Risk-Aware Portfolio Optimization for Healthcare Investments Using Hybrid AI and Time-Series Modelling
Keywords:
risk-aware, portfolio optimization, healthcare investments, reinforcement learningAbstract
Hybrid AI and advanced time-series modelling control healthcare investment risk. In volatile markets, reinforcement learning-based healthcare asset allocation boosts revenue and reduces risk. Investment viability and ESG indices are examined using Bayesian deep learning using asset performance prediction uncertainty. Research uses historical and simulated biotechnology, medical device, and digital health platform return distributions, volatility patterns, and correlations to develop robust capital allocation strategies that account for market microstructure, regulatory constraints, and strategic risk exposures. Hybrid AI-time-series models improve complex healthcare financial ecosystem portfolio resilience, risk-adjusted returns, and evidence-based investments. Financial engineering using AI for medical portfolio management: predictive modelling, uncertainty quantification, and dynamic decision-making.
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References
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