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Sort articles by: Volume | Date | Most Rates | Most Views | Reviews | Alphabet
1.

Additional effect on the deformation zone during plastic metal flow Pages 113-126 Right click to download the paper Download PDF

Authors: Valeriy Chigirinsky, Irina Volokitina

DOI: 10.5267/j.esm.2023.11.001

Keywords: Additional effect, Metal, Plastic deformation, Rolling Forecast

Abstract:
In the article, on the basis of previously conducted theoretical and experimental studies, a phys-ical model of the process of additional impact on the basis of non-uniform plastic deformation is developed and investigated. On the basis of the physical model, a mathematical model of the additional impact is developed, the generalizing factor of which in the case of asymmetric de-formation is the external moment from the side lines.
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Journal: ESM | Year: 2024 | Volume: 12 | Issue: 2 | Views: 552 | Reviews: 0

 
2.

Forecasting the cross-sectional stock returns: Evidence from the United Kingdom Pages 289-298 Right click to download the paper Download PDF

Authors: Vu Hoang Tran, Khoa Dang Duong, Trung Nam Nguyen, Van Ngoc Pham

DOI: 10.5267/j.dsl.2022.2.004

Keywords: Asset Pricing, Rolling Forecast, Return predictability, Stock returns, London Stock Exchange

Abstract:
The study provides the forecasts of expected returns based on cross-sectional estimates from the Fama-Macbeth regressions in the United Kingdom. We collected the data of listed firms on the London Stock Exchange on the DataStream from January 1980 to December 2020. We analyze the data sample by employing three cross-sectional models' ten-year rolling estimates of Fama-Macbeth slopes. The empirical findings demonstrate that an investor can derive a composite estimate of the expected return by integrating various company-specific variables in real-time. Model 1 indicates that the expected-return estimates have a predictive slope for future monthly returns of 95.07%, with a standard error of 0.1981. Moreover, model 2 and model 3 report the predictability of returns are 77.57% and 76.94%. In short, our empirical evidence suggests that investors and stakeholders may consider using model 1 to estimate the cost of equity due to its simplicity and effective prediction capability. Our findings are consistent with trade-off theory and prior literature.
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Journal: DSL | Year: 2022 | Volume: 11 | Issue: 3 | Views: 1254 | Reviews: 0

 

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