Should investment decisions rely on historical performance or forward-looking expectations? Why not use both – and add a third perspective?
I've been asked several times why my BICon Investment Decision Lab uses forward-looking expected returns and volatilities rather than historical data.
My reasoning: Historical returns don't necessarily predict future performance. But forecasts are equally uncertain.
With the latest update, the Investment Decision Lab now combines three perspectives:
- Forward-looking expectations – Long-term return and volatility assumptions based on capital market expectations.
- Your own assumptions – Expected returns and volatilities are fully adjustable. My expectations don't have to be yours.
- Historical performance – Actual annualised returns (CAGR) and volatilities calculated from up to 10 years of ETF market data via EODHD.
One Switch – Three Important Applications
Users can switch between forecast-based and historical assumptions to see how they influence three core components:
- Portfolio construction – Asset allocation and Sharpe-ratio-based portfolio tilts.
- Monte Carlo simulations – Simulated portfolio outcomes and probability distributions.
- Risk & performance metrics – Expected returns, volatility, Sharpe ratio and other risk indicators.
The interesting question is not necessarily which dataset is correct, but how sensitive investment decisions are to the underlying assumptions.
A portfolio that looks attractive under one set of assumptions might look very different under another.
From Black Box to Transparency
The Investment Decision Lab follows a deterministic, transparent approach. Users can inspect assumptions, modify them and understand their impact on investment outcomes.
The best of three worlds? Perhaps. But certainly a better way to challenge investment assumptions.
🌐 Investment Decision Lab
🌐 About the platform
Note: Due to market-data licensing restrictions, the historical data feature is currently not available in the public version of the Investment Decision Lab.