Real-Time Portfolio Rebalancing with Machine Learning: Optimizing Financial Perf

This paper presents an innovative approach to portfolio rebalancing using machine learning, specifically reinforcement learning, to dynamically adjust asset allocations in response to real-time market changes. Unlike traditional static strategies, this model adapts to market volatility, optimizing returns while minimizing risk. Extensive backtesting and simulations demonstrate its superior performance, especially during volatile and bear market conditions.

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