Portfolio balance strategy. 1.43 times better than index!
Hands-on quantitative trading portfolio strategies
Table of contents
- Main idea
- Dataset
- Back test
- Result
- Final thoughts
- Links
1. Main idea
I constructed a portfolio named ‘SP182’ using end-of-day data from the 182 largest companies with a 44-year history, beginning in 1980. Each company was equally weighted.
I developed a monthly rebalancing strategy to enhance performance. After a 10-year AI experimentation phase, 34 years of backtesting demonstrated a 1.43x improvement in returns while maintaining similar diversification and risk levels.
2. Dataset
3. Backtest
4. Result
- A custom portfolio (SP182) was created by combining 182 of the largest companies with a 44-year history.
- An AI-driven rebalancing strategy was developed and tested over a 10-year period.
- Backtesting over 34 years showed a 1.43x improvement in performance compared to a simple equal-weighted portfolio, while maintaining similar diversification and risk levels.
5. Final thoughts
The AI-driven rebalancing strategy employed in the SP182 portfolio yielded a substantial 1.43x performance enhancement over a 34-year period compared to a traditional equal-weighted approach. This outcome underscores the potential benefits of active management and quantitative modeling in portfolio optimization.
To further validate the strategy, we plan to implement the same rebalancing approach on well-known indices like the NASDAQ 100 and S&P 500.