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This paper presents a statistical arbitrage trading strategy that utilizes an ensemble of machine learning algorithms for asset return prediction and dynamic asset selection. The approach demonstrates superior performance in risk-return analysis compared to baseline strategies, particularly during volatile market conditions. The methodology is validated using historical data from the S&P500 index, showcasing its applicability across various asset classes for risk-controlled trading.

Author

slatsmug7i

Language

EN