This thesis discusses the criticism of the businessman of the 1920s from the twenties through the 1960s.

The primary purpose of this research is to perform an empirical test using Arbitrage Pricing Theory (APT) in order to investigate the relationship between the Chinese stock market performance and domestic economic activity.China's stock market was established in early 1990s and has operated through a period of strong economic growth. Generally, it has been recognized that the development of a sound financial market is necessary to sustain and support a high growth economy. In turn, a growing economy will drive financial market growth. This research is designed to shed light on this unique relationship by investigating the links between China's booming national economy and the domestic stock returns.Using both time-series and cross-section regressions, several identified macro economic variables are shown to be significant in their influence on stock returns. These variables include the growth rate of industrial production, the growth rate of total social retail sales, the growth rate of terms of trade and the growth rate of total social saving deposits. Stock market indexes are found significantly related to the stock portfolio returns in time-series regressions.Overall, the empirical results suggest that the rapid growth of the Chinese economy is factored into stock returns by the market. It also indicates that the market index has strong explanatory power over, the time-series returns, providing empirical support for the market model Capital Asset Pricing Model (CAPM). However, the explanations of cross-section returns need to be further explored.

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This study investigates stock market anomalies in the Kuala Lumpur Stock Exchange (KLSE), Malaysia, with some comparisons with three other Far-Eastern markets, namely the Stock Exchange of Singapore (SES), the Stock Exchange of Thailand (SET) and the Stock Exchange of Hong Kong (SEHK). The main anomaly investigated is overreaction in the KLSE. Seasonality and firm size effects, which are usually associated with the overreaction effect, are also examined individually, and in the context of the overreaction effect. The impact of time-varying risk on overreaction is also investigated. First, stock market seasonality across four markets - KLSE, SES, SET and SEHK- is examined. The evidence suggests the existence of December and January effects in Singapore and Hong Kong respectively. A Chinese New Year effect is observed in all countries except Thailand. Next, stock market overreaction in the KLSE is investigated. Two portfolios of extreme stocks (based on their past 3-year excess returns) are formed, and their performance is measured in the next three years for evidence of overreaction. The initial results are consistent with overreaction; winner (loser) portfolios, which outperform (underperform) the market in the prior period, underperform (outperform) the market in the next period. The reversal in performance is more dramatic for losers. Further analyses show that risk and size factors cannot explain fully the observed phenomenon. A seasonal pattern is revealed in the excess returns of winners and losers; there is a pronounced February effect in both. Moreover, the February effect is observed to be greater for smaller firms. Lastly, a post-script chapter is included whereby the effect of the recent Asian economic turmoil on the markets, and on KLSE overreaction, is looked at. It is found that several months into the crisis, both winners and losers underperform the market.

Master of Science / Department of Agricultural Economics / Glynn T. Tonsor / Over the past decade, social media has risen from an emerging novelty to the normative form of expression for many Americans. As these platforms have risen in popularity, researchers have recognized the potential for capturing information users are self-reporting about their beliefs and preferences. Simultaneously, social media corporations have become privy to the value of this information being freely shared by consumers and have safeguarded much of their historical data to monetize the data. Faced with both an enticing new source of data, but a steep price to obtain it, researchers must evaluate the potential gains that can be extracted from the often difficult to analyze data. This study explored the acquisition of social media, namely Twitter, data and the potential uses in the field of agriculture economics. A contract was secured with Sysomos, a social media analytics firm, in July of 2017 to collect raw Twitter data over the proceeding thirteen months. Changes in frequency of tweets and sentiment scoring of tweets were used to attempt to explain election results from November 2017 proposed legislations pertaining to marijuana and minimum wage as well as to explain and predict changes in the stock prices of selected publicly traded firms in the food producing sector. Twitter frequency changes were then compared to changes in traditional print media articles in an effort to determine the exchangeability of the two media sources when used to track events pertaining to animal health. Results of this study suggested that Twitter data possess little power to explain the studied election results, but creation of a strong model was difficult due to the limited number of months of data available. Changes in the frequency of tweets were not found to be a strong indicator of changes in the stock market on the average day, but were shown to explain potentially highly valued information to investors on days with large changes in price. Twitter and traditional print media were shown to be unique sources of data when exploring the topic of animal health events.

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.

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.