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AQR’s Reality Check About Machine Learning in Asset Management Exploring Benefits Beyond Alpha Generation At Rosenblatt, we are believers in the long-term potential of Machine Learning (ML) in financial services and are seeing first-hand proof of new and innovative ML-based FinTechs emerging, and investors keen to fund 2, pp. 5, pp. 1, pp. Krauss, C., Do, X., and Huck, N. (2017): “Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500.” European Journal of Operational Research, Vol. 2, pp. COST / MACHINE. 591–94. 1915–53. Wang, Q., Li, J., Qin, Q., and Ge, S. (2011): “Linear, Adaptive and Nonlinear Trading Models for Singapore Stock Market with Random Forests.” In Proceedings of the 9th IEEE International Conference on Control and Automation, pp. 6, pp. Posted on November 4, 2020 by . 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In 2014, we published a ViewPoint titled The Role of Technology within Asset Management, which documented how asset managers utilize technology in trading, risk management, operations and client services.
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