The "Swiss Grand Eight" portfolio, introduced last year, was designed to reflect key segments of the Swiss market. It includes eight prominent stocks: ABB, Georg Fischer, Sika, Roche, Lonza, Nestlé, Novartis, and UBS. This portfolio offers a unique opportunity to explore the potential of AI-driven portfolio management by comparing its performance to traditional approaches.
Between 2019 and 2023, an equally weighted portfolio of these stocks delivered an impressive annualized performance of 13.28%, significantly outpacing the Swiss Market Index’s (SMI) 5.73%. However, this strong performance came at the cost of higher volatility, with the portfolio showing a volatility of 17.97% compared to the SMI’s 15.49%.
When the portfolio was managed using Aisot Technologies’ AI platform during the same period, the annualized performance increased to 15.35%—a 15% improvement—while volatility dropped to 15.40%, matching the stability of the SMI. This demonstrated the potential of AI to enhance returns while maintaining control over risk.
For 2024, we tested the "Swiss Grand Eight" portfolio under similar conditions. An equally weighted portfolio would have outperformed the SMI once again, generating a return of 10.20% compared to the SMI’s 4.19%. However, it exhibited slightly higher volatility than the benchmark.
In contrast, the AI-optimized portfolio took a different trajectory. After a relatively slow start in early 2024, it gained momentum by March, ultimately delivering a performance of 25.43% by year-end. This represented a significant outperformance, not only against the SMI but also against the equally weighted portfolio.
The portfolio’s composition throughout 2024 highlights the strategic role of AI in managing diversification and risk. At the start of the year, the AI-optimized portfolio was concentrated in just two stocks. As the year progressed, it dynamically adjusted, diversifying across all of the eight stocks by December. This adaptability was crucial in navigating the year’s market conditions.
The 2024 results reaffirm the effectiveness of AI in portfolio management. By dynamically adjusting weights, introducing cash during volatile periods, and increasing diversification over time, the AI-optimized portfolio achieved superior performance while maintaining controlled risk levels.
These findings underscore how AI, when used as a second opinion, can complement traditional strategies. It enhances decision-making by providing data-driven insights that help maximize returns while minimizing unnecessary risk—offering a glimpse into the future of investment management.