Portfolio optimization using predictive auxiliary classifier generative adversarial networks

Abstract

In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. Despite numerous studies on shallow learning models, they have shown limited success in analyzing the complex nature of massive stock data, a task where recent deep learning models excel. However, the deterministic nature of conventional deep learning models impedes their consideration of portfolio risk due to an inherent lack of uncertainty quantification in their predictions. This paper proposes a novel portfolio weighting strategy, incorporating both risk and return considerations within a deep learning framework. We propose the Predictive Auxiliary Classifier Generative Adversarial Networks (PredACGAN), a probabilistic deep learning model, to measure prediction uncertainty. The PredACGAN generator leverages latent vectors and historical stock prices to predict future returns. The model synthesizes predictive distributions from various latent vectors and past prices. The associated risk is produced via the entropy of these distributions, facilitating portfolio optimization through both return and risk considerations. The proposed algorithm removes high-risk assets from the investment universe at rebalancing moments, enabling PredACGAN to optimize portfolios considering both return and risk. We evaluated PredACGAN and the accompanying algorithm with S&P 500 stocks from 1990 to 2020, with portfolios rebalanced monthly based on PredACGAN predictions and risk measures. The PredACGAN portfolios yielded 9.123% annual returns and a 1.054 Sharpe ratio, outperforming a risk-agnostic portfolio yielding 1.024% annual returns and a 0.236 Sharpe ratio. The PredACGAN portfolio also exhibited lower maximum drawdowns, highlighting its effectiveness.