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 N.F. Kaplan Professor of Business at Columbia Business School and chair of the Finance and Economics Division. The second is scaled investment implementation, where he was a managing director at BlackRock and led work across factors, sustainability, and investment solutions. And now, he is focused on 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 had some race riots during the 1960s and 70s. My parents wanted somewhere safe to raise their family, and right after the White Australia policy was rescinded in Australia, they migrated there. So I grew up in Australia.

Both of my parents didn’t go to college. My dad at one stage had a corner shop. I did really well with exams, and I thank my parents for migrating because it gave me so much more opportunity. But when I was growing up, I was the only non-white kid in class for a long time. So you ask why. Does it matter? How is it different? That questioning led to me becoming an academic. I think later on I realized that I chose finance and financial economics primarily because my family didn’t have as much money as our friends. That led to this searching. So I went to the United States, got a PhD, 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, and 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, the individual investor’s portfolio, and what matters for individuals.

Jack Sharry: So there’s a theme I’m picking up on from way back when, and it runs throughout your career. It’s improving portfolios and how they’re built and managed. Let’s talk a little bit about some of the work you did at that firm we all know so well, the largest asset manager 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 got started, where you went with it, and in a little bit, we’ll talk about where it’s all going.

Andrew Ang: Factors are interesting because they’re the standard paradigm for academics, and have been for the last several decades, going back to the 1970s. There have been five or six Nobel Prizes awarded in this area, but I think it really only went mainstream over the last 20 or 30 years. Where I saw it make a big difference in practice was when I was a lowly assistant professor. I worked for the Norwegian Sovereign Wealth Fund for over a decade. They had terrible performance during the global financial crisis in 2008, and I was part of a commission to study the investment strategy of that fund. Factors explained two-thirds of the fund’s active returns. They explained the really terrible loss that happened during the financial crisis, and they explained the rebound afterward. Factors like buying cheap, or value, finding high-quality names, finding trends, or momentum investing, go way, way back. I think all investors have always wanted to buy cheap, find trends, and find companies with sustainable, durable business models. But what Norway didn’t have was a proactive, communicative stance of explaining its active strategies. These are really good things to hold in the long run, but you do need to proactively manage them. It’s not just for institutions. This was in the 2000s and during the global financial crisis in 2008. But these factors ought to be for everybody. Everybody should be able to find a bargain. Everybody should be able to participate in trends. Everybody should be able to find good, high-quality companies. What we want to do is make a difference to individual portfolios. You have to do that with tax efficiency, you want to do it at low cost, and you want to find the right way to put it into someone’s portfolio. So I think it’s really democratizing access to these long-run drivers of return, but doing it in a way that’s friendly to an individual’s portfolio.

Jack Sharry: Talk a little bit about that transition from being an academic, understanding it, studying it, theorizing about it, and then running portfolios. And if you would, talk about the new approach you’re doing with SEI, the SEI Ang Research Enhanced U.S. Large Cap ETF. Talk about that transition and how it applies 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 for BlackRock. The real difference is that if you really want to see a bloated bureaucracy, you go to a university. Companies, especially large companies, may have bureaucracy, but they do get things done. The really big thing I learned as department chair was that it’s really hard to hire academics. And because people are tenured, it’s also really hard to exit them. Naively, I thought that coming into industry, where employment is at will and you have a very large supply of people, it should be fairly easy to hire the right people and form a team. But hiring good people is really hard anywhere you are. I did like the speed at which the business world moves. You’re able to get research out and really make a difference. That was the biggest change, to actually see it put into portfolios, see people using it, and make a contribution to someone’s financial well-being.

Jack Sharry: Let’s talk about one of the factors you pay attention to, which is taxes and tax-efficient investing. I’d love to hear more about how you do that and some of the innovations you’ve discovered, uncovered, revealed, and studied. Talk a little bit about that, and then we’ll get into how you’re applying it to some of the current work you’re doing.

Andrew Ang: The first lesson is that taxes are a really big deal for individuals. Often, the world of investing has come from an institutional framework, where most of those institutions, pension funds, sovereign wealth funds, foundations, are tax exempt. Taxes constitute the biggest wedge between the returns that are reported and the returns that you actually get to keep. So you have to deal with taxes. First, you want to put your money into the right vehicle. There, ETFs dominate. 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. Thanks to the creation and redemption feature of ETFs, a well-managed ETF will generate no pass-through taxes for an individual. Hopefully, you will pay taxes on a gain when you actually sell the ETF, but there are no ongoing taxes for ETFs that are well managed. So lesson number one is to put these factor strategies into the right vehicle, into an ETF. The second thing is that you should take advantage of all these different tax locations, or asset locations, as academics call them. These are things like your 401(k)s, Roths, 529s if you’re saving for college. I’m an academic, so I have things like a 403(b). These are all different vehicles where either you don’t pay any tax at all, like a Roth, or you defer the tax, like a 401(k) or an IRA. We should use those to reduce our tax bill. The question is which assets go into which accounts. That does make a difference. We should take advantage of all these different accounts available to us. For wealthy individuals, you may have a couple of trusts, maybe some corporations, and all of these should be used to be more tax efficient. The third area is evaluating managers. Some managers or active strategies are very tax onerous. If you are considering them, put them in the right place. Other types of active managers might have a return that looks pretty modest before taxes, but they look great compared to some other active managers after taxes. We want to have a tax lens. Look at these managers not on reported or pre-tax returns, but on what you actually get to keep. You want to keep more of what you earn. So 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 your household. It’s usually several people. So those accounts for an individual should be considered with the accounts for the entire household. They also have to work together. This is a hugely complex problem. But if you spend some time with a financial advisor thinking about these things and getting them right, my research shows that the federal tax drag on U.S. equities for a taxable investor is more than one-third. It’s about 36% or 37%. If you could keep that for your family or your consumption, that’s a game-changing amount.

Jack Sharry: Talk a little bit about how you’re applying it, because I know you’re in the process of doing some work with SEI around the SEI Ang Research Enhanced U.S. Large Cap ETF. Talk a little bit about what you’re doing and how you’re applying what you just mentioned.

Andrew Ang: It’s a big mouthful, the SEI Ang Research Enhanced U.S. Large Cap ETF. I had to practice that. But the ticker is really good. It’s ANGU. I really like that name. It’s designed to be a core holding for your portfolio, with low tracking error of around 1%. It has exposure to three factors: quality, value, and momentum. Quality is fairly well understood. It has different metrics you want to measure it with, but at heart, it’s about how durable your business model is and what the actual content of your earnings is. They shouldn’t be transitory. They should be fairly permanent. Momentum is investing in trends. Value is the interesting one because we split value into two parts. There’s traditional cyclical value. These are things like earnings yield, or the inverse of price-to-earnings ratios, and book-to-market. Those are the traditional valuation signals. They are cyclical. But there’s a different form of value that we call enhanced value. That’s very important with the rise of intangible capital. We can look at economic value added, human capital, goodwill, and other forms of intangible capital that are sometimes not reflected in accounting statements for book value or earnings. With the rise of technology firms and the increasing importance of intangibles, now 50% of companies have some form of intangible capital that is material. That provides a different return stream. ANGU contains these factors, and then we time them. The catchphrase I like to use is the right factors at the right time. We have several timing signals. We look at market similarity, meaning how similar today’s environment is to different market conditions in the past. That gives us a clue as to the kinds of factor positions you might hold. The factors have momentum themselves, so there’s a factor momentum indicator. Finally, there’s a model we call the factor of factors. That looks at the valueness, or dispersion, in each of these factors. The value of value, so to speak. The momentum characteristics of momentum. The quality of quality. If those are high, if dispersion is high or there is a large exposure, then that also bodes well for those factor positions. So it’s diversification across the factors, diversification in the factor signals themselves, like intangible capital measures and cyclical value, and finally, diversification across time in how we tilt and manage those factor positions.

Jack Sharry: Sounds like a very solid portfolio from your description. That’s a lot to keep track of. I know you’re doing a lot of work around AI, which we’ll talk about in a bit. How does AI fit into the management of this stable, solid core holding?

Andrew Ang: AI and machine learning are present in there. We like to reflect it in the actual signals, and that constitutes some fraction of intangible capital that we pick up through those measures, which enters enhanced value. We also incorporate AI and machine learning techniques in the way we tilt factors. The market similarity model, in particular, uses machine learning techniques across hundreds of market conditions to reduce the dimension. We look at the distance, which is defined in machine terms, between today and periods in the past across hundreds of vectors of different market indicators. Then we come up with a forecasting model that is also based on a machine learning model.

Jack Sharry: Continuing on with the AI theme, you’ve written about what you call the self-driving portfolio. I know you’re doing a lot of work around that, and it’s informing your thinking across the board. Talk a little bit about your vision around what you’re calling the self-driving portfolio.

Andrew Ang: This is a very interesting paper, and I should put it first in historical perspective.

Most people now have engaged with ChatGPT or Claude, or whatever your favorite large language model is. They’ve become my BFF, for sure. But the way people have been using them has really been to substitute. Jack, you mentioned preparing for this podcast and talking to me. That’s a task where you can get AI to help you. I hope everyone does that because lots of our jobs consist of laborious, tedious work. Let’s outsource that to AI. But the really big gains come when we don’t just substitute something with AI, but we completely change the way we work with AI. Historically, you can look at the movement to electrification in the late 1800s and early 1900s. Originally, factories had shafts and belts. But the really big gains from electrification didn’t come until you decentralized that and had machines in different parts of the factory. When computing and the internet came on, the big changes didn’t come when we moved forms to punch cards or centralized mainframes. The big change came when everyone had a PC on their desktop, or when we could get information instantaneously on a mobile computing device or phone.

We’re right there now. It’s about not just using AI to substitute, but completely reimagining the way we work with AI. That’s the self-driving portfolio paper. What exactly is it? It’s asset allocation done with agents. The agents give us three things. They give us scale, they interact with each other, and they learn. In asset allocation, we come up with capital market assumptions. Usually, the capital market assumptions don’t embed tax, which they should. But now we can create an agent for every single asset class or sub-asset class. That makes sense because the expected return for commodities is different from large-cap equity, small-cap equity, investment-grade bonds, mortgages, and now private credit. Each of those is different, but you can have an agent specialize in each one. That wasn’t possible before, unless you had a huge team, and very few people could do that. Then we have portfolio construction methods. Usually, the asset allocation procedure is just one method, often some variant of mean variance, with thanks to Harry Markowitz from 1952, plus some constraints. But there are dozens, thousands of portfolio construction methods. In the paper, we go through about two dozen. They range from purely formulaic approaches like market cap or equal weight to complex portfolio construction methods like total portfolio allocation, hierarchical risk parity, and maximum entropy. You can now have an agent, dozens of agents, for every single portfolio construction methodology. This wasn’t achievable before. So we have scale. The second thing that’s really interesting is that the agents interact with each other. One public way that your agent could potentially talk with other agents is through OpenClaw. We’ve developed our own internal system, but the agents critique each other’s proposals for expected returns and portfolio construction. They vote on each other’s proposals, they referee, and they revise their proposals. I want to know what that vote is. This is really interesting because I call this productive dissent. It is possible with human teams, but it’s hard to disagree in many institutional or committee meetings, or at least to disagree 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. But the agents don’t care. I find this fascinating because where the agents disagree, those are the most interesting and important areas for investing. Those are the ones we should focus on. That’s probably where alpha lies, or where we have to be very careful and construct robust portfolios to guard against those risks. That was present in human-based workflows, but now it’s at a completely different level, and we can harness that advantage. Finally, the agents learn. They can change the way they act, the descriptions, the skills they use, the code they call, and they can develop new skills. So we call this the self-driving portfolio. It’s one of several agentic investment strategies that we’re running. After all of this, 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 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 essentially in the same way. I use the power of organizing, researching, learning, and understanding. 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: Yeah, and on the portfolio management side, that’s actually hard-coded through what’s called an investment policy statement. It’s the same type of guidance that a financial advisor would sit across the table from a mom and dad to discuss. We want guidelines and objectives for the portfolio. We want bounds. We want to discuss how much risk there is and what types of assets should be held.

That IPS, the investment policy statement, guides how the agents should work.

Jack Sharry: What’s interesting with all this, I find, is that it helps me think better. It helps me discern better. It gives me information where there’s opportunity. I’ve found myself in discussions with colleagues about AI, and some like it and some don’t. I find that it just gets me to think better. Do you find the same?

Andrew Ang: Yes, I completely agree with that. Absolutely. It surfaces things, like the term I use, productive dissent, that you wouldn’t have encountered before and that would have been very hard to generate in traditional ways.

Jack Sharry: That’s great. Our time grows short. This has been great to catch up with you, Andrew. I’m excited for what you’re building and where things are headed. You’ve got a lot of things on your plate and exciting stuff ahead. As we look to close our discussion, what do you do outside of work? When you’re not thinking about factor investing, tax-aware investing, AI, and how it helps focus the mind, what do you do for fun?

Andrew Ang: I’m a pianist, mostly classical, but I have played in a few corporate bands. I can do some cheesy 1980s and 1990s songs, but that’s my escape.

Jack Sharry: That’s great. Love it. Andrew, this has been a lot of fun. I really enjoyed our conversation. Your work has consistently challenged conventional thinking and pushed the industry toward more thoughtful, more personalized, and more effective implementation. Thanks for all that. I look forward to seeing more to come on all fronts.

Thanks for sharing your insights and your journey with us. Your path from Malaysia to Australia, to academia, to BlackRock, and now to what you’re building today is fascinating. To our audience, thanks for tuning in. If you enjoyed today’s episode, please rate, review, subscribe, and share what we’re doing at WealthTech on Deck. You can find us at wealthtechondeck.com. Andrew, thanks. This was really enjoyable. A real pleasure.

Andrew Ang: Thank you.