State-dependent asset allocation using neural networks
| Autor: | Davood Pirayesh Neghab, Reza Bradrania |
|---|---|
| Přispěvatelé: | Bradrania, Reza, Neghab, Davood Pirayesh |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
I.2
Mathematical optimization Tactical asset allocation Artificial neural network Computer science G.1 Economics Econometrics and Finance (miscellaneous) G.3 conditional asset allocation Asset allocation tactical asset allocation portfolio optimization market state FOS: Economics and business performance ratio machine learning Performance ratio State dependent Portfolio optimization Quantitative Finance - General Finance General Finance (q-fin.GN) artificial neural network Market conditions |
| Popis: | Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods. Refereed/Peer-reviewed |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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