The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Enhancing Portfolio Optimization With Deep Learning: Evidence From African Stock Markets
Abstract
Emerging markets, particularly in Africa, present unique challenges for portfolio optimization due to increased volatility and limited historical data. This study explores the potential of deep learning models, specifically Long Short-Term Memory (LSTM), Deep Multilayer Perceptron (DMLP), and Convolutional Neural Networks (CNN), to predict the movements of three key African stock indices: MASI (Morocco), BRVM Composite (West Africa) and TUNINDEX (Tunisia). By comparing the performance of these architectures, this research aims to identify the models most suitable for prediction in a the context of limited data and high volatility. The results of this study will provide crucial insights for investors, portfolio managers, and researchers, thereby contributing to the development of more robust and effective portfolio optimization strategies for African markets, often overlooked by traditional methods.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|