The Reference Point Effect, M&A Misvaluations and Merger Decisions

This thesis investigates the reference point effect on M&As. Prospect theory proposes that the reference dependence bias is firmly rooted in people’s minds, affirming that people rely heavily on a piece of relevant information while making decisions. This thesis applies this reference point to M&As, showing that M&A participants are subject to reference-dependence bias. The reference point theory explains the M&A motives in a new way. The results of this thesis offer important implications to M&A practitioners. Baker et al. (2012) suggest that the target’s reference price enhances the target’s bargaining power in the U.S. market, inspiring the author’s thinking that the reference point effect is likely to be reinforced in a competitive market and in the scenario where the bidder is in the face of considerable information barriers. In this pursuit, Chapter 3 studies the reference point effect on a sample of public acquisitions involving a U.K. target. Using the target 52-week high as the reference price, a positive relationship emerges between the reference price and the offer premium. Further, there is evidence of overpayment among domestic bidders whereas little evidence among cross-border bidders, indicating that the market acquiesces the payment according to the target reference point among cross-border bidders. Evidence that reference- dependence bias serves as the bargaining power is confirmed outside the United States. However, the real M&A motive of the bidder is unlikely to be revealed solely depending on the target reference point. Therefore, Chapter 4 extends Baker et al.’s paper (2012) by adding both the target and the bidder reference points. A Relative Reference Point (RRP) is proposed for M&A misvaluations as per Shleifer and Vishny (2003), indicating the extent to which the bidder is more overvalued relatively to the target. The joint reference point effects can explain the motivation of why bidders paying high offer premiums. The results obtained in this chapter show that bidders are likely to pay with stocks when RRP increases, suggesting that bidders accelerating the process of overvaluation dilution is a sign of bidder overvaluation. In addition, the offer premium increases with the RRP, indicating the motive of diluting overvaluation is to be seen by the target, leading targets to bargain over high offer premiums. When assessing the long-term performance of stock bidders, it became evident that bidders time the market by paying stocks according to the RRP. A direct implication of prospect theory is that people seek risk in the face of loss gauged by a reference point. Chapter 5 proposes the bidder reference point to assess the market anticipation effect. Investors suffer mental loss when their firm’s current performance is significantly below its best recently achieved performance. They anticipate that the firm will take risks to turn the table round. It was evident that the firm is rewarded with positive market reactions when it takes risks according to the market’s anticipation. In a further analysis, it also emerged that managers taking the market-anticipated risks exhibit greater efforts in the processes of M&A negotiation and post-merger integration, reflected in the low offer premiums paid to the target shareholders and positive long-term abnormal returns earned. This thesis has empirically investigated many implications of the reference point effect in the M&A context. The main aim of this thesis is to simplify M&A decisions for managers in order to structure M&A effectively

A Differential Game Modeling Approach to Dynamic Traffic Assignment and Signal Control

This paper addresses a theoretical issue related to combined dynamic traffic assignment and signal control under conditions of congestion through a brief review of previous research and the discussion on interaction between dynamic traffic assignment and signal control. The dynamic characteristics of the interaction are approached using a differential game modeling approach here to formulate the decision-making process for solving the problem inherent in this combination. Specifically, the combined dynamic traffic assignment and signal control problem is formulated as a leader−follower differential game, where a leader and multiple followers engage interactively to finding optimal strategies under the assumption of an openloop information structure. Discretization in time is used to find a numerical solution for the proposed game model, and a simulated annealing algorithm is applied to obtain optimal strategies. Finally, a simulation study is conducted on a simple traffic network in which numerical results demonstrate the effectiveness of the proposed approach

Helmert Variance Component Estimation for Multi-GNSS Relative Positioning

The Multi-constellation Global Navigation Satellite System (Multi-GNSS) has become the standard implementation of high accuracy positioning and navigation applications. It is well known that the noise of code and phase measurements depend on GNSS constellation. Then, Helmert variance component estimation (HVCE) is usually used to adjust the contributions of di¿erent GNSS constellations by determining their individual variances of unit weight. However, HVCE requires a heavy computation load. In this study, the HVCE posterior weighting was employed to carry out a kinematic relative Multi-GNSS positioning experiment with six short-baselines from day of year (DoY) 171 to 200 in 2019. As a result, the HVCE posterior weighting strategy improved Multi-GNSS positioning accuracy by 20.5%, 15.7% and 13.2% ineast-north-up(ENU) components, compared to an elevation-dependent (ED) priori weighting strategy. We observed that the weight proportion of both code and phase observations for each GNSS constellation were consistent during the entire 30 days, which indicates that the weight proportions of both code and phase observations are stable over a long period of time. It was also found that the quality of a phase observation is almost equivalent in each baseline and GNSS constellation, whereas that of a code observation is different. In order to reduce the time consumption off the HVCE method without sacrificing positioning accuracy, the stable variances of unit weights of both phase and code observations obtained over 30 days were averaged and then frozen as a priori information in the positioning experiment. The result demonstrated similar ENU improvements of 20.0%, 14.1% and 11.1% with respect to the ED method but saving 88% of the computation time of the HCVE strategy. Our study concludes with the observations that the frozen variances of unit weight (FVUW) could be applied to the positioning experiment for the next 30 days, that is, from DoY 201 to 230 in 2019, improving the positioning ENU accuracy of the ED method by 18.1%, 13.2% and 10.6%, indicating the effectiveness of the FVUW.Peer ReviewedPostprint (published version

Differential Privacy for Edge Weights in Social Networks

Social networks can be analyzed to discover important social issues; however, it will cause privacy disclosure in the process. The edge weights play an important role in social graphs, which are associated with sensitive information (e.g., the price of commercial trade). In the paper, we propose the MB-CI (Merging Barrels and Consistency Inference) strategy to protect weighted social graphs. By viewing the edge-weight sequence as an unattributed histogram, differential privacy for edge weights can be implemented based on the histogram. Considering that some edges have the same weight in a social network, we merge the barrels with the same count into one group to reduce the noise required. Moreover, k-indistinguishability between groups is proposed to fulfill differential privacy not to be violated, because simple merging operation may disclose some information by the magnitude of noise itself. For keeping most of the shortest paths unchanged, we do consistency inference according to original order of the sequence as an important postprocessing step. Experimental results show that the proposed approach effectively improved the accuracy and utility of the released data