This thesis introduces two stock analysis software packages. The software packages are designed to gather stock data over the Internet, and process the gathered data to determine an approximate correlation (A-correlation) between a reference stock and a set of comparison stocks. / In this thesis, the A-correlation is obtained from historical stock data using data mining techniques. The data mining techniques include a data retrieval and analysis algorithm. First, a large pool of stock data is retrieved from the CNBC web site by employing an Internet data retrieval algorithm. Then an analysis algorithm is applied to determine the A-correlation between a reference stock and each of a set of the comparison stocks. The analysis algorithm is based on the accumulation of individual stock movements (up/down) over a period of time. / We have designed and implemented the two programs using Microsoft Excel/VBA. The two programs share common functionalities such as providing the A-correlation as a function of time, ranking the A-correlation results in a predefined order, and allowing the user to spot possible A-correlation trends. The difference between the two programs is that the first program analyses A-correlation with no time delay and the second program deals with the time delayed A-correlation. Each program can be useful in revealing different aspects of the relationships between stocks. These two programs can be useful tools for stock market analysis.

<p> High Frequency Trading (HFT) represents an ever growing proportion of all financial transactions as most markets have now switched to electronic order book systems. This dissertation proposes a novel methodology to analyze idiosyncrasies of the high frequency market microstructure and embed them in classical continuous time models. </p><p> The main technical result is the derivation of continuous time equations which generalize the self-financing relationships of frictionless markets to electronic markets with limit order books. We use NASDAQ ITCH data to identify significant empirical features such as price impact and recovery, rough paths of inventories and vanishing bid-ask spreads. Starting from these features, we identify microscopic identities holding on the trade clock, and through a diffusion limit argument, derive continuous time equations which provide a macroscopic description of properties of the order book. </p><p> These equations naturally differentiate between trading via limit and market orders. We give several applications to illustrate their impact and how they can be used to the benefit of Low Frequency Traders (LFTs). In particular, option pricing and market making models are proposed and solved, leading to new insights as to the impact of limit orders and market orders on trading strategies.</p>

<p> There are conservative estimates that the amount of money laundered each year is anywhere between 2 and 5 percent of the annual gross domestic product of the world economy. That figure in dollars is between $800 billion and $2 trillion in U.S. dollars. Research has shown that the majority of money that is laundered across the globe either enters the United States or comes from the United States. It can be argued that the use of wire transfers is the preferred method used by criminals to move their funds. By using wire transfers it is more difficult for those tasked with prevention and investigation to track the money. </p><p> Wire transfers have become the preferred method for criminals to launder money. This is because they are fast, secure and very hard to trace. It is electronically sending cash to another individual or account. It is also easy to initiate a wire transfer. It can be done from a personal computer in a residence or from a public computer. It can be done through an established relationship with a financial institution or by using a wire transfer service such as Western Union or MoneyGram. It can be sent to another account or sent to an individual for immediate pick-up depending upon the type of service a person chooses to use. </p><p> This paper researches the argument that there is complicity within the wire transfer system that has supported this method for illegal activity and money laundering. The research argues that given the lack of effort by financial institutions and Federal regulations and enforcement efforts this activity will continue to increase. There have been successful efforts employed to reduce money laundering through wire transfers. These methods have not been embraced by the larger community of enforcement officials nor the Federal Government. </p><p> By using proven methods and analyzing trends and data within the wire transfer process an effective set of tools can be deployed by regulators, investigators and financial institutions to mitigate money laundering.</p>

<p> The financial economics profession has determined that identical agents in a dynamic, stochastic, general equilibrium (DSGE) model does not provide price and trading dynamics realized in financial markets. There has been quite a bit of research over the last three decades extending heterogeneity to the Lucas asset pricing framework, to address this issue. Once the assumption of homogeneous agents is relaxed, the problem becomes increasingly complex due to a state space including the wealth distribution, continuation utilities, and wealth distribution dynamics. To establish a more computationally feasible model, specical modifications have been made such as heterogeneity in idiosyncratic shocks and not risk aversion, including aggregate or idiosyncratic risk (but not both), or assuming no growth in the economy (steady state). </p><p> In this research, I will define a DSGE model with heterogeneous agents. This heterogeneity will refer to differing CRRA utilities through risk aversion. The economy will have growth due to the assumed dividend process. Agents will face idiosyncratic and aggregate shocks in a complete markets setting. The framework of the provided algorithm will enable issues to be addressed beyond homogeneous agent models. </p><p> The numerical simulation results of this model provide considerable asset price volatility and high trading volume. These results occur even in the complete markets setting, where investors are expected to fully insure. Given these dynamics from the simulations of the algorithm, I demonstrate the ability to calibrate this model to address specific financial economic issues, such as the equity premium puzzle. More importantly this exercise will assume realistic agent parameters of risk aversion and discount factors, relative to economic theory.</p>

<p> A longstanding controversy in financial economics is whether investors' rational forces or their emotional responses govern the asset pricing of the financial markets. Some psychology researchers use dual- process models to understand peoples' information processing. The problem is that some investors allow cognitive biases which operate quickly and automatically in the <i> System 1</i> domain, to affect their decisions rather than respond deliberatively and rationally which are ascribed to the <i>System 2</i> domain. The purpose of this study was to explore how and why investors, when faced with extreme stress impelled during the 2008 Financial Crisis, yielded to either <i>System 1</i> or <i>System 2</i> axis decision-making. Without evaluating the role that cognitive biases play in information processing, investors will not understand why they make inauspicious automatic decisions or grasp the steps that could help avoid realized losses in their stock portfolio. This qualitative research consisted of a multiple-case study that included in-depth semi-structured interviews of 12 investors who had at least $1 million invested in stocks and bonds and triangulation data analysis. The research findings indicated that <i>stock market literacy</i> and risk profiling are foundations for sound investing. When faced with a financial crisis, some investors displayed cognitive biases such as nervousness, worry, and fear that led to myopic loss aversion that caused them to sell their entire stock portfolio or reallocated into more conservative, less risky bonds. Some investors with no emotions and higher <i>stock market literacy </i> considered the financial crisis as a blip in the long-term upward trend performance of stocks and viewed the financial crisis as an opportunity to buy more stocks. For those investors that displayed emotions because of the financial crisis, emotion regulation strategies helped them make more controlled and deliberative investment decisions. Nevertheless, the decisions made by investors may be satisficing because of peoples' bounded rationality, the inherent information processing limitation of the human mind. The specific role of emotion in the duality of information processing was undetermined because the crisis evolved over time rather than a singular event. It is possible that quantitative determination of <i>stock market literacy</i> and the application of Epstein's Rational-Experiential Questionnaire and personality tests including satisfaction questions could shed further information on the dual-process mechanisms.</p>

<p>Given the complexity of transportation systems, generating optimal routing decisions is a critical issue. This thesis focuses on how routing decisions can be computed by considering the distribution of travel time and associated risks. More specifically, the routing decision process is modeled in a way that explicitly considers the dependence between the travel times of different links and the risks associated with the volatility of travel time. Furthermore, the computation of this volatility allows for the development of the travel time derivative, which is a financial derivative based on travel time. It serves as a value or congestion pricing scheme based not only on the level of congestion but also its uncertainties. In addition to the introduction (Chapter 1), the literature review (Chapter 2), and the conclusion (Chapter 6), the thesis consists of two major parts: </p><p> In part one (Chapters 3 and 4), the travel time distribution for transportation links and paths, conditioned on the latest observations, is estimated to enable routing decisions based on risk. Chapter 3 sets up the basic decision framework by modeling the dependent structure between the travel time distributions for nearby links using the copula method. In Chapter 4, the framework is generalized to estimate the travel time distribution for a given path using Gaussian copula mixture models (GCMM). To explore the data from fundamental traffic conditions, a scenario-based GCMM is studied. A distribution of the path scenario representing path traffic status is first defined; then, the dependent structure between constructing links in the path is modeled as a Gaussian copula for each path scenario and the scenario-wise path travel time distribution is obtained based on this copula. The final estimates are calculated by integrating the scenario-wise path travel time distributions over the distribution of the path scenario. In a discrete setting, it is a weighted sum of these conditional travel time distributions. Different estimation methods are employed based on whether or not the path scenarios are observable: An explicit two-step maximum likelihood method is used for the GCMM based on observable path scenarios; for GCMM based on unobservable path scenarios, extended Expectation Maximum algorithms are designed to estimate the model parameters, which introduces innovative copula-based machine learning methods. </p><p> In part two (Chapter 5), travel time derivatives are introduced as financial derivatives based on road travel times&mdash;a non-tradable underlying asset. This is proposed as a more fundamental approach to value pricing. The chapter addresses (a) the motivation for introducing such derivatives (that is, the demand for hedging), (b) the potential market, and (c) the product design and pricing schemes. Pricing schemes are designed based on the travel time data captured by real time sensors, which are modeled as Ornstein-Uhlenbeck processes and more generally, continuous time auto regression moving average (CARMA) models. The risk neutral pricing principle is used to generate the derivative price, with reasonably designed procedures to identify the market value of risk. </p>

<p> Due to the increase in corporate bankruptcy, financial distress studies have flourished since 1968. Firms do find themselves in financially distressful situations because of several factors including changing economic environment such as a decrease in aggregate demand, an increase in the cost of borrowed funds, and changes in government regulation. In addition to the Altman's z-score model, the sustainable growth rate (SGR) is another tool that is used primarily for financial planning. The problem with Altman's z-score model is that it does not consider whether a firm can be financially distressed or not if the sustainable growth rate of the firm is in fact higher than the growth rate of the firm's reported revenues. The purpose of this quantitative study was to investigate the efficacy of using ltman's z-score in forecasting financial distress of a firm when the sustainable growth rate was higher than the growth rate of the reported revenues. The sample for this study was drawn from all non-financial firms traded on the NYSE. The research question was investigated using two group design in two phases. Phase 1 involved the calculation of the sustainable growth rate (SGR), the growth rate of reported revenues, and the calculation of Altman's z-score. The Altman's z-score of the two groups were compared using Mann-Whitney <i>U</i> test to determine whether a statistically significant difference exists in the z-score. Phase 2 involved the correlation between the values of SGR and the values of Altman's z-score to determine if there was a statistically significant relationship between the two scores. The result of this research indicates that the Alman's z-score and the sustainable growth rate are conceptually independent and both can be used to ascertain whether a firm is financially distressed or not. In addition, result of this study provide practical application that could help management of firms reach important financial and managerial decisions. While the result of this study provided useful information and added to existing knowledge on financial distress, additional research using more than one year of financial data is recommended in order to confirm the results of this study.</p>

<p> Modern portfolio theory states that investments with greater beta, a common measure of risk, require greater returns from investors in order to compensate them for taking greater risk. Therefore, under the premise that market participants act rationally and therefore markets run efficiently, investments with higher beta should generate higher returns vis-&agrave;-vis investments with lower beta over the long run. In fact, many studies suggest that investments with lower beta actually generate equal to or higher returns relative to investments with higher beta. In looking at data for the S&P; 500 going back 22 years between 1990 and 2012, this study found that there was very low correlation between beta and returns. In fact, portfolios with very low risk generated commensurate to better returns versus portfolios with very high beta. Therefore, we find that beta appears to be a poor measure of risk as it relates to the stock market. In addition to beta and returns, this study looked at the fundamental characteristics of each company specifically corporate profitability and balance sheet leverage which are commonly used by investors in assessing the underlying quality of a company. We find that companies with higher levels of return on equity combined with lower levels of balance sheet leverage tend to outperform companies with lower levels of profitability and higher balance sheet leverage. As a result, we find a high correlation between balance sheet leverage, ROE and stock returns. This paper suggests that in fact, fundamental factors such as leverage and ROE tend to be better measures of risk vis-&agrave;-vis beta. One important final observation is the fact that while in general, companies with high ROEs and low leverage tend to outperform companies with low profitability and high leverage, portfolios of those companies with the highest ROE and lowest leverage and portfolios of those companies with the lowest ROE and highest leverage actually underperform on the whole other portfolios. In other words, portfolios of companies that exhibit the most extreme of characteristics in terms of ROE and leverage underperform portfolios of companies with more moderate characteristics. One plausible explanation for these observations is rooted in behavioral economic theory known as the favorite long shot bias and the opposite favorite long shot bias. The opposite favorite long shot bias suggests that market participants tend to "over-bet" an asset and/or an investment with high probability of a payoff but low overall return if the payoff occurs (ie the sure bet). In fact, market participants go so far to secure a payoff that they actually place a higher bet on the probability of success than the actual odds would suggest. In stock market terms, investors will tend to over-value the least-riskiest stocks to the point where risk and return is no longer favorable. Similar phenomenon can be observed in horse race betting and sports drafts. The favorite long shot bias is the inverse of the opposite favorite longshot bias. This theory suggests that market participants actually "over bet" an asset and/or an investment with the lowest probability of a payoff but with significant overall returns if the payoff occurs. Similar phenomenon takes place in the purchase of insurance to insure against large potential losses with small probabilities as well as lottery ticket purchases. We see the most striking evidence of this when looking at the returns of stocks with the highest ROEs and the lowest levels of debt/capital as of 1990. In that year, investors would have based their investments in stocks using current attributes at that time. We can see that stocks with the highest ROEs and lowest levels of debt/capital garner higher valuations relative to the broad stock market. We also see that stocks with the lowest ROEs and highest debt/capital also command premium valuations to the market as a whole. Therefore, risk-averse investors will tend to overvalue companies with the least risky prospects while risk loving investors will tend to overvalue companies with the riskiest prospects at the same time. As a result, we can see from looking at the future returns that companies that exhibit extreme characteristics in terms of ROE and debt/capital tend to underperform the broad market. Similar to high profile athletes and horse track betting, we find that investors tend to over-bet sure shot investments while simultaneously over-betting long shot investments.</p>

Since the 1994 economic crisis, Mexico's inclusion in the globalization era has been questioned. To discover if Mexico is moving in the right direction, this study has as its objective the examination of the different regulations that, in Mexico, are related to Foreign Direct Investment. These regulations include the 1993 Foreign Investment Law ("Ley de Inversion Extranjera"); the Competition Act ("Ley Federal de Competencia Economica"); and the North American Free Trade Agreement. The aim of this study is to find out if these regulations are capable of attracting Foreign Direct Investment, which is the most convenient foreign capital flow needed, in order to assist Mexico in its search for economic growth. The importance of the rule of law as an effective instrument to attract FDI is also an element considered in this study. / Finally, this thesis, after the above examination, concludes that Mexico has the potential to overcome its latest economic crisis by using its existing regulations. However, some improvements would benefit its place within the global competition to attract FDI. Most of these improvements are needed at the multilateral level, where Mexico should address the importance that FDI has as a counterbalance to the risks associated with short-term investments. As shown, short-term investments were one of the conditions that provoked the 1994 crisis.

Thesis (Ph.D.)--Indiana University, Dept. of Economics and Dept. of Finance, 2007. / Source: Dissertation Abstracts International, Volume: 68-05, Section: A, page: 2094. Advisers: Eric L. Leeper; Gregory F. Udell. "Title from dissertation home page (viewed Jan. 24, 2008)."