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"Exciting News! 📢
In today's ever-changing market landscape, traditional portfolio selection methods fall short due to the dynamic nature of financial data. That's where our pattern matching approach with clustering shines.
🌟In our new paper, we've developed an online portfolio selection strategy that adapts to market changes, maximizing expected growth while considering ESG factors. 🌍📊
Our research tackles the challenge of non-stationarity in finance, which often trips up conventional machine learning algorithms. By incorporating nonlinear entropy between the shares and creating a dynamic pattern-matching approach, we beat many benchmark algorithms in live trading.
🌟🌟One key finding: ESG portfolios ♻️obtained the best deflated Sharpe ratio—indicating their strong performance beyond market growth. These findings highlight the tremendous potential of responsible investing.🌿💰
I'd like to extend my gratitude to the team, Ali Fereydoni and Mehrzad Asadi, which have the main contributions to this research. 🚀
BY @machinelearningnet
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