4,631 research outputs found

    Tobin’s q and intangible assets

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    In a recent paper Laitner & Stolyarov (2003) assert that measured Tobin’s q has usually been well above 1, and use this to back up their conclusion that there are signiïŹcant quantities of unrecorded intangible assets. This key feature of q turns out however to be entirely due to errors and omissions in the authors’ calculations. The corrected q series turns out to be usually well below unity

    Decomposition Algorithms for Stochastic Programming on a Computational Grid

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    We describe algorithms for two-stage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the L-shaped method and a trust-region method. The parallel platform of choice is the dynamic, heterogeneous, opportunistic platform provided by the Condor system. The algorithms are of master-worker type (with the workers being used to solve second-stage problems, and the MW runtime support library (which supports master-worker computations) is key to the implementation. Computational results are presented on large sample average approximations of problems from the literature.Comment: 44 page

    The limits to stock return predictability

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    We examine predictive return regressions from a new angle. We ask what observable univariate properties of returns tell us about the “predictive space” that deïŹnes the true predictive model: the triplet ÂĄ λ, R2 x, ÏÂą , where λ is the predictor’s persistence, R2 x is the predictive R-squared, and ρ is the "Stambaugh Correlation" (between innovations in the predictive system). When returns are nearly white noise, and the variance ratio slopes downwards, the predictive space can be tightly constrained. Data on real annual US stock returns suggest limited scope for even the best possible predictive regression to out-predict the univariate representation, particularly over long horizons