Random matrix theory and portfolio optimization in Moroccan stock exchange

Alaoui, Marwane El

doi:10.1016/j.physa.2015.03.081

Cited by 14 publications

(8 citation statements)

References 31 publications

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“…Nobi et al [ 49 ] tested for the daily data of 20 global financial indexes from 2006 to 2011 and also found out that most eigenvalues fell into the theoretical range, suggesting a high presence of noises and few eigenvectors with very highly relevant information; particularly, this effect was even more prominent during a financial crisis. Although studies like El Alaoui [ 50 ] found a larger percentage of informative eigenvalues, the reported results show that the wide majority of principal components is still dominated by noisy information.…”

Section: Theoretical Backgroundmentioning

confidence: 64%

Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

Peng

Albuquerque

Nascimento

et al. 2019

Entropy

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This paper discusses the effects of introducing nonlinear interactions and noise-filtering to the covariance matrix used in Markowitz’s portfolio allocation model, evaluating the technique’s performances for daily data from seven financial markets between January 2000 and August 2018. We estimated the covariance matrix by applying Kernel functions, and applied filtering following the theoretical distribution of the eigenvalues based on the Random Matrix Theory. The results were compared with the traditional linear Pearson estimator and robust estimation methods for covariance matrices. The results showed that noise-filtering yielded portfolios with significantly larger risk-adjusted profitability than its non-filtered counterpart for almost half of the tested cases. Moreover, we analyzed the improvements and setbacks of the nonlinear approaches over linear ones, discussing in which circumstances the additional complexity of nonlinear features seemed to predominantly add more noise or predictive performance.

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Section: Theoretical Backgroundmentioning

confidence: 64%

Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

Peng

Albuquerque

Nascimento

et al. 2019

Entropy

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“…Cloud Environment data resource discovery context, SOA model includes five main entities; data resources service provider, service requestor data resources, data resources and services also registered. Data resources via cloud service providers encapsulate data resources, cloud services in a common interface defines its functional parameters and registration were also registered; service users according to their application needs, service registrations also matching service, select their needs and match service [7][8][9] . Finally, according to the results of matching service, the use of data resources service description node resource information and data resource provider binding, and call the However, in practical applications, SOA-based data resource management and to find the model, data resources and services registered in the Universal Description, Discovery and Integration protocol is a centralized architecture and periodically synchronize multiple data sources (realization when unified data resource management and search.…”

Section: Cloud Computing Big Data Developmentmentioning

confidence: 99%

Mobile Cloud-based Big Data Library Management System

Jing¹,

Cui²

2016

IJGDC

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Mobile Internet can enable the reader to obtain a digital library resources from anywhere, but because of the huge amount of data in large data quickly retrieve the required information, the problem faced by mobile cloud library, aiming at this problem, we propose a big data mobile cloud Books and memory retrieval system with associated services, according to this system, it is possible to quickly search and develop reading programs for the reader's query and search time.

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“…For the Athens Stock Market correlations were investigated by Garas and Argyrakis 16 . In another study 17 investigates the Moroccan Stock Exchange. Some applications use world stock market indices such as Sensoy et al 18 that suggested that after the 2008 crisis markets' co‐movement behaviors were increased.…”

Section: Introductionmentioning

confidence: 99%

The analysis of cross‐correlation between Istanbul Stock Exchange and major stock markets and indices: An empirical analysis using Random Matrix Theory

Tastan¹,

İMAMOĞLU²

2022

Concurrency and Computation

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This study attempts to investigate the cross-correlation between stocks listed under the XU100 index of Borsa Istanbul with several ratios and indices of the stock markets worldwide by using the Random Matrix Theory approach through a correlation matrix.In addition, Eigenvector Analysis, Network Analysis, Dimension Reduction will be carried out to investigate cross-correlation between markets. It was found that XU100, which is an index that includes 100 stocks highest in volume, has a distinguishing behavior compared to other indices and rates in terms of eigenvalue and related eigenvector structures. Furthermore, mean-value portfolio analysis showed that the empirical correlation matrix underestimates the portfolio risks than the correlation matrix obtained by filtering the noise. Coronavirus pandemic also affected Borsa Istanbul by breaking periodic behavior of volatility and correlation cycle.

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Random matrix theory and portfolio optimization in Moroccan stock exchange

Cited by 14 publications

References 31 publications

Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

Mobile Cloud-based Big Data Library Management System

The analysis of cross‐correlation between Istanbul Stock Exchange and major stock markets and indices: An empirical analysis using Random Matrix Theory

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