Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies

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Springer

Paru le : 2024-06-18

This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-n...
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Éditeur

Collection
n.c

Parution
2024-06-18

Pages
71 pages

EAN papier
9783031620607

Auteur(s) du livre


Manuel Moura is currently doing a MBA at London Business School. Prior to that he worked at LFO since 2019. He received a Master’s Degree in Electrical Engineering and Computer Science with a specialization in Control Systems from Instituto Superior Técnico. At LFO, he worked as a quantitative researcher developing models to invest in the stock market and manage risk but also as a portfolio manager. He did internships in consulting at Bain & Company in Brussels and in private equity at Advent International in London.   Rui Ferreira Neves is a professor at Instituto Superior Técnico since 2005. He received the Eng -Diploma and the PhD degrees in Electrical and Computer Engineering from the Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 1993 and 2001, respectively. In 2006 he joined Instituto de Telecomunicações (IT) as a research Associate. His research activity deals with evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems and mixed signal integrated circuits. He uses both fundamental, technical and pattern matching indicators to find the evolution of the financial markets. During his research activities he has collaborated/coordinated several EU and National projects. He supervised 50 MSc Theses. He published more than 60 works, respectively, 7 books, 4 book chapters, 20 journal papers and 30 conference papers.

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9783031620614
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Nombre pages imprimables
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EAN EPUB
9783031620614
Prix
147,69 €
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Nombre pages imprimables
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