Each individual is both a consumer and an investor in the market. It is the common goal of every investor to achieve a high return on investment through the portfolio of profit maximization. As a result, the ratio of assets in a portfolio has become a hot topic. In China, real estate and the stock market are two main markets favoured by both individual and institutional investors. And there is a significant economic link between the two markets. Therefore, their mutual relationship and long-term and short-term causality can provide good guidance for investors. This paper studies the causality and correlation between stock trading volume and real estate trading volume in 31 provinces of mainland China. The empirical results in this paper is based on a panel data from 2000 to 2016 and divides 31 provinces into three different economic regions. The panel unit root test and the Pedroni co-integration test were carried out. The Hausman test was used to select among different estimation methods. Panel Mean Group is found the most suitable analysis method. It is found that the main industries in different provinces may affect the short-term causal relationship between real estate and the stock market. But in the long run, the causal relationship between real estate and the stock market is two-way and stable.

Background: The credit rating agencies have been heavily contested and criticized. In addition to this, other informational sources may potentially deliver the information that the CRA is intended to provide. This may have changed their role in reducing information asymmetry in the financial market. Purpose: This thesis will investigate (i) whether changes (upgrade/downgrade) in credit ratings lead to abnormal returns in share value, and thereby provide useful information to potential and current investors. The thesis will also (ii) examine whether there are significant differences between the periods before and after the GFC in 2008. Method: Regression based event study using a dummy-variable approach. Conclusions: No strong evidence that credit ratings have a significant effect on stock prices in the European stock market. Small indications that the market is responding more strongly to a rating change announcement during the period 2000-2008 compared to 2009-2019.

Prior studies on the impact of the termination of overfunded defined benefit pension plans on shareholders' wealth have produced conflicting findings. The first study on the stock market reaction to pension plan termination was conducted by Alderson and Chen (1986); this study claimed that shareholders realize significant positive abnormal returns around the termination announcement date. A more recent study, by Moore and Pruitt (1990), disclaimed the findings of Alderson and Chen. Reexamination of these two studies with additional evidence and the use of the appropriate announcement date suggests that termination of pension plans is associated with significant wealth gain to shareholders. This study also analyzes samples from periods prior to and after the imposition in 1986 of a 10 percent excise tax on recaptured excess pension assets. The empirical results suggest that shareholders experience significant positive wealth effects for the pre-tax (1980-85) period and no wealth effects for the post-tax (1986-88) period. The primary purpose of this study is to determine the impact of stock market reaction upon shareholders' wealth under the partial anticipation hypothesis. The pre-tax sample is analyzed by isolating the expected terminators using the multiple discriminant analysis model. This study finds significant positive abnormal returns only for firms that are not anticipated by the investors as potential terminators. The results of this study do not lend support to either the "separation" or the "integration" hypothesis as proposed by Alderson and Chen (1986). Instead, the results are consistent with the information hypothesis that the market reacts to unanticipated events that provide new information. Cross-sectional regression analysis of unexpected terminators suggests that the abnormal performance of stocks of pension terminating firms is explained by the firms' debt ratio and the amount of surplus pension assets. It can be inferred that firms may resort to recapturing excess pension assets as a way of financing investments internally when faced with unfavorable credit markets.

This study investigates whether the Buffett indicator can be used to make investment decisions in China. The investigation has two approaches. First, this study determines the scaling relationship between the Buffett Indicator and the GDP in China. Previous research and findings in this research regarding the scaling relationship can help international investors when comparing China with a different country as potential investment opportunities. Second, this study also examines whether the Buffett Indicator, the P/E ratio and composite models including the Buffett Indicator can be used as tools for international investors in predicting the Shanghai Index and making investment decisions for the Chinese stock market. The analysis is based on Chinese data from the World Bank, the National Bureau of Statistics of China, the Federal Reserve and the Yahoo Finance. This study finds that there is a sublinear relationship between the Buffett indicator and GDP in China and that the composite models which include the Buffett Indicator perform better to forecast the stock market in China than other indicators.

Using an early warning system (EWS) methodology, this thesis analyses the predictability of stock market crises from the perspective of behavioural fnance. Specifcally, in our EWS based on the multinomial logit model, we consider in- vestor sentiment as one of the potential crisis indicators. Identifcation of the relevant crisis indicators is based on Bayesian model averaging. The empir- ical results reveal that price-earnings ratio, short-term interest rate, current account, credit growth, as well as investor sentiment proxies are the most rele- vant indicators for anticipating stock market crises within a one-year horizon. Our thesis hence provides evidence that investor sentiment proxies should be a part of the routinely considered variables in the EWS literature. In general, the predictive power of our EWS model as evaluated by both in-sample and out-of-sample performance is promising. JEL Classifcation G01, G02, G17, G41 Keywords Stock market crises, Early warning system, In- vestor sentiment, Crisis prediction, Bayesian model averaging Title Predicting stock market crises using investor sentiment indicators

This thesis aims to investigate the long-term behaviour of the Swedish stock market under quantitative easing (QE) between the years 2015-2019 in comparison to an equally long period before the implementation of QE. The relationship is analysed within the framework of transmission channels of monetary policy and with considerations for previous research on the topic. By the means of an autoregressive distributed lag (ARDL) model, we conduct a regression analysis using the price level of the OMX Stockholm 30 (OMXS30), the value of Riksbank’s assets, the short-term interest rate and the industrial production index. The results show significant but weak evidence of a positive relationship between the OMXS30 index and the Riksbank’s assets value. Furthermore, we analyse the findings to provide an insight into the transmission of unconventional monetary policy to the stock market in a small open economy. Finally, we present some broad implications of our study, as well as suggestions for future research on the topic.

In this study, the authors examined the relationship between crude oil price and the Swedish and Norwegian stock markets. Using linear regression models the authors found that the Swedish stock market and Norwegian stock market both have a positive relation with crude oil price. This supports the hypothesis that crude oil price has a positive impact on Norwegian stock market, since Norway is an oil exporting country. However, this result contradicts a hypothesis of a negative relationship for an oil importing country like Sweden. The authors also looked into the relationship between exchange rates (Swedish krona and Norwegian krone) and oil price, which reveals that oil price is significantly negatively correlated with Swedish krona and Norwegian Krone. The study contributes with evidence from underexplored regions of the world.

Social trading platforms are an increasingly popular venue for sharing investment ideas. We investigate if followers on these platforms herd, i.e., copy leader traders’ trades, to the extent that it affects stock markets. To do so, we use an event study to detect abnormal returns and trading volume after a trade is conducted by a leader trader. Furthermore, we investigate if the leader trader’s sentiment on the stock and the leader trader’s perceived trustworthiness have explanatory power over the herding behavior. We find signs of herding in terms of abnormal returns within thirty-minutes after the leader trader’s trade. However, we do not find signs of herding for longer event windows, nor do we find that the sentiment of the leader trader can explain herding. Finally, we find some signs that affect-based signals of trustworthiness can explain herding. However, most signals of trustworthiness are non-significant.

In this thesis, we have built an optimal portfolio using five assets from the Japanese market. We have investigated the use of GARCH(1,1) when forecasting the volatility of our optimal portfolio. Different time periods have been considered for optimizing our results. An equally-weighted portfolio has been used as a benchmark. Our results show that the optimal portfolio we constructed is more efficient than the equally-weighted portfolio in all chosen situations.

This thesis investigates and compares the performance and characteristics of defensive and cyclical sectors on the Swedish stock market during 2003-2020 and the financial crisis in2007-2008, taking monthly price developments from nine sectors. The purpose is to examine the differences in sector performances based on the estimations of systematic risk. Using the relationship between risk and return, we aim to find the most beneficial investment strategy for investors with a long-term investment horizon and provide knowledge to investors who may want to change investment schemes during stock market crises to protect their portfolios from risk. To determine the sectors' classifications, the beta coefficient from CAPM is used. Moreover, alpha and Sharpe ratios are used as performance measures with the aim to find evidence of differences in performance between the classifications. The results show that beta is inconstant over time, and sectors behave differently depending on their dependence to business conditions, demonstrated by different patterns in beta for the two different classifications when comparing the crisis to the full period. The empirical evidence indicates that a defensive investment strategy is beneficial when considering the relationship between risk and return.