This study focuses on empirical investigations and seeks implications by utilizing three different methodologies to test various aspects of trader behavior. The first methodology utilizes Prospect Theory to determine trader behavior during periods of extreme wealth contracting periods. Secondly, a threshold model to examine the sentiment variable is formulated and thirdly a study is made of the contagion effect and trader behavior. The connection between consumers' sense of financial well-being or sentiment and stock market performance has been studied at length. However, without data on actual versus experimental performance, implications based on this relationship are meaningless. The empirical agenda included examining a proprietary file of daily trader activities over a five-year period. Overall, during periods of extreme wealth altering conditions, traders "satisfice" rather than choose the "best" alternative. A trader's degree of loss aversion depends on his/her prior investment performance. A model that explains the behavior of traders during periods of turmoil is developed. Prospect Theory and the data file influenced the design of the model. Additional research included testing a model that permitted the data to signal the crisis through a threshold model. The third empirical study sought to investigate the existence of contagion caused by declining global wealth effects using evidence from the mining industry in Canada. Contagion, where a financial crisis begins locally and subsequently spreads elsewhere, has been studied in terms of correlations among similar regions. The results provide support for Prospect Theory in two out of the three empirical studies. The dissertation emphasizes the need for specifying precise, testable models of investors' expectations by providing tools to identify paradoxical behavior patterns. True enhancements in this field must include empirical research utilizing reliable data sources to mitigate data mining problems and allow researchers to distinguish between expectations-based and risk-based explanations of behavior. Through this type of research, it may be possible to systematically exploit "irrational" market behavior.

Since the seminal works of Markowitz (1952), Sharpe (1964), and Lintner (1965), numerous studies on portfolio selection and performance measure have been based upon the mean-variance framework. However, several researchers [e.g., Arditti (1967, and 1971), Samuelson (1970), and Rubinstein (1973)] argue that the higher moments cannot be neglected unless there is reason to believe that: (i) the asset returns are normally distributed and the investor's utility function is quadratic, or (ii) the empirical evidence demonstrates that higher moments are irrelevant to the investor's decision. Based on the same argument, this dissertation investigates the impact of higher moments of return distributions on three issues concerning the 14 international stock markets. First, the portfolio selection with skewness is determined using the Polynomial Goal Programming in which investor preferences for skewness can be incorporated. The empirical findings suggest that the return distributions of international stock markets are not normally distributed, and that the incorporation of skewness into an investor's portfolio decision causes a major change in the construction of his optimal portfolio. The evidence also indicates that an investor will trade expected return of the portfolio for skewness. Moreover, when short sales are allowed, investors are better off as they attain higher expected return and skewness simultaneously. Second, the performance of international stock markets are evaluated using two types of performance measures: (i) the two-moment performance measures of Sharpe (1966), and Treynor (1965), and (ii) the higher-moment performance measures of Prakash and Bear (1986), and Stephens and Proffitt (1991). The empirical evidence indicates that higher moments of return distributions are significant and relevant to the investor's decision. Thus, the higher moment performance measures should be more appropriate to evaluate the performances of international stock markets. The evidence also indicates that various measures provide a vastly different performance ranking of the markets, albeit in the same direction. Finally, the inter-temporal stability of the international stock markets is investigated using the Parhizgari and Prakash (1989) algorithm for the Sen and Puri (1968) test which accounts for non-normality of return distributions. The empirical finding indicates that there is strong evidence to support the stability in international stock market movements. However, when the Anderson test which assumes normality of return distributions is employed, the stability in the correlation structure is rejected. This suggests that the non-normality of the return distribution is an important factor that cannot be ignored in the investigation of inter-temporal stability of international stock markets.

Prices of U.S. Treasury securities vary over time and across maturities. When the market in Treasurys is sufficiently complete and frictionless, these prices may be modeled by a function time and maturity. A cross-section of this function for time held fixed is called the yield curve; the aggregate of these sections is the evolution of the yield curve. This dissertation studies aspects of this evolution. There are two complementary approaches to the study of yield curve evolution here. The first is principal components analysis; the second is wavelet analysis. In both approaches both the time and maturity variables are discretized. In principal components analysis the vectors of yield curve shifts are viewed as observations of a multivariate normal distribution. The resulting covariance matrix is diagonalized; the resulting eigenvalues and eigenvectors (the principal components) are used to draw inferences about the yield curve evolution. In wavelet analysis, the vectors of shifts are resolved into hierarchies of localized fundamental shifts (wavelets) that leave specified global properties invariant (average change and duration change). The hierarchies relate to the degree of localization with movements restricted to a single maturity at the base and general movements at the apex. Second generation wavelet techniques allow better adaptation of the model to economic observables. Statistically, the wavelet approach is inherently nonparametric while the wavelets themselves are better adapted to describing a complete market. Principal components analysis provides information on the dimension of the yield curve process. While there is no clear demarkation between operative factors and noise, the top six principal components pick up 99% of total interest rate variation 95% of the time. An economically justified basis of this process is hard to find; for example a simple linear model will not suffice for the first principal component and the shape of this component is nonstationary. Wavelet analysis works more directly with yield curve observations than principal components analysis. In fact the complete process from bond data to multiresolution is presented, including the dedicated Perl programs and the details of the portfolio metrics and specially adapted wavelet construction. The result is more robust statistics which provide balance to the more fragile principal components analysis.

Forecasting foreign exchange rates is a perennial dilemma for exporters, importers, foreign exchange rate traders, and the business community as a whole. Foreign exchange rate models using popular linear and non-linear specifications do not produce particularly accurate forecasts. In point of fact, these models have not improved much upon the random walk model, especially in out-of-sample forecasting. Given these results, this dissertation constructs and evaluates new forecasting models to generate as accurate as possible out-of-sample forecasts of foreign exchange rates. The information content of futures contracts on foreign exchange rates is investigated and used to forecast future exchange rates using alternative techniques, both structural (econometric) and non-structural (fuzzy) models. The results of two specifications of a structural model are compared against the well-known random walk model. The first specification assumes future exchange rates are determined by futures prices and a lagged structure of spot rates. The second specification assumes that future spot rates are a function of only a lagged structure of the futures prices. The forecasting accuracy of the models is tested for both in-sample and out-of-sample periods; out-of-sample tests range from the short term to the long term (30- to 180-day forecasts). The results indicate that the random walk model remains a competitive alternative. In out-of-sample predictions, however, we can improve upon it in certain cases. The results also show that the predictive accuracy of the models is better in the short term (30 to 60 days) than in the longer term (180 days).

The purpose of this study was to empirically investigate the adoption of retail electronic commerce (REC). REC is a business transaction which takes place over the Internet between a casual consumer and a firm. The consumer has no long-term relationship with the firm, orders a good or service, and pays with a credit card. To date, most REC applications have not been profitable. To build profitable REC applications a better understanding of the system's users is required. The research model hypothesizes that the level of REC buying is dependent upon the Buying Characteristics of Internet Use and Search Experience plus the Channel Characteristics of Beliefs About Internet Vendors and Beliefs About Internet Security. The effect of these factors is modified by Time. Additional research questions ask about the different types of REC buyers, the differences between these groups, and how these groups evolved over time. To answer these research questions I analyzed publically available data collected over a three-year period by the Georgia Institute of Technology Graphics and Visualization Unit over the Internet. Findings indicate the model best predicts Number of Purchases in a future period, and that Buyer Characteristics are most important to this determination. Further, this model is evolving over Time making Buyer Characteristics predict Number of Purchases better in more recent survey administrations. Buyers clustered into five groups based on level of buying and move through various levels and buy increasing Number of Purchases over time. This is the first large scale research project to investigate the evolution of REC. This implications are significant. Practitioners with casual consumer customers need to deploy a finely tuned REC strategy, understand their buyers, capitalize on the company reputation on the Internet, install an Internet-compatible infrastructure, and web-enable order-entry/inventory/fulfillment/ shipping applications. Researchers might wish to expand on the Buyer Characteristics of the model and/or explore alternative dependent variables. Further, alternative theories such as Population Ecology or Transaction Cost Economics might further illuminate this new I.S. research domain.

Research on the consumer behavior of the Hispanic population has recently attracted the attention of marketing practitioners as well as researchers. This study's purpose was to develop a model and scales to examine the acculturation process of Hispanic consumers with income levels of $35,000 and above, and its effects on their consumer behavior. The proposed model defined acculturation as a bilinear and multidimensional change process, measuring consumers' selective change process in four dimensions: language preference, Hispanic identification, American identification, and familism. A national sample of 653 consumers was analyzed. The scales developed for testing the model showed good to high internal consistency and adequate concurrent validity. According to the results, consumers' contact with Hispanic and Anglo acculturation agents generates change or reinforces consumers' language preferences. Language preference fully mediates the effects of the agents on consumers' American identification and familism; however, the effects of the acculturation agents on Hispanic identification are only partially mediated by individuals' language preference change. It was proposed that the acculturation process would have an effect on consumers' brand loyalty, attitudes towards high quality and prestigious brands, purchase frequency, and savings allocation for their children. Given the lack of significant differences between Hispanic and Anglo consumers and among Hispanic generations, only savings allocation for children's future was studied intensively. According to these results, Hispanic consumers' savings for their children is affected by consumers' language preference through their ethnic identification and familism. No moderating effects were found for consumers' gender, age, and country of origin, suggesting that individual differences do not affect consumers' acculturation process. Additionally, the effects of familism were tested among ethnic groups. The results suggest not only that familism discriminates among Hispanic and Anglo consumers, but also is a significant predictor of consumers' brand loyalty, brand quality attitudes, and savings allocation. Three acculturation segments were obtained through cluster analysis: bicultural, high acculturation, and low acculturation groups, supporting the biculturalism proposition.

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This thesis consists of three essays that attempt to provide novel empirical analyses of important problems in finance. The first essay deals with the returns of actively managed mutual funds; the second and third essays attempt to bring further understanding to ultra high-frequency market microstructure data. The thread that binds these chapters together---if any---is the use of latent structure models. The first chapter attempts to identify unobserved populations of managers, the second looks for hidden structure in order book shapes, and the third searches for connections between order book events. Evaluating the performance of actively managed equity mutual funds is among the most important topics in the field of finance. In the first chapter, I present a new assessment of the stock picking ability of actively managed funds that accounts for the occurrence of false positives, an issue that complicates traditional assessments. I find that while the data is consistent with a small group of alpha-generating funds, the composition of this population experiences significant annual turnover and is, therefore, difficult to identify in advance. Between 1975 and 2015, the returns to a fund selection strategy based on the classification method fail to generate alpha. The second chapter begins a study of high-frequency limit order book data. With a view towards exploring the information content of limit orders, as opposed to market orders, I propose a factor model of order book shape. I start by building a unique dataset of Nasdaq limit order books that tracks order activity at ultra high-frequency. Analyzing over 20,000 stock-days, I find that the limit order book comprises three common factors, which I characterize as level, slope, and curvature. By combining these factors alongside price increments in a vector autoregression, I demonstrate that the factors not only explain limit order book shape but also predict returns over one-minute time intervals. In agreement with the claim that high-frequency traders serve a role in increasing market efficiency, I find a negative correlation between predictability and high-frequency trade activity. In the third chapter, I explore a continuous-time, event-driven model of limit order book dynamics. It is the first analysis of its kind to examine the microstructure of a broad cross-section of markets, as well as the first to introduce a Bayesian framework for the study of mutually-exciting Poisson processes in order-driven microstructure models. The picture that emerges is that of a strongly self- and mutually-exciting process characterized by intensity ``spikes'' lasting mere fractions of a second. The largest of these spikes are expected to generate between 0.5 and 2.0 order book events--up to 120 times the number of expected events per second. In the typical order book, market orders demonstrate a significant influence on limit orders and cancellations, but the relationship is non-reciprocating: while limit orders and cancellations exhibit strong interactions with each other, they have no effect on the arrival of market orders. Over 99.5% of the markets examined are stable, and in every market examined, the network model significantly improves in-sample fit relative to a baseline Poisson model. I argue that this improvement is due almost entirely to the most active 20-25% of connections.

Many international companies have entered China because of its expanding opportunities. However, for expatriate managers to innovate and implement their strategic plans in order to exploit these opportunities, they must know and work with their Chinese subordinates. But conflict is inevitable within organizations, especially when people with different cultural backgrounds work together. Culture not only affects people’s preferred ways of doing things but also influences their styles to deal with conflicts. Compared with Westerners, Chinese people have been found to employ indirect ways and prefer to avoid conflict. To facilitate effective communication, it is imperative for Western managers to understand why local subordinates might avoid conflicts and what strategies they will use. This paper explores the dynamic structure of conflict avoidance between Western managers and Chinese employees; we want to understand the different strategies used to avoid conflict. Specifically, this study uses the theory of cooperation and competition to predict people’s responses toward conflict avoidance. We hypothesize that conflict avoidance is not always negative but depends on the specific actions the protagonists adopt and their perceptions of the goal interdependence with each other (cooperative or competitive) greatly influence their tactics to avoid conflict. The study extends research on conflict avoidance to foreign invested companies in China and develops a typology of the dynamics of conflict avoidance. Altogether 132 face-to-face interviews were carried out in Hong Kong and Beijing, China. Participants who work with Western managers were asked to describe an incident in which they avoided a conflict with their foreign superiors; it included the setting, what occurred, the reasons, and the consequences. Then they rated specific questions on 7-point Likert-type scale based on the recalled incidents. Employees whose bosses are local managers were also recruited as a control group. Structural equation modeling and other analyses will explore the proposed model and help to compare cultural differences in handling conflicts between the Western and Chinese managers. The paper draws implications for managing in foreign invested firms.

Recent technological advances in sensor and computer technology allow the observation of business and industrial processes at fairly high frequencies. For example, data used for monitoring critical parameters of industrial furnaces, conveyor belts or chemical processes may be sampled every minute or second. A high sampling rate is also possible in business related processes such as mail order distribution, fast food restaurant operations, and electronic commerce. Data obtained from frequently monitored business processes are likely to be autocorrelated time series that may or may not be stationary. If left alone, processes will typically not be stable, and hence they will usually not posses a fixed mean, thus exhibiting homogeneous non-stationarity. For monitoring, control, and forecasting purposes of such potentially non-stationary processes it is often important to develop an understanding of the dynamic properties of processes. However, it is sometimes difficult if not impossible to conduct deliberate experiments on full scale industrial plants or business processes to gain the necessary insight of their dynamic properties. Fortunately, intentional or inadvertent process changes that occur in the course of normal operation sometimes offer an opportunity to identify and estimate aspects of the dynamic behavior. To determine if a time series is stationary, the standard exploratory data analytic approach is to check that the sample autocorrelation function (ACF) fades out relatively quickly. An alternative, and at times a sounder approach is to use the variogram – a data exploratory tool widely used in spatial (geo) statistics for the investigation of spatial correlation of data. The first objective of this dissertation is to derive the basic properties of the variogram and to provide the literature on confidence intervals for the variogram. We then show how to use the multivariate Delta method to derive asymptotic confidence intervals for the variogram that are both practical and computationally appealing. The second objective of this dissertation is to review the theory of dynamic process modeling based on time series intervention analysis and to show how this theory can be used for an assessment of the dynamic properties of business and industrial processes. This is accompanied by a detailed example of the study of a large scale ceramic plant that was exposed to an intentional but unplanned structural change (a quasi experiment). The third objective of this dissertation concerns the analysis of multiple interventions. Multiple interventions occur either as a result of multiple changes made to the same process or because of a single change having non-homogeneous effects on time series. For evaluating the effects of undertaken structural changes, it is important to assess and compare the effects, such as gains or losses, of multiple interventions. A statistical hypothesis test for comparing the effects among multiple interventions on process dynamics is developed. Further, we investigate the statistical power of the suggested test and elucidate the results with examples.