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Liquidity and Mispricing: Decomposing Disagreement
|Issue Date: ||2021-02-01 14:00:10 (UTC+8)|
|Abstract: ||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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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0102357502|
|Data Type: ||thesis|
|Appears in Collections:||[財務管理學系] 學位論文|
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