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Artificial Intelligence-Driven Financial Risk Analytics and Portfolio Optimization

Abstract: Simulation and optimization algorithms are used in quantitative finance and risk management to model, evaluate, hedge and optimally rebalance portfolios of financial assets. The primary goal of simulation is to model uncertainty in asset values over time. Optimization techniques help to minimize risk and maximize performance of financial portfolios. As performance, numerical stability and practical applicability of simulation and optimization algorithms still remain a challenge in financial modeling, we look at machine learning practice to improve the accuracy of financial modeling. Moreover, we investigate how we can enhance formulating financial modeling and optimization problems with Artificial Intelligence algorithms such as Natural Language Processing and Neural Nets. Natural language understanding algorithms for portfolio stress-testing and for financial optimization problems such as sentiment analysis and chat-bots will be discussed and demonstrated.

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McMaster University - School of Computational Science and Engineering