Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target
We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective fun...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
27.06.2020
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Subjects | |
Online Access | Get full text |
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Summary: | We propose a data-driven Neural Network (NN) optimization framework to
determine the optimal multi-period dynamic asset allocation strategy for
outperforming a general stochastic target. We formulate the problem as an
optimal stochastic control with an asymmetric, distribution shaping, objective
function. The proposed framework is illustrated with the asset allocation
problem in the accumulation phase of a defined contribution pension plan, with
the goal of achieving a higher terminal wealth than a stochastic benchmark. We
demonstrate that the data-driven approach is capable of learning an adaptive
asset allocation strategy directly from historical market returns, without
assuming any parametric model of the financial market dynamics. Following the
optimal adaptive strategy, investors can make allocation decisions simply
depending on the current state of the portfolio. The optimal adaptive strategy
outperforms the benchmark constant proportion strategy, achieving a higher
terminal wealth with a 90% probability, a 46% higher median terminal wealth,
and a significantly more right-skewed terminal wealth distribution. We further
demonstrate the robustness of the optimal adaptive strategy by testing the
performance of the strategy on bootstrap resampled market data, which has
different distributions compared to the training data. |
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DOI: | 10.48550/arxiv.2006.15384 |