投资者学习行为与股票市场波动
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摘要
股票市场中投资者的有限理性和异质信念对其学习行为产生了深刻的影响,进而影响其投资行为,最终引发了股票市场的波动。基于此,研究以投资者有限生命且群体规模改变为假设,构建了包含经验、社会和政策学习行为的股票价格增长率预期模型,并采用上证A股2008年3月至2015年12月的股利数据进行数值模拟分析。研究发现,价格股利比(Pt/Dt)的模拟值与实际值的变化趋势和波动幅度基本一致,相关系数达90%以上。投资者学习行为是影响股票市场波动的重要因素,投资者在暴涨暴跌期更倾向于社会学习和个别政策学习,在较稳定期个人经验学习的权重会相对增加。
The limited rationality and heterogeneous beliefs of investors in the stock market have a profound impact on their learning behavior,which can affect their investment behavior and finally lead to the fluctuation of the stock market. Based on the assumption of limited life of investors and the change of group size,our research constructs an expected growth rate model of stock price that includes empirical learning,social learning and policy learning behaviors. We also use the dividend data of Shanghai A-share from March 2008 to December 2015 for numerical simulation analysis. The results show that the simulated value of price-dividend ratio( Pt/Dt) is basically consistent with the actual value in variation trend and fluctuation range,and the correlation coefficient is more than 90%.It has been also found that the investor's learning behavior is an important factor affecting stock market volatility.Specifically,investors tend to favor social and specific policy learning during the periods of boom and bust,and the weight of individual experience learning will increase relatively in relatively stable periods.
The limited rationality and heterogeneous beliefs of investors in the stock market have a profound impact on their learning behavior,which can affect their investment behavior and finally lead to the fluctuation of the stock market. Based on the assumption of limited life of investors and the change of group size,our research constructs an expected growth rate model of stock price that includes empirical learning,social learning and policy learning behaviors. We also use the dividend data of Shanghai A-share from March 2008 to December 2015 for numerical simulation analysis. The results show that the simulated value of price-dividend ratio( Pt/Dt) is basically consistent with the actual value in variation trend and fluctuation range,and the correlation coefficient is more than 90%.It has been also found that the investor's learning behavior is an important factor affecting stock market volatility.Specifically,investors tend to favor social and specific policy learning during the periods of boom and bust,and the weight of individual experience learning will increase relatively in relatively stable periods.
引文
[1]Malmendier U,Nagel S.Depression babies:Do macroeconomic experiences affect risk taking?[J].Quarterly Journal of Economics,2011,126(1):373-416.
[2]Ehling P,Graniero A,Heyerdahl-Larsen C.Asset prices and portfolio choice with learning from experience[J].Review of Economic Studies,2018,85(3):1752-1780.
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[17]顾荣宝,刘海飞,李心丹,李龙.股票市场的羊群行为与波动:关联及其演化---来自深圳股票市场的证据[J].管理科学学报,2015,18(11):82-94.
[18]许年行,于上尧,伊志宏.机构投资者羊群行为与股价崩盘风险[J].管理世界,2013(7):31-43.
[19]王明涛,路磊,宋锴.政策因素对股票市场波动的非对称性影响[J].管理科学学报,2012,15(12):40-57.
[20]陈国进,张润泽,赵向琴.经济政策不确定性与股票风险特征[J].管理科学学报,2018,21(4):1-27.
[21]徐亚平.公众学习、预期引导与货币政策的有效性[J].金融研究,2009(1):50-65.
[22]朱小能,周磊.未预期货币政策与股票市场---基于媒体数据的实证研究[J].金融研究,2018(1):102-120.
[23]Dunlop C A,Radaelli C M.Does policy learning meet the standards of an analytical framework of the policy process?[J].Policy Studies Journal,2018,46(S1):48-68.
[24]Brown A A,Rogers L C G.Heterogeneous beliefs with finite-lived agents[J/OL].SSRN Electronic Journal,2009-07-28.
[25]Allen F,Morris S,Shin H S.Beauty contests and iterated expectations in asset markets[J].Review of Financial Studies,2006,19(3):719-752.
[26]Cespa G,Vives X.The beauty contest and short-term trading[J].Journal of Finance,2015,70(5):2099-2154.
[27]张维,武自强,张永杰,熊熊,冯绪.基于复杂金融系统视角的计算实验金融:进展与展望[J].管理科学学报,2013,16(6):85-94.
[28]卞志村,高洁超.适应性学习、宏观经济预期与中国最优货币政策[J].经济研究,2014(4):32-46.
[29]Shiller R J.Irrational exuberance[M].2nd ed.New Jersey:Princeton University Press,2005.
[30]王远林.中国股票市场股利、股票价格之间非线性Granger因果关系的实证研究[J].预测,2014(1):45-49.
(1)注:在研究仅有政策预期的作用时,为匹配股票价格的剧烈波动,改变总供应量L为50000000,其他参数值不变;而模拟其他预期的作用时,使用的参数值完全相同。此外,因模型包含随机数,其结果均为模拟100次的平均值。
[2]Ehling P,Graniero A,Heyerdahl-Larsen C.Asset prices and portfolio choice with learning from experience[J].Review of Economic Studies,2018,85(3):1752-1780.
[3]List J A.Does market experience eliminate market anomalies?The case of exogenous market experience[J].American Economic Review,2011,101(3):313-317.
[4]Malmendier U,Nagel S.Learning from inflation experiences[J].Quarterly Journal of Economics,2016,131(1):53-87.
[5]Adam K,Marcet A.Internal rationality,imperfect market knowledge and asset prices[J].Journal of Economic Theory,2011,146(3):1224-1252.
[6]Adam K,Marcet A,Nicolini J P.Stock market volatility and learning[J].Journal of Finance,2016,71(1):33-82.
[7]Nakov A,Nu1o G.Learning from experience in the stock market[J].Journal of Economic Dynamics&Control,2015,52:224-239.
[8]Hussam R N,Porter D,Smith V L.Thar she blows:Can bubbles be rekindled with experienced subjects?[J].A-merican Economic Review,2008,98(3):924-937.
[9]Xiong W,Yu J L.The Chinese warrants bubble[J].A-merican Economic Review,2011,101(6):2723-2753.
[10]Greenwood R,Nagel S.Inexperienced investors and bubbles[J].Journal of Financial Economics,2009,93(2):239-258.
[11]潘越,戴亦一,陈梅婷.基金经理的投资经验、交易行为与股市泡沫[J].中国工业经济,2011(1):120-129.
[12]Seru A,Shumway T,Stoffman N.Learning by trading[J].Review of Financial Studies,2010,23(2):705-739.
[13]Xiao E.Justification and conformity[J].Journal of Economic Behavior&Organization,2017,136:15-28.
[14]Kluger B D,McBride M E.Intraday trading patterns in an intelligent autonomous agent-based stock market[J].Journal of Economic Behavior&Organization,2011,79(3):226-245.
[15]Heller Y,Mohlin E.Social learning and the shadow of the past[J].Journal of Economic Theory,2018,177:426-460.
[16]孟涓涓,赵龙凯,刘玉珍,尤炜.社会性学习、从众心理和股市参与决策[J].金融研究,2013(7):153-165.
[17]顾荣宝,刘海飞,李心丹,李龙.股票市场的羊群行为与波动:关联及其演化---来自深圳股票市场的证据[J].管理科学学报,2015,18(11):82-94.
[18]许年行,于上尧,伊志宏.机构投资者羊群行为与股价崩盘风险[J].管理世界,2013(7):31-43.
[19]王明涛,路磊,宋锴.政策因素对股票市场波动的非对称性影响[J].管理科学学报,2012,15(12):40-57.
[20]陈国进,张润泽,赵向琴.经济政策不确定性与股票风险特征[J].管理科学学报,2018,21(4):1-27.
[21]徐亚平.公众学习、预期引导与货币政策的有效性[J].金融研究,2009(1):50-65.
[22]朱小能,周磊.未预期货币政策与股票市场---基于媒体数据的实证研究[J].金融研究,2018(1):102-120.
[23]Dunlop C A,Radaelli C M.Does policy learning meet the standards of an analytical framework of the policy process?[J].Policy Studies Journal,2018,46(S1):48-68.
[24]Brown A A,Rogers L C G.Heterogeneous beliefs with finite-lived agents[J/OL].SSRN Electronic Journal,2009-07-28.
[25]Allen F,Morris S,Shin H S.Beauty contests and iterated expectations in asset markets[J].Review of Financial Studies,2006,19(3):719-752.
[26]Cespa G,Vives X.The beauty contest and short-term trading[J].Journal of Finance,2015,70(5):2099-2154.
[27]张维,武自强,张永杰,熊熊,冯绪.基于复杂金融系统视角的计算实验金融:进展与展望[J].管理科学学报,2013,16(6):85-94.
[28]卞志村,高洁超.适应性学习、宏观经济预期与中国最优货币政策[J].经济研究,2014(4):32-46.
[29]Shiller R J.Irrational exuberance[M].2nd ed.New Jersey:Princeton University Press,2005.
[30]王远林.中国股票市场股利、股票价格之间非线性Granger因果关系的实证研究[J].预测,2014(1):45-49.
(1)注:在研究仅有政策预期的作用时,为匹配股票价格的剧烈波动,改变总供应量L为50000000,其他参数值不变;而模拟其他预期的作用时,使用的参数值完全相同。此外,因模型包含随机数,其结果均为模拟100次的平均值。