In this scientific webinar, led by Senior Machine Learning Engineer Julian Brooke, we will share empirical evidence on experiments run on Boosted Insights that highlight the utility of signal optimization as measured by risk-adjusted returns. We will walk through portfolio construction, how using signal optimization can improve risk-adjusted returns in those portfolios, and showcase the experiments.

You will learn:

• The steps to portfolio construction within an AI platform like Boosted Insights
• How signal optimization works to help mitigate risk, including some mathematical foundations (briefly)
• How we have proven empirically that signal optimization is effective across a range of different portfolios
• How to pick good settings for successful signal optimization using different portfolios