<div>In the first chapter, our empirical tests use data from three sources. First, we obtain the Loughran and McDonald’s (hereafter LM wordlist) positive/negative wordlist and from the authors’ website. Second, we obtain the monthly Fama and French (1993 and 2015) factors (i.e. SMB, HML, Rm-Rf, CMA, and RMW) and momentum factor (MOM) from Kenneth French’s website for the sample period January 1994 through December 2016. Third, we obtain the monthly stock returns, monthly index returns, month end market value from the Center for Research in Security Prices (CRSP) as well as accounting information such as annual book</div><div>I the second chapter, we utilize five main datasets in this study. The first dataset is the stock market transaction level data for S&P 500 stocks, obtained from Trades and Quote (TAQ). The second dataset is the corporate bond transaction data from Trade Reporting and Compliance Engine (TRACE) through Wharton Research Data Services (WRDS) for the S&P 500 firms. The TRACE data provides over the counter (OTC) corporate bond market real-time prices.To examine the price discovery of bonds in equity prices we use a sample period of over 1,000 trading days from January 2004 through December 2008.</div><div>Our third data source is the institutional level transaction data from ANcerno, which provides transactional level trade data for corporate bonds and stocks for the first quarter of 2006 through the third quarter of 2010. Several studies have used equity transaction dataset to examine the ANcerno institutional trading behavior. See for example Puckett and Yan (2011), Bethel, Hu and Wang (2009), Chemmanur, He and Hu (2009), Goldstein, Irvine, Kandel and Wiener (2009). Additionally, Hu, Jo, Wang and Xie (2018) provide a comprehensive review of ANcerno dataset. The fourth source of data comes from Mergent Fixed Income Security Database (FISD), which provides details of bond characteristics and credit ratings from standard and poor’s (S&P) and Moody’s. Finally, we obtain the daily stock returns data from center for security prices (CRSP) database and match it with the daily bond returns to examine the lead-lag relationships.</div><div><br></div>

This thesis extends the 52-week high momentum literature, which was first published by George and Hwang in 2004, by stressing the parameters of the trading strategy to investigate its robustness. George and Hwang, in their seminal paper, find that the ratio of a stock’s close price to its 52-week high price is a good predictor of future returns. The thesis stresses various parameters of the strategy - such as the percent of total stocks bought and sold each period – and applies the strategy over different time periods – such as bull and bear markets. The study finds that the strategy is more profitable over the later half of the data set due to underperformance in bear markets such as the 1929 market crash and subsequent Great Depression. The results also show a significant difference in profitability between bull and bear market periods. The second half of the thesis looks at a new area in momentum, the absolute 52-week high. The strategy buys stocks whose price has increased over the previous six months, and who also close to their 52-week high price. Stocks are only bought (sold) if their price has increased (decreased) over the past six months and is close to (far from) the 52-week high price. The aim is to cut out stocks that are considered to be underperforming in the 52-week high momentum strategy, leaving only true winner and loser stocks. This strategy was found to increase the strength of the 52-week high momentum strategy, and the results show that there is no longer a significant difference between bull and bear market returns.

This study will validate several key results from previous studies of bank loan announcement effects by using the data from Hong Kong market following the 1997 Asian crisis. Banks are believed to play a unique role in financial market which could effectively reduce the problem of information asymmetry and moral hazard. Banks could access borrowers’ inside information which is not available to other participants. Thus bank loan announcements convey valuable information to the market, and market response of the stock price should be positive. However, because of the significant reform in both financial market and information market, the valuation of bank loan announcement conveyed need to be reconsidered. This study investigates whether banks are still “unique” in the financial market or whether they are like middlemen between borrowers and investors. Data used in this study is collected from the Hong Kong Stock Exchange Index, and a standard event study with the market model is applied in the research to conduct the empirical analysis. The results suggest bank loan announcements are associated with significantly higher positive abnormal returns than non-bank loan announcements. Based on the market model of event study, market response is found to be significantly positive for loan syndication, short maturity loan and borrower’s debt ratio, and negatively related to firm size and loan size. Bank loans with refinancing and capital expenditure and no specific purpose have significantly higher positive abnormal returns, and borrowers with property and industrial industry type have more significant positive abnormal returns compared to other industry type. The findings also suggest the Hong Kong stock market is efficient in both strong and semi-strong form for bank loan announcements. A strong evidence of information leakage problem is found for non-bank loan announcements. The results are generally consistent with the existing literature.

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There has been a rapid growth of private participation in infrastructure projects throughout the world in the last three decades. China as the largest developing country in the world has a huge amount of demands for high quality infrastructure projects and public services. The government has actively developed and used the Build-Operation-Transfer (BOT) model to deliver public facilities and services, particularly after 2002. Certain benefits have been brought by the adoption of the BOT model in China's motorway and water sectors. However, issues were also found in the processes behind BOT applications. International organisations as well as scholars suggested that China should learn experiences and lessons from the UK and its PFI scheme. The UK has been the leading country to use private finance in developing public buildings and services. However, very little has been written addressing how lessons from British PFIs can be learnt by China to improve its BOT practice. This study is an attempt to address this vacuum in the existed literature. It was designed to explore the current problems of using BOTs in Chinese motorway and water sectors and looks at what needs to be improved, based upon the PFI lessons in the UK. A mixture of qualitative and quantitative methods has been employed and various methods of data collection used in this study. These included: 21 interviews, 2 observations and one focus group and the analysis on 14 government reports about BOTs in China. The governments' decision-makers, directors, project managers and contractors were involved in the research to explore the results and emerging issues involving the implementation of BOT models in 87 Chinese motorway and water projects in 10 cities covering six provinces. As a result of its findings, the research is able to discuss and identify the relevant experiences and lessons from PFIs in the UK to improve further application of China's BOTs. This study fills the gap in knowledge regarding comparisons between PFIs and BOTs. It also gives recommendations for good practice in relation to Chinese BOT policy decision making, development and evaluation. Finally, the study hopes to give recommendations that enable the implementation of BOT model in Chinese motorway and water sectors to be more successful in the future.