Il giorno mercoledì 21 marzo alle ore 14.30 presso la Aula Seminari 4026 del DISMEQ al IV piano dell'edificio U7, il dott. Marco Bianchetti di Intesa Sanpaolo terrà un seminario su
Learning the Optimal Risk - Advanced Risk-Based Portfolio Management with Global Optimization and Machine Learning Algorithms
Abstract
We consider the portfolio optimization problem from a trading and risk management point of view. Given a generic initial portfolio of financial instruments (e.g. stocks, derivatives and securities) with a given risk profile and subject to risk management limits, we aim to find optimal trading strategies, based on eligible tradable and liquid market instruments (i.e. quoted stocks and plain vanilla derivatives and securities), able to globally optimize the portfolio risk figures while respecting the required risk limits. We make use of an evolutionary approach based on genetic algorithms, where a population of candidate trading strategies is evolved using crossover, mutation and elitism, until optimal solutions are found as the best trading strategies that both minimize the selected risk measures and respect the risk limits. We apply our idea to different test cases based on real portfolios including linear and non-linear real financial instruments. We show that different optimal trading strategies may exist, depending on the combination of risk-based objective functions and limits. Our approach is general with respect to initial portfolios, risk profiles, measures and limits, eligible optimization instruments, trading strategies, and optimization algorithms. It can be applied, in principle, to real portfolios of financial institutions, provided that, in practice, a single operational framework and sufficient computational resources are available. Strong financial insight is needed both to select the optimization parameters, i.e. the eligible trading strategies, and to understand the financial soundness of the solutions proposed by the optimization metaheuristic (joint work with Marco Scaringi).
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