Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Guillaume Coqueret"'
Autor:
Guillaume Coqueret
Publikováno v:
Journal of Finance and Data Science, Vol 8, Iss , Pp 12-34 (2022)
In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns
Externí odkaz:
https://doaj.org/article/3d705e58b42b4cf7a6d9cbe210af6116
Autor:
Guillaume Coqueret
Publikováno v:
Healthcare Analytics, Vol 2, Iss , Pp 100106- (2022)
In this article, we exploit a large dataset of surveys to answer a simple questions: which factors drive good (or bad) health? Using a set of 14 very diverse predictors (both socioeconomic and physiological), we perform sets of supervised learning ta
Externí odkaz:
https://doaj.org/article/b4ea0bee06924b67873d42989e8490f0
Publikováno v:
Quantitative Finance. 22:2275-2295
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d49cb45e7d3242bc39ac6f29ba6353b
https://doi.org/10.1080/14697688.2022.2122543
https://doi.org/10.1080/14697688.2022.2122543
We quantify equity and bond market sensitivity to sovereign ESG scores and their variations which, theoretically, is equivalent to evaluating the demand for ESG at the global scale. We do so by estimating a longitudinal model, at the issue level, tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05dbae43240ef3aa8efbad68b6433d5b
http://hdl.handle.net/10044/1/101760
http://hdl.handle.net/10044/1/101760
Autor:
Guillaume Coqueret
Publikováno v:
Quantitative Finance. 20:1531-1551
This paper quantifies the impact of stock-specific news sentiment on future financial returns. Daily predictive regressions yield significant t-statistics for 7% at most of our sample of more than ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22ad03f44f673efd6f9d94319aeac590
https://doi.org/10.1080/14697688.2020.1736314
https://doi.org/10.1080/14697688.2020.1736314