NEWSLETTER
In April, we improved the user experience for features related to creating new portfolios. These enhancements allow users to create portfolios in just a few clicks, making the overall workflow smoother and more intuitive. Our development team also successfully completed AWS Certification exams, bringing GM Advisors closer to achieving tiered AWS Partner status. As our pipeline includes multiple improvements in both financial modelling and AI deployment, we have engaged experts to help create a series of YouTube videos. These videos will introduce the platform, review its features, and support broader financial literacy among the public.
- Data entry in the new (edit) portfolio screen has been automated for faster workflows.
- Market data extraction now includes a more refined integrity verification flow.
- Additional refinements and small improvements were made across the platform.
Platform updates
We have continued advancing the InvestLens agentic system designed to intelligently orchestrate tools across the platform. In the coming months, we expect to make an iteration of this system available to testers. In parallel, our R&D team has started work on a scenario stress-testing module that will evaluate portfolio outcomes against historical data distributions classified as bear, neutral, or bull market environments. We also expect to release a white paper on our implied volatility estimation methodology. As with our broader product development, this work remains centered on human accountability, expert oversight, and strict guardrails designed to support transparency, reliability, and trust.
Analytics in Practice
This month, we present a brief workflow demonstrating how InvestLens can be used to quickly evaluate momentum within a selected industry or sector. In this example, we focus on major AI infrastructure companies, including leading semiconductor firms and hyperscalers.
For this exercise we assign equal weights at portfolio creation, and set the horizon to one year. We name the portfolio Chip, and use the backtester in our optimization module. We disable all other objectives except the equal-weight trajectory.
Over the tested one-year window, the equal-weight Chip Portfolio generated a strong cumulative return, with a particularly sharp increase near the end of the period. The return path, however, was not linear. The portfolio experienced periods of volatility and temporary weakness before recovering, highlighting the importance of evaluating both upside potential and risk.
This example shows how InvestLens helps users quickly test an investment theme and move beyond return alone. For AI infrastructure exposure, the backtest suggests strong recent momentum, while also reminding investors that thematic portfolios should be assessed across performance, volatility, drawdowns, and risk-adjusted metrics.
