The Economics of Leveraged Takeovers

Financing of hostile takeovers has emerged as a central issue in the ongoing debate concerning corporate takeovers. Concomitant with the increase in the dollar value of takeovers during the past few years has been a significant increase in the percentage of tender offer financing accounted for by bank borrowing and the issuance of high yield debt, (that is, debt securities which are rated below Standard and Poor\u27s BBB-or Moody\u27s Baa3), hereafter referred to as junk bonds, have accounted for an increasingly greater percentage of takeover financing. This Article examines these concerns about debt financing of corporate takeovers from an efficient markets perspective. The efficient-market hypothesis has important implications for public policy toward corporate takeovers. Because a takeover involves the payment of premiums to target shareholders, prospective bidders must perceive a way to raise the target firm\u27s value, that is, the discounted cash flow of the target firm. We argue that junk-bond financing facilitates takeovers which in turn promote economic efficiency. Critics of leveraged takeovers, in our view, exaggerate the risks associated with these transactions, and in some instances, misunderstand the nature of corporate debt. After illustrating the structure of a leveraged takeover with an analysis of Mesa Petroleum\u27s unsuccessful bid for Unocal, this Article seeks to correct the misperceptions about corporate debt with a discussion of the economics of corporate leverage. Finally, we use the Mesa-Unocal case to evaluate the claims made by critics of leveraged takeovers

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Selectivity, Information And The Return To Futures Trading

One of the more controversial issues in modern financial economics and in futures trading in particular is whether traders have the ability to earn returns above what they could with a buy-and-hold strategy.  The weight of evidence in support of Martingale price movements generally has been considered to be evidence that the expected value of “trading returns” is zero.  This paper shows, however, that, when the contingent claims in a futures contract are taken into account in defining return, expected trading returns may not be zero, even if prices follow the Martingale pattern.  We also point out that, if a sample of trades is representative of some trading strategy with its corresponding trading information, the impact of that strategy on a trader’s expected return can be represented by a  probability model of the strategy’s success.  This results because, to be successful, a trading strategy must select trades nonrandomly.  Using these results, the paper specifies a model of the expected return of an arbitrary trading strategy.  As an illustration, the model is estimated for an artificially constructed strategy in gold futures that imitates what the industry claims is the epitome of futures trading performance – many small losses more than offset by a few big gains.  Statistical tests based on the estimation of this model support the characterization of returns being due to the trading strategy using information to nonrandomly select trades