从投资者情绪看资产定价理论发展与演进

传统资产定价理论的假设在投资者实践当中难以有效运用,存在很多资产定价模型所无法解释的金融异象。基于投资者情绪的行为资产定价理论一直是解释金融市场异象的主要理论基础之一,从投资者情绪的角度去研究资产定价理论是非常有意义的。文章针对当前研究的不足,构建了基于异质情绪的资产定价模型,提出了加强投资者情绪理论与市场微观结构理论相结合,与非线性资产定价研究模式相结合,考虑政策、文化因素的资产定价研究模式。     

互联网基金对股票市场的影响——基于大数据情绪指数的实证研究

投资者在进行投资决策时易受到自身情绪的影响，并且投资者行为是影响金融市场间波动溢出的直接原因。运用文本挖掘技术对新浪微博2014年4月至2016年7月的博文进行文本分析和随机森林主成分分析并构建微博大数据投资者情绪指数，根据投资者情绪指数研究互联网基金市场对股票市场的影响，结果表明互联网基金市场对股票市场具有波动溢出效应。     

金融市场投资者情绪研究述评

投资者情绪是行为金融研究的重点,它对投资者行为和金融市场运行造成重大影响。文章从投资者情绪定义、影响因素、测度指标、研究方法及现状几方面对国内外的相关研究进行归纳总结,提出了现有研究情绪指标指标单一、适宜性较差等不足,结合中国金融市场与国外市场相比仍然存在个人投资者占比较高、市场投机较大、金融产品结构单一等特点,提出了未来关于投资者情绪方面的研究应关注投资者结构、投资者心理、投资者指标构建。     

期权定价的“有限理性”视角：基于投资者情绪的研究

2013年的种种迹象表明我国金融市场将进入期权时代。期权价值的确定是期权功能发挥的前提和基础。本文从行为金融学的角度出发,在传统二叉树期权定价模型的基础上,通过引入投资者情绪变量构建基于投资者情绪的欧式看涨期权定价模型。模型表明,投资者情绪不仅通过行为随机折现因子直接影响期权价值,而且通过影响标的证券的价值运行概率间接影响期权的最终价值;投资者情绪与期权价格之间呈现正相关关系。最后,基于长虹CWB1的实证研究也表明了传统期权定价模型存在的缺陷,通过求解权证实际交易价格与理论价格之间的偏差,可以反算出投资者情绪,进而预测权证的行为价值。     

中国股市春节“节前效应”与投资者情绪——基于31个行业面板数据的实证研究

本文根据1991~2013年的上证综指数据,对中国股市春节是否存在"节前效应"进行研究。结果发现,春节前超额收益现象在早期并不稳健,但是随着中国股市的发展而逐渐增强。随后应用主成分分析法构建投资者情绪综合指标,并建立面板数据模型,针对行为金融学关于"节前效应"的解释进行实证检验,发现投资者情绪对春节前超额收益具有明显的正向作用,且不同行业对投资者情绪的敏感程度不同,导致了"春节效应"的行业差别。     

基于行业关联网络的行业集群划分与应用

基于改进的时间序列相似性度量构建行业关联网络,运用最小生成树算法简化网络结构。通过对中国股票市场进行实证研究,考察网络结构得到行业群的划分,并通过统计得到行业群在不同经济周期阶段下表现的一般规律,为战略性行业配置和投资决策提供有价值的参考信息。     

会计信息与投资者情绪相关性研究

本文构建了基于换手率的投资者情绪测量指标,随机抽取沪深两市300家上市公司2009年的年报数据作为样本,对会计信息与投资者情绪之间的关系进行了分析。结果显示:反映股东获利能力和公司盈利能力的会计信息能显著影响投资者情绪,除每股收益外其他会计信息与投资者情绪之间并非呈简单的线性关系。     

基于网络嵌入的企业优势资源重构

网络嵌入是研究优势资源重构重要手段,本文把优势资源重构分为识别与引进、共享、吸收和创新应用四个阶段,分析了网络嵌入与优势资源重构的关联性;构建了系统动力学模型,通过仿真分析,得出适度的网络嵌入能促进优势资源重构的结论 ;基于上述结论,结合海洋装备制造企业自身的优势资源状况、业务类型与地理状况,提出了适合的网络嵌入路径及减少重构过程优势资源损失量的策略。     

投资者有限关注、综合情绪指标与股票收益北大核心

本文选取2008年和2015年两次"股灾"时间段内A股主板市场数据,采用股吧发帖量作为投资者关注度的衡量指标,并将投资者关注度加入到综合情绪指标的构建中,验证了投资者关注度作为情绪代理指标的合理性。同时,针对不同特征的投资组合构建了不同的情绪指标,采用GARCH(1,1)-M模型分析投资者情绪变化对股票收益的横截面效应。结果显示:小市值股、低价股、高市盈率股、高市净率股的投资组合在情绪高涨时能够获得更多的收益。通过比较投资者在面对两次"股灾"时的反应,发现投资者并没有因为受到第一次股灾时的"教训"而变得更加理性,情绪变化对股票收益的影响反而更大。     

基于WEKA平台的文本聚类研究与实现

文本聚类是文本挖掘领域的一个重要研究分支,是聚类方法在文本处理领域的应用。本文首先对基于空间向量模型的文本聚类过程做了较深入的讨论和总结。另外,本文回顾了现有的文本聚类算法,以及常用的文本聚类效果评价指标。在研究了已有成果的基础上,本文利用20Newsgroup文本语料库,针对向量空间表示模型,在开源的数据挖掘平台WEKA上实现了文本预处理和k-means聚类算法,并根据实际聚类效果,就文本表示、特征选择、特征降维等方面提出优化方案。     

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.     

The fluctuation correlation between investor sentiment and stock index using VMD-LSTM: Evidence from China stock market

The impact of the investor sentiment on China’s capital market price volatility is concerned under the perspective of the behavioral finance. Firstly, in terms of the existing methods of establishing the investor sentiment index, the composite investor sentiment index which include six indicators (five objective indicators and a subjective indicator) are obtained. Secondly, VMD-LSTM (Variational Mode Decomposition and Long Short Term Memory) hybrid neural network model is used to decompose and restructure the investor sentiment index and the Shanghai Security Exchange Composite Index (SSEC) into the short-term, medium-term and long-term trend. Each trend is trained to obtain the forecasting results in three different time scales, and then to achieve the final predicting results by superimposing the output of each trend. Furthermore, compare with other prediction methods, the model can indeed improve the overall predicting accuracy. Finally, GARCH model and the co-integration error regression model are used to discuss the fluctuation correlation and VAR (Vector Auto-regression) models are established to analyze the causality between the stock market indices and the investor sentiment index.     

Information asymmetry,sentiment interactions,and asset price

We develop an asset pricing model with sentiment interactions between institutional and individual investors under the condition of information asymmetry. Our model considers private information and investor sentiment, two imperfections in securities markets, and integrates them into a theoretical model to investigate the role of the interaction between information asymmetry and investor sentiment in asset pricing. We show that the joint effect of private information and investor sentiment deviate the price of risky assets and efficiently explains anomalies in the stock market. Investor sentiment changes the effect of information on the equilibrium price relative to a world where all investors are completely rational. Private information changes the effect of investor sentiment on the equilibrium price in comparison with a scenario with symmetric market information. In addition, the individual investors’ learning and the disclosure of information both allow private information to be better integrated into the price and simultaneously changes the effect of investor sentiment on the equilibrium price.     

Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.     

Geopolitical risks and investor sentiment: Causality and TVP-VAR analysis

This paper aims to explore the relationship between geopolitical risks (GPR) and investor sentiment in the US stock market based on Granger causality test and time-varying parameter vector autoregression (TVP-VAR) analysis. Empirical results indicate that changes in geopolitical risks can affect investor sentiment, whereas investor sentiment cannot affect geopolitical risks. More importantly, geopolitical risks have significant negative effects on investor sentiment, suggesting that higher (lower) geopolitical risks dampen (promote) investor sentiment directly or indirectly. Specifically, the negative effects of geopolitical risks show substantial time variation and generally decrease over time. The response of investor sentiment appears to be more pronounced in the short and medium term than in the long term, and is more sensitive to domestic geopolitical events. There is no significant difference in the impacts of geopolitical risks (GPR), geopolitical threats (GPT), and geopolitical acts (GPA). The results obtained are robust for alternative investor sentiment and geopolitical risk indicators.     

Excess co-movement of agricultural futures prices: Perspective from contagious investor sentiment

This study investigates the excess co-movement of agricultural futures prices from a new perspective of contagious investor sentiment. This study shows that contagious investor sentiment is a key determinant of excess co-movement of agricultural futures prices, by using contagious investor sentiment among different agricultural futures. Further, this study decomposes contagious investor sentiment into expected and unexpected contagious investor sentiment. Results show that both of them can positively affect excess co-movement of agricultural futures prices. More interestingly, expected contagious investor sentiment outperforms unexpected contagious investor sentiment in soybean 1 future, soymeal future, and strong wheat future. In general, the results of this study can provide strong support for the significant roles of contagious investor sentiment in asset pricing applications.     

基于投资者情绪的IPO首日收益研究

本文借鉴国内外学者关于投资者情绪度量指标选取的方法,遴选出适合中国证券市场特征的四类投资者情绪指标,通过主成分分析方法提取反映投资者情绪的主要因素,并在此基础上,利用VAR模型对投资者情绪与IPO首日收益之间的领先滞后关系进行刻画,主要考察投资者情绪的新息冲击对IPO首日收益动态脉冲影响.结果表明,投资者情绪显著影响IPO首日收益,投资者对未来的预期同样受到IPO首日表现的影响,投资者情绪指标之间存在显著的扩散和弥漫效应.     

The impacts of investor sentiment on returns and conditional volatility of international stock markets

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean–variance relation. This paper takes an international approach using weekly market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.