Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Abdolreza Nazemi"'
Autor:
Markus Höchstötter, Abdolreza Nazemi
Publikováno v:
Investment Management & Financial Innovations, Vol 10, Iss 4 (2013)
Externí odkaz:
https://doaj.org/article/02d8a1943d524b8098bb4c60f9700402
Publikováno v:
European Journal of Operational Research. 297:1162-1177
The recovery rate on defaulted corporate bonds has a time-varying distribution, a topic that has received limited attention in the literature. We apply machine learning approaches for intertemporal analysis of U.S. corporate bonds’ recovery rates.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c87cbddd8274bfe1c7ed828697193a51
https://doi.org/10.1016/j.ejor.2021.06.047
https://doi.org/10.1016/j.ejor.2021.06.047
Publikováno v:
International Journal of Forecasting. 38:240-252
This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::400e2e92b267f0b2d83684c6ea1a9ebc
https://doi.org/10.1016/j.ijforecast.2021.03.013
https://doi.org/10.1016/j.ijforecast.2021.03.013
Autor:
Francesco A. Fabozzi, Abdolreza Nazemi
Publikováno v:
Journal of International Money and Finance. :102864
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::839bac8b07b880779db039a4010ecbf3
https://doi.org/10.1016/j.jimonfin.2023.102864
https://doi.org/10.1016/j.jimonfin.2023.102864
Publikováno v:
The Journal of Portfolio Management. 45:55-67
Academics and analysts have mostly employed stochastic and statistical default models to project defaults for properties backing commercial mortgage-backed securities. Although over the last few years there has been increased interest in using machin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3f022c5fdd78d639edd3c2c383d0ef5
https://doi.org/10.3905/jpm.2019.1.104
https://doi.org/10.3905/jpm.2019.1.104
Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks
Publikováno v:
SSRN Electronic Journal.
In this paper we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose outperforms other ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25c5d72eeaa2320bd49eb4a7930af179
https://doi.org/10.2139/ssrn.3733398
https://doi.org/10.2139/ssrn.3733398
Autor:
Abdolreza Nazemi, Frank J. Fabozzi
Publikováno v:
Journal of Banking & Finance. 89:14-25
We study the relationship between U.S. corporate bond recovery rates and macroeconomic variables used in the credit risk literature. The least absolute shrinkage and selection operator (LASSO) is used in selecting macroeconomic variables. The LASSO-s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3345a186f5cfd95d6125209397d0912
https://doi.org/10.1016/j.jbankfin.2018.01.006
https://doi.org/10.1016/j.jbankfin.2018.01.006
Publikováno v:
European Journal of Operational Research. 262:780-791
In this paper, fuzzy decision fusion techniques are applied to predict loss-given-default of corporate bonds. In our model, we add the principal components derived from more than 100 macroeconomic variables as explanatory variables. However, in order
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c0b635392188a9d20a1aff76c23fd01
https://doi.org/10.1016/j.ejor.2017.04.008
https://doi.org/10.1016/j.ejor.2017.04.008
