Doctor of Philosophy in Business Administration

dissertationAccording to classical portfolio theory, two implications follow when an asset has a positive alpha against some benchmark: (1) the benchmark is mean-variance inefficient; (2) by combining the positive alpha asset with the benchmark, one can improve the mean- variance efficiency of the benchmark. The first implication is well known, but the second is largely ignored in the existing literature. This dissertation tests and applies the second implication. The dissertation has two chapters. Chapter 1 empirically tests the theory. Specifically, we test whether the alpha of an investment relative to one's existing portfolio can be used to improve out-of-sample performance as measured by Sharpe ratio and four-factor alpha. For the period 2000 - 2014, we confirm this for the Vanguard S&P 500 Index Fund and the Growth and Small Index Fund, which we extend by adding various Exchange Traded Funds. Chapter 2 applies the theory in the mutual fund context in order to shed light on the relation between active management and fund performance. Recent studies have documented a positive relation between the degree of active management and mutual fund performance. We show that this relation holds only for fund managers who trade in an optimal way. The optimality measure that we develop, "investment alpha," captures whether a mutual fund is trading towards mean-variance optimality, which, we argue, is the first-best choice for mutual fund managers within a mean-variance framework. This investment alpha is similar to previous work using evaluation alphas such as Jensen's alpha, except that our benchmark is the manager's own portfolio. We show that if the investment alpha of a fund's incremental portfolio - defined as the portfolio obtained by collecting the changes in a manager's positions over a given period - is positive then the fund is trading in the "right" direction. We show empirically that managers who do so outperform, and the more so if they are more active, and that investors react to the correct direction through increases in fund flows in the subsequent quarter. Actively managed funds that don't trade toward mean-variance optimality do not outperform

Toward a Mathematical Theory of Behavioral-Social Dynamics for Pedestrian Crowds

This paper presents a new approach to behavioral-social dynamics of pedestrian crowds by suitable development of methods of the kinetic theory. It is shown how heterogeneous individual behaviors can modify the collective dynamics, as well as how local unusual behaviors can propagate in the crowd. The main feature of this approach is a detailed analysis of the interactions between dynamics and social behaviors.Comment: 22 pages, 5 figure

The Role of Perceived Uncertainty, Ego Identity, and Perceived Behavioral Control in Predicting Patient's Attitude Toward Medical Surgery

Medical surgery has sometimes become the only best choice for a patient's well-being. Unfortunately, not all patients have the willingness to live it. Often, therapeutic failure is caused by uncooperative attitudes of the patients which originate from their negative attitudes toward the surgery. This research is aimed at finding a theoretical model to explain psychological factors forming the patient's attitudes. This predictive correlational research was conducted on 99 patients suffering heart disease and cancer continuum who require medical surgery in DKI Jakarta, Indonesia. Research results showed that a commitment aspect of ego identity is able to indirectly predict attitude toward medical surgery through mediation of perceived uncertainty. Perceived behavioral control directly predicts the attitude in a negative direction. This research concludes that patients' commitment towards their identity plays a significant role as they deal with medical surgery

Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate

Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.Defense Advanced Projects Research Agency SyNaPSE program (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC #801881-BS under HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (NSF SBE-0354378

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088

Predicting Students’ Physical Activity and Health-Related Well-Being: A Prospective Cross-Domain Investigation of Motivation Across School Physical Education and Exercise Settings \ud

A three-wave prospective design was used to assess a model of motivation guided by self-determination theory (Ryan & Deci, 2008) spanning the contexts of school physical education (PE) and exercise. The outcome variables examined were health-related quality of life (HRQoL), physical self-concept (PSC), and 4 days of objectively assessed estimates of activity. Secondary school students (n = 494) completed questionnaires at three separate time points and were familiarized with how to use a sealed pedometer. Results of structural equation modeling supported a model in which perceptions of autonomy support from a PE teacher positively predicted PE-related need satisfaction (autonomy, competence, and relatedness). Competence predicted PSC, whereas relatedness predicted HRQoL. Autonomy and competence positively predicted autonomous motivation toward PE, which in turn positively predicted autonomous motivation toward exercise (i.e., 4-day pedometer step count). Autonomous motivation toward exercise positively predicted step count, HRQoL, and PSC. Results of multisample structural equation modeling supported gender invariance. Suggestions for future work are discussed.\ud \u

Toward an analytical and methodological understanding of actor-network theory

Actor-Network theory (ANT) is well developed within social studies of science and technology. The last two decades have seen an increasing awareness and interest in ANT within the social sciences and it has increasingly been invoked to theorise the role of ‘nonhumans’ in social life.  In this respect the conceptual repertoire of ANT has been increasingly drawn upon to examine the relational dimensions between artefacts and people. Despite this the use of ANT as an analytical and/or methodological approach occupies a peripheral within social science research.  In part, the reticence towards ANT may be explained by its lack of theoretical unity. Analytically and methodologically the application of ANT and thought which is closely associated with the approach is considerably varied. ANT informed research often differs quite considerably in terms of methodological approach and style of analyses. This is further complicated by the disparate emphases of ANT proponents and the proliferation of different versions of ANT. Thus, there is no generic way to ‘apply’ actor-network theory and it lacks methodological prescription. This article intends to articulate the analytical and methodological possibilities of ANT. For those who are encountering ANT for the first time or for whom ANT has been regarded as a somewhat left field and inaccessible theory obscured by its own vocabularies and heterogeneity this article may provide a useful conceptual map through which the key elements of ANT can be navigated

Residual Value Analysis Post-Privatization Of Jakarta Stock Exchange From August 1992 Until July 1995)

The more-and-more efficient market shows the lessening difference between actual return and expected return on stocks being traded. In other words, the residual values are growing smaller. Such market brings in signals that the prices of stocks reflect all the necessary information suitable for the concept of the market efficiency itself. By making most use of all information available either in the form of regularly published reports or in any other relevant information on the movement of the capital market from year to year historically, it is highly expected that the efficiency be reached where the price has significance upon true fundamental values. The competition includes market participants as well as the competition in numbers and security. The steadily increasing informational efficiency results positively in the development of the upcoming market, namely bringing great credit to contribute to public interest. Naturally, the consequence is that there is more and more people involved in the market participating actively and thus each transaction is getting more competitive. In the end, illegal practices can be minimized and the market mechanism can roll forward propelled by the natural power of demand and supply for stocks expected having a prospect as provided by the available information