Market Simulations & Financial Planning (With John Yang) | Rational Reminder 411

Why It Matters

More realistic return simulations change asset-allocation trade-offs and long-term plan outcomes, potentially altering advice on savings rates, portfolio mix, and risk assessments. Using academic-grade, time-series-preserving methods in planning software could improve clients’ retirement projections and firm-level decision-making.

Summary

In this Rational Reminder episode, Benjamin Felix and Braden Warwick discuss improving expected-return modeling for financial planning, emphasizing that mean returns, distribution shape, and time-series features like volatility clustering and mean reversion materially affect portfolio decisions. They describe engaging Columbia engineering student John Yang and his classmates to develop simulation methods—building on block-bootstrap approaches—that preserve historical return dynamics and can be uploaded into PWL’s Conquest planning software for more realistic thousand-run projections. The hosts preview comparisons between the new student-developed simulations and PWL’s previous methods and note the firm’s broader effort to leverage academic collaboration. They also briefly share an anonymized client testimonial explaining a recent switch from DIY investing to an AUM adviser.

Original Description

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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.

Timestamps:

0:00:00 Intro

0:17:46 Discussing PWL's Current Methodology

0:22:16 Braden's Note on Geometric vs Arithmetic

0:32:37 John Yang Interview & Presentation

1:04:22 Braden's Post-Interview Analysis

Paper's From Today's Episode:

https://pwlcapital.com/financial-planning-assumptions-market-capitalization-weighted-portfolio/

https://pwlcapital.com/financial-planning-assumptions-factor-tilted-portfolio/

https://pwlcapital.com/financial-planning-assumptions-for-market-cap-weighted-and-factor-tilted-portfolios-methodology-guide/

Links From Today’s Episode:

Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582.

Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/

Rational Reminder on YouTube — https://www.youtube.com/channel/

Benjamin Felix — https://pwlcapital.com/our-team/

Benjamin on X — https://x.com/benjaminwfelix

Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/