785 research outputs found
Dynamic behavior of value and growth stocks
The difference between the performance of growth and value portfolios presents an interesting puzzle for researchers in finance. Most studies showed that value stocks outperform growth stocks. This is the so-called value premium. In this article, we try to find an answer to the question as to why value stocks generate superior returns to growth stocks by dividing growth and value stocks into switching- and fixed-style stocks. We show that the difference in returns between value and growth stocks is caused by frequently rebalancing portfolios and find a value premium for the switching-style stocks and a growth premium for the fixed-style stocks. We will try to find an explanation for this phenomenon using the behavioral finance explanation that investors are unable to process information correctly. We use earnings announcement return data to test whether expectations of investors about future growth are too extreme.
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A study of the validity of a battery of mental tests in predicting college success
A Book of Generations â Writing at the Frontier
We address the problem of finding viewpoints that preserve the relational structure of a three-dimensional graph drawing under orthographic parallel projection. Previously, algorithms for finding the best viewpoints under two natural models of viewpoint âgoodnessâ were proposed. Unfortunately, the inherent combinatorial complexity of the problem makes finding exact solutions is impractical. In this paper, we propose two approximation algorithms for the problem, commenting on their design, and presenting results on their performance
The Virtues of Thisness Presentism
Presentists believe that only present things exist. But opponents insist this view has unacceptable implications: if only present things exist, we canât express singular propositions about the past, since the obvious propositional constituents donât exist, nor can we account for temporal passage, or the openness of the future. According to such opponents, and in spite of the apparent âcommon senseâ status of the view, presentism should be rejected on the basis of these unacceptable implications. In this paper, I present and defend a version of presentism (âThisness Presentismâ) that avoids the unacceptable implications. The basic strategy I employ is familiarâI postulate presently existing entities to serve as surrogates (or âproxiesâ) for non-present entitiesâbut some of the details of my proposal are more novel, and their application to these problems is certainly novel. One overarching thesis of this paper is that Thisness Presentism is preferable to other versions of presentism since it solves important problems facing standard iterations of the view. And I assume that this is a good positive reason in favour of the underlying thisness ontology
Recognition of 3-D Objects from Multiple 2-D Views by a Self-Organizing Neural Architecture
The recognition of 3-D objects from sequences of their 2-D views is modeled by a neural architecture, called VIEWNET that uses View Information Encoded With NETworks. VIEWNET illustrates how several types of noise and varialbility in image data can be progressively removed while incornplcte image features are restored and invariant features are discovered using an appropriately designed cascade of processing stages. VIEWNET first processes 2-D views of 3-D objects using the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and removes noise from the images. Boundary regularization and cornpletion are achieved by the same mechanisms that suppress image noise. A log-polar transform is taken with respect to the centroid of the resulting figure and then re-centered to achieve 2-D scale and rotation invariance. The invariant images are coarse coded to further reduce noise, reduce foreshortening effects, and increase generalization. These compressed codes are input into a supervised learning system based on the fuzzy ARTMAP algorithm. Recognition categories of 2-D views are learned before evidence from sequences of 2-D view categories is accumulated to improve object recognition. Recognition is studied with noisy and clean images using slow and fast learning. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of 2-D views of jet aircraft with and without additive noise. A recognition rate of 90% is achieved with one 2-D view category and of 98.5% correct with three 2-D view categories.National Science Foundation (IRI 90-24877); Office of Naval Research (N00014-91-J-1309, N00014-91-J-4100, N00014-92-J-0499); Air Force Office of Scientific Research (F9620-92-J-0499, 90-0083
The parent analogy: a reassessment
According to the parent analogy, as a caretakerâs goodness, ability and intelligence increase, the likelihood that the caretaker will make arrangements for the attainment of future goods that are unnoticed or underappreciated by their dependents also increases. Consequently, if this analogy accurately represents our relationship to God, then we should expect to find many instances of inscrutable evil in the world. This argument in support of skeptical theism has recently been criticized by Dougherty. I argue that Doughertyâs argument is incomplete, for there are two plausible ways of construing the parent analogyâs conclusion. I supplement Doughertyâs case by offering a new argument against the parent analogy based on failed expectations concerning the amount of inscrutable evils encountered in the world. Consequently, there remains a significant empirical hurdle for skeptical theism to overcome if it is to maintain its status as a defeater for our reliability when tracking gratuitous evils.Publisher PDFPeer reviewe
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