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机构投资者的交易行为研究:来自中国台湾股市的证据
引用本文:陈志娟,马长峰,林苍祥.机构投资者的交易行为研究:来自中国台湾股市的证据[J].商业经济与管理,2012(3):52-64.
作者姓名:陈志娟  马长峰  林苍祥
作者单位:1. 浙江工商大学金融学院,浙江杭州,310018
2. 淡江大学商学院,台湾台北25137
基金项目:国家自然科学基金面上项目,教育部人文社科一般项目,教育部青年基金,教育部青年基金,福建省自然科学基金,浙江省高校人文社会科学重点研究基地(浙江工商大学金融学研究中心)资助项目
摘    要:文章以中国台湾股市为研究对象,主要研究机构投资者的交易行为。文章构建了机构投资者交易不平衡性指标——净交易,在此基础上研究机构投资者的交易行为。研究结果表明:台湾股票市场中以外资和投信基金为代表的金融机构投资者表现为正反馈的交易策略,并且交易的信息含量较高;一般法人的交易表现为负反馈的交易策略,并且交易的信息含量不足;而自营商由于交易动机复杂,交易的信息含量不明确。可见不同的机构投资者的交易行为并不相同。

关 键 词:机构投资者  交易策略  收益预测  信息

Research on the Trading Behavior of Institutional Investors: Evidence from Taiwan Stock Market
CHEN Zhi-juan , MA Chang-feng , LIN William T.Research on the Trading Behavior of Institutional Investors: Evidence from Taiwan Stock Market[J].Business Economics and Administration,2012(3):52-64.
Authors:CHEN Zhi-juan  MA Chang-feng  LIN William T
Institution:CHEN Zhi-juan1,MA Chang-feng1,LIN William T2(1.School of Finance,Zhejiang Gongshang University,Hangzhou 310018,China; 2.School of Business,Danjiang University,Taibei 25137,China)
Abstract:This paper mainly investigates the trading behavior of institutional investors by studying Taiwan stock market.First,we construct an indicator to measure institutional investors’ trading imbalance ─ investors’ net trading.This index reflects the intensity of investors’ trading imbalance.Then,we use the trading imbalance index to construct portfolios to study the trading behavior of institutional investors.The paper finds: first,foreigners and mutual funds in Taiwan stock market on behalf of financial institutional investors adopt positive feedback trading strategies,and the information contents of their transactions are high.Second,corporations’ trading performance shows the negative feedback trading strategies and their trading lacks information content.Third,due to dealers’ complex trading motives,the information content of their transactions is unclear.In summary,the research shows that the trading behavior of different institutional investors is not the same.
Keywords:institutional investors  trading strategy  return prediction  information
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