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 for educational purposes and does not constitute financial advice.
How Monte Carlo Simulation Works in Investing
Monte Carlo simulation works by randomly sampling historical or assumed return distributions — typically equities, bonds, CPF OA/SA rates — 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 $1 million SGD portfolio over 30 years with 5% average annual return and 15% standard deviation, you might find that 87% of scenarios end with a positive balance. That 87% is your “probability of success.”
Singapore-specific tools like the TKN Retirement Planning Calculator incorporate similar probabilistic thinking for local investors.
Why Monte Carlo Simulation Matters for Singapore Investors
Monte Carlo is especially relevant for Singapore investors because:
- CPF integration: CPF LIFE payouts are guaranteed, but supplementary investment portfolios (SRS, brokerage) are subject to market risk. Simulations help size how much of your retirement income should come from CPF vs investments.
- Longevity risk: Singaporeans have among the highest life expectancies globally (average 84 years as at 2025). A 30–35 year retirement horizon makes probabilistic planning essential.
- S-REIT exposure: Many local portfolios are heavy in S-REITs, which have distribution income but also NAV volatility. Simulation captures both dimensions.
Read more: Retirement Calculator Singapore
Limitations of Monte Carlo Simulation
Monte Carlo simulation has limitations Singapore investors should understand:
- Garbage in, garbage out: Results depend heavily on assumed return and volatility inputs. Using pre-GFC data may overstate equity returns.
- Correlation assumptions: In market crises (2008, 2020), correlations between asset classes spike — simulations that assume constant correlations underestimate tail risk.
- Does not account for behavioural responses: Investors often reduce spending or return to work during downturns — simulations typically assume fixed withdrawals.
- Black swan events: Extreme events are underrepresented in historical data. The simulation may underestimate the probability of a catastrophic scenario.
For a complementary approach, consider CPF FIRE Number Calculator to stress-test your retirement number.
How to Interpret Monte Carlo Results
Most sophisticated retirement planning platforms — including Endowus, Syfe, and robo-advisors — run Monte Carlo analyses behind the scenes. Here’s how to interpret the output:
- Probability of success ≥ 85%: Generally considered acceptable for retirement planning. Some planners target 90–95% for conservative clients.
- Median outcome vs 10th percentile: Don’t just look at the median. The 10th percentile (worst 10% of scenarios) tells you the floor you should be able to survive.
- Sensitivity analysis: Run scenarios with spending reduced by 10% or returns reduced by 1% to see how robust your plan is.
If using Endowus or Syfe, ask your advisor how their projections are modelled — deterministic or Monte Carlo.