<p> This dissertation studies trading and investment in financial markets through the lens of financial econometrics. Chapter 1 develops a continuous-time model of the optimal strategies of high-frequency traders (HFTs) to rationalize their pinging activities - defined as rapid submissions and subsequent cancellations of limit orders inside the bid-ask spread. The current worry is that HFTs ping inside the spread to manipulate the market. In contrast, the HFT in my model uses pinging to control inventory or to chase short-term price momentum without any learning or manipulative motives. I use historical message data to reconstruct limit order books, and characterize the HFT's optimal strategies under the viscosity solution to my model. By gauging the model's implications against data, I show that pinging is not necessarily manipulative and is rationalizable as part of the dynamic trading strategies of HFTs. </p><p> In Chapter 2, joint with Harrison Hong, we use overdispersed Poisson regression models to study social networks in finance. We count an investor's social connections in different cities as proportional to the number of stocks held by this investor that are headquartered in those cities. When connections are formed in an i.i.d. manner, the count of such connections in any city follows a Poisson distribution. Using data from institutional investors' holdings, we find instead overdispersion for a number of cities like San Jose and San Diego, which suggests that investors have non-i.i.d. propensities to be connected to these cities. Overdispersed cities have a large number of graduates from local universities who work in the fund industry. Managers with relatively high non-i.i.d. propensities to have social contacts significantly outperform other managers. </p><p> In Chapter 3, I propose a continuous-time model for the joint stochastic process of asset price and trading volume to study the transmission mechanism from changes in trading volume to price movements at the high-frequency level. A GMM-based estimation procedure is developed based on the model's closed-form moment conditions. I estimate the model on real-world high frequency financial data and find that, jumps in volume have a strong cross-excitation effect on jumps in price. Other implications of the model are also discussed. </p>

<p> This dissertation introduces a panel data method to estimate country-specific steady state levels of output in an augmented Solow growth model. The use of panel data permits the estimation of a country-specific effect which can explain the surprising result that many developing economies are above their steady states. These empirical results also confirm that the augmented Solow model can explain the present cross-country income <i> divergence</i> of developed and developing economies. Another application finds evidence that the post-Soviet economies began their transition toward markets with initial conditions of overcapitalization. Finally, when the results are sufficient, there is also the possibility of describing an entire period of growth and gaining insights into future periods. This is shown with the OECD economies. </p><p> In Islam (1995), panel data is first used to estimate the parameters of the Solow growth model. The following year, Cho and Graham (1996) published a small paper which illustrates a simple way to compute steady state levels of per capita income by using the results of cross-sectional convergence tests. This dissertation simply combines these two methods with the result that the interpretations are more satisfying. In sum, we find that countries can begin a period of development above or below their steady states and that countries converging from above should be considered to be overcapitalized. This implies that development through investment can only succeed when there is convergence from below the steady state. Above the steady state, total factor productivity is too low to sustain the relatively high levels of capital. </p><p> The organization of the dissertation is linear with an introduction preceding the second chapter's literature review and the development of a theoretical and empirical model in the third chapter. The applications of the method then follow. Chapter 4 uses a worldwide sample to compare the result to other work and to show that this fundamental model of growth theory can explain the observed increasing levels of international inequality. Chapter 5 takes a look at the transition economies. In addition to finding evidence of overcapitalization, this dissertation finds a positive correlation between growth and the privatization of small business under transition. Additionally, there is a negative impact of price liberalization under the conditions of repressed inflation experienced by many Soviet-era planned economies. Chapter 6 uses a sample of OECD economies to obtain a significant deterministic, technological growth rate. This is possible because the countries are similar enough to make the assumption that they have the same growth rate more realistic. This enables an understanding of steady states after the initial period and leading into the most contemporaneous period of the sample.</p><p> <b>Keywords:</b> macroeconomic analyses of economic development; institutions and growth; measurement of economic growth; cross-country output convergence; models with panel data; government policy; socialist systems and transitional economies: political economy, property rights; socialist institutions and their transitions</p>

<p> Highways have become a symbol of modern America (Levinson, 2004), and infrastructure investment plays a pivotal role both in short-term and long-term economic growth and in job creation. In the US, it represents 16% of the gross national product, and every dollar of public investment in highways has a net rate of return of 22 cents, and every billion dollars of federal highway investment generates 47,500 jobs (AASHTO 2003). In response to the inabilities to raise government revenues in the US, aging infrastructure systems, and high construction and O/M costs, infrastructure development has steadily become a collaboration work between the public and private sector. In liberalized infrastructure markets, various governance structures are being tested for application of public-private partnerships (PPPs or P3s) strategies in infrastructure development (Estache, 2004). </p><p> This thesis aims to review the key complex PPP issues in transportation renewal projects in the US that adopt PPPs. While PPPs can be applied to a range of agreements, the PPP projects to be studied and analyzed in this paper will be limited to those involving complex financing, design, construction and long-term operation and maintenance of transportation infrastructure of at least 10 years. These issues are examined in the context of six case studies in six different state across the US by means of interview and archival record. Findings resulting from this work suggested that PPPs have been increasingly implemented by departments of transportation in the US as a mean to tape into private resources. In addition, this research identified four key complex PPP issues in transportation projects as such Economic issue, Procurement issue, Risk Issue, and Governance issue. States have established a dedicated organizational unit to facilitate the use of PPPs, for example High Performance Enterprise (HPTE) in Colorado and Innovative Project Delivery Division in Virginia, but there exist no standards or best practices in the United States for procurement, concession terms, or risk-sharing.</p>

<p> This dissertation consists of three essays that examine the role that financial intermediaries played in the housing market and the credit default swap market in the years leading up to the U.S. financial crisis in 2008. The first essay studies the impact that increased mortgage lending during the 2004-2006 housing boom period had on future 2007-2009 home price crashes in Florida. The second essay focuses on the predictability of home prices prior to 2006 and looks at how bank lending across different metropolitan statistical areas responded to past increases in home prices in 2006. The third essay turns to the credit default swap market and investigates how the market perceived investment bank counterparty risk by analyzing credit default swap prices during the time period leading up to and immediately after the collapse of Bear Stearns in 2008.</p><p> In the first essay, "Loan Supply and Home Price Crashes: Evidence from Florida," I examine whether neighborhoods that experienced higher loan growth during the recent housing bubble later experienced larger home price crashes when the bubble burst. I use microeconomic data on mortgages and home sales to answer this question in Florida. A simple OLS regression reveals that, within Florida's metropolitan statistical areas, a 1 percent increase in 2004 to 2006 census tract loan growth is associated with a relatively modest 0.1 percent decline in 2007 to 2010 census tract home prices. However, this exercise ignores the fact that loan growth may be driven by demand as well as supply factors. Using information on bank lending practices outside of Florida, I am able to isolate changes in loan growth that are uncorrelated with local demand. From the more sophisticated analysis, I find that a 1 percent increase in earlier loan growth actually results in a 0.9 percent decline in home prices. I find some evidence that supply-driven loan growth led to greater home price crashes by pushing home prices up to unsustainable levels. These results contribute to an understanding of the relationship between credit booms and financial crises by linking loan growth to asset prices.</p><p> In the second essay, "The Subprime Crisis and House Price Appreciation," (co-authored with William Goetzmann and Liang Peng) we argue that econometric analysis of housing price indexes before 2006 generated forecasts of future long-term price growth and low estimated probabilities of extreme price decreases. These forecasts of future increases in home-loan collateral values may have affected both the demand and the supply of mortgages. Standard time series models using repeat-sales indexes suggest that positive trends had a long half-life. Expectations based on such models support expectations that could lead to an asset bubble. Analysis of data from the HMDA loan database and LoanPerformance.com at the MSA level and at the loan level substantiates the effects of past price trends on the demand and supply of subprime mortgages. On the demand side, at the MSA level, past home price increases are associated with more subprime applications, higher loan to income ratios and lower loan to value ratios of applications for both prime and subprime mortgages. This is consistent with the notion that households not only borrowed more but also invested more in home equity conditional on greater past house price increases. On the supply side, past home price appreciation had a significantly greater impact on the approval rate of subprime applications than the approval rate of prime applications. Loan level analysis indicates that past home price appreciation increased the approval rate of subprime applications but did not affect the approval rate of prime applications. Further, approved HMDA subprime loans had higher loan to income ratios in MSAs with greater past house price trends.</p><p> In the last essay, "Counterparty Risk in the Credit Default Swap Market," I use a panel data set of credit default swap (CDS) spreads that covers 157 companies over 188 weeks from 2005 to 2008 to test whether counterparty risk in the CDS market, as measured by the financial health of the major CDS dealers, has an effect on market-wide CDS spreads. The results suggest that after controlling for changes in firm and market conditions, a rise in counterparty risk increases CDS spreads of investment grade firms. Counterparty risk's ability to explain changes in CDS spreads of investment grade firms remains the same before and after the Bear Stearns crisis, suggesting that perhaps investors were unprepared when Lehman Brothers was not saved by the Fed and went bankrupt six months later.</p>

<p> <b>Employee Ownership and Corporate Governance:</b> I find that firms that actively promote employee ownership through profit sharing and equity ownership plans pay their executives less and adopt more provisions favorable to shareholders. Furthermore, my empirical evidence shows that the shareholders in firms with higher employee ownership tend to be more active in corporate governance through the execution of proxy voting. The corporate boards in firms with higher employee ownership are younger, more diverse, and more representative of employees. My findings suggest that in the shareholder-manager conflict, employee ownership tends to shift power in the direction of shareholders and could significantly mitigate existing agency problems in the firm. </p><p> <b>Leadership and Corporate Culture:</b> Evidence from Executive Migrations across Firms This paper examines the importance of leadership for corporate culture by studying changes in firm environmental policy around executive successions. I find that firms improve significantly their environmental performance following the arrival of executives from firms with strong pro-environmental culture and firms tend to decrease their environmental standards following the arrival of executives with poor environmental record. However, the economic impact is much weaker for an executive with poor environmental record. The findings provide insight into the formation of organizational culture and the diffusion of cultural norms in the economy.</p>

In today's financial markets, when new information is disseminated with lightning speed across the investment community, individual investors turn to trading systems as a way to generate profit. Based primarily on Technical Analysis, a trading system can take advantage of a plethora of advanced modeling tools available today ranging from chart pattern recognition to genetic optimization of technical indicators and trading rules. This paper offers a systematic approach to financial system development involving neural networks and genetic algorithms. A trading system that forecasts S&P500 index is developed and analyzed.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.

Knowledge of the investment risk of investment-grade commercial real estate (‘ICRE’) is important because it determines the approaches which should be taken to portfolio management. However, relatively little is known about this risk. This research expands the body of knowledge of ICRE investment risk by producing conclusions about the information content of prices and the distribution of returns in the ICRE context. It is broken into three main parts. First, the ICRE returns-generating process is characterised to form a basis for deducing theoretical conclusions about the information content of prices and the stochastic attributes of returns. The rationale for this approach lies in capital markets literature, which demonstrates that the characteristics of the information structure of markets, the decision-making processes of investors and the market trading mechanism determine the main attributes of the process of price evolution (which is assumed to be the main driver of returns). The analysis concludes that ICRE prices are partially informed, and changes in prices are described by a ‘jump’ process. Second, analysis of a database of ‘large’ price changes supplied by the Property Council of Australia is undertaken to empirically test the jump process hypothesis. This analysis provides evidence that natural events associated with changes in the leasing structure of properties are a primary driver of relatively large, infrequent dislocations in valuation-based prices. With parts one and two as a backdrop, the third part of this research empirically tests a discrete mixture of normals (‘DMON’) model of investment risk. Capital markets research shows that a DMON model flows naturally from jump price processes. DMON models fitted to cross-sectional returns on individual properties supplied by the PCA are found to be superior to the normal and stable Paretian models previously proposed by other researchers. In aggregate these conclusions have serious implications for the management of ICRE portfolios, and suggest a need for additional research. Some implications include: (1) Mean-lower partial variance is superior to mean-variance optimisation. (2) Forecasting the distribution of ICRE returns forms a new tool for active management. (3) Passive portfolio management is inappropriate. (4) Comparables-based valuations may be unreliable for investment decisions. / Subscription resource available via Digital Dissertations only.