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:: year 19, Issue 69 (12-2026) ::
JMBR 2026, 19(69): 609-644 Back to browse issues page
Risk Parity Portfolio Based on Long Short-Term Memory Networks with Cholesky Decomposition Approach
Amir Mohamzadeh , Seifollah Khishtandar , Seifollah Heidari *1
Abstract:   (75 Views)
Abstract:
Risk parity portfolio , with balanced risk distribution among all assets in the portfolio ,strives to prevent risk concentration on a specific asset or group ,contributing to the stability andresilience of the investmemt portfolio against market shocks . However the effective implementation of risk parity strategy requires the dynamic understanding and manageement of the asset covariance matrix , as relationships and correlations among assets change over time . The present research utilizes Short - Term (LSTM) networks for the dynamic prediction covariance matrix with itc innovation lying in addressing the issue of the predicted covariance matrix not being positive definite by employing the Cholesky decomposition approach. In this study , the risk parity approach was used to optimize a portfplio consisthng of 10 major Tehran Stock Exchange indices over the period 1390 to 1404 , comparing three models : LSTMnetworks with Cholesky decomposition , Hidden Markov and DCC -GARCH. The results showed that the LSTM model has advantages over the other two models in terms of weekly average return , Shape ratio , cumulative return , and maximum drawdown . This superiority stems from LSTMs abilityto model the complex and dynamic patterns of the Iranian capital market .
Article number: 6
Full-Text [PDF 1504 kb]   (52 Downloads)    
Type of Study: Theoretical Article | Subject: Financial Institutions and Services (G2)
Received: 2026/02/17 | Accepted: 2026/06/1 | Published: 2026/10/17
References
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year 19, Issue 69 (12-2026) Back to browse issues page
فصلنامه پژوهش‌های پولی-بانکی Journal of Monetary & Banking Research
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