Investing with ETFs: a simple strategy for big results

Institutional investors have long relied on systematic strategies to optimize portfolios, enhance returns, and navigate market trends. Now, asset and wealth managers of all sizes can use the same structured approach—without the complexity. Dr. Nino Antulov-Fantulin (aisot’s Co-Founder and Head of Research), Dr. Petter Kolm (aisot’s AI & Quant Advisory Lead), together with Aisot Labs, have developed a simple but systematic methodology with Exchange-Traded Funds (ETFs). This approach is now accessible through aisot’s Investment Co-Pilot, a Large-Language-Models-powered investment chat tool. 

This article walks through a simple approach to adjusting portfolios using ETFs and aisot’s Investment Co-Pilot. For those looking to fine-tune their strategy with cost-effective, instantly tradable ETFs, the Investment Co-Pilot offers an intuitive chat-based interface for portfolio analysis and optimization. Powered by advanced Large Language Models (LLMs)—including the open-source Llama 400B—it connects seamlessly with aisot’s AI Insights Platform or any external investment platform. The best part? You can interact with it just like you would in a chat. Just ask, and it delivers insights on portfolio performance, risk assessment, and strategy optimization in plain language. 

How it works

1. Upload Portfolio – Import the portfolio as a CSV file into aisot’s Investment Co-Pilot.
2. Portfolio Overview – Instantly access a breakdown of ETFs and stocks and their weights.
3. Risk Analysis – Identify key risk factors, such as sector tilts or lack of diversification.
4. Adjust Investments – Use the Investment Co-Pilot to implement your views while maintaining benchmark alignment.
5. Performance Tracking – Monitor profit and loss over time to assess the effectiveness of adjustments.
6. Rebalancing – Periodically fine-tune the portfolio, optimizing allocations for better returns and risk management.

By layering in multiple views, you can refine your approach while keeping risk under control.

Real world examples

Watch how aisot’s Investment Co-pilot assists portfolio manager Bob in analyzing his portfolio and executing his bullish AI view with ETFs, all while maintaining close alignment with the S&P 500.

With one click, Bob uploads his portfolio and instantly sees his holdings, including ETFs and big-name stocks. Analyzing his risk, he realizes he is heavily tilted toward value stocks and lacks tech exposure. To adjust, he reallocates toward a Tech ETF while staying aligned with the S&P 500. Using aisot’s Investment Co-Pilot, he simplifies systematic investing, customizing risk factors and fine-tuning his strategy efficiently. (video was produced at the end of March 2025)

But what happens when market uncertainty increases?

Take Sara’s approach as another example. She manages an equity mandate with the goal of outperforming the S&P 500, tracking the benchmark with a 4% target tracking error while maintaining a technology sector tilt via Technology Select Sector SPDR Fund (XLK). However, in January 2025, facing heightened market volatility, she expanded her tilt portfolio to include Utilities Select Sector SPDR Fund (XLU) and SPDR Gold Trust (GLD) alongside XLK. Using a risk parity approach, she structured the tilt portfolio to ensure that each view contributes equally to the tilt portfolio risk. Additionally, she increased exposure to tilt portfolio, while maintaining the tracking error to the benchmark. To allow greater flexibility in tracking the benchmark, she increased the tracking error target to 8%.
Simplifying systematic investing

With aisot’s Investment Co-Pilot, systematic ETF investing becomes simple—no large resources or complex instruments required. Customize risk factors, adjust horizons, and fine-tune portfolios with ease. 

Want to see real-time portfolio adjustments in action? Book a demo today!

For a deeper understanding of systematic investing with ETFs, real-world examples like Sara’s case above, and strategies for improving investment outcomes, explore our white paper, “Implementing Systematic Risk Premia, Factor-Based Strategies, and Sector Rotation with ETFs.” This document provides an in-depth look at the methodology developed by Aisot Lab.

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