882 resultados para Abnormal returns
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This chapter reviews studies on the effects of mycotoxins on embryonic and fetal development, especially those toxins that are global food and feed contaminants. The toxins discussed include aflatoxin produced by Aspergillus flavus and A. parasiticus, ochratoxin which is produced by Aspergillus species particularly A. ochraceus as well as Penicillium verrucosum, ergot alkaloids produced by Claviceps spp., and the Fusarium toxins (fumonisins, deoxynivalenol [vomitoxin], and zearalenone). These toxins have been shown to be teratogenic and/or embryotoxic in different animal bioassays. The implications of toxicity on embryogenesis, and the progress of research on these mycotoxins, are also examined.
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Reactive oxygen species are recognised as important signalling molecules within cells of the immune system. This is, at least in part, due to the reversible activation of kinases, phosphatases and transcription factors by modification of critical thiol residues. However, in the chronic inflammatory disease rheumatoid arthritis, cells of the immune system are exposed to increased levels of oxidative stress and the T cell becomes refractory to growth and death stimuli. This contributes to the perpetuation of the immune response. As many of the effective therapies used in the treatment of rheumatoid arthritis modulate intracellular redox state, this raises the question of whether increased oxidative stress is causative of T-cell hyporesponsiveness. To address this hypothesis, this review considers the putative sources of ROS involved in normal intracellular signalling in T cells and the evidence in support of abnormal ROS fluxes contributing to T-cell hyporesponsiveness. © W. S. Maney & Son Ltd.
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Abnormal protein aggregates, in the form of either extracellular plaques or intracellular inclusions, are an important pathological feature of the majority of neurodegenerative disorders. The major molecular constituents of these lesions, viz., beta-amyloid (Abeta), tau, and alpha-synuclein, have played a defining role in the diagnosis and classification of disease and in studies of pathogenesis. The molecular composition of a protein aggregate, however, is often complex and could be the direct or indirect consequence of a pathogenic gene mutation, be the result of cell degeneration, or reflect the acquisition of new substances by diffusion and molecular binding to existing proteins. This review examines the molecular composition of the major protein aggregates found in the neurodegenerative diseases including the Abeta and prion protein (PrP) plaques found in Alzheimer's disease (AD) and prion disease, respectively, and the cellular inclusions found in the tauopathies and synucleinopathies. The data suggest that the molecular constituents of a protein aggregate do not directly cause cell death but are largely the consequence of cell degeneration or are acquired during the disease process. These findings are discussed in relation to diagnosis and to studies of to disease pathogenesis.
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This article examines whether UK portfolio returns are time varying so that expected returns follow an AR(1) process as proposed by Conrad and Kaul for the USA. It explores this hypothesis for four portfolios that have been formed on the basis of market capitalization. The portfolio returns are modelled using a kalman filter signal extraction model in which the unobservable expected return is the state variable and is allowed to evolve as a stationary first order autoregressive process. It finds that this model is a good representation of returns and can account for most of the autocorrelation present in observed portfolio returns. This study concludes that UK portfolio returns are time varying and the nature of the time variation appears to introduce a substantial amount of autocorrelation to portfolio returns. Like Conrad and Kaul if finds a link between the extent to which portfolio returns are time varying and the size of firms within a portfolio but not the monotonic one found for the USA.
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In this paper the performance of opening and closing returns, for the components of the FT-30 will be studied. It will be shown that for these stocks opening returns have higher volatility and a greater tendency towards negative serial correlation than closing returns. Unlike previous studies this contrasting performance cannot solely be attributed to differences in the trading mechanism across the trading day. All the stocks used in our sample trade thought the day using a uniform trading mechanism. In this paper, we suggest that it is differences in the speed that closing and opening returns adjust to new information that causes differences in return performance. By estimating the Amihud and Mendelson (1987) [Amihud, Yakov, & Mendelson, Haim (1987). Trading mechanisms and stock returns: An empirical investigation, Journal of Finance, 62 533-553.] partial adjustment model with noise, we show that opening returns have a tendency towards over-reaction, while closing returns have a tendency towards under-reaction. We suggest that it is these differences that cause a substantial proportion (although not all) of the asymmetric return patterns associated with opening and closing returns. © 2005 Elsevier Inc. All rights reserved.
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In this article a partial-adjustment model, which shows how equity prices fail to adjust instantaneously to new information, is estimated using a Kalman filter. For the components of the Dow Jones Industrial 30 index I aim to identify whether overreaction or noise is the cause of serial correlation and high volatility associated with opening returns. I find that the tendency for overreaction in opening prices is much stronger than for closing prices; therefore, overreaction rather than noise may account for differences in the return behavior of opening and closing returns.
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One of the central explanations of the recent Asian Crisis has been the problem of moral hazard as the source of over-investment and excessive external borrowing. There is however rather limited firm-level empirical evidence to characterise inefficient use of internal and external finances. Using a large firm-level panel data-set from four badly affected Asian countries, this paper compares the rates of return to various internal and external funds among firms with low and high debt financing (relative to equity) among financially constrained and other firms. Selectivity-corrected estimates obtained from random effects panel data model do suggest evidence of significantly lower rates of return to long-term debt, even among firms relying more on debt relative to equity in our sample. There is also evidence that average effective interest rates often significantly exceeded the average returns to long-term debt in the sample countries in the pre-crisis period. © 2006 Elsevier Inc. All rights reserved.
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Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study,GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk,despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.