This dissertation mainly explores the effect of investor sentiment on stock returns and volatility on Growth Enterprise in China using monthly data from Shenzhen Stock Exchange of China from June 2010 to November 2019. Using five explicit and market-related implicit indicators an investor sentiment has been measured and constructed with the help of principal component analysis. The analysis has been done by employing a vector autoregression(VAR) model and impulse response functions (IRFs) generated from a VAR model to examine the relationship between the unanticipated changes in investor sentiment and stock returns and volatility. We also establish EGARCH model to test the validity of previous results and if the asymmetric impact of positive and negative news on market returns volatility. The results show a significant impact of investor sentiment on stock return and volatility. We also document that there is a positive leverage effect between investor sentiment and the volatility of returns. The findings of this paper can help both individual and institutional investors have a better understanding of GEM market and improve their investment returns by incorporating investor sentiment into their asset forecasting model. This paper also provides policymakers guidance on reducing volatility on stock markets from the perspective of investor sentiment. Additionally, this paper has important contributions to behavioral finance and adds to the limited number of studies on investor sentiment and stock return in not only the Chinese market but emerging markets.

More and more events and anomalies that have happened in recent years cannot be explained by traditional models, which leads to a pervasive doubt of the effectiveness of the efficient market hypothesis. In particular, over ninety percent of Taiwan¡¦s stock market investors are individuals, and the noise trading phenomenon is very common and has a great effect upon the return of stock. Hence, the measure of investor sentiment formed by noise traders becomes a task for the researcher studying the factors which effect the stock return in Taiwan. The objective of this paper is to find the investor sentiment proxy variables which can be a significant factor in explaining stock return. This analysis adopts the arbitrage pricing model of the macroeconomic factors. The sample contains data for most listed stocks on the Taiwan Stock Exchange from 1984 to 2002. By combining the stock or company characteristic related to the noise traders¡¦ perception, including market value, stock and etc., and phenomenons effect by investor sentiment, including closed-end fund discount, initial returns on IPOs, and number of IPOs to the arbitrage pricing model , we found that closed-end fund discount and initial returns on IPOs are significant and appropriate to investor sentiment proxy variables. However, the number of IPOs is not significant enough

This work finds that Managerial and Investor Sentiment are determined by differing sets of economic variables, that share some common factors: inflation, liquidity and the term premium. Decomposing the Sentiment Indices, it is found that the Investor Sentiment Model Component and the Managerial Sentiment Residual Component are primarily responsible for the predictive power of predicting cross-sectional stock returns that is much stronger than previous results. Evidence is presented that part of the predictive power is due to the components predicting priced market factors. Overall, there is strong evidence that the predictive power of Managerial Sentiment is driven primarily by private information, while the predictive power of Investor Sentiment is driven by public information.

Traditional asset pricing models such as Capital Asset Pricing Model (CAPM) and Discounted Cash Flow (DCF) have been used widely in academics and practice due to their simplicity and popularity. The CAPM is a prescriptive model that describes the relationship between a stock’s required return and risk relative to the movements in the market, while the DCF is a descriptive model that measures the realized rate of return on a stock based on the market price of the stock, which in turn incorporates investor perceptions about the stock and the market. In an ideal, efficient market where investors behave rationally, we should not see much of a difference between stock returns estimated from these two models. However, because investor perceptions affect the DCF estimate of returns, changes in investor confidence without accompanying changes in firm risk can affect the DCF estimate without changing the CAPM estimate. High growth firm returns are more likely to incorporate changes in investor perception because more of their value is generated from realization of future growth opportunities. In this research, I study whether investor sentiment affects the DCF estimate of stock return more than the CAPM estimate, and whether this impact is more pronounced for high growth firms. I find results consistent with this hypothesis. I find that investor sentiment causes a divergence between the CAPM and DCF estimates of stock returns, and this divergence is higher for high growth firms compared to low growth firms. My findings suggest that high growth firm stock prices are more prone to distortions due to hype or investor pessimism.

This study shows that investor sentiment plays an important role in affecting the pricing dynamics between the spot and futures markets. The empirical evidence suggests that investor sentiment has a positive impact on price volatility and the bid–ask spread on both the spot and futures markets, which induces higher arbitrage risk and trading costs during high sentiment periods. As a consequence, during high sentiment periods, informed traders become less willing to leverage their information advantages on the futures market, which diminishes the futures markets’ leading informational role and contributions to price discovery. My findings provide support for the theory of limits to arbitrage.

Yes / This paper investigates the impact of investor sentiment on the mean-variance relationship in 14 European stock markets. Applying three approaches to define investors’ neutrality and determine high and low sentiment periods, we find that individual investors’ increased presence and trading over high-sentiment periods would undermine the risk-return tradeoff. More importantly, we report that investors’ optimism (pessimism) is more determined by their normal sentiment state, represented by the all-period average sentiment level, rather than the neutrality value set in sentiment surveys.

Yes / This paper investigates the role of institutional investor sentiment in the mean–variance relation. We find market returns are negatively (positively) related to market’s conditional volatility over bullish (bearish) periods. The evidence indicates institutional investors to be sentiment traders as well.

This thesis addresses asset pricing in Chinese A-share stock markets using a dataset consisting of all shares listed in Shanghai and Shenzhen stock exchanges from January 1997 to December 2007. The empirical work is carried out based on two theoretical foundations: the efficient market hypothesis and behavioural finance. It examines and compares the validity of two traditional asset pricing models and two behavioural asset pricing models. The investigation is initially performed within a traditional asset pricing framework. The three-factor Fama-French model is estimated and then augmented by additional macroeconomic and bond market variables. The results suggest that these traditional asset pricing models fail to explain fully the time-variation of stock returns in Chinese stock markets, leaving non-normally distributed and heteroskedastic residuals, calling for further explanatory variables and suggesting the existence of a structure break. Indeed, the macroeconomic and bond market factors provide little help to the asset pricing model. Using the Fama-French model as the benchmark, further research is done by investigating investor sentiment as the third dimension beside returns and risks. Investor sentiment helps explain the mis-pricing component of returns in the Fama-French model and the time-variation in the factors themselves. Incorporating investor sentiment into the asset pricing model improves the model performance, lessening the importance of the Fama-French factors, and suggesting that in China, sentiment affects both the way in which investors judge risks as well as portfolio returns directly. The sentiment effect on asset pricing is also examined under a nonlinear Markov-switching framework. The stochastic regime-dependent model reveals that stock returns in China are driven by fundamental factors in bear and low volatility markets but are prone to sentiment and become uncoupled from fundamental risks in bull and high volatility markets.

The objectives of this thesis are: first, to investigate the impact of investor sentiment in UK financial markets in different investment intervals through the construction of separate sentiment measures for UK investors and UK institutional investors; second, to examine institutional herding behaviour by studying UK mutual fund data; third, to explore the causal relation between institutional herding and investor sentiment. The study uses US, German and UK financial market data and investor sentiment survey data from 1st January 1996 to 30th June 2011. The impact of investor sentiment on UK equity returns is studied both in general, and more specifically by distinguishing between tranquil and financial crisis periods. It is found that UK equity returns are significantly influenced by US individual and institutional sentiment and hardly at all by local UK investor sentiment. The sentiment contagion across borders is more pronounced in the shorter investment interval. The investigation of institutional herding behaviour is conducted by examining return dispersions and the Beta dispersions of UK mutual funds. Little evidence of herding in return is found, however strong evidence of Beta herding is presented. The study also suggests that beta herding is not caused by market fundamental and macroeconomic factors, instead, it perhaps arises from investor sentiment. This is consistent between closed-end and open-ended funds. The relation between institutional herding and investor sentiment is investigated by examining the measures of herding against the measures of investor sentiment in the UK and US. It suggests that UK institutional herding is influenced by investor sentiment, and UK institutional sentiment has a greater impact as compared to UK market sentiment. Open-end fund managers are more likely to be affected by individual investor sentiment, whereas closed-end fund managers herd on institutional sentiment.

This dissertation addresses issues concerning liquidity and its volatility. It consists of two essays. The first essay, "Liquidity, Macro Factors and the U.S. Equity Flows to Emerging Markets", examines the role of liquidity on equity flows from the U.S. to fifteen emerging markets around the world. Since liquidity has many dimensions, an emphasis is placed on utilizing various measures of liquidity. Moreover, both static and dynamic analyses, as well as short and long-horizon regressions, are performed to investigate the research questions. The results suggest that a liquid market attracts flows, after controlling for market size, political openness, exchange rate and other macro factors. Additionally, evidence indicates that the importance of liquidity varies across regions. For instance in the Asian region, the relation between equity flows and volume-related liquidity is weak while that between flows and price impacts of trading is strong. Evidence also supports the relevance of macro factors such as a country's economic freedom. The second essay, "Liquidity Risk Premium Puzzle and Possible Explanations", attempts to resolve the liquidity risk puzzle: a negative relation between returns and liquidity risk, documented by Chordia, Subrahmanyam, and Anshuman (2001b), by employing alternative liquidity measures and by incorporating factors that might potentially affect the relation. The main findings are as follows. The relation between stock returns and volatility of liquidity depends on the measure of liquidity. When liquidity measures are based on trading volume, the results are largely mixed, but when liquidity is measured based on price impact of trading, the relation between returns and volatility of price impacts is positive, as expected. The results are sensitive to time periods examined. Moreover, during extreme down markets, the aversion to liquidity volatility is lower, suggesting behavioral bias might potentially address the puzzle. Empirical findings also suggest that liquidity risk premium tends to be greater for small stocks. Finally, when the VIX index is included as a proxy for investor sentiment, the results indicate that the relation between returns and liquidity risk is significantly positive in four out of five liquidity measures. In sum, the empirical analysis partially but not completely addresses the puzzle.