Abstract
Deep learning techniques have provided a fresh outlook on the evergreen subject of portfolio optimization within the finance domain. This article selects the stocks of Google, Tesla, Tractor Supply Company, Analog Devices, and Duke Energy Corporation and deploys four deep learning models to estimate returns and covariance respectively. The mean-variance model is utilized to generate the target portfolio for each deep learning model, incorporating the predicted outcomes. Ultimately, the returns of each portfolio are compared to the market benchmark (S&P 500) returns. The findings demonstrate that the proposed target model outperforms the market benchmark (S&P 500) across multiple financial metrics. This study highlights the groundbreaking and promising applications of deep learning in the financial sector, providing valuable insights into innovative portfolio allocation strategies for risk-averse investors who aim to achieve stable and positive returns even in turbulent market conditions.
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