Scenario optimization for robo-advisory analytics.

In recent years, improvements in technology provide the opportunity for investors to use computer algorithms to produce low-cost guidance on possible portfolio investment mixes and strategies. This project is directed at the research and development of one such “Robo- Advisor” algorithm based on forward-looking scenario optimization, in order to determine the efficacy of the strategy. Here optimal portfolios are selected based on investor’s views on future scenarios, goals and risk tolerance. Given an investor’s goals, optimal portfolio selection under various risk constraints will be compared to determine the trade-off between risk and reward in achieving desired goals. This approach will be tested with portfolios of ETFs, and compared to some of the more widely used approaches that are based on the historical average and variance of returns.

Faculty Supervisor:

David Lozinski


Koopa Hakimi


RiskGrid Technologies Inc.





McMaster University



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