In this research we conduct an in-depth examination of the several financial theories as well asevaluating different implementations of a hypothetical Robo-advisor and the correspondingperformance under market stress (COVID-19). Thus, we contribute a view on Robo-advisors’ benefitsand limitations, providing a foundation for better understanding its potential. Attained with knowledgeof Robo-advisors’ and the underlying models the historical data is collected from Handelsbankenplatform and different portfolios is created using Value at Risk and Expected Shortfall as the measureof risk. Finally, the different risk measures are compared through back testing where the frame of thetest align with the period of the chosen market stress COVID-19. The results shows that an investmentportfolio utilizing the underlying statistics to construct the Value at Risk and respectively ExpectedShortfall manages to outperform both the Mean Variance Optimization portfolios, as well asoutperforms the historical Value at Risk and Expected Shortfall.