Market Simulations & Financial Planning | #411 (John Yang)
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What is expected return modeling and why is it important for financial planning?
This is the Rational Reminder Podcast, a weekly reality check on sensible investing and financial decision making from two Canadians. We're hosted by me, Benjamin Felix, Chief Investment Officer and Braden Warwick, Financial Planning Product Architect at PWL Capital.
Welcome to Episode 411. Today we have a pretty nerdy but very interesting episode on expected returns.
It is really interesting. So it's like figuring out what to use as an expected return for stocks and bonds. Like what is the average return that you expect or any other asset class is super important. It's one of the most important parts of financial planning and portfolio management. It ultimately informs how you allocate your assets, how much you need to save or spend in your financial plan. Higher expected returns make an asset class more attractive, all else equal, but of course all else is not always equal.
What role does cognitive decline play in financial decision-making?
Higher volatility makes an asset less attractive, all else equal. But again, all else is not always equal. And lower cross asset correlations make assets more attractive together in a portfolio, all else equal. But there are always trade-offs. It's never actually all else equal. Modeling those relationships is hard enough, but there are other aspects of expected returns that get less attention while being similarly important.
How do traditional Monte Carlo methods fail to capture market behaviors?
The big one there is the shape of the distribution. And another one is the time series characteristics of asset class returns. That's like volatility clustering in stocks where a little bit of volatility tends to be followed by a lot of volatility, mean reversion in stocks, mean aversion in bonds. Those are all characteristics that can materially change optimal asset allocations and modeling outcomes like long-term financial planning outcomes. Now, listeners will be familiar with Scott Cederberg's work on this, which we found illuminating. He and his coauthors used block bootstrap to preserve the time series characteristics of historical stock and bond and cash returns to show that doing that changes the relative attractiveness of stocks and nominal bonds and cash for long-term investors.
So since hearing from Scott and learning about his research and reading his paper, we've been trying to figure out, basically me and you Braden have been trying to figure out how we can improve our expected returns modeling when we're running financial planning projections. And so that's what we're going to talk about in this episode. Braden, you're going to talk about some of the modeling considerations that go into expected returns modeling and what we're doing now. And then, and this part's pretty cool. We're going to hear from John Yang, who's a financial engineering student at Columbia Engineering, the engineering school at Columbia University.
What innovative methods did John Yang's team use for synthetic return data?
PWL engaged with John and some of his classmates for their industry project where the students, supported by their professor, Professor Michael Robbins, aim to solve a real problem for a firm. So it's pretty cool. There are a bunch of industry projects that are available to students and they get to choose which one they want to do. And so I talked to Professor Michael Robbins, explained this expected returns modeling challenge that we had. And he was like, cool, I'll propose that as a project. And John and his group of students for the class chose our project and worked through it for a semester. So it was pretty cool experience.
It's stuff that I did when I was in academia, working with industry partners. And I always found that pretty impactful when you're actually able to make a difference on real product and real tangible research that can be applied in the real world. So I think hopefully they took that same experience in working with us, but it was definitely really cool to be on the other side of that, where we were able to leverage academia and feel like we're in the bleeding edge of this type of research. So yeah, really cool experience all around.
Yeah, I thought it was cool. Our financial planning software that PWL uses right now is called Conquest Planning.
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Chapters
8 chapters
1
What is expected return modeling and why is it important for financial planning?
0:04–0:50
2
What role does cognitive decline play in financial decision-making?
0:50–1:14
3
How do traditional Monte Carlo methods fail to capture market behaviors?
1:14–2:29
4
What innovative methods did John Yang's team use for synthetic return data?
2:29–4:22
5
How does the new simulation framework improve financial planning outcomes?
4:22–5:18
6
How do expected returns differ between Gaussian and empirical distributions?
5:18–6:07
7
What implications do the new modeling methods have for wealth accumulation?
6:07–6:50
8
How can these findings impact financial advice for clients?
6:50–1:17:23
Speakers
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