The Rational Reminder Podcast
Market Simulations & Financial Planning | #411 (John Yang)
28 May 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: 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.
Chapter 2: 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.
Chapter 3: 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.
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Chapter 4: 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. They're just Conquest. The way that its returns analysis works, when you want to look at how sustainable is this financial planning projection over a projected range of future outcomes, we upload a thousand runs of simulated data.
Doing it that way is really cool because some other financial planning softwares will run a Monte Carlo inside the software, but you don't get to change any of the parameters or the shape of the distribution. But because Conquest lets us, or the only way that you can do it is by uploading your thousand runs of data,
You can use whatever simulation method that you want and then upload that into the software. Because of that, we're able to do stuff like what John and his team worked on, where we define our own way of creating a distribution of returns, and then we can test that in financial planning software.
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Chapter 5: How does the new simulation framework improve financial planning outcomes?
We don't yet have this loaded up in Conquest, but Braden has done some modeling work just to look at how John's method of simulating returns compares to what PWL had been doing previously. in a long-term projection. How does it change the outcome in these projections? So we will get to that in a minute. So that's the setup.
Now, I do want to speak real quickly about something cool that happened before we just get into the main content. A new PWL client who is characteristically as, as many of them are highly analytical and had previously been a successful DIY investor. They wrote down a detailed analysis of their decision to become a PWL client.
I believe that they wrote this to share in a group chat with some of their friends, but the cool thing is they also shared the analysis with their new PWL advisor. who then shared it with me. And then we got permission to talk about it on the podcast because I thought it was pretty thoughtful analysis.
Chapter 6: How do expected returns differ between Gaussian and empirical distributions?
I do have to make some quick disclosures before we get into this. The person who wrote this is a client of PWL Capital, which is a subsidiary of One Digital, who also operates One Digital Investment Advisors LLC in the U.S., As some of our audience is US-based, we wanted to properly disclose that the client was not compensated by PWL Capital or OneDigital for writing this.
Also keep in mind that clients' experiences vary and therefore their views are not representative of all client experiences. Additionally, the testimonial is not an indication of past or future investment performance. All right, nice disclosure there to make Ross happy. So I'm going to get into what this client wrote.
So keep in mind, this is a long post that they wrote in a group chat type setting. Here's what factored into my decision to make the change from DIY couch potato style investing to an assets under management financial advisor. This is just my own thought process.
Chapter 7: What implications do the new modeling methods have for wealth accumulation?
I'm not trying to change anyone's opinion here, but hopefully the share is interesting. I've been self-directed my entire life and have spent the past 10 plus years getting serious about building up more in-depth knowledge about the pros and cons of different approaches to investing. I don't have particularly complex needs. And I've been a long VEQT for ages now.
So it took quite a while to work through my hesitations to the AUM, the asset center management model. Even a few months ago, I was pretty sure I wanted a fee only advisor. So this is pretty fresh and not informed by actual experience yet. I'll start with why I went with an advisor at all, roughly ordered from most important to me to least.
Financial planning, my top reason for finding an advisor. I'm starting to think about how to optimize for funding my life after I stopped working.
Chapter 8: How can these findings impact financial advice for clients?
I built fairly complex spreadsheets to project out drawdowns across my accounts to get a baseline idea of where I'm at. But even with tax planning included, they're still crude and unoptimized compared to something like Conquest, which again is the software that PWL uses. An advisor can use it to model and optimize drawdown scenarios, then run thousands of Monte Carlo market return simulations.
That's actually a good tie-in because that's what we're talking about. To help buffer against unexpected conditions that build robustness and security into the plan. I knew that I couldn't effectively plan for the future without this level of specific planning capability. And that was totally validated the first time we ran our numbers through Conquest.
I was way off, but in a pleasant surprise kind of way. The next point is cross-border complexity. One spouse in this couple is an American citizen, which has prevented us from combining our finances beyond our mortgage. We'll miss out on long-term planning strategies, for example, spousal RSPs without qualified guidance on how we should be approaching this.
And the spouse was looking for more peace of mind from qualified advice on dealing with the IRS. Despite having separate accountants to deal with taxes in each country, there have been some unexpected tax bills due to filing issues. The next point is estate security.
We don't have kids, but my wife and I are in different financial positions from both a knowledge standpoint, as well as account balances. Odds are all die first, so establishing a relationship now with an advisor we both trust who can be relied on to look out for both spouses' best interests in the event that one of them is gone is a huge benefit to everyone's peace of mind.
The next point is a hedge against decline. They write, my father-in-law is in the early stages of dementia and we're already seeing how that's having an impact on his finances, having an outside observer who can spot when conditions change and help intervene before costly mistakes are made. is great insurance. And I did want to add a note here.
PWL always encourages clients to assign a trusted contact person, which is someone that the client trusts who PWL is pre-authorized to contact if something doesn't seem quite right.
So if someone wants to make a trade or move money to a different country or something like that, if the PWL advisor is like, I don't know if this, if something doesn't seem right here, they can contact the trusted contact person who can then speak with a client or confirm whether everything's okay or whatever the case may be.
There is a paper that found that older people tend to underestimate their own cognitive decline and that those who have experienced a severe cognitive decline but are unaware of it are more likely to suffer wealth losses compared to those who are aware or did not experience a severe decline. This is a 2024 study.
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