Run Monte Carlo simulations on a retirement portfolio
Poster used Claude to rebuild their retirement portfolio and test sequence-of-returns risk by inserting past bear markets into growth paths. Task: Suggested implementation: Use Claude to model a 60:40 retirement portfolio over 15-20 years with a 6% baseline return. Randomly insert two to three historical bear markets and layer in one-time home-improvement costs. First, map a month-by-month budget to stabilize cash flow assumptions. The human role involves defining these inputs and validating the model's logic.; tools: Claude. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include accurate historical market data and defined expense assumptions. Integration uncertainty exists regarding Claude's ability to perform complex stochastic modeling without external code execution. A commenter noted dedicated tools cost ~$120/year, but the AI setup's runtime and cost are unstated. Human approval is critical for financial decisions..
Tools
Published on Agent Work Atlas:
Overview
Poster used Claude to rebuild their retirement portfolio and test sequence-of-returns risk by inserting past bear markets into growth paths.
Workflow
Suggested implementation: Use Claude to model a 60:40 retirement portfolio over 15-20 years with a 6% baseline return. Randomly insert two to three historical bear markets and layer in one-time home-improvement costs. First, map a month-by-month budget to stabilize cash flow assumptions. The human role involves defining these inputs and validating the model's logic.
Reported result
Actual outcomes are unknown as the source is inaccessible. The proposed workflow aims to test sequence-of-returns risk. A measurable acceptance check is to verify if the simulated portfolio sustains withdrawals for 20+ years under stress scenarios. No specific success metrics or failures are reported in the available data.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include accurate historical market data and defined expense assumptions. Integration uncertainty exists regarding Claude's ability to perform complex stochastic modeling without external code execution. A commenter noted dedicated tools cost ~$120/year, but the AI setup's runtime and cost are unstated. Human approval is critical for financial decisions.
Try it
Act as a financial analyst. I need to simulate a retirement portfolio using Claude. Inputs: - Asset Allocation: [replaceable, e.g., 60% stocks, 40% bonds] - Time Horizon: [replaceable, e.g., 20 years] - Baseline Return: [replaceable, e.g., 6%] - Stress Events: [replaceable, e.g., 2008 and 2020 bear markets] - One-time Costs: [replaceable, e.g., $15,000 home renovation in year 5] - Monthly Budget: [replaceable, e.g., $4,000 expenses] Steps: 1. First, create a month-by-month cash flow table based on the budget. 2. Model the portfolio growth using the baseline return. 3. Randomly insert the specified historical bear market drops into the growth path. 4. Apply the one-time costs at the specified times. 5. Calculate the probability of the portfolio lasting the full time horizon. Output: A summary report with the survival probability and a table of key stress test results. Safeguards: Do not provide financial advice. This is a simulation. I will review the assumptions before proceeding. If inputs are missing, ask for clarification. Start with a small sample dry run for the first 5 years to verify logic before running the full 20-year simulation.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.