiquidity and pricing errors, while DI reduces illiquidity and increases pricing errors, and DO reduces illiquidity and pricing errors. The empirical results support the theoretical model. Moreover, we find that stocks with high AI or high DI tend to be overpriced, and stocks with low DO tend to be underpriced.en_US
dc.description.tableofcontents 1. Introduction 1
2. Literature Review 17
2.1. Asymmetirc Information and the Market Microstructure 18
2.2. Asymmetric Information and Asset Pricing 20
2.3. Different Information and the Market Microstructure 21
2.4. Different Information and Asset Pricing 23
2.5. Different Opinion and the Market Microstructure 23
2.6. Diffeent Opinion and Asset Pricing 25
3. The Model of Analyst Disagreement and Informed Trading (ADIT) 27
3.1. Equilibrium 29
3.2. Proof of Equilibrium 30
3.3. Analysis 32
3.4. Empirical Research Questions 36
3.5. How Can Analyst Disagreement Be Decomposed? 39
4. Decomposing Analyst Disagreement 42
4.1. Sample Selection 42
4.2. The Correlations between Candidates of Disagreement Components 44
4.3. Empirical Decomposition of Analyst Disagreement 46
5. Disagreement Components, Liquidity and Mispricing 49
5.1. Liquidity and Disagreement Components 49
5.2. Mispricing and Disagreement Components 51
5.3. Linear and Non-Linear Effects of Disagreement Components 53
6. Robustness Checks 55
6.1. Size-Neutral Portfolio Analysis 55
6.2. Portfolio Analysis for the Alternative Disagreement Components 56
6.3. Fama-MacBeth Regressions: Using Alternative Disagreement Components 59
6.4. Portfolio Analysis for the Subperiods 61
7. Summary and Conclusions 65
7.1. Summary 65
7.2. Conclusions 68
Appendix A. Theoretical Definition of Analyst Disagreement 73
Appendix B. Definitions of Variables 75
References 81
zh_TW
dc.format.extent 6427285 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102357502en_US
dc.subject (關鍵詞) 市場微結構zh_TW
dc.subject (關鍵詞) 定價錯誤zh_TW
dc.subject (關鍵詞) 異質信念zh_TW
dc.subject (關鍵詞) Market microstructureen_US
dc.subject (關鍵詞) Mispricingen_US
dc.subject (關鍵詞) Heterogeneous beliefsen_US
dc.title (題名) 流動性與定價錯誤:解構意見分歧zh_TW
dc.title (題名) Liquidity and Mispricing: Decomposing Disagreementen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abarbanell, Jeffery S, William N Lanen, and Robert E Verrecchia, 1995, Analysts’ Forecasts as Proxies for Investor Beliefs in Empirical Research, Journal of Accounting and Economics 20, 31–60.
Akins, Brian K, Jeffrey Ng, and Rodrigo S Verdi, 2012, Investor Competition over Information and the Pricing of Information Asymmetry, The Accounting Review 87, 35–58.
Amihud, Yakov, 2002, Illiquidity and Stock Returns: Cross-Section and Time-Series Effects, Journal of Financial Markets 5, 31–56.
Amihud, Yakov, Allaudeen Hameed, Wenjin Kang, and Huiping Zhang, 2015, The Illiquidity Premium: International Evidence, Journal of Financial Economics 117, 350–368.
Amihud, Yakov, and Haim Mendelson, 1986, Asset Pricing and the Bid-Ask Spread, Journal of Financial Economics 17, 223–249.
Back, Kerry, and Shmuel Baruch, 2004, Information in Securities Markets: Kyle Meets Glosten and Milgrom, Econometrica 72, 433–465.
Back, Kerry, Kevin Crotty, and Tao Li, 2018, Identifying Information Asymmetry in Securities Markets, The Review of Financial Studies 31, 2277–2325.
Baker, Malcolm, and Jeffrey Wurgler, 2006, Investor Sentiment and the Cross-Section of Stock Returns, The Journal of Finance 61, 1645–1680.
Bamber, Linda Smith, Orie E Barron, and Thomas L Stober, 1997, Trading Volume and Different Aspects of Disagreement Coincident with Earnings Announcements, The Accounting Review 72, 575–597.
Barron, Orie E, Oliver Kim, Steve C Lim, and Douglas E Stevens, 1998, Using Analysts’ Forecasts to Measure Properties of Analysts’ Information Environment, The Accounting Review 73, 421– 433.
Barron, Orie E, and Pamela S Stuerke, 1998, Dispersion in Analysts’ Earnings Forecasts as a Measure of Uncertainty, Journal of Accounting, Auditing & Finance 13, 245–270.
Barry, Christopher B, and Robert H Jennings, 1992, Information and Diversity of Analyst Opinion, Journal of Financial and Quantitative Analysis 27, 169–183.
Berkman, Henk, Valentin Dimitrov, Prem C Jain, Paul D Koch, and Sheri Tice, 2009, Sell on the News: Differences of Opinion, Short-Sales Constraints, and Returns Around Earnings Announcements, Journal of Financial Economics 92, 376–399.
Boehmer, Ekkehart, Gideon Saar, and Lei Yu, 2005, Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE, The Journal of Finance 60, 783–815.
Brennan, Michael J., Sahn-Wook Huh, and Avanidhar Subrahmanyam, 2013, An Analysis of the Amihud Illiquidity Premium, Review of Asset Pricing Studies 3, 133–176.
Brennan, Michael J., and Avanidhar Subrahmanyam, 1996, Market microstructure and asset pricing: On the compensation for illiquidity in stock returns, Journal of Financial Economics 41, 441–464.
Caballe, Jordi, and Murugappa Krishnan, 1994, Imperfect Competition in a Multi-Security Market with Risk Neutrality, Econometrica 62, 695.
Cen, Ling, K.C. John Wei, and Liyan Yang, 2016, Disagreement, Underreaction, and Stock Returns, Management Science 63, 1214–1231.
Chordia, Tarun, Sahn-Wook Huh, and Avanidhar Subrahmanyam, 2009, Theory-Based Illiquidity and Asset Pricing, The Review of Financial Studies 22, 3629–3668.
Daniel, Kent, and Sheridan Titman, 1997, Evidence on the Characteristics of Cross Sectional Variation in Stock Returns, The Journal of Finance 52, 1–33.
De Bondt, Werner FM, and Richard Thaler, 1985, Does the Stock Market Overreact?, The Journal of Finance 40, 793–805.
De Long, J Bradford, Andrei Shleifer, and Lawrence H Summers, 1990a, Noise Trader Risk in Financial Markets, Journal of Political Economy 98, 703–738.
De Long, J Bradford, Andrei Shleifer, Lawrence H Summers, and Robert J Waldmann, 1990b, Positive Feedback Investment Strategies and Destabilizing Rational Speculation, The Journal of Finance 45, 379–395.
Diamond, Douglas W, and Robert E Verrecchia, 1987, Constraints on Short-Selling and Asset Price Adjustment to Private Information, Journal of Financial Economics 18, 277–311.
Diether, Karl B, Christopher J Malloy, and Anna Scherbina, 2002, Differences of Opinion and the Cross Section of Stock Returns, The Journal of Finance 57, 2113–2141.
Duarte, Jefferson, and Lance Young, 2009, Why Is PIN Priced?, Journal of Financial Economics 91, 119–138.
Easley, David, S Hvidkjaer, and Maureen O’Hara, 2002, Is Information Risk a Determinant of Asset Returns?, The Journal of Finance 57, 2185–2221.
Easley, David, Soeren Hvidkjaer, and Maureen OH́ara, 2010, Factoring Information into Returns, Journal of Financial and Quantitative Analysis 45, 293–309.
Easley, David, N M Kiefer, and Maureen O’Hara, 1996, Liquidity, Information, and Infrequently Traded Stocks, The Journal of Finance 51, 1405.
Easley, David, and Maureen O’Hara, 2004, Information and the Cost of Capital, The Journal of Finance 59, 1553–1583.
Fama, Eugene F, and Kenneth R French, 1993, Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics 33, 3–56.
Fama, Eugene F, and Kenneth R French, 2015, A Five-Factor Asset Pricing Model, Journal of Financial Economics 116, 1–22.
Fama, Eugene F, and J D MacBeth, 1973, Risk, Return, and Equilibrium: Empirical Tests, Journal of political economy 81, 607–636.
Garfinkel, Jon A, 2009, Measuring Investors’ Opinion Divergence, Journal of Accounting Research 47, 1317–1348.
Givoly, Dan, and Josef Lakonishok, 1979, The Information Content of Financial Analysts’ Forecasts of Earnings: Some Evidence on Semi-Strong Inefficiency, Journal of Accounting and Economics 1, 165–185.
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dc.identifier.doi (DOI) 10.6814/NCCU202100150en_US