Download Deep-Learning Solution To Portfolio Selection With Serially-Dependent Returns PDF Free - Full Version
Download Deep-Learning Solution To Portfolio Selection With Serially-Dependent Returns by ['Yosef Gavriel'] in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About Deep-Learning Solution To Portfolio Selection With Serially-Dependent Returns
This paper proposes using a deep neural network (DNN) to solve high-dimensional multi-period portfolio optimization problems with serially-dependent asset returns. The DNN consists of subnetworks for each decision step, with the control function determined by network parameters. This links the portfolio problem to DNN training. The paper proves convergence of the approach for a quadratic utility function when returns follow AR(1) or GARCH(1,1) models. Numerical tests show the DNN outperforms alternatives in various return dynamics for portfolios up to 100 assets. The paper establishes theoretical convergence of the DNN architecture under certain conditions.
Detailed Information
| Author: | ['Yosef Gavriel'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 24 |
| Language: | English |
| Format: | |
| Price: | FREE |
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