1,721,164 research outputs found
Firm size and the Italian Stock Exchange
No full textThe presence of a relation between firm size and asset returns is investigated by referring to the Italian Stock Exchange. In order to explain asset return variability, the excess return on a market portfolio as well as the difference between the return on a portfolio of small stocks and the return on a portfolio of large stocks are considered. The resultant two-factor model seems to improve the explanation of the returns of the portfolios formed on size
Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects
Full text linkMachine vision systems for automatic defect detection commonly adopt 2D image-based systems or 3D laser triangulation systems. 2D and 3D systems present opposite advantages and disadvantages depending on the typology and position of defects to be detected. When the variety of defects is large, none of them performs defect detection accurately. To overcome this limitation, this paper illustrates a hybrid Deep Learning-supported system where the 2D- and 3D-generated data are juxtaposed and analyzed contextually. Anomaly scores are subsequently determined to distinguish suitable and uncompliant parts. The implementation of the hybrid system allowed the identification of defective parts in an aluminium die-cast component with an accuracy concerning true positives of over 95% by comparing the system outputs with human defect detection. The inspection time was reduced by approximately 20% if compared, once again, with the same activities performed by humans. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
From chronic overnutrition to insulin resistance: the role of fat-storing capacity and inflammation. CO-FIRST AUTHOR
No full textAims: We analyze how the inflammatory state in adipose tissue caused by a condition of chronically positive energy balance can lead to insulin resistance first in adipose tissue, then in all insulin-sensitive tissues.Data synthesis: Chronic nutrient overload causes an increase in adipose depots that, if adipose tissue expandability is tow, are characterized by an increased presence of hypertrophic adipocytes. This adipocyte hypertrophy is a possible stress condition for the endoplasmic reticulum (ER) that would lead to a proinflammatory state in adipose tissue. In this condition, ER stress would activate metabolic pathways that trigger insulin resistance, release of macrophage chemoattractant proteins, and in chronic inflammation, the death of the hypertrophic adipocyte. The infiltrated macrophages in turn release inflammatory proteins causing further recruitment of macrophages to adipose tissue and the release of inflammatory cytokines. Following these events, insulin resistance becomes extended to all adipose tissue. Insulin-resistant adipocytes, characterized by low liposynthetic capacity and high lipolytic capacity, cause increased release of free fatty acids (FFA). FFA released by lipolitic adipocytes may also activate Toll-like receptors 4 and then chemokines and cytokines release amplifying insulin resistance, lipolysis and inflammation in all. adipose tissue. Moreover, increased circulating FFA levels, reduced circulating adiponectin levels and leptin resistance lead to decreased lipid oxidation in non-adipose tissues, thereby triggering ectopic accumulation of lipids, lipotoxicity and insulin resistance.Conclusion: All. the conditions that increase circulating fatty acids and cause lipid overloading (obesity, lipoatrophy, lipodystrophy, catabolic states, etc.) induce a lipotoxic state in non-adipose tissues that gives rise to insulin resistance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An identification and testing strategy for proxy-SVARs with weak proxies
Full text linkWhen proxies (external instruments) used to identify target structural shocks are weak, inference in proxy-SVARs (SVAR-IVs) is nonstandard and the construction of asymptotically valid confi-dence sets for the impulse responses of interest requires weak-instrument robust methods. In the presence of multiple target shocks, test inversion techniques require extra restrictions on the proxy-SVAR parameters other than those implied by the proxies that may be difficult to interpret and test. We show that frequentist asymptotic inference in these situations can be conducted through Minimum Distance estimation and standard asymptotic methods if the proxy-SVAR can be identified by using 'strong' instruments for the non-target shocks; i.e., the shocks which are not of primary interest in the analysis. The suggested identification strategy hinges on a novel pre-test for the null of instrument relevance, based on bootstrap resampling, which is not subject to pre-testing issues. Specifically, the validity of post-test asymptotic inferences remains unaffected by the test outcomes due to an asymptotic independence result between the bootstrap and non -bootstrap statistics. The test is robust to conditionally heteroskedastic and/or zero-censored proxies, is computationally straightforward and applicable regardless of the number of shocks being instrumented. Some illustrative examples show the empirical usefulness of the suggested identification and testing strategy
Fundamentals and asset price dynamics
No full textThe relation between fundamentals and asset returns is analyzed by means of Markov-switching regression models with time-varying transition probabilities. By referring to the Italian Stock Exchange over the 1973-2002 period, we find that (i) returns 'switch' between a zero-expected return/low volatility state and a high expected return/high volatility state; (ii) states are persistent and hence state changes can be forecast to some extent; (iii) the probability of state changes can be explained in terms of changes in the fundamentals; (iv) fundamentals do not have a direct impact on the expected returns but they only affect the transition probability matrix. Overall, our results show that a non-linear relation between market price changes and market fundamentals can be caught within the framework of (Markov) switching regession models. © Springer-Verlag 2003
PARAMETERS ON THE BOUNDARY IN PREDICTIVE REGRESSION
Full text linkWe consider bootstrap inference in predictive (or Granger-causality) regressions when the parameter of interest may lie on the boundary of the parameter space, here defined by means of a smooth inequality constraint. For instance, this situation occurs when the definition of the parameter space allows for the cases of either no predictability or sign-restricted predictability. We show that in this context constrained estimation gives rise to bootstrap statistics whose limit distribution is, in general, random, and thus distinct from the limit null distribution of the original statistics of interest. This is due to both (i) the possible location of the true parameter vector on the boundary of the parameter space and (ii) the possible non-stationarity of the posited predicting (resp. Granger-causing) variable. We discuss a modification of the standard fixed-regressor wild bootstrap scheme where the bootstrap parameter space is shifted by a data-dependent function in order to eliminate the portion of limiting bootstrap randomness attributable to the boundary and prove validity of the associated bootstrap inference under non-stationarity of the predicting variable as the only remaining source of limiting bootstrap randomness. Our approach, which is initially presented in a simple location model, has bearing on inference in parameter-on-the-boundary situations beyond the predictive regression problem
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