Announcer: You’re listening to WealthTech on Deck, a podcast about the future of wealth management technology, brought to you by SEI. Here’s your host, Jack Sharry.
Jack Sharry: Hello, everyone, and welcome to this week’s edition of WealthTech on Deck. My guest today is Andrew Ang. Andrew is one of the industry’s leading voices in quantitative investing, factor research, tax-efficient portfolio construction, and the growing use of AI in investing.
Andrew’s career spans three major frontiers in modern investing. The first is academic research, where he served as the Ann F. Kaplan Professor of Business at Columbia Business School and chair of the Finance and Economics Division. The second is investment implementation at scale, where he was a managing director at BlackRock and led work across factors, sustainability, and investment solutions. And now, it’s the application of technology, taxes, and AI to portfolio construction as co-founder of Tao Balance, a fintech firm focused on tax-efficient asset allocation and wealth management tools.
Today, we’ll connect Andrew’s career journey to his current work in research-enhanced indexing, tax-efficient asset allocation, and agentic investment systems. Andrew has authored more than 100 publications, written Asset Management: A Systematic Approach to Factor Investing, and influenced how institutional and wealth management firms think about factors, risk, return, taxes, and implementation.
Andrew, welcome to WealthTech on Deck.
Andrew Ang: Thank you, Jack. It’s a pleasure to be here.
Jack Sharry: Good to have you here. So, Andrew, you’ve had a fascinating career across research, asset management, and technology. Tell us your story. How did you get started? How did you wind up where you are today?
Andrew Ang: Well, if you go way, way back, I was born in Malaysia, and that country experienced race riots during the 1960s and ’70s. My parents wanted somewhere safe to raise their family, so right after the White Australia policy was rescinded, they migrated to Australia.
I grew up in Australia. Neither of my parents went to college. At one stage, my dad had a corner shop. I did really well on exams, and I thank my parents for migrating because it gave me so many more opportunities.
When I was growing up, I was the only non-white kid in class for a long time. That made me ask, “Why? Does it matter? How is it different?” That questioning led to me becoming an academic.
Jack Sharry: Interesting. You were just trying to figure things out.
Andrew Ang: Yeah. I only realized later that I had first chosen actuarial studies and then switched to finance. I think I studied financial economics primarily because my family didn’t have as much money as our friends. That led to this searching.
I went to the United States, earned a Ph.D., became a professor, and was at Columbia Business School for 15 years. I think I could have done without the last three. I was chair of the Finance and Economics Division. It was a terrible, terrible job that I don’t wish on anyone.
Then I led the factor investing business, among other things, at BlackRock for 10 years. Now, as you say, I’m independent and really thinking about taxes, individual investors’ portfolios, and what truly matters for individuals.
Jack Sharry: There’s a theme I’m picking up on that goes way back and runs throughout your career. It’s about improving portfolios, including how they’re built and managed.
Let’s talk about some of the work you did at that firm we all know so well, the largest in the world. You spent much of your career advancing factor-based investing. How has your thinking about factors evolved over the years? Talk about how it started, where you went with it, and how it grew. Then we’ll talk about where it’s all going.
Andrew Ang: Factors are interesting because they’ve been the standard paradigm for academics for several decades, really since the 1970s. There have been five or six Nobel Prizes awarded in this area, but I think factor investing has only gone mainstream over the past 20 or 30 years.
Where I saw factors make a big difference in practice was when I was a lowly assistant professor. I worked for the Norwegian sovereign wealth fund for more than a decade. The fund had terrible performance during the global financial crisis in 2008, and I was part of a commission that studied its investment strategy.
Factors explained two-thirds of the fund’s active returns. They explained the terrible loss during the financial crisis, and they explained the rebound afterward.
Factors such as buying cheap, or value investing, finding high-quality names, and identifying trends, or momentum investing, go way, way back. Investors have always wanted to buy cheap, find trends, and identify companies with sustainable, durable business models.
What we didn’t always have was a proactive, communicative approach to explaining active strategies. These are very good things to hold over the long run, but you do need to manage them proactively.
And it’s not just about institutions. This work was happening in the 2000s around the global financial crisis, but these factors ought to be for everybody. Everybody should be able to find a bargain. Everybody should be able to identify and participate in trends. Everybody should be able to find good, high-quality companies.
We want to make a difference in individual portfolios. You have to do that with tax efficiency and low costs, and you have to find the right way to incorporate it into someone’s portfolio. I think it’s really about democratizing access to these long-run drivers of return, but doing so in a way that’s friendly to an individual’s portfolio.
Jack Sharry: Talk about the transition from being an academic who understood, studied, and theorized about these ideas to running portfolios. How did that transition apply to the real world of managing money and producing performance?
Andrew Ang: To be honest, I think there was far more in common between my existence as a professor at Columbia and heading the factor-based strategy group and factor business at BlackRock than people might think.
The real difference is that if you truly want to see a bureaucracy, you go to a university. Companies, especially large companies, may have bureaucracy, but they do get things done.
The biggest thing I learned as department chair was that it’s really hard to hire academics. Because people are tenured, it’s also really hard to exit them. I naively thought that when I entered the industry, because employment is at will and there’s a very large supply of people, it should be fairly easy to hire the right people and reform a team.
But hiring good people is really hard wherever you are. It’s just really, really hard.
I did like the speed at which the business world moves. You’re able to get research out and make a real difference. I think the biggest change was seeing the research put into portfolios, seeing people use it, and then making a contribution to someone’s financial well-being.
Jack Sharry: Let’s talk about one of the factors you pay attention to. I’d love to hear more about how you approach taxes and tax-efficient investing, as well as some of the innovations you’ve discovered, uncovered, revealed, or studied.
Talk about that, and then we’ll get into how you’re applying it to some of your current work.
Andrew Ang: The first lesson is that taxes are a really big deal for individuals. The world of investing has often come from an institutional framework in which most institutions, including pension funds, sovereign wealth funds, and foundations, are tax-exempt.
But taxes constitute the biggest wedge between the returns you appear to participate in, or the returns that are reported, and the returns you actually get to keep. So you have to deal with taxes.
First, you want to put your money into the right vehicle. ETFs dominate there. That’s not to say there’s no room for mutual funds, but mutual funds are pass-through vehicles, and there are ongoing tax liabilities associated with them.
Because of the creation and redemption feature of ETFs, a well-managed ETF will generate no pass-through taxes for the individual. Hopefully, you’ll have to pay taxes on a gain when you sell the ETF, but there are no ongoing taxes for well-managed ETFs.
Lesson number one is to put these factor strategies into the right vehicle, an ETF.
The second thing is that you should take advantage of the different tax locations, or asset locations, as academics call them. These include accounts such as 401(k)s, Roth IRAs, and 529 plans if you’re saving for college. I’m an academic, so I have things like a 403(b).
These vehicles either allow you to pay no tax at all, as with a Roth, or to defer the tax, as with a 401(k) or an IRA. We should use them to reduce our tax bill.
The question is: Which assets go into which accounts? That does make a difference. But we should take advantage of all the different accounts available to us. These include IRAs, 401(k)s, Roth accounts, and, for wealthy individuals, perhaps trusts or corporate accounts. They should all be used to become more tax-efficient.
The third area is manager selection. Some managers or active strategies are very tax-onerous. If you’re considering them, put them in the right place.
Other active managers might have a return that looks fairly modest before taxes, but they look great after taxes compared with other active managers. We want to apply a tax lens. Don’t look only at reported or pre-tax returns. Ask, “What do you actually get to keep?”
You want to keep more of what you earn, so let’s evaluate active managers on an after-tax basis.
Finally, you have to knit all these things together. It’s usually not just one person in a household. There are often several people. An individual’s accounts should be considered alongside the accounts of the entire household, and all those accounts have to work together.
This is a hugely complex problem. But if you spend time with a financial advisor thinking through it and getting it right, it can make a major difference. Some of my research shows that the federal tax drag on U.S. equities for a taxable investor is more than one-third, around 36% or 37%.
If you could keep that for your family or your consumption, that’s a game-changing amount.
Jack Sharry: That’s great. You’ve written about what you call the self-driving portfolio, and I know you’re doing a lot of work in that area. I’m sure it’s informing your thinking across the board. Talk about your vision for the self-driving portfolio.
Andrew Ang: This is a very interesting paper, and I should first put it into historical perspective. Most people have now engaged with ChatGPT, Claude, or another favorite large language model. They’ve become my BFFs, for sure.
The way people have been using them has largely been as substitutes. Jack, you mentioned preparing for this podcast and talking to me. That’s a task you can get AI to help you do. I hope everyone does that because much of our work consists of laborious, tedious tasks. Let’s outsource those tasks to AI.
But the really big gains come when we don’t simply substitute AI for something. They come when we completely change the way we work with AI.
Historically, you can look at the move to electrification in the late 1800s and early 1900s. Factories originally had shafts and belts, but the really big gains from electrification didn’t come until power was decentralized and machines could be placed in different parts of the factory.
When computing or the internet emerged, the big change didn’t happen when we moved forms to punch cards or centralized mainframes. The really big change came when everyone had a PC on their desk, or when we could access information instantaneously on a mobile computing device or phone.
We’re at a similar point. It’s not just about using AI as a substitute. It’s about completely reimagining the way we work with AI. That’s what the self-driving portfolio paper is about.
So, what exactly is it? It’s asset allocation done with agents. The agents give us three things: scale, interaction with one another, and learning.
In asset allocation, we develop capital market assumptions. Those assumptions usually don’t embed tax, although they should. We can now create an agent for every single asset class or sub-asset class.
That makes sense because the expected return for commodities is different from the expected return for large-cap equity, small-cap equity, investment-grade bonds, mortgages, or private credit. Each is different, but you can have an agent that specializes in each one.
That wasn’t possible before. You could assemble a huge human team, but that’s a luxury very few people could afford.
Then we have portfolio construction methods. The asset allocation procedure usually relies on one method, often some variant of mean-variance optimization, with thanks to Harry Markowitz and his work from 1952, plus some constraints.
But there are dozens, even thousands, of construction methods. In the paper, we go through about two dozen. They range from purely formulaic approaches, such as market-cap weighting or equal weighting, to complex portfolio construction methods such as total portfolio allocation, hierarchical risk parity, and maximum entropy.
You can now have an agent for every single portfolio construction methodology. This wasn’t achievable before. So that gives us scale.
The second interesting thing is that the agents interact with one another. One public way your agents could potentially communicate with other agents is through open core. We’ve developed our own internal system, but the agents critique one another’s proposals for expected returns and portfolio construction.
They vote on one another’s proposals. They referee, and then they revise their proposals. I want to know what that vote is.
This is really interesting because I call it “productive dissent.” Productive dissent is possible with human teams, but it can be hard to disagree in many institutional or committee meetings, at least to the degree that agents can disagree.
In the worst case for humans, HR gets involved, and you might lose talented people from your organization. The agents don’t care.
I find this fascinating because the areas where the agents disagree are the most interesting and important areas for investing. Those are the areas we should focus on. That’s probably where alpha lies, or where we need to be especially careful and construct robust portfolios to guard against adverse outcomes.
This was present in human-based workflows, but it’s now possible at a completely different level, and we can harness that advantage.
Finally, the agents learn. They can change the way they act, update their descriptions, change the skills they use, change the code they call, and develop new skills.
We call this the self-driving portfolio. It’s one of several agentic investment strategies we’re running.
After saying all that, it sounds cool. I think it’s really cool. It gives us things we haven’t had before. But can you prove that it’s better? No. We’ll see. It’s going to take a few years.
I wouldn’t give up on the human yet. I think the human still has to be there. But technology really does help, and we’re very interested to see how it goes.
Jack Sharry: I use AI very differently than you do, but also in much the same way. I use its power to organize, research, understand, and learn, but I’m still in charge. I still get to ask the questions. It sounds like you’re doing the same thing on the portfolio management side.
Andrew Ang: Yes. On the portfolio management side, that’s hard-coded through what’s called an investment policy statement.
It’s the same type of guidance a financial advisor would develop while sitting across the table from a mom and dad. We want guidelines and objectives for the portfolio. We want boundaries. We want to discuss how much risk is appropriate and what types of assets should be held.
The IPS, or investment policy statement, guides how the agents should work.
Jack Sharry: What I find interesting is that all of this helps me think and discern better. I assume you agree. In other words, it surfaces new information or reveals opportunities within existing information.
Andrew Ang: Yes, I completely agree.
Jack Sharry: That’s what I find. I’ve had discussions with colleagues about AI. Some like it, and some don’t. I find that it simply gets me to think better.
Andrew Ang: Yes, absolutely. It surfaces things like productive dissent that you wouldn’t have encountered before. That would have been very hard to generate in traditional ways.
Jack Sharry: That’s great. Our time is growing short. It has been great catching up with you, Andrew. I’m excited about what you’re building, what you’re doing, and where things are headed. You have a lot on your plate and some exciting work ahead.
As we close our discussion, what do you do outside of work when you’re not thinking about factor investing, tax-aware investing, AI, and how all of it interacts and helps focus the mind? What do you do for fun?
Andrew Ang: I’m a pianist, mostly classical, but I’ve played in a few corporate bands. I can also play some cheesy 1980s and 1990s songs. That’s my escape.
Jack Sharry: That’s great. I love it.
Andrew, this has been a lot of fun. I’ve really enjoyed our conversation. Thank you. Your work has consistently challenged conventional thinking and pushed the industry toward more thoughtful, personalized, and effective implementation.
Thank you for all of that. I look forward to seeing what’s to come on all fronts, and I appreciate you sharing your insights and your journey with us. It was fascinating to hear how a little Malaysian boy wound up in Australia and went on to do what you’re doing today.
To our audience, thanks for tuning in. If you enjoyed today’s episode, please rate, review, subscribe, and share WealthTech on Deck. You can find us at wealthtechondeck.com.
Andrew, thank you. This was really enjoyable. It was a pleasure.
Andrew Ang: Thank you.
Announcer: Thanks for listening to this episode of WealthTech on Deck, our ongoing conversation about improving financial outcomes for all. This podcast is brought to you by SEI and produced by Turncast.
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