Assessing the Effects of Mergers and Acquisitions on Firm Performance, Plant Productivity, and Workers: New Evidence from Matched Employer-Employee Data

Studies of the effects of mergers and acquisitions focus on a single unit of analysis: firms, plants, or workers. In contrast, we model these events as transactions that simultaneously have cross-levels effects. Based on the theory of human capital, we generate a set of predictions regarding the antecedents and consequences of firm, plant, and worker turnover. Our empirical analysis is based on longitudinal, linked employer-employee data for virtually the entire population of Swedish manufacturing firms and employees for the period 1985-1998. These data allow us to assess the effects of mergers and acquisitions on firm performance, plant productivity, levels of employment, and compensation. Consistent with human capital theory, we find that mergers and acquisitions lead to improvements in firm performance and plant productivity, although they also result in the downsizing of establishments and firms. These transactions also appear to enhance the careers of workers because they provide a mechanism for improving the sorting and matching or workers and managers to firms and industries that best suit their skills.

The Value of Bitcoin: A Closer Look at How Investor Attention Affects the Value of Bitcoin

This paper looks at two measure of the value of Bitcoin (the price and volume traded in the last month of Bitcoin) and sees if investor attention causes any changes in the values of Bitcoin. This paper also adds exchange rates, the S&P 500, and the price of gold as other possible explanations for the value of Bitcoin. This paper examines the variables at a worldwide level and at the countries with the top 10 GDP in the world. The results of this paper find that investor attention has a significant positive relationship with the value of Bitcoin. Specifically at the country level, Russia consistently has a significant relationship with the value of Bitcoin

K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc

Confronting the Peppercorn Settlement in Merger Litigation: An Empirical Analysis and a Proposal for Reform

Shareholder litigation challenging corporate mergers is ubiquitous, with the likelihood of a shareholder suit exceeding 90%. The value of this litigation, however, is questionable. The vast majority of merger cases settle for nothing more than supplemental disclosures in the merger proxy statement. The attorneys that bring these lawsuits are compensated for their efforts with a court-awarded fee. This leads critics to charge that merger litigation benefits only the lawyers who bring the claims, not the shareholders they represent. In response, defenders of merger litigation argue that the lawsuits serve a useful oversight function and that the improved disclosures that result are beneficial to shareholders. This Article offers a new approach to assessing the value of these claims by empirically testing the relationship between merger litigation and shareholder voting on the merger. If the supplemental disclosures produced by the settlement of merger litigation are valuable, they should affect shareholder voting behavior. Specifically, supplemental disclosures that are, in effect, “compelled” by settlement should produce new and unfavorable information about the merger and lead to a lower percentage of shares voted in favor of it. Applying this hypothesis to a hand-collected sample of 453 large public company mergers from 2005-2012, we find no such effect. We find no significant evidence that disclosure-only settlements affect shareholder voting. These findings warrant a reconsideration of Delaware merger law. Specifically, under current law, supplemental disclosures are viewed by courts as providing a substantial benefit to the shareholder class. In turn, this substantial benefit entitles the plaintiffs’ lawyers to an award of attorneys’ fees. Our evidence suggests that this legal analysis is misguided and that supplemental disclosures do not in fact constitute a substantial benefit. As a result, and in light of the substantial costs generated by public company merger litigation, we argue that courts should reject disclosure settlements as a basis for attorney fee awards. Our approach responds to critiques of merger litigation as excessive and frivolous by reducing the incentive for plaintiffs’ lawyers to bring weak cases, but it would have an additional benefit. Current practice drags state court judges into the task of indirectly promulgating disclosure standards in connection with the approval of fee awards. We argue, instead, for a more efficient specialization between state and federal courts in the regulation of mergers: public company merger disclosure should be policed by the federal securities laws while state corporate law focuses on substantive fairness

Confronting the Peppercorn Settlement in Merger Litigation: An Empirical Analysis and a Proposal for Reform

Shareholder litigation challenging corporate mergers is ubiquitous, with the likelihood of a shareholder suit exceeding 90%. The value of this litigation, however, is questionable. The vast majority of merger cases settle for nothing more than supplemental disclosures in the merger proxy statement. The attorneys that bring these lawsuits are compensated for their efforts with a court-awarded fee. This leads critics to charge that merger litigation benefits only the lawyers who bring the claims, not the shareholders they represent. In response, defenders of merger litigation argue that the lawsuits serve a useful oversight function and that the improved disclosures that result are beneficial to shareholders. This Article offers a new approach to assessing the value of these claims by empirically testing the relationship between merger litigation and shareholder voting on the merger. If the supplemental disclosures produced by the settlement of merger litigation are valuable, they should affect shareholder voting behavior. Specifically, supplemental disclosures that are, in effect, “compelled” by settlement should produce new and unfavorable information about the merger and lead to a lower percentage of shares voted in favor of it. Applying this hypothesis to a hand-collected sample of 453 large public company mergers from 2005-2012, we find no such effect. We find no significant evidence that disclosure-only settlements affect shareholder voting. These findings warrant a reconsideration of Delaware merger law. Specifically, under current law, supplemental disclosures are viewed by courts as providing a substantial benefit to the shareholder class. In turn, this substantial benefit entitles the plaintiffs’ lawyers to an award of attorneys’ fees. Our evidence suggests that this legal analysis is misguided and that supplemental disclosures do not in fact constitute a substantial benefit. As a result, and in light of the substantial costs generated by public company merger litigation, we argue that courts should reject disclosure settlements as a basis for attorney fee awards. Our approach responds to critiques of merger litigation as excessive and frivolous by reducing the incentive for plaintiffs’ lawyers to bring weak cases, but it would have an additional benefit. Current practice drags state court judges into the task of indirectly promulgating disclosure standards in connection with the approval of fee awards. We argue, instead, for a more efficient specialization between state and federal courts in the regulation of mergers: public company merger disclosure should be policed by the federal securities laws while state corporate law focuses on substantive fairness

Legality and venture governance around the world

We analyze governance with a dataset on investments of venture capitalists in 3848 portfolio firms in 39 countries from North and South America, Europe and Asia spanning 1971-2003. We find that cross-country differences in Legality have a significant impact on the governance structure of investments in the VC industry: better laws facilitate faster deal screening and deal origination, a higher probability of syndication and a lower probability of potentially harmful co-investment, and facilitate board representation of the investor. We also show better laws reduce the probability that the investor requires periodic cash flows prior to exit, which is in conjunction with an increased probability of investment in high-tech companies. Klassifikation: G24, G31, G32

An automated technique for identifying associations between medications, laboratory results and problems

AbstractBackgroundThe patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete.ObjectiveTo determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list.DesignAssociation rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women’s Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results.MeasurementsCandidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby’s Diagnostic and Laboratory Test Reference for laboratory results.ResultsWe were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest).ConclusionAssociation rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches