Market Simulations & Financial Planning | #411 (John Yang)

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 Monte Carlo simulations were central to the client's decision.  (0:07:22) Cross-border financial complexity and the value of professional advice.  (0:08:03) Estate planning, cognitive decline, and the role of trusted financial relationships.  (0:10:02) Research on cognitive decline and its impact on financial decision-making.  (0:12:00) Delegation, accountability, and reducing mental overhead through advisory relationships.  (0:13:47) Why the client chose PWL specifically and the appeal of evidence-based investing.  (0:15:25) Ben and Braden discuss the perceived disconnect between online discourse and demand for AUM advisors.  (0:16:12) Overview of PWL's methodology for estimating expected returns across asset classes.  (0:17:05) How PWL combines historical returns with market-implied expected returns.  (0:18:07) The use of factor premiums and expected return composition in taxable projections.  (0:18:48) Why PWL previously relied on Gaussian multivariate normal distributions for simulations.  (0:19:41) Arithmetic vs. geometric mean returns and why the distinction matters.  (0:21:01) A simple example illustrating volatility drag.  (0:23:29) Why diversification benefits must be incorporated into expected portfolio returns.  (0:25:15) How correcting portfolio math improved expected return estimates by 20–30 basis points.  (0:27:12) Transition to John Yang's interview and introduction to synthetic data generation.  (0:30:07) John explains the limitations of Gaussian return assumptions.  (0:31:04) Why realistic sequences of returns matter for retirement planning.  (0:32:16) Empirical evidence that returns are not truly random.  (0:33:25) The three modeling challenges: unique asset behavior, realistic co-movement, and tail risk.  (0:37:49) Separating marginal distributions from dependency structures in the modeling process.  (0:38:48) Using a t-copula to better model asset co-movement during market stress.  (0:39:39) Why historical data alone struggles to capture rare crisis events.  (0:40:06) Applying Extreme Value Theory and Generalized Pareto Distributions to model tail risk.  (0:42:15) How Monte Carlo simulations generate many realistic future return paths.  (0:43:00) Imposing forward-looking expected returns and volatility assumptions onto the simulations.  (0:44:56) How the new framework better preserves skewness and kurtosis.  (0:46:38) Evaluating the new model using marginal shape, tail behavior, and co-movement scores.  (0:48:10) Why the new model significantly imp