This thesis investigates the relative merits and drawbacks of six portfolio weighting techniques, including two traditional (equal and value-weighting), two optimization-based (mean-variance and risk parity), and two statistical (principal component analysis and ridge regression) techniques. The focus is thus on selecting the weighting technique, an aspect of portfolio construction that market practitioners sometimes overlook. The analysis, implemented on the Swedish market, employs historical backtesting, Monte Carlo simulations, and stress tests to evaluate various techniques under diverse market conditions. The results reveal that no single portfolio consistently outperforms or underperforms across all metrics and scenarios, highlighting the importance of a comprehensive set of performance and risk measures for informed investment decisions. Furthermore, the statistical techniques, principal component analysis and ridge regression demonstrate competitive risk-adjusted returns relative to the traditional and optimization-based techniques implemented in this thesis. The results suggest that market practitioners should consider incorporating these somewhat uncommon techniques alongside more traditional techniques in portfolio management, depending on their investment objectives and risk tolerance.