Adaptive data-driven portfolio optimization in the non-stationary financial market under constraints
In this work we propose a novel methodology for optimal dynamic allocation of a portfolio of risky financial assets under hard constraints on trading volume amounts. Our approach is direct in that it uses directly the observed historical data to construct an adaptive algorithm for online portfolio selection. The problem of portfolio optimization is stated as a dynamic problem of tracking a financial benchmark. We use the model predictive control (MPC) methodology in order to solve the problem. The main features of our approach are (a) the ability to adapt to non-stationary market environments by dynamically incorporating new information into the decision process; (b) no stochastic assumptions are needed about the stock prices, and (c) the flexibility of dealing with portfolio constraints. We also present the numerical modeling results, based on futures traded on the Russian Stock Exchange FORTS that give evidence of capacity and effectiveness of proposed approach.
Keywords
investment portfolio,
non-stationary financial market,
adaptive optimization,
model predictive control,
инвестиционный портфель,
нестационарный финансовый рынок,
адаптивная оптимизация,
управление с прогнозирующей модельюAuthors
| Dombrovskii Vladimir V. | Tomsk State University | dombrovs@ef.tsu.ru |
Всего: 1
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Dombrovskii V.V., Ob'edko T.Yu. Portfolio optimization in the financial market with serially dependent returns under constraints //Вестник Томского государственного университета. Управление, вычислительная техника и информатика. (Tomsk State University Jo
