4 resultados para Fama-French model

em Aston University Research Archive


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Processes of European integration and growing consumer scrutiny of public services have served to place the spotlight on the traditional French model of public/private interaction in the urban services domain. This article discusses recent debates within France of the institutionalised approach to local public/private partnership, and presents case study evidence from three urban agglomerations of a possible divergence from this approach. Drawing on the work of French academic, Dominique Lorrain, whose historical institutionalist accounts of the French model are perhaps the most comprehensive and best known, the article develops two hypotheses of institutional change, one from the historical institutionalist perspective of institutional stability and persistence, and the other from an explicitly sociological perspective, which emphasises the legitimating benefits of following appropriate rules of conduct. It argues that further studying the French model as an institution offers valuable empirical insight into processes of institutional change and persistence. © 2004 Taylor & Francis Ltd.

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This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

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This paper deals with the grammaticalization of venir into aspectual auxiliary of immediate anteriority, against the traditional approach (Gougenheim 1929/1971) according to which venir de + inf., would express recent past and so would be a temporal auxiliary. On the basis of the (revised) Reichenbachian model, it shows that venir de + inf. bears upon the relationship between R and E (aspect) and not on the relationship between R and S (time). This analysis allows explain why venir, in this periphrasis, is defective (i.e. why venir cannot be conjugated in the passé simple or in any compound tense).

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This paper presents a novel approach to the computation of primitive geometrical structures, where no prior knowledge about the visual scene is available and a high level of noise is expected. We based our work on the grouping principles of proximity and similarity, of points and preliminary models. The former was realized using Minimum Spanning Trees (MST), on which we apply a stable alignment and goodness of fit criteria. As for the latter, we used spectral clustering of preliminary models. The algorithm can be generalized to various model fitting settings, without tuning of run parameters. Experiments demonstrate the significant improvement in the localization accuracy of models in plane, homography and motion segmentation examples. The efficiency of the algorithm is not dependent on fine tuning of run parameters like most others in the field.