V
Vitor G. Azevedo
Researcher at Technische Universität München
Publications - 15
Citations - 177
Vitor G. Azevedo is an academic researcher from Technische Universität München. The author has contributed to research in topics: Capital asset pricing model & Earnings response coefficient. The author has an hindex of 4, co-authored 11 publications receiving 114 citations.
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CO2 emissions: A quantitative analysis among the BRICS nations
TL;DR: In this article, the volume of CO2 emissions by lag of the emissions and by the Gross Domestic Product (GDP) for the BRICS (Brazil, Russia, India, China, and South Africa) countries from 1980 to 2011 was examined.
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Combination of forecasts for the price of crude oil on the spot market
TL;DR: In this article, a combination of three forecast models, ARIMA, exponential smoothing and dynamic regression, was used to predict the West Texas Intermediate (WTI) crude oil spot price and the Brent North Sea (Brent) crudeoil spot price.
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Corporate sustainability and asset pricing models: empirical evidence for the Brazilian stock market
TL;DR: In this paper, the authors investigate the impact of corporate sustainability on asset prices and test the extent to which this factor is priced in an augmented four-factor version of the traditional Fama & French (1993) asset pricing model.
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Earnings Forecasts: The Case for Combining Analysts' Estimates with a Mechanical Model
TL;DR: In this article, a cross-sectional model was proposed to forecast corporate earnings, which combines the accuracy of analysts' forecasts with the unbiasedness of a crosssectional model, and showed that using their estimates in the implied cost of capital calculation leads to a substantially stronger correlation with realized returns compared to earnings estimates from extant cross-section models.
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Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model
TL;DR: In this article, a cross-sectional model is proposed to forecast corporate earnings, which combines the accuracy of analysts' forecasts with the unbiasedness of a crosssectional model, and the model outperforms the most popular methods from the literature in terms of forecast accuracy, bias, and earnings response coefficient.