Summary
Timeline Planning uses long-term, historical asset class data rather than individual fund data.
Asset classes enable stress testing of plans against over 100 years of real-world market conditions.
Fund data is limited in historical depth and does not allow for consistent long-term scenario analysis.
Asset mapping is automated via the Morningstar API to ensure accurate alignment with asset classes.
Description
Timeline Planning takes an evidence-based approach to long-term financial modelling by using asset class data instead of fund-specific data. This ensures that forecasts are grounded in over a century of actual market performance, allowing advisers to simulate and stress test client plans against a wide range of economic conditions—including wars, recessions, pandemics, and inflationary periods.
While fund data is useful for short-term performance tracking, it typically lacks the historical depth required for reliable long-term planning. In contrast, Timeline’s asset class database extends back to January 1915, providing access to rolling return scenarios that span multiple generations and market events.
Capturing Market Events Across 100+ Years
Let’s take the example of major bear markets. From the Great Depression to the 2008 financial crisis and the 2020 COVID-19 crash, Timeline’s historical asset class data allows us to observe how markets behaved in each of these periods. Since individual fund data often doesn’t extend further back than a few decades, it fails to provide the depth needed to identify long-term trends or assess the resilience of a plan during earlier crises.
Mapping Portfolios to Asset Classes
To make full use of historical asset data, Timeline employs an automated investment mapping system powered by the Morningstar API. Here’s how it works:
Advisers input the client’s portfolio, either by ISIN or fund name.
Timeline automatically maps these holdings to their respective asset classes, based on Morningstar categories.
The resulting allocation is then used to simulate outcomes using over 100 years of historical returns.
For example, a portfolio like Tracker 50—with a 50/50 split between global equities and bonds—is mapped to global equity and bond asset classes. This mapping enables us to forecast long-term performance based on real historical scenarios.
Conclusion
While fund-level data may offer more granularity in the short term, it lacks the depth and historical perspective required for robust, long-term financial planning. Asset class data enables Timeline to model plans against more than a century of market events—giving both advisers and clients a clearer understanding of potential outcomes. Through automated mapping and rigorous scenario testing, Timeline delivers a more evidence-based approach to long-term financial modelling.