Class of 2011 Abstract / OBJECTIVES: To establish the risk versus return of pharmacy corporations those are traded publicly on the open market. METHODS: Descriptive retrospective study of financial data obtained through Center of Research in Security Prices (CRSP). Pharmacy corporations were selected by the Standard Industrial Classification Code (SIC code) of 5912. Information that was gathered were monthly security-level stock market prices, value-weighted stock market index, the 30-day return on Treasury bill, SMB, HML, and MOM. Analysis timeframe: 1929-2009. RESULTS: CAPM and Fama-French three factor and four models calculated the data results. CAPM resulted in statistically significant overall beta= 1.04 (p≤0.05). Fama-French three factor model resulted in significant overall beta= 0.87 and overall SMB= 0.79. Fama-French four factor model resulted in significant overall beta= 0.86 and overall SMB= 0.78. CONCLUSION: Over the 80 year time period pharmacy corporations suggested mixed volatility. Risk of investment has never suggested being a viable gain on return of investment versus a 30-day Treasury bill.

Traditional portfolio theory suggests that, in equilibrium, only the market risk is priced in the cross-section of expected stock returns. However, if the market is not perfect and investors are constantly changing investing behaviors based on their perceptions about future market outlook, then non-traditional risk factors could potentially provide significant power of describing the expected stock returns. This dissertation has two essays on the pricing of volatility, in which the market is not assumed to be frictionless or perfect. Essay 1 focuses on the pricing of individual volatility in penny stocks. Empirical results show that individual volatility plays an important role in describing the average cross-sectional returns of penny stocks. Resorting to the rolling portfolio approach, evidences indicate that portfolios consisting of penny stocks with high individual volatilities, on average, earned much higher returns than portfolios consisting of penny stocks with low individual volatilities. This effect is statistically significant when multiple factors are controlled simultaneously. Essay 2 focuses on the pricing of the market volatility among individual stocks. Following the rolling portfolio method, Essay 2 constructs portfolios that consist of individual stocks with various market volatility exposures. Traditional risk factors such as market beta, size, book-to-market, and momentum are controlled respectively to obtain more detailed analyses. Empirical results yield a negative pricing of the market volatility and it is more prominent in stocks that have high market beta, small size, and high book-to-market.

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The paper analyzes effects of the financial crisis on stock market of the Czech Republic and Spain. We employ BEKK-GARCH model in order to study volatility spillovers and transmissions from the US stock market to stock markets of the Czech Republic and Spain. The multivariate GARCH models results show statistically significant, but relatively small, almost irrelevant volatility spillovers from the US stock market to stock markets of the Czech Republic and Spain. The Czech stock market exhibits higher conditional correlation coefficient than the Spanish stock market.

Thesis (M.B.A.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2031-01-01

Student Number : 0300191P - PhD thesis - School of Accountancy - Faculty of Commerce, Law and Management / The main objective of the study has been to provide new insights into ongoing recent studies examining the impact of stock market liberalization at both macro and micro (firm) levels. The study focused on a single country, South Africa, whose exchange, the Johannesburg Stock Exchange (JSE), liberalized in the 1990s. Consistent with empirical evidence from other studies the study finds support at market, firm and sectoral level for the prediction by international asset pricing models that stock market liberalization reduces the cost of capital. More important, the study makes five major contributions to the literature on the impact of stock market liberalization in emerging markets. First, it demonstrates that some emerging market specific risks such as political and economic risks can act stronger binding constraints to foreign investment than direct legal barriers which foreign investors are frequently able to circumvent. The second contribution is the observation that there are some firms (in the minority however) that will experience a significant increase in the cost of capital following liberalization, a situation where the local price of risk is higher than the global price of risk, contrary to international asset pricing theory. The third contribution is that it has been empirically proved that the reduction in firms’ cost of capital following stock market liberalization is permanent. It is not a transitory phenomenon. The fourth contribution of the study highlights the influence of firm specific characteristics such as size of the firm, book-to-market ratios and leverage ratios on firms’ response to impact of stock market liberalization. The preference for large firms by foreign investors is supported, contrary to Merton’s (1987) recognition hypothesis, and hence highlights the inconclusiveness of the debate on whether stock market liberalization benefits both large firms and small firms. The fifth contribution is the observation that the effective liberalization date is not the same for all firms but varies from firm to firm.

Master of Science / Department of Statistics / Michael Higgins / The amount of data generated from stock market trading is massive. For example, roughly 10 million trades are performed each day on the NASDAQ stock exchange. A significant proportion of these trades are made by high-frequency traders. These entities make on the order of thousands or more trades a day. However, the stock-market factors that drive the decisions of high-frequency traders are poorly understood. Recently, hybridized threshold clustering (HTC) has been proposed as a way of clustering large-to-massive datasets. In this report, we use three months of NASDAQ HFT data---a dataset containing information on all trades of 120 different stocks including identifiers on whether the buyer and/or seller were high-frequency traders---to investigate the trading patterns of high-frequency traders, and we explore the use of HTC to identify these patterns. We find that, while HTC can be successfully performed on the NASDAQ HFT dataset, the amount of information gleaned from this clustering is limited. Instead, we show that an understanding of the habits of high-frequency traders may be gained by looking at \textit{janky} trades---those in which the number of shares traded is not a multiple of 10. We demonstrate evidence that janky trades are more common for high-frequency traders. Additionally, we suggest that a large number of small, janky trades may help signal that a large trade will happen shortly afterward.

<p>There have been countless empirical studies conducted to test the validity of the Capital Asset Pricing Model（CAPM）since its naissance. However, few have considered the Chinese Stock Market. The purpose of this paper is to test the CAPM to see if it holds true in the Shanghai Stock Exchange (SSE). We use weekly stock returns from 100 companies listed on the SSE during 2000.1.1 to 2005.12.31. Black, Jensen and Scholes (1972) (time-series test) and Fama and MacBeth (1973) (cross-sectional test) methods were used to test the CAPM.</p><p>We found that the excepted returns and betas are linear related with each other during the entire period of 2000.1.1 to 2005.12.31, which implies a strong support of the CAPM hypothesis.</p><p>On the other hand, as the CAPM hypothesizes for the intercept, is it should equal zero and the slope should equal to the average risk premium. However, the results from the test refute the above hypothesizes and offer evidence against the CAPM.</p><p>According to the findings of the empirical test, we conclude that the Capital Asset Pricing Model does not give a valid description of the Chinese Stock Market during 2000.1.1 to 2005.12.31.</p>

This paper studies the persistence of daily returns of 21 German stocks from 1960 to 2008. We apply a widely used test based upon the modified R/S-Method by Lo [1991]. As an extension to Lux [1996] and Carbone et al. [2004] and in analogy to moving average or moving volatility, the statistics is calculated for moving windows of length 4, 8, and 16 years for every time series. Periods of persistence or long memory in returns can be found in some but not all time series. Robustness of results is verified by investigating stationarity and short memory effects.

There have been countless empirical studies conducted to test the validity of the Capital Asset Pricing Model（CAPM）since its naissance. However, few have considered the Chinese Stock Market. The purpose of this paper is to test the CAPM to see if it holds true in the Shanghai Stock Exchange (SSE). We use weekly stock returns from 100 companies listed on the SSE during 2000.1.1 to 2005.12.31. Black, Jensen and Scholes (1972) (time-series test) and Fama and MacBeth (1973) (cross-sectional test) methods were used to test the CAPM. We found that the excepted returns and betas are linear related with each other during the entire period of 2000.1.1 to 2005.12.31, which implies a strong support of the CAPM hypothesis. On the other hand, as the CAPM hypothesizes for the intercept, is it should equal zero and the slope should equal to the average risk premium. However, the results from the test refute the above hypothesizes and offer evidence against the CAPM. According to the findings of the empirical test, we conclude that the Capital Asset Pricing Model does not give a valid description of the Chinese Stock Market during 2000.1.1 to 2005.12.31.