用深度学习提升多资产投资组合优化，增强市场波动下的稳定性。
Enhancing Portfolio Optimization with Deep Learning Insights
- 预训练+Transformer架构，用有限数据训练模型
- 在波动市场中表现优于传统方法，提升预测准确性
- 适合量化投资、金融工程领域研究者参考
本研究聚焦深度学习在投资组合优化中的应用，针对长期单一策略在多市场周期下的挑战。提出使用预训练技术在有限周期数据上训练模型，并引入Transformer架构以纳入状态变量。与传统方法对比评估显示，该方法在动荡市场中表现出更强的鲁棒性，验证了其在动态市场环境下提升预测精度的潜力。研究强调了深度学习驱动的投资组合优化正不断演进，亟需适应性强的策略应对复杂市场变化。
原文摘要 · Abstract (English)
Our work focuses on deep learning (DL) portfolio optimization, tackling challenges in long-only, multi-asset strategies across market cycles. We propose training models with limited regime data using pre-training techniques and leveraging transformer architectures for state variable inclusion. Evaluating our approach against traditional methods shows promising results, demonstrating our models' resilience in volatile markets. These findings emphasize the evolving landscape of DL-driven portfolio optimization, stressing the need for adaptive strategies to navigate dynamic market conditions and improve predictive accuracy.
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