Adaptive rolling window selection for minimum variance portfolio estimation based on reinforcement learning (CROSBI ID 699493)
Prilog sa skupa u zborniku | ostalo | međunarodna recenzija
Podaci o odgovornosti
Gašperov, Bruno ; Šarić, Fredi ; Begušić, Stjepan ; Kostanjčar, Zvonko
engleski
Adaptive rolling window selection for minimum variance portfolio estimation based on reinforcement learning
When allocating wealth to a set of financial assets, portfolio optimization techniques are used to select optimal portfolio allocations for given investment goals. Among benchmark portfolios commonly used in modern portfolio theory, the global minimum variance portfolio is becoming increasingly popular with investors due to its relatively good performance which stems from both the low- volatility anomaly and the avoidance of the estimation of first moments i.e. mean returns. However, estimates of minimum variance portfolio weights significantly depend on the size of the rolling window used for estimation, especially considering the non- stationarity of the underlying market dynamics. In this paper, we use a model-free policy-based reinforcement learning framework in order to directly and adaptively determine the optimal size of the rolling window. Training is done on a subset of trading stocks from the NYSE. The resulting agent achieves superior performance when compared against multiple benchmarks, including those with fixed rolling window sizes.
reinforcement learning ; portfolio optimization ; covariance estimation
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Podaci o prilogu
20166276
2020.
objavljeno
10.23919/mipro48935.2020.9245435
Podaci o matičnoj publikaciji
2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)
Institute of Electrical and Electronics Engineers (IEEE)
Podaci o skupu
MIPRO 2020
predavanje
28.09.2020-02.10.2020
Opatija, Hrvatska