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

Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

In this paper, a resource-efficient FPGA-based neural network regression model is developed for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned upgrades, the entirely new FPGA-based hardware muon trigger system will be installed in 2025-2026 that will process full muon detector data within a 10 ${\mu}s$ latency window. The planned large FPGA devices should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our model promises to improve the rejection of the dominant source of background events in the central detector region, which are due to muon candidates with low transverse momenta. This neural network was implemented in the hardware description language using 65 digital signal processors and about 10,000 lookup tables. The simulated network latency and deadtime are 245 and 60 ns, respectively, when implemented in the FPGA device using a 400 MHz clock frequency. These results are well within the requirements of the future ATLAS muon trigger system, therefore opening a possibility for deploying machine learning methods for data taking by the ATLAS experiment at the High Luminosity LHC.Comment: 12 pages, 17 figure

Gesture-Controlled Quadcopter System

According to the statistic given by the National Law Enforcement Officers Memorial Fund, 514 police officers casualties have been attributed to gunfire in the last decade(2008-2017). It is the leading cause of death among police officers and accounts for more than a third of the total 1511 police casualties in the past decade. In this project, we want to provide a safer solution to police officers that are surveying a building for one or multiple potential dangerous personnel. We are working to design and build a gesture-controlled quadcopter that can scout ahead of the officer and provide information about the area so the officers can avoid a dangerous situation. This gestured-controlled quadcopter system will be compact and lightweight, offering greater mobility and ease-of-use over joystick-based systems. We hope that our system’s simple, intuitive design will allow a user to learn how to pilot it in less than an hour. These goals are targeted to create a system that is useful for any police officer, regardless of their technical skills

Kelangsungan industri lada hitam

In current Cloud computing environments, management of data reliability has become a challenge. For data-intensive scientific applications, storing data in the Cloud with the typical 3-replica replication strategy for managing the data reliability would incur huge storage cost. To address this issue, in this paper we present a novel cost-effective data reliability management mechanism named PRCR, which proactively checks the availability of replicas for maintaining the reliability. Our simulation indicates that, comparing with the typical 3 replica replication strategy, PRCR can significantly reduce the storage space consumption, hence storage cost in the Cloud