Deep Learning for Portfolio Optimization
This work addresses portfolio optimization for financial investors by providing a method that avoids forecasting returns, but it is incremental as it applies deep learning to an existing problem with specific data.
The paper tackled portfolio optimization by using deep learning to directly optimize the Sharpe ratio, trading ETFs of market indices to reduce asset selection complexity, and achieved the best performance compared to other algorithms during the testing period from 2011 to April 2020, including financial instabilities.
We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimise portfolio weights by updating model parameters. Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. Indices of different asset classes show robust correlations and trading them substantially reduces the spectrum of available assets to choose from. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period, from 2011 to the end of April 2020, including the financial instabilities of the first quarter of 2020. A sensitivity analysis is included to understand the relevance of input features and we further study the performance of our approach under different cost rates and different risk levels via volatility scaling.