Reputation–Quality Mechanism in the Context of Mergers and Acquisitions: Financial Advisors, Financial Analysts and Acquirer Performance

This thesis focuses on the reputation–quality mechanism in the context of mergers and acquisitions (M&A). This study specifically examines whether investment bank reputation is a determinant of M&A advisory service quality, and whether sell-side analyst reputation is a determinant of the predictive ability of stock recommendations. To begin with, this research investigates whether top-tier M&A financial advisors improve their acquirer clients’ performance in both the short and long term, and whether top-tier advisors can help their acquirer clients to gain bargaining advantage, allowing them to pay lower bid premiums. I find that acquirers advised by top-tier advisors outperform in the long term and pay lower bid premiums, suggesting that top-tier advisors do have superior skills. Furthermore, the social loafing hypothesis suggests that individuals exercise less effort when they work collectively. My research therefore explores whether multiple top-tier financial advisors can cooperate effectively to create value for their clients or whether they suffer from social loafing. This study finds that acquirers advised by multiple top-tier advisors gain greater long-term returns and pay lower bid premiums than acquirers advised by a single top-tier advisor. The results suggest that top-tier advisors care more about their reputational capital, and therefore do not suffer from social loafing. Instead, they can make concerted efforts to improve their clients’ performance and bargaining power. In addition, my study examines whether the pre-acquisition stock recommendations of sell-side analysts can be used to predict acquirer performance, and more importantly whether the recommendations of star analysts have stronger predictive ability for acquirer announcement performance than those of non-star analysts. I find that pre-deal consensus recommendations are an effective predictor of acquirer performance; however, star recommendations are not predictive of acquirer performance, while acquirers with more favourable non star consensus recommendations gain higher announcement returns. In other words, non-star recommendations have stronger predictive ability than star recommendations. Overall, this thesis provides new evidence on the reputation–quality mechanism in the context of M&A. The results suggest that market share-based league tables are reliable to reflect financial advisors’ skills, while sell-side analyst rankings are a kind of popularity contest

Hierarchical Structure with Highly Ordered Macroporous-Mesoporous Metal-Organic Frameworks as Dual Function for CO2 Fixation.

As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials

Neuron Coverage-Guided Domain Generalization

This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e., misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples. As such, the decision behavior of the DNN is optimized, avoiding the arbitrary neurons that are deleterious for the unseen samples, and leading to the trained DNN that can be better generalized to out-of-distribution samples. Extensive studies on various domain generalization tasks based on both single and multiple domain(s) setting demonstrate the effectiveness of our proposed approach compared with state-of-the-art baseline methods. We also analyze our method by conducting visualization based on network dissection. The results further provide useful evidence on the rationality and effectiveness of our approach

Motion-aware Memory Network for Fast Video Salient Object Detection

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. Furthermore, previous methods only considered single-frame prediction without temporal association. As a result, the model may not focus on the temporal information sufficiently. Thus, we initially introduce object motion prediction between inter-frame into VSOD. Our model follows standard encoder--decoder architecture. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. This approach is more efficient than the optical flow-based methods. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The semantic information of the high-level features is used to fuse the object details in the low-level features, and then the spatiotemporal features are obtained step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity. Extensive experiments on several datasets demonstrated the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.Comment: 12 pages, 10 figure

Aqueous electrosynthesis of an electrochromic material based water-soluble EDOT-MeNH2 hydrochloride

2\u27-Aminomethyl-3,4-ethylenedioxythiophene (EDOT-MeNH2) showed unsatisfactory results when its polymerization occurred in organic solvent in our previous report. Therefore, a water-soluble EDOT derivative was designed by using hydrochloric modified EDOT-MeNH2 (EDOT-MeNH2·HCl) and electropolymerized in aqueous solution to form the corresponding polymer with excellent electrochromic properties. Moreover, the polymer was systematically explored, including electrochemical, optical properties and structure characterization. Cyclic voltammetry showed low oxidation potential of EDOT-MeNH2·HCl (0.85 V) in aqueous solution, leading to the facile electrodeposition of uniform the polymer film with outstanding electroactivity. Compared with poly(2′-aminomethyl- 3,4-ethylenedioxythiophene) (PEDOT-MeNH2), poly(2′-aminomethyl-3,4-ethylenedioxythiophene salt) (PEDOT-MeNH3 +A-) revealed higher efficiencies (156 cm2 C-1), lower bandgap (1.68 eV), and faster response time (1.4 s). Satisfactory results implied that salinization can not only change the polymerization system, but also adjust the optical absorption, thereby increase the electrochromic properties