Backtests covering 2014-2024 across seven equity indices show that the model achieves higher Sharpe ratios than the baselines while maintaining near-zero benchmark correlations and competitive drawdowns.
Abstract
Market-neutral portfolios aim to generate consistent returns while offsetting systematic market risk. Traditional approaches based on factor models or convex optimization often underperform during market regime shifts or when structural assumptions break down. We propose AlphaZeroBeta, a deep reinforcement learning framework designed to deliver benchmark-relative alpha (excess returns) with near-zero beta (market neutrality). AlphaZeroBeta combines a composite reward function that balances risk-adjusted excess return, benchmark correlation, and transaction costs with a CNN-GRU policy trained end-to-end via Recurrent PPO and evaluated through a rolling walk-forward protocol. Backtests covering 2014-2024 across seven equity indices show that the model achieves higher Sharpe ratios than the baselines while maintaining near-zero benchmark correlations and competitive drawdowns.
When economic structures and market dynamics shift, classic portfolio rebalancing algorithms often suffer from unstable and degraded performance. To improve the return and robustness of portfolio management, we explore reinforcement learning (RL) and propose Scenario-Context Rollout (SCR), a macroeconomics-guided feedb...
Vanya Priscillia Bendatu, Yao Lu· Proceedings of the 32nd ACM...· 0 citations
No individual tabular deep learning architecture outperforms gradient-boosted trees, but combining XGBoost and TabNet using rank aggregation produces a Hybrid ensemble with an annualised return of 51.26%, a Sharpe ratio of 2.44, and a statistically significant CAPM alpha of 0.423.
The results show that deep learning models perform best in highly efficient markets where signals are weak but consistent, and in moderately and least efficient markets, traditional strategies often achieve similar or better returns.
H. Sahu, Avishek Bhandari· Discover Artificial Intellig...· 0 citations
Portfolio optimization is a fundamental problem in finance which has normally been addressed by mean-variance frameworks and their extensions. However, these methods rely on assumptions such as normally distributed returns and covariance estimates which often fail to capture the dynamics of real markets. Advances in ma...
Steven Itti Leon, Rishi V. N., Venkatakrishnan K. V. et al.· International Conference on...· 0 citations
This work offers a highly adaptable framework that successfully aligns multi-objective algorithmic trading with diverse, real-world human sustainability preferences and integrates a Preference Elicitation framework using Gaussian Processes.
Giovanni Dispoto, Marcello Restelli, Carmine Ventre· 0 citations
Financial markets are challenging to navigate due to changing regimes, high volatility, and unpredictable investor behavior, often leading to model misspecification in classical stationary frameworks like Moving Average (MA) and Autoregressive (AR) models. To address this, we propose an uncertainty-aware framework that...
A. Verma, Arti M. K., Surjeet Kumar· International Conference Com...· 0 citations
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