Can AI Build a Diversified ETF Portfolio in Under Five Minutes?

Building a diversified investment portfolio traditionally requires hours of research. Investors need to compare market trends, economic data, sector performance, valuations, and risk before deciding how to allocate capital.

But what if AI could complete the first stage of that research in just a few minutes?

To demonstrate the process, we asked our AI Research Agent to evaluate five popular ETFs representing different parts of the global market:

• SPY – U.S. Equities

• QQQ – Technology

• VGK – European Equities

• EEM – Emerging Markets

• TLT – U.S. Treasury Bonds

The analysis suggested that QQQ and SPY continued to benefit from strong earnings momentum, while TLT became increasingly attractive as expectations for lower interest rates improved. VGK and EEM provided diversification benefits but faced weaker economic momentum and greater uncertainty.

Rather than simply comparing historical returns, the AI analyzed recent market momentum, macroeconomic conditions, news sentiment, volatility, and relative market strength. Within minutes, it produced a structured research report highlighting the strengths and risks of each ETF.

Generating investment ideas is relatively easy. The real challenge is determining whether those ideas would have worked in the real market.

Why we are doing something special with backtesting?

At Boosting Alpha, every AI-generated strategy is validated using our proprietary event-driven backtesting engine. Instead of evaluating historical data as one complete dataset, the engine processes the market sequentially—one bar, one event, and one trading decision at a time. Orders are generated only from information that would have been available at that exact moment, closely simulating how a strategy would behave in live markets.

This approach provides a more realistic assessment of performance, transaction timing, portfolio evolution, and risk before moving to forward testing.

We believe AI should not only generate investment ideas—it should also validate them using institutional-grade research standards. By combining AI-driven analysis with robust backtesting, investors can move from promising ideas to evidence-based investment decisions with greater confidence.