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- W3214185135 abstract "We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments. Our framework has the flexibility of optimizing various objective functions including Sharpe ratio, mean-variance trade-off etc. Further, we allow for short selling and study several constraints attached to objective functions. In particular, we consider cardinality, maximum position for individual instrument and leverage. These constraints are formulated into objective functions by utilizing several neural layers and gradient ascent can be adopted for optimization. To ensure the robustness of our framework, we test our methods on two datasets. Firstly, we look at a synthetic dataset where we demonstrate that weights obtained from our end-to-end approach are better than classical predictive methods. Secondly, we apply our framework on a real-life dataset with historical observations of hundreds of instruments with a testing period of more than 20 years." @default.
- W3214185135 created "2021-11-22" @default.
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- W3214185135 date "2021-11-17" @default.
- W3214185135 modified "2023-09-23" @default.
- W3214185135 title "A Universal End-to-End Approach to Portfolio Optimization via Deep Learning" @default.
- W3214185135 doi "https://doi.org/10.48550/arxiv.2111.09170" @default.
- W3214185135 hasPublicationYear "2021" @default.
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