0637014 20251007090158.320250701235959.9 Praha ČVUT 2025 34 s. portfolio optimization structure estimation multivariate linear regression linear quadratic regulator decision making dynamic programming cav_un_auth*0489870 Procházka Tomáš UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS CZ 100 Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2025/AS/prochazka-0637014.pdf CA21169 EU-COST XE cav_un_auth*0452289 This research project presents a combination of techniques to develop a sound mathematical approach to the portfolio optimization problem. The problem is formulated as a Linear Quadratic Regulator and solved using Dynamic Programming. The key contributions include integrating multivariate regression modeling of returns with structure estimation for the regressor subset and employing exponential forgetting with an algorithm for varying forgetting factor. The optimal allocation is obtained by solving a constrained quadratic programming problem featuring a custom reward function. We highlight the importance of structure estimation and the sequential approach, while also exploring the potential of modeling optimal allocation using the same regression framework as for returns. BB 20000 20200 20205 2026 RVO:67985556 https://hdl.handle.net/11104/0370102 S 2025 2025 Praha ČVUT