Sustainable vs. Non-Sustainable Assets: A Deep Learning-Based Dynamic Portfolio Allocation Strategy

This article aims to investigate the impact of sustainable assets on dynamic portfolio optimization under varying levels of investor risk aversion, particularly during turbulent market conditions. The analysis compares the performance of two portfolio types: (i) portfolios composed of non-sustainable assets such as fossil energy commodities and conventional equity indices, and (ii) mixed portfolios that combine non-sustainable and sustainable assets, including renewable energy, green bonds, and precious metals using advanced Deep Reinforcement Learning models (including TD3 and DDPG) based on risk and transaction cost- sensitive in portfolio optimization against the traditional Mean-Variance model. Results show that incorporating clean and sustainable assets significantly enhances portfolio returns and reduces volatility across all risk aversion profiles. Moreover, the Deep Reinforcing Learning optimization models outperform classical MV optimization, and the RTC-LSTM-TD3 optimization strategy outperforms all others. The RTC-LSTM-TD3 optimization achieves an annual return of 24.18% and a Sharpe ratio of 2.91 in mixed portfolios (sustainable and non-sustainable assets) under low risk aversion (λ = 0.005), compared to a return of only 8.73% and a Sharpe ratio of 0.67 in portfolios excluding sustainable assets. To the best of the authors’ knowledge, this is the first study that employs the DRL framework integrating risk sensitivity and transaction costs to evaluate the diversification benefits of sustainable assets. Findings offer important implications for portfolio managers to leverage the benefits of sustainable diversification, and for policymakers to encourage the integration of sustainable assets, while addressing fiduciary responsibilities.