360 research outputs found

    The Politics of the Income Tax

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    A Review of Dimensions of Law in the Service of Order: Origins of the Federal Income Tax by Robert Stanle

    The Effect of Anti-Discrimination Provisions on Rank-and-File Compensation

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    The purpose of this Article is to provide a more considered, though still quite basic, exposition of the effect the anti-discrimination provisions are likely to have on rank-and-file compensation

    The Business Purpose Doctrine and the Sociology of Tax

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    The Venture Capital Investment Bust: Did Agency Costs Play a Role? Was it Something Lawyers Helped Structure?

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    This Article examines the question of why venture capital firms would continue to raise technology funds, and then invest those funds, when they were certain that the business markets for such investments were overvalued preceding the “crash” of April 2000. We interviewed a number of venture capitalists, lawyers, entrepreneurs, and other industry observers in search of an explanation. The explanations offered by key decision makers for the observed investment behavior can be categorized as of three types of theories: agency cost theories, herd behavior and other cognitive bias theories, and non–agency cost theories. Agency cost theories suggest that the activity took place because of the divergence between the long-term reputational and other interests of fund general partners (venture capital firms), and the short-term interests of their limited partner investors. Herd behavior explanations apply herding theory to the general movement of venture capital firms, but fail to provide a satisfactory explanation for the direction of the “herd.” Non-agency cost theories include explanations premised upon gaming strategies by better-informed venture capitalists in the context of less-informed public markets at the end of the investment pipeline. All of the theories surveyed are problematic in at least some respects, and none fully explains the pattern of investment observed

    Substitutes for Insider Trading

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    When insider trading prohibitions limit the ability of insiders (or of a corporation itself) to use material nonpublic information to trade a particular firm\u27s stock, there may be incentive to use the information to trade instead in the stock of that firm\u27s rivals, suppliers, customers, or the manufacturers of complementary products. We refer to this form of trading as trading in stock substitutes. Stock substitute trading by a firm is legal. In many circumstances, substitute trading by employees is also legal. Trading in stock substitutes may be quite profitable, and there is anecdotal evidence that employees often engage in such trading. Our analysis suggests that substitute trading is less socially desirable than traditional insider trading. We recommend a set of disclosure rules designed to clarify existing law and provide information on the extent of stock substitute trading. We also discuss possible changes in the law that might limit inefficient trading in stock substitutes

    Why Start-ups ?

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    The prototypical start-up involves an employee leaving her job with an idea and selling a portion of that idea to a venture capitalist. In many respects, however, the idea should be worth more to the former employer. The former employer can be expected to have better information concerning the employee-entrepreneur and the technology, have opportunities to capture economies of scale and scope not available to a venture capital-backed start-up, and will receive more favorable tax treatment than the start-up should the innovation fail. In connection with an auction of the idea, the former employer should have both a more accurate estimate of its value and receive an element of private value not available to the venture capitalist. In turn, this should give rise to a powerful winner\u27s curse: each time a venture capitalist wins the auction, it will have paid more than a party that has better information and receives an element of private value. The puzzle, then, is why do we ever observe start-ups? Professors Joseph Bankman and Ronald J. Gilson suggest three interrelated explanations. First, the venture capitalist may have superior information with respect to some subset of employee innovations. Second, employer bids on employee innovation can create an incentive for employees to establish internal property rights in their research efforts that may reduce the future output of the employer\u27s research and development efforts. Finally, employees are not homogenous. The attractiveness of venture capital financing depends critically on employee personal characteristics, such as risk aversion. The employer sets the internal payoff to discovery – its bid – to equalize the marginal benefit of retaining employees who might otherwise leave to the marginal cost of establishing unfavorable incentives for future research and development for those employees who do not find venture capital financing a close substitute for continued employment. In some cases, this calculus might lead to a no bid policy. In virtually all cases, the pay-off will be set too loll to retain all employees, and start-ups ensue

    A neural network prototyping package within IRAF

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    We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do
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