My dissertation studies the measurement of investor disagreement and the effects of investor disagreement on asset prices. In my first essay, I clarify the seemingly contradicting theoretical predictions of Miller (1977), and Varian (1985, 1989) and Abel (1989), and design empirical analysis to test the predictions in a unified framework. Miller models the effect of the level of disagreement on asset prices and predicts a negative relation between investor disagreement and subsequent asset returns. Varian and Abel present results on the effect of the change in disagreement on asset prices and the resulting positive relation between disagreement and subsequent asset returns. I find that, consistent with Varian (1985) and Abel (1989), increases (decreases) in disagreement are always associated with lower (higher) contemporaneous stock returns, regardless of the prior levels of disagreement. Because the level of investor disagreement is highly persistent, stocks with high prior levels of disagreement earn lower subsequent returns as disagreements on these stocks remain high or continue to increase. Consequently, changes in investor disagreement and their impact on stock prices, not overvaluation as in Miller (1977), drive the relation between investor disagreement and subsequent stock returns documented in the existing literature. Empirical analyses based on changing short-sale constraints and earnings announcements provide further support to the central role of changing investor disagreement in asset pricing. In my second essay, I emphasize and examine the role of the consensus investor opinion in the relation between heterogeneous investor beliefs and stock prices, which is largely overlooked in the prior empirical literature. I measure investors' opinions based on financial analysts' stock recommendations and study how both investors' opinions and their disagreement jointly affect stock prices. I show that the consensus opinion is at least as important as the dispersion of opinion in predicting stock returns. When the consensus opinion is pessimistic, investor disagreement leads to lower stock returns, but the opposite is true when the consensus opinion is optimistic. Moreover, strong investor agreement predicts stock returns and largely drives the return difference between high- and low-agreement stocks. In supporting evidence, I show that both the investor opinion and its dispersion are related to short-sale constraints and strong optimistic agreement is significantly associated with binding short-sale constraints.

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This dissertation proposes an analysis and assessment of the two regimes of macrofinancial risk management in the United States since the 1950s: one based on financial regulation and government controls, from 1950 to 1971, and another based on financial derivatives, from 1972 to the present. It compares the two regimes and discusses the means to assess their relative effectiveness in mitigating financial volatility for nonfinancial corporations. Without exhausting all the different criteria to adjudicate between the two regimes, the research emphasizes some specific characteristics that must be part of any coherent mechanism of risk management such as coverage of exposed firms, impacts on financial stability and systemic risks, scope for speculative activities, degree of complexity and transparency, creation of additional risks, and potential for failures. The use of these criteria suggests that financial derivatives might be relatively less effective in mitigating financial risks and their consequences. In order to test the proposition that financial derivatives are potentially less effective than financial regulation, an econometric analysis of the liquidity preference of nonfinancial firms, assumed to be an important hedging mechanism under capitalism, is carried out. At the aggregate level, the time series show that nonfinancial corporations have been accumulating more liquid assets since the beginning of the eighties, in contrast to the declining liquidity preference under the regulatory regime. A cointegration analysis suggests that the relationship between liquidity preference and financial risk was stable for the period 1948-2005, notwithstanding the rise of derivatives. At the firm level, using a panel consisting of balance sheet and financial statements for 170 firms over the period 1993-2005, the liquidity preference of firms is regressed against a measure of the intensity of financial derivatives usage. The evidence here suggests that, after controlling for other firms' characteristics, financial derivatives do not substantially reduce the necessity of holding liquid assets as a behavior toward financial risk. These results seem to indicate that financial derivatives are relatively less effective in hedging financial risks, making the case for financial regulation.

Beginning in 1977, Connecticut has been developing a school finance equalization plan, funded out of General Revenue. This dissertation examines Connecticut's school finance equalization plan, and concludes that it is inadequate for its stated goals. The dissertation begins with an examination of theory. There are two parts to this examination: (1) taxation theory; and (2) school finance theory. Taxation theory includes a vigorous debate on the merits of progressive taxation. The author favors progressive taxation based on the "proportionate sacrifice" standard. Taxation theory has developed attribution mechanisms for each tax to determine its incidence. School finance theory is the theory of how to measure school finance equity. Different measures of equal per-pupil expenditures are developed and explained here. The author explains his methodology, and then derives 14 measures of school finance equity. Using data from Connecticut, the author shows that the state's system of school finance is inequitable, even in comparison to other states. The author examines Connecticut's school finance equalization plan, its history, and institutional evolution. From these, he presents arguments why Connecticut's equalization plan may not be working. The author then examines Connecticut's state taxes, and its property tax. The methodology of the study is explained at length. The author then derives the incidence of Connecticut's state taxes and fees, and of Connecticut's property tax. Both taxes are regressive, of roughly equal degree. Consequently, the tax system is inequitable, as well. Both Connecticut's school finance and the taxation funding it are inequitable. The author concludes the study with suggestions for further research, and for improving equity in school finance and taxation.

Both volatility and the tail of the stock return distribution are impacted by discontinuities ("large jumps") in the stock price process. In the first chapter, "The Tail in the Volatility Index"1, we construct model-free volatility and tail indexes in a manner that clearly distinguishes one from the other. Our tail index measures time-variation in jump intensity, and is constructed non-parametrically from the identical set of 30-day index options used to construct a volatility index. We use the indexes to examine the economic importance of volatility and the tail in predicting market returns over 1996–2011. We find that discontinuities predict market returns through both potential channels, volatility and tail risk, respectively. In the second chapter, "The Pricing of Market Risks in Equity Options: Evidence From Individual Variance Risk Premiums", I examine the pricing of market level risks in equity options by studying the model-free individual variance risk premiums (VRP) constructed from options and stocks data. I document significant empirical findings that a positive market equity risk premium, a negative market variance risk premium, and a negative market jump risk premium are priced in the cross-section of individual VRPs. The results are robust after controlling for firm-level variables, including firm size, book-to-market ratio, momentum, stock illiquidity, idiosyncratic volatility and options trading volume. I propose a non-parametric way to decompose the individual VRPs into three components: the common unexplained component, the systematic exposure component, and the idiosyncratic component. I find that the (conditional) common unexplained component is significantly negative and time varying, and it strongly predicts future 6-month to 2-year stock market return and future 3-month to 2-year growth of economic activities. My findings support a missing risk factor story and suggest that the cross-section of individual VRPs can be understood as risk premiums rather than mispricing. In the last chapter, "Option Trading Activities and the Future Stock Returns", I present empirical finding that the trading activities in equity options market contain information about future stock movement. I test two variables of options trading activities, the aggregate options trading volume (volume) and the call-put ratio from options volume (C/P). Stocks with low volume outperform stocks with high volume; stocks with high C/P outperform stocks with low C/P. The predictability is strong with both economical and statistical significance; it is present when I track future stock returns from one week up to two months. I find stronger predictability for stocks with lower liquidity in the equities market. The results are robust when I control for firm size, firm book-to-market ratio, previous stock return and previous stock variance, or when I control for the effect of earnings announcements. Considering that the volume and C/P are publicly observable, my results question the market efficiency across options market and equities market. 1The first chapter, "The Tail in the Volatility Index", is co-authored with Nikunj Kapadia.

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Modeling and simulation of financial instruments is accomplished from multiple approaches but most completely from an engineering perspective. Aeronautical engineering yields a wave model created for stock indices in the 1970s. This comprehensive methodology models stock markets as waves for the intention of trading or investing yet has not been applied on time periods smaller than daily or weekly, known as intraday. Stakeholders trading intraday waves need to utilize wave analysis for price capture, analytics, and profitability. It is the purpose of this thesis to present a model to harness wave analytics for the needs of traders seeking price capture of the Standard and Poor's 500 Index on an hourly and minute time periods, or intraday. This paper applies wave analytics in time frames never accomplished before for the sufficing the needs of index day traders.

At present, we are experiencing a new technological revolution. Information technology, represented by the Internet of Things, big data, cloud computing, artificial intelligence, and blockchain, is driving the transformation of social production mode, guiding various traditional industries to transition to informatization, digitization and intelligence, and giving birth to many emerging industries with technological innovation and business model innovation. Intelligent building is a product combining building technology and modern information technology based on Internet of Things technology. It is an important part of the smart city. In the financial field, driven by emerging cutting-edge technologies such as big data, cloud computing, IoT, blockchain and artificial intelligence, major innovative changes have taken place in the financial market and financial service business, resulting in a large number of emerging business models, new technology applications, new products and services, known as fintech. The development of a series of emerging intelligent industries, including intelligent buildings and intelligent cities, is inseparable from the participation of finance. Fintech is undoubtedly an important part of the whole ecosystem of intelligent buildings. To promote the development of intelligent buildings, it is a topic of far-reaching significance to explore the business model and the innovative application of fintech. This topic has thoroughly investigated the development status, research progress and technical level of China’s and foreign countries’ intelligent building industry, and on this basis, carried out the research on the innovative application of IoT-based water level monitoring system in public catastrophe insurance. Relying on HC Tech, we developed the integrated intelligent building management system, constructed the integrated intelligent building management mode of "finance + IoT + building" and financial innovative application, and explored a new market operation mode of "technology + insurance + operation & maintenance" in the application of intelligent buildings. In practical application, the IoT-based automatic water level monitoring system is adopted in public catastrophe insurance in N city to meet the requirements of real-time water level monitoring, remote transmission, forecasting and early warning. A remote disaster damage assessment management system is constructed to achieve intelligent effects such as real-time monitoring and transmission of field data, forecasting and early warning of the highest water level, and automatic calculation of damage assessment results. Through the implementation of the project, the risk coefficient of heavy rain weather and field investigation by claim adjusters can be reduced and at the same time, the information of the water level inundation of the disaster site can be obtained in a real time manner so that leaders can make emergency decisions in time and realize express claim settlement and damage assessment. The application of this new mode can greatly improve the working efficiency of insurance companies in flood claims, and increase the efficiency of flood claims by four times. / Business Administration/Finance