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Random Matrix Approach for Analysis of the Johannesburg Stock Exchange
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Author(s): Boris Tizi Zourmba (University of Ngaoundere, Cameroon), Jimbo Henri Claver (Samarkand International University of Technology, Uzbekistan), Nzotem Tchoumi Cyrille Audrey (University of Ngaoundere, Cameroon), Paul Tchoua (University of Ngaoundere, Cameroon)and Georges Nguefack-Tsague (University of Yaounde 1, Cameroon)
Copyright: 2025
Pages: 32
Source title:
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
Source Author(s)/Editor(s): Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Jimbo Henri Claver (Samarkand Interntional University of Technology, Uzbekistan), A. V. Sriharsha (Mohan Babu University, India), Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania)and Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)
DOI: 10.4018/979-8-3693-6215-0.ch011
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Abstract
Random Matrix Theory gives us the theatrical framework to analyze computational and statistical properties of cross-correlation matrix. Usually, the cross-correlation matrix is noisy because of the estimation's error for the length and the finite nature of the underlying time series. The present work studies the Johannesburg Stock Market and the effects of eigenvalues filtering techniques on portfolio optimization in the mean-variance framework. The data stock prices are scraped on the Yahoo Financial website from 1st January 2020 to 1st January 2024. Further, we applied the LCPB, PG+, and KR cleaning techniques and observed that the predicted risk was closer to the realized risk in the prediction period. We conclude that the KR method is the best technique. In addition, the Johannesburg Stock Exchange reacts more to bad events than to good ones. This result considerably improves the constructive approach often used by practitioners in this market.
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