市场情绪、溢价与波动

本文在投资者异质性条件下建立了市场情绪与情绪溢价的理论模型,利用封闭式基金折价率、换手率、月度新股首次发行数量、月度新股首次发行收益和基金现金持有比例构建一个衡量市场情绪的综合指标。研究结果表明:中国股市市场情绪产生溢价并使深沪两市的收益产生波动;中国股市不仅有情绪的短期持续性和长期的逆转性,而且存在短期收益惯性和长期收益反转效应;市场情绪是导致中国股市非理性大起大落的重要因素。     

沪深股市收益和风险分析

通过采用ECM模型及GARCH模型对沪深股市进行了研究,结果发现两市波动性存在非对称性和杠杆效应,沪深两市对的利空消息反应均大于利好消息的反应,但是深市风险大于沪市风险,当然其收益率也比较高。     

机构投资者、波动性和股票收益——基于沪深A股股票市场的实证研究

本文以2003—2011年沪深A股为样本,考察机构投资者对股市波动性及股票收益的影响。本文研究结果表明:(1)机构投资者持股比例的提高加剧了股票的波动,但股票波动加剧并不会吸引更多的机构投资者;(2)机构投资者持股比例提高会增加当期股票收益,机构投资者持股比例的净增加会显著提高下期股票收益率;(3)机构投资者持股比例较高的资产组合当期夏普比率较高,但机构投资者持股比例较高的资产组合下期夏普比率较低。     

卖方分析师研报与股票横截面收益分析

卖方分析师是股票市场中重要的信息中介,其研究报告中所推荐的标的股与其他非标的股之间在横截面上存在显著的收益差异.若根据单位时间内卖方分析师研报的发布数量对沪深两市A股进行滚动分组并构造套利组合,在短期内可以获得显著为正的溢价,但溢价会随即出现反转.个体投资者若不能在研报发布前或研报发布时成功捕获标的股并建仓,滞后于分析师研报的建仓行为会导致投资亏损.当然,在政策条件允许的前提下,融券卖空分析师研报推荐的股票也是一种明智的策略.     

股票市场收益跳跃性风险研究

中国股市是一个“政策市”，政策因素是造成我国股票市场收益（价格）跳跃性行为的最重要的原因。首先，本文深入而系统地阐述了股票收益（价格）发生跳跃性行为的经济机制，并将跳跃风险从总体风险中分离出来；然后描述了跳跃性风险的测度方法、跳跃性风险的定价及其对于风险管理的影响，以便能够为投资者和政府决策者提供一些有益的理论支撑，     

我国股票市场羊群行为实证研究

金融市场中的羊群行为是一种非理性的行为,对于投资者的效用和整个市场的稳定都有着消极的作用。文章通过三种实证方法对深沪两市股票市场的羊群行为进行了实证检验,发现在2004年以前深沪两市A股市场存在着显著的羊群行为,但在随后的近几年里,羊群行为并不显著。最后,文章探讨了影响中国股票市场羊群行为的几点原因。     

投资者情绪对我国IPO首日超额收益影响的实证研究

本文分析了投资者情绪对我国IPO首目超额收益的影响,主要参考了异质预期假说、狂热投资者假说和正向反馈交易者假说,分别用首日换手率作为异质预期指标,用中签率等作为狂热投资者情绪指标,用首日收盘价相对开盘价的涨跌幅作为正向反馈交易者情绪指标,分析了各种投资者情绪对我IPO首日超额收益率的影响,实证检验发现,投资者情绪是造成我国IPO首日超额收益的主要原因。     

我国证券市场收益波动度及相关性分析

本文以沪深两市A股指数为样本，采用GARCH（1，1）模型，研究收益波动度的性质特征，并探讨两个市场波动度的相关关系。实证结果表明，两市收益率存在尖峰厚尾与波动集簇等ARCH特征，它们的波动度存在Granger的因果关系，在预测时应综合考虑两市的市场走势。     

上市公司股利分配政策与其收益的实证分析

股利的发放来源于公司的自由现金流量,因而股利支付水平与公司收益之间必然存在一种紧密的联系。本文利用2004年沪、深两市上市公司的相关数据,采用实证分析的方法,对股利分配政策与上市公司的收益水平之间的关系及可能的原因进行了分析,表明我国上市公司股利分配政策与其收益确实存在着相关关系,收益水平越高,相应的股利支付水平也越高。     

异质情绪成分行为特征与资产收益动态关系

使用主成分分析法提取情绪成分是构建复合情绪指数最为有效的方法之一,但仅提取单一成分作为复合指数的方法没有考虑到不同投资者群体行为特征的异质性。文章在Baker和Wurgler(2006)的研究基础上,分析了由主成分分析得出的各情绪成分的行为异质性。进一步地采用VAR模型探讨了不同市场状态下,各异质情绪成分行为特征与沪深指数加权收益率的动态关系。     

Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility

Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time‐varying parameter that is created via the similarity of realized volatility and investor sentiment. Out‐of‐sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed.     

Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models

This paper applies the threshold quantile autoregressive model to study stock return autocorrelations and predictability in the Chinese stock market from 2005 to 2014. The results show that the Shanghai A-share stock index has significant negative autocorrelations in the lower regime and has significant positive autocorrelations in the higher regime. It attributes that Chinese investors overreact and underreact in two different states. These results are similar when we employ individual stocks. Besides, we investigate stock return autocorrelations by different stock characteristics, including liquidity, volatility, market to book ratio and investor sentiment. The results show autocorrelations are significantly large in the middle and higher regimes of market to book ratio and volatility. Psychological biases can result into return autocorrelations by using investor sentiment proxy since autocorrelations are significantly larger in the middle and higher regime of investor sentiment. The empirical results show that predictability exists in the Chinese stock market.     

Threshold effect in the relationship between investor sentiment and stock market returns: a PSTR specification

This article verifies whether the hypothesis of heterogeneous agent modelling and the behavioural heterogeneity framework can reproduce recent stylized facts regarding stock markets (e.g. the 1987 crash, internet bubble, and subprime crisis). To this end, we investigate the relationship between investor sentiment and stock market returns for the G7 countries from June 1987 to February 2014. We propose an empirical non-linear panel data specification based on the panel switching transition model to capture the investor sentiment-stock return relationship, while enabling investor sentiment to act asymmetrically, non-linearly, and time varyingly according to the market state and investor attitude towards risk. Our findings are twofold. First, we show that the hypotheses of efficiency, rationality, and representative agent do not hold in reproducing stock market dynamics. Second, investor sentiment affects stock returns significantly and non-linearly, but its effects vary with the market conditions. Indeed, the market appears predominated by fundamental investors in the first regime. In the second regime, investor sentiment effect is positively activated, increasing stock returns; however, when their overconfidence sentiment exceeds some threshold, this effect becomes inverse in the third regime for a high threshold level of market confidence and investor over-optimism.     

The impacts of investor sentiment and price uncertainty on the effects of open-market share repurchase announcements

This study probes into relationship between investor sentiment and cumulative abnormal returns (CAR) of share repurchase announcements, and it treats market return as threshold variable. By threshold regression model, it tries to find the effect of market situations on relation between investor sentiment and CAR. According to empirical result, in share market of Taiwan, investor sentiment can explain CAR. When share market is extremely pessimistic (market return lower than ?16.0053%), relation between investor sentiment and CAR will change to some degree. In addition, relation between price risk of announcement company and CAR will disappear with the extremely pessimistic situation of market.     

Daily market news sentiment and stock prices

In recent years there has been a tremendous growth in readily available news related to traded assets in international financial markets. This financial news is now available through real-time online sources such as Internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially significant impact on market stock price movement as these news items are swiftly transformed into investors sentiment which in turn drives prices. In this study, we use the Thomson Reuters News Analytics (TRNA) data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index constituents. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock returns of these constituents using a multi-factor model. We augment the Fama–French three-factor model with the day’s sentiment score along with lagged scores to evaluate the additional effects of financial news sentiment on stock prices in the context of this model using Ordinary Least Square (OLS) and Quantile Regression (QR) to analyse the effect around the tail of the return distribution. We also conduct the analysis using the seven-day simple moving average (SMA) of the scores to account for news released on non-trading days. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series. The results also indicate that the SMA measure does not have a significant effect on the returns. The analysis using Quantile Regression provides evidence that the news has more impact on left tail compared to the right tail of the returns.     

投资者情绪对资本市场稳定性的实证研究——来自截面效应的分析

文章借鉴心理学家在赛马赌博中发现的规律,对股票市场的截面效应进行了理论推测。同时,通过构建一个新的投资者情绪指标,采用非参数统计和回归模型实证检验了情绪指标的变动对特征组合收益率的影响并给出解释,并通过考虑系统风险的情绪变化与其他情绪代理变量验证了实证结果的稳健性。     

The role of sentiment in global risk premia

This article examines the role of sentiment for global risk premia. We analyse whether the global risk premia on macroeconomic fundamentals can be estimated more thoroughly if sentiment is included as additional conditioning information. The analysis is performed in the framework of a conditional multiple beta pricing model. The focus of analysis is the asset excess returns of the G-7 stock markets in the period from February 1999 to February 2012. The obtained results indicate that sentiment as conditioning information is able to contribute to the explanation of the general macroeconomic risk premia.     

Language sentiment in fundamental and noise trading: evidence from crude oil

ABSTRACT

Recent research has found the language sentiment in financial news to be a substantial driver of prices in financial markets, though there are two diametrically opposed interpretations for this: either markets perceive news sentiment as fundamental information (thus leading to changes in the valuation of assets) or news sentiment conveys a noise signal (thus contributing to the stochastic component of prices). The opposite roles are resolved in the context of crude oil prices by decomposing price movements into two components referring to fundamental and noise trading. Contrary to theoretical arguments in prior literature, we find empirical results supporting both interpretations.     

Enhanced index tracking with multiple time-scale analysis

This paper considers the process optimal strategies for an enhanced index tracking problem. The investment goals are set to achieve a higher return than the benchmark by setting the portfolio's risk profile identical to the primary index risk factors. Return differences between the index and the tracking portfolio are classified as positive and negative series. Multiple time-scale features of each series are extracted by the method of empirical mode decomposition. Then the positive return deviations are modeled by trend-like low frequency behavior and the negative return deviations are modeled by a trendless high frequency behavior. By adopting an immunity-based multi-objective optimization algorithm, the solutions for the process optimal enhanced index tracking are developed. Five data sets drawn from major world markets are adopted to implement our approach. The computational results show the superiority of our model.     

Where does return and volatility come from? The case of Asian ETFs

We analyze return and volatility of Asian iShares traded in the U.S. The difference in trading schedules between the U.S. and Asia offers a unique market setting that allows us to distinguish various return and volatility sources. We find Asian ETFs have higher overnight volatility than daytime volatility, explained by public information released during each local market's trading session. Local Asian markets also play an important role in determining each Asian ETF return. Nonetheless, returns for these funds are highly correlated with U.S. markets, indicative of the effects of investor sentiment and location of trade. Finally, returns in the U.S. market Granger-cause returns in all six Asian markets are analyzed.