How Would the Date of 52-week High/low of a Bidder Affect M&A?

This thesis consists of three empirical studies. First, it discusses how the acquirers' date of 52-week high/low affects the completeness and the performance of the acquirers in M&A. Second, it compares the conflicting market timing effect and signalling effect in M&A and discusses which is the domination power in the deals. Finally, it examines how the choice of financial advisors affects bidders in M&A under the influence of payment methods and the psychological reference points at or near the dates of 52-week high and low. Based on the US M&As undertaken in the period between 1/1/1985 - 03/31/2015, the thesis finds that 1) an M&A announcement closer to the date of the 52-week peak will positively influence the completion of the transaction but negatively affect the cumulative abnormal returns (CARs) and buy-and-hold abnormal returns (BHARs), while an M&A announcement closer to the date of the 52-week low will be significantly associated with higher 36-month and 60-month BHARs but lower deal completion given stock exchange as the payment method. 2) Anti-signallers (bidders that announce pure cash deals close to the date of the 52-week low) have the highest short-term return, while timers (bidders that announce pure stock deals close to the date of the 52-week high) perform the worst in terms of CARs after the announcement. In the long term, reversals exist in all categories of bidders. The anti-signallers have the lowest reversal in the long term, while the timers have the highest long-term reversal. 3) When payment methods and the announcement timing are controlled, neither the top- nor the median-tier financial advisors bring significant gains whatsoever; the low-tier advisors even incur remarkable loss to the acquirers. The in-house deal announcements are recognized by the market with significantly positive cumulative abnormal returns in the short term; however, they are unlikely to be completed due to the lack of experience in M&A. Comparatively speaking, the median-tier advisors are the most cost effective in terms of the deal completion and consequent performances. This thesis contributes to the existing literature 1) by establishing a bidder reference timing point at the date of the 52-week high/low, when the announcement significantly impacts the M&A. 2) In addition, the contradictive recommendation for payment methods from market timing theory and signalling theory are reconciled and complemented with more details. 3) Finally, it empirically proves that the influence from the reference timing point of the dates of the 52-week high/low is even more decisive than that of the financial advisors

Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

Toeplitz operators on Lp\mathcal L^p-spaces of a tree

Let $T$ be a rooted, countable infinite tree without terminal vertices. In the present paper, we characterize the spectra, self-adjointness and positivity of Toeplitz operators on the spaces of $p$-summable functions on $T$. Moreover, we obtain a necessary and sufficient condition for Toeplitz operators to have finite rank on such function spaces

Localization-delocalization wavepacket transition in Pythagorean aperiodic potentials

We introduce a composite optical lattice created by two mutually rotated square patterns and allowing observation of continuous transformation between incommensurate and completely periodic structures upon variation of the rotation angle θ. Such lattices acquire periodicity only for rotation angles cosθ=a/c, sinθ=b/c, set by Pythagorean triples of natural numbers (a, b, c). While linear eigenmodes supported by lattices associated with Pythagorean triples are always extended, composite patterns generated for intermediate rotation angles allow observation of the localizationdelocalization transition of eigenmodes upon modification of the relative strength of two sublattices forming the composite pattern. Sharp delocalization of supported modes for certain θ values can be used for visualization of Pythagorean triples. The effects predicted here are general and also take place in composite structures generated by two rotated hexagonal latticesPeer ReviewedPostprint (published version

A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain

Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers. Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model. Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time. Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints. Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great practical significance to the dynamic relationship establishment of multi-supplier-multi-customer in current market.Peer Reviewe

A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain

Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers. Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model. Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time. Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints. Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great practical significance to the dynamic relationship establishment of multi-supplier-multi-customer in current market.Peer Reviewe

Monitoring Crop Carotenoids Concentration by Remote Sensing

Assessment of carotenoids (Car) content provides a valuable insight into clarifying the mechanisms of plant photoprotection and light-adaption and is critical for stress diagnoses in plants. Due to their small proportion in the overall total pigment content and to the overlapping of spectral absorption features with chlorophylls (Chl) in the blue region of the spectrum, accurate estimation of Car content in plants, from remotely sensed data, is challenging. Previous studies made progress in Car content estimation at both the leaf and canopy level with remote sensing techniques. However, established spectral indices and methods for Car estimation in most studies that generally rely on specific and limited measured data might lack predictive accuracy for Car estimation and lack sensitivity to low or high Car content in various species and at different growth stages. In this chapter, a new carotenoid index (CARI) was proposed for foliar Car assessment with abundant simulated leaf data and various measured leaf reflectances. Detailed analysis on the mechanism, formation and performance of the new spectral index on Car retrieval was presented. Analysis results suggested that accurate nondestructive estimation of foliar Car content with CARI could be achieved at the leaf scale, through remote sensing techniques