This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability…
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
Mergers and acquisitions (M&As) have been playing a very significant role in the capital market. Many companies regard mergers and acquisitions as an important way for their business expansion and transformation. This paper begins with a review of literature on firm’s motivations of and outcomes in M&A, and followed…
Mergers and acquisitions (M&As) have been playing a very significant role in the capital market. Many companies regard mergers and acquisitions as an important way for their business expansion and transformation. This paper begins with a review of literature on firm’s motivations of and outcomes in M&A, and followed by a critical examination of three case studies of actual M&A transactions based on the insights provided from the literature review. For each case study, a firm’s motivations and related managerial initiatives for M&A activities were examined, followed by an assessment of the firm’s post M&A performance results. This allows the study to discerns the insights of why and how a firm proceed in its M&A transactions from its strategic intent to its post M&A managerial actions. Collectively, the results show that the key drivers for a firm’s M&A successes rest on a firm’s abilities to manage the M&A activities consistent with its strategic intent (e.g., creating synergies or transformation through diversification) and followed by its post M&A integration efforts in achieving its strategic intent.
This study investigates the performance effects of cross-industry mergers and acquisitions (M&A) using a sample of firms listed in China’s Growth Entrepreses Market (GEM). Compared to firms listed in the Shanghai and Shenzhen Stock Exchanges, firms listed in the GEM are much smaller and tend to derive the majority of…
This study investigates the performance effects of cross-industry mergers and acquisitions (M&A) using a sample of firms listed in China’s Growth Entrepreses Market (GEM). Compared to firms listed in the Shanghai and Shenzhen Stock Exchanges, firms listed in the GEM are much smaller and tend to derive the majority of their revenues from a single industry. I first analyze the motives for firms listed in the GEM to engage in M&As and propose a set of factors that may influence their likelihood of M&A activities. Using data on 55 cross-industry M&As between January 1, 2012 and December 31, 2016, I find that investor generally responded positively in short-term, as indicated by the positive accumulated abonormal returns over the first five trading days following the announcements. Meanwhile, I found no evidence that investors benefited from cross-industry M&As in long-term over three years after the event. Further analysis suggests that the short-term effects of cross-industry M&As by GEM listed firms were influenced by the target firm’s market valuation, whether the M&A was paid by cash, the amount of the payment, and the degree of difference between the acquiring firm’s and the target firm’s industries. These findings have important implications for the investors and senior executives of firms listed in the GEM.
On January 30, 2019, the China Securities Regulatory Commission issued the Implementation Opinions on the Establishment of the Science and Technology Innovation Board on the Shanghai Stock Exchange and the Pilot Registration-based System, announcing the establishment of a new Science and Technology Innovation Board(STAR). The STAR Market is an important…
On January 30, 2019, the China Securities Regulatory Commission issued the Implementation Opinions on the Establishment of the Science and Technology Innovation Board on the Shanghai Stock Exchange and the Pilot Registration-based System, announcing the establishment of a new Science and Technology Innovation Board(STAR). The STAR Market is an important measure in China's capital market reform, aiming to promote the transformation of China's economy from a stage of rapid growth to a stage of high-quality development. The companies listed on the Science and Technology Innovation Board are mainly scientific and technological innovation enterprises that are at the forefront of the world's science and technology, the main battlefield of the economy, and the major needs of the country, in line with the national strategy, breaking through key core technologies, and with high market recognition. Since its launch on July 22, 2019, to May, 15, 2023, there are 522 companies have been listed on the STAR Market, with a total market capitalization of more than RMB 7 trillion. The successful listing of these enterprises will provide strong support for the deep integration of China's high-tech industries and strategic emerging industries.This paper analyzes the influencing factors of IPO listing pricing on the STAR Market, and studies 1478 companies listed on the three listing platforms of the STAR Market, ChiNext and Hong Kong stocks. Through descriptive statistical analysis and multivariate regression model, the influencing factors of the 1st day and the 20th day were empirically studied. The results of the study will provide a pricing reference for
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listed companies in the future, and provide a reference for policymakers to meet the expectations of the new regulatory reforms.
Through analysis of multiple factors includes but not limited as the NR,IPE, LEAD, ISCA, T10, AOL, BC, STL, RDI, CAGR, DTOR, these influencing factors have an important impact on the IPO of the STAR Market.