Episode
Market Simulations & Financial Planning | #411 (John Yang)
- Published
- May 28, 2026
- Duration seconds
- 4644
- Processing state
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Summary
In this episode, Ben Felix and Braden Warwick unpack the surprisingly complex world of expected return modeling and why it matters so much for retirement projections, portfolio construction, and financial advice. They explain how PWL Capital currently estimates expected returns across asset classes, why traditional Monte Carlo methods relying on Gaussian distributions may miss important market behaviors, and how new research could improve the realism of long-term financial planning simulations. The conversation also explores a fascinating collaboration between PWL and Columbia Engineering student John Yang, who worked with Professor Michael Robbins on a project to build more realistic synthetic return data for financial planning. John explains how his team used empirical distributions, t-copulas, and Extreme Value Theory to better capture market crashes, fat tails, and asset co-movements during periods of stress. Ben and Braden then analyze how these improved simulation methods affect financial planning outcomes, sustainable spending estimates, and projections for long-term wealth accumulation. Key Points From This Episode: (0:00:00) Introduction to expected return modeling and why it matters for financial planning. (0:00:25) The importance of volatility, correlations, distribution shape, and time-series behavior in portfolio projections. (0:01:26) How Scott Cederburg's research on block bootstrapping influenced PWL's thinking on simulations. (0:02:03) Introduction to Columbia Engineering student John Yang and the industry research collaboration. (0:03:30) How Conquest Planning allows PWL to upload custom return simulations. (0:04:05) A new PWL client's detailed reasoning for moving from DIY investing to working with an advisor. (0:06:22) Why financial planning and Mon…