Monte Carlo Simulation in Investing: What Singapore Investors Need to Know
A Monte Carlo simulation is a computational technique that runs thousands of randomised scenarios to estimate the range of possible investment outcomes. In investing, it helps Singapore investors assess the probability that a retirement portfolio will last through a given period — accounting for market volatility, inflation, and sequencing risk. This article is educational only and does not constitute financial advice.
How Monte Carlo Simulation Works
Monte Carlo simulation works by randomly sampling historical or assumed return distributions and running them through a portfolio model thousands of times. Each run produces a different outcome based on random draws, simulating real-world uncertainty.
For example, if you run 10,000 simulations of a $1M SGD portfolio over 30 years with 5% average return and 15% standard deviation, you might find 87% of scenarios end with a positive balance. That 87% is your probability of success.
The TKN Retirement Planning Calculator incorporates similar probabilistic thinking for local investors.
Why It Matters for Singapore Investors
Monte Carlo is especially relevant because CPF LIFE payouts are guaranteed but supplementary investment portfolios (SRS, brokerage) carry market risk. Simulations help size how much retirement income should come from CPF vs investments. With Singapore’s high life expectancy (avg. 84 years), a 30–35 year horizon makes probabilistic planning essential. Many local portfolios are heavy in S-REITs, which have distribution income but also NAV volatility — simulation captures both dimensions.
Key Limitations
Results depend heavily on assumed return and volatility inputs. In market crises, correlations between asset classes spike — simulations assuming constant correlations underestimate tail risk. Simulations typically assume fixed withdrawals, not accounting for behavioural responses like spending cuts. Extreme events are underrepresented in historical data. Use the CPF FIRE Number Calculator to complement your stress-testing.
Interpreting Monte Carlo Results
A probability of success ≥ 85% is generally considered acceptable for retirement planning. Don’t just look at the median — the 10th percentile (worst 10% of scenarios) tells you the floor you must survive. Run sensitivity analysis: reduce spending by 10% or returns by 1% to see how robust your plan is. If using Endowus or Syfe, ask how their projections are modelled.