Yang Tang, Arch Indices
Systematic Smart Beta Indexing
Yang Tang is the founder of Arch Indices and the manager behind VWI, an actively managed ETF focused on variable weighted indexing. Yang spent over a decade in quantitative finance, working at firms including Goldman Sachs, before building his own indexing methodology from scratch. His background combines deep quantitative research with practical experience in portfolio construction and risk management at the institutional level.
On this episode, Yang talks with Brad about why traditional market cap weighting creates structural problems for investors, how VWI's variable weighting methodology works under the hood, and the research behind his claim that smarter weighting alone can add meaningful alpha over long periods without requiring better stock selection.
The Problem with Market Cap Weighting
Yang's central thesis is straightforward: market cap weighting systematically overweights overvalued stocks and underweights undervalued ones. As a stock's price rises, its market cap grows, and index funds are forced to buy more of it at increasingly expensive levels. The reverse happens on the way down. This creates a built-in buy-high, sell-low dynamic that costs investors performance over full market cycles. Yang points out that this isn't a theoretical problem. Decades of academic research, including the foundational work of Rob Arnott at Research Affiliates, have demonstrated that alternative weighting schemes consistently outperform market cap benchmarks over long periods with similar or lower risk.
What makes VWI different from existing smart beta or fundamental indexing approaches is the variable component. Rather than applying a fixed alternative weighting scheme like equal weight or revenue weight, VWI dynamically adjusts its weighting methodology based on market conditions. The system evaluates which weighting factors are most likely to add value in the current environment and tilts the portfolio accordingly. This means the fund might lean more toward value-oriented weighting when valuations are stretched and shift toward momentum-friendly weighting when trends are strong. The dynamic adjustment is what separates VWI from static smart beta products that apply the same formula regardless of market conditions.
How VWI Constructs the Portfolio
The investment process starts with a universe of large cap US stocks, similar to the S&P 500. From there, the system applies multiple weighting factors simultaneously, combining fundamental metrics (revenue, earnings, book value, dividends) with quantitative signals (momentum, volatility, quality scores). Each factor gets a dynamic weight based on its expected contribution to risk-adjusted returns over the next period. The portfolio typically holds several hundred names, providing broad market exposure, but with very different weights than a traditional index fund would assign.
Rebalancing happens quarterly, with the system recalculating optimal factor weights and position sizes at each interval. Yang notes that turnover stays relatively low because the system is designed to avoid excessive trading that would erode returns through transaction costs. The changes tend to be gradual shifts in emphasis rather than wholesale portfolio reconstruction. Transaction costs are tightly managed, and the ETF structure provides tax efficiency through the in-kind creation and redemption process that allows capital gains to be exported from the fund. Yang emphasizes that VWI is designed to be a core equity holding, not a factor tilt or satellite position. The goal is to capture the equity risk premium more efficiently than market cap weighting allows while maintaining the diversification and liquidity characteristics investors expect from a broad market fund.
Research and Performance
Yang walks through the backtesting and live performance data behind VWI's methodology. The research shows that variable weighting has historically added 100-200 basis points of annualized alpha over market cap benchmarks with similar or lower volatility. The improvement comes almost entirely from the weighting methodology rather than stock selection, which Yang argues is a more durable source of alpha because it exploits a structural feature of how markets organize prices rather than relying on information advantages that erode as more participants compete for the same edge.
He also addresses the capacity question that frequently comes up with smart beta and alternative weighting strategies. Because VWI operates in the large cap space and maintains broad diversification across hundreds of names, the strategy can handle significant assets without degrading performance. The positions are all highly liquid names that can be traded efficiently even at meaningful scale, which means the fund won't run into the capacity walls that constrain more concentrated or small-cap-focused quantitative strategies.
Key Takeaways
- VWI dynamically adjusts its weighting methodology based on market conditions rather than applying a fixed alternative weighting scheme like equal weight or fundamental weight.
- The system evaluates multiple weighting factors (revenue, earnings, momentum, volatility, quality) and adjusts their emphasis quarterly based on expected contribution to risk-adjusted returns.
- Market cap weighting systematically overweights expensive stocks and underweights cheap ones, creating a structural performance drag that alternative weighting can exploit over full market cycles.
- Historical research behind VWI shows 100-200 basis points of annualized alpha over market cap benchmarks with comparable or lower volatility, driven by weighting methodology rather than stock selection.
- VWI is designed as a core equity replacement rather than a satellite position, targeting broad large cap exposure with a more efficient weighting approach that maintains full diversification.
Listen to the full conversation on Spotify, Apple Podcasts, or YouTube.
Full Transcript
5,331 wordsMachine transcribed from Brad Roth's conversation with Yang Tang, Arch Indices, with speakers identified automatically. Timestamps link to that moment on YouTube. Lightly cleaned, otherwise unedited.
Welcome to Behind the Ticker. I'm Brad Roth, Chief Investment Officer of Thor Financial Technologies and Portfolio Manager of THLV, the Thor Low Volatility ETF. Behind the Ticker uncovers the inner workings of the ETF industry. We will interview portfolio managers and ETF service providers to dive deep into their work lives and their businesses. We will learn the inner workings of their strategies and what drives them as they continue to grow their company. Many of these individuals are entrepreneurs and will have unique and compelling insights to share as much goes on behind the ticker. Please note, nothing in this show is investment advice and it is meant solely for educational and entertainment purposes only.
Welcome to Behind the Ticker. Today we have on Yang Tang. He is from Arch Indices and we are talking about their new issue, ticker VWI, the Arch Indices VOI, Absolute Income ETF. It is an all-in-one product. They use optimization techniques to try and drive the highest yield with the lowest volatility out of dividend paying stocks as well as some fixed income securities and ETFs. We talk about that low volatility factor, why it's important. And this product is really great for those investors that are in or nearing retirement. They're looking for yield and also looking for a level of low volatility.
So without further ado, please welcome Yang Tang. Hey Yang, welcome to the show. Hi, thank you so much for having me.
So why don't you tell everybody a bit about your background and then how eventually you end up starting Arch Indices?
Read the full transcript (52 more sections)Collapse transcript
Yeah, I'd love to. So I started my career in sales. I started in the commodity sales group at Barclays and then I went to business school at Columbia. And after business school, I was recruited into Morgan Stanley in a group called Macro Solutions. And the idea of Macro Solutions was to cover banks, insurers, asset managers for products that were non-vanilla. So everything bespoke, everything structured. I was very much focused on interest rates, effects, macro products. After that, I was in the same group at Citi where I met my co-founder, Dr. Jacob Kuo, who was a PhD in math and started at Salomon Brothers SA in Quant in the 90s.
And then I was recruited to Deutsche Bank to start the same group in the Americas. And then I was recruited to Credit Agrico to start the same group also for the Americas. So my career really was on the sales side and working institutional clients. And what's really interesting about that is, a lot of the clients are investors, but they're not investors in the way that many people are investors. They're investors because they have a purpose. It's really called liability-driven investing. So if you think about an insurance company, the goal of an insurance company is to meet the obligations with as little volatility as possible. So very rarely is the, the return, the main factor. It's the return in context of volatility experience, as well as the
Capital efficiency. During COVID, my co-founder and I were kind of talking as more as friends, and we came up with the ideas how to build a better portfolio. And the genesis of what we do is pretty much all passive portfolios out there are either market cap or equal. And if you think about this, historically, market cap was actually a negative factor, right? If you look at factor research, small caps have generally outperformed large caps over a long period of times. Of course, the last 17 years is not a good example of that. But nonetheless, the way people try to correct for that is to look at equal. So one of the most interesting charts you'll see is the equal weight versus market cap weight, S&P 500. But when you think about it, those are just one and two ways of
Weighting a portfolio. We want to build something around weighting portfolios from risk-adjusted contribution. And this takes into account a little bit of the modern portfolio theory of, the optimal portfolio and where the efficient frontier is. So what we do in weighting is really to look for the efficient frontier of assets. And that's building about the idea of risk-adjusted contribution. So we launched, we started the company in August 2022. We spent about the first year as, you as an ETF entrepreneur will know it's a bit of a lift to just start an ETF. It's a lot more than the 75-day filing period. So we built a lot of intellectual property. We put everything together at an ETF wrapper. we actually custom index ourselves with a third-party calc agent that
Was a wonderful partner for us. And we launched the income ETF last October. And the income ETF really is ticker VWI. What it is, it combines dividend stocks and bond ETFs. It uses our optimal weighting to do risk-adjusted contribution. And it looks for the most amount of income you can get and the least amount of volatility. So I guess that's a kind of a long, wider way to jump into our ETF as well as my
Career backgrounds. Yeah, sure. So we're going to get into all this because I do find this very interesting and appealing. But I always like to ask people before we get into the kind of the nitty-gritty and the business side, any hobbies? What do you like to do when you're not working?
I think if you as an entrepreneur, we'll know this thing consumes almost every waking moment of your day. outside of work, my wife and I love the art scene, the culture scene, as well as I'm a big San Francisco 49ers fan and a big San Jose Sharks fan. So those are very consuming. Although I'm at the point in life where, I used to watch all 16 or 17 Niners games on DirecTV. Now I'm down to about four, one of the Sunday night game,
One of the Monday night game. Yeah, you and I both are football and hockey guys. So it's good to know. So we talked a little bit about Arch indices as a whole. I kind of the audience probably knows what you do, but let's get into really, I think, the intellectual property and the IP. Can you talk about what like variance optimization is and just what it is and how it can help build better portfolios?
Yeah. So the idea of an optimization actually came from the modern portfolio theory, what Harry Markowitz. He introduced the idea, I think, in the 50s. So realistically, optimization is not a new idea, right? It's about as old as say, the tabular computer and the first computer. The idea of an optimization is, knowing what the world looks like. How do you build a portfolio that achieves your goal, whether it's total return, income, a price return, and also have the least amount of volatility or other constraining factors. So, the way an investor would think about it is if I have two portfolios and they both have the same return, I want, of course, the least volatile portfolio. I think there are some people
That want to go, no, I want the opposite. I want as much volatility as possible. But a rational investor would generally say, I want the least amount of portfolio as a buy and hold investor, not a quote unquote trading mentality. So that's what it is. The optimization is actually very technical. So if you think about what MPT entailed, right, it entailed a linear algebra approach, which is the covariance matrix. And that's taking into account the volatility multiplied by the correlation and essentially linear algebra formant and using a solver to hit that, waiting like waiting scheme. That's actually, a lot of people don't realize the modern portfolio theory is actually very flawed on that perspective. The first flaw is it expects you to, as a human,
To run expected returns. no human I've ever met has had any level of consistency in expected returns. And fun fact, even when I was in, the sales and trading side, the market side of the banking world, our best traders on the trading floor had on average, low fifties in terms of win rate, right? So what separated a good trader from a unemployed trader was this idea of risk management. I've met traders that are right about one in three times, but they're extremely profitable because they know how to manage risk, control risk and maximize opportunities. So that's kind of the, the active approach where you take a optimization approach, which is really kind of passive with a factor. So we did a few things.
The first thing that we did was we don't do expected returns because that's crazy. We look for some kind of output that correlates to total return. And if you think about just using our, income fund as a case study, most income investors will correlate yield and total return, right? Because a lot of income products have little to no price appreciation potential, and even dividend stocks are somewhat limited in price appreciation potential relative to more speculative stocks or, more growth stocks. We can talk about that later, of course. The second is, people think of the idea of, expect the volatility, expect the correlation. We kind of corrected for that by using market observed volatility, market observed correlation. And really the last thing that I think is the huge differentiator,
What we built is we don't use a covariance matrix. We use something called a recursive approach. So what a recursive approach does, it solves for the flaws of a matrix optimization. And, it doesn't take a linear algebra. It takes a portfolio starting with two assets. That becomes one portfolio, right? And then you continuously and recursively add assets until adding more assets. Does not, one, decrease the expected volatility, or number two, increase the expected output? So in essence, what you're trying to do is you're thinking of it as, what is the optimal path in a large number of assets? And just to put this in perspective, one of the hardest things about explaining this to people is people are used to, two-dimensional charts. People like to see one graph, right? People love to see, you know,
I use a chart correlation between the S&P 500 and 30-year bonds. Well, guess what? That's just two assets. In our ETF alone, there's 600 available assets between dividend stocks and 12 bond ETFs. 600 assets gives you 180,000 to portfolio permutations. So what is the, out of that 180,000 permutations, how do you use that to build the quote-unquote optimal portfolio? And of course, there's no way to visualize this, right? Like, you would just, your eyes would blow out if you, try to look at too many of these graphs. You have to use a bit of a mathematical and getting people to understand that and talking about that is, I think, really the
The power of our product. Yeah. And I want to get into the product, but let's just stay on volatility management here just at a high level. We talk about the power of kind of keeping vol suppressed as much as you can in a portfolio. And you had mentioned taking, two total returns of the same amount, one with lower volatility, right? An investor would want, a logical investor would choose one that's got a lower volatility tilt. So, but what though mathematically does reducing volatility in an overall portfolio do to long-term return outcomes?
Yeah. I think pretty much every study, right? Has shown two things. Number one, it's near impossible to time to move. And number two is people are really exposed to sequence of return risk. This is probably most relevant for a withdrawal portfolio, such as a retiree, but even, for someone who's accumulating assets, sequence of return is, it's not just a mathematical concept, it's also psychological. So let's start with kind of the idea of timing markets, right? I think it's very, you've met a lot of people, I think either probably in the social media space or talking heads, and they talk about signals, all these things, it's almost flawed in some level, because no one has consistently shown being able to time the market. And I think it's very obvious in hindsight, what a market bottom
And a market top is. But, I think if you were an investor, say, during the recent experience of COVID, right, the stock market crashed almost 40% from February to March. If you bought stocks in February, and you sold out of panic in March, that generally was poor. And just conversely, right, if you bought in March, and then you went through probably three more phases of volatility over the summer of 2020, 2021, probably the biggest Fed hike, you've experienced significant volatility, and it's not clear that every investor has the stomach to have high volatility. So reducing volatility, not timing markets itself, makes a portfolio more palatable over the long run. And the key, I think, of course, is not sacrificing returns in the
Process. So that's another important thing. But the first is, being invested in the market, staying in the market, having the right amount of, tolerance and getting the most returns for that level of tolerance is really what keeps people and makes people good investment. The second is volatility risk is actually very real for people that have a certain goal. So if we talk about retirement plan, people talk about sequence of returns. And, you can have the same amount of returns over a portfolio. And in the long run, the portfolio values are the same. But if you experience, positive or negative returns early relative to later, that impacts number one, the amount of money you have for that goal. And number two, the amount of money you can withdraw over time. So as any retirement
Planning person will tell you is you want to reduce as much portfolio risk, if you are having a specific goal. And that, of course, applies for accumulation as well. You could be saving for a house, right? And, instead of putting your money into a 5% T-bill, maybe you want to put a little bit of the stock market to get extra returns to stay invested while you look for the house, because you just don't know when that perfect house is going to come. So why not have a little bit of extra returns? But at the same time, you don't want to be so overexposed to risk that the house comes and you go, oh, shoot, I just took a 10% drawdown, buy a house, right? Yeah, of course. So let's talk about the ETF,
VWI. It's the ARC Indices VOI, Absolute Income ETF. And from the naming convention, I thought it was going to be a fixed income product until I started reading more. So this will be good. Let's just talk at a high level. And then we'll get into kind of the details. What is this fund
Really trying to accomplish? Yeah, so the idea of this fund is to do two things, is to generate income, really is targeted towards income focused investors. And the second is, is to reduce volatility. So you want the most amount of income and the least amount of volatility. And we do this by using two key assets, which is dividend paying stocks and bond ETFs. So we have a screener approach for dividend stocks. We have minimum market cap, minimum daily average trading volume, minimum yield and minimum regular dividend history. And of course, just to note, that's actually the criteria for inclusion. We don't exclude assets if they don't, if they breach those criteria, solely because that would actually create volatility instead of reducing both.
And the second is, we combine that with 12 bond ETFs we picked. 10 of them are low cost Vanguard ETFs. And the other two are products that are very, I think, beneficial to the portfolio, but Vanguard does not. Yeah. So, and the idea is, well, how can you mix that? You dynamically rebalance every quarter to catch up with changing market conditions. And, the majority of the assets are traditionally dividend stocks, which also have potential for capital appreciation. So the idea is generate as much income as possible, have the potential for capital appreciation, and have the least amount of volatility in getting.
So do you have a target vol you're trying to achieve when you start building out the asset mix? Or is it just, and do you have a yield target? Like, how are you kind of honing this in to try to optimize it to get both the lowest volatility and the highest possible yield?
Yeah. So because this is a dynamic product, there is no specific target. So right now the index yield is probably 6.8% when we rebalanced beginning of May. And it's historically been probably high fives on the low side to over 7%. So you're probably really thinking of a common a 6.7% type product. The volatility. So I think that one of the, you bring a really interesting point, Brad, is kind of targeting volatility. I think anyone who's been an investor will know that there's low volatility periods and high volatility periods. So one thing that we don't do is we don't introduce leverage in this product. So without, using leverage, you cannot achieve the right volatility target.
So in essence, it's just looking for the lowest amount of volatility in today's market. And it's really beneficial because, there's going to be periods where you cannot achieve low volatility, right? If it was in March, 2020, or October, 2008, there's just not a whole lot you can do if you want to stay invested and you should stay invested. You shouldn't give up because volatility is high and you shouldn't. So if you were targeting volatility, right? Because volatility went up, you would have to reduce exposure, which actually leads to adverse outcomes for investors. And just by the same kind of coin flip, if times were low, like 2019, 2017, so forth, you shouldn't lever, right? Because levering up and increasing exposure and volatility
Is low generally will give you adverse outcomes if volatility spikes.
Interesting. So we have, you explained the kind of the screening mechanism to be able to get to a universe. Now, do you dwindle down from that screen? Because you said it gives you about 600 names. Are you, are you then dwindling it down to a more concentrated set of holdings or are you using all of them?
So yeah, so those are the criteria for inclusion. So if you, the way we actually do our optimization, we don't actually target yield directly. We target something we call the performance ratio, which is yield relative to asset volatility. So what you want is you want a lot of assets that have extremely high yield relative to its underlying volatility. So that doesn't mean that you want 26% dividend paying stocks, right? You want say something that has high yield, but also extremely low volatility. And the second is the second layer of defense is looking for assets that don't move together. So of the 600 available stocks, in any given moment on the low end, the optimization will only include about 75. And on the high end, it could be about 125, 130. We're probably about 118 right
Now, which is on the high end. So in this process, what's, what's great is number one, it'll dynamically catch when things, when stocks change, because, as being a market participant, no stock has gone from a hundred to zero overnight, right? It's taken massive drops, it's bounced back, it's introduced a lot of volatility on that process down. So when the volatility of the asset increases, naturally, it's not attractive for our frame. So the optimization will go, oh, well, you were doing 5%, yield at a 10% volatility, but now you're doing 7% yield at 20% volatility, right? That naturally is unattractive. So you will be de-emphasized on those being.
Yeah, whereas conversely, somebody that can go from 5% volatility, 10% yield to say, around your 4% volatility, or sorry, 4% yield, and maybe 3% volatility, that actually becomes extremely attractive from a performance ratio perspective.
That makes sense to me. So you've now dwindled, you've, you've have your inclusion universe, you dwindle it down to 75 to 125 names. Now the weighting process would kick in on. So on a quarterly basis, you're running a new optimization to figure out what the new weights are. Is that correct? That's correct. So we dynamically rebalance with changing market conditions. And do you put any guardrails inside of that optimization or around the rebalance to kind of put any guardrails on sector or industry concentrations? Or are you allowing the process and the optimization to kind of take you wherever you need to go?
So we allow to take it wherever we need to go. I think, over time, there's been, some kind of sector changes. It's actually quite, it actually captures sector changes quite well. So in that may rebalance as an interesting point, it's almost 50% between bond ETFs, S-plus and BDCs. And that's actually quite noticeable because number one, correlation between stocks and bonds have been dropping all year and bond volatility actually declined by a third. Whereas if you look at kind of our October rebalance last year, even though yields on 30 year bonds probably hit, gosh, a decade high in October, it actually didn't increase its allocation to bond ETFs solely because that increase in yield came with an increase in volatility. So nothing was interesting
About the performance ratio. It does a pretty, I think it does a pretty strong job of getting by itself the sector allocations and the diversifications correctly. And I think, it's important for investors to appreciate that sectors are really a human invention, right? It's not like when you think about it, like, Facebook is in the communication sector, right? While Microsoft is an information technology, Visa, MasterCard are in the financial sector. So by itself, you can make an argument that perhaps MasterCard is more of an information technology company, and maybe Facebook is more of a, information technology company than say some things even in the information technology sector. So I think it's important that, like the investor is not too hung up on
Having just a quote unquote, sector allocation, because of all the underlying dynamics that happens within companies and within sectors. So you mentioned you have fixed income exposure,
Some by Vanguard, two others. Can you talk about what types of fixed income exposures you're targeting? Is it traditional fixed income? Do you have some other things in there that might generate some more yield like CLOs? can you talk about the fixed income side?
Yeah, so we try to be as close to the, I guess the broad investment bond index for dollars. So it's going to be, the different duration sectors of both IG corporates, as well as US government bonds, we do have AAA CLOs. So yes, we do have securities product exposure. We have mortgage back exposure as well. It's not always included. Also, we only have actually eight or nine bond ETFs used at the moment. And then we try to capture as much of the dollar bond investment universe as we can from a pure sector approach. This is actually, I think, an area where the ETF industry has not quite caught up to perhaps the overall broad asset management industry in terms of products offered. But in general, we want to, as long as the cost and the exposure is correct,
Want to be able to include it into our framework, because having more assets is generally beneficial. And then I think, yeah, we have BND, we have, yeah, we have a short term, yeah, we have USHY, which is the other non-vanguard, which is the high yield exposure. So we're trying to capture as much exposure as we possibly can.
So how often is VWI kicking off income? Is it a monthly distribution?
It is. So we pay income as we receive it. So the US corporate world likes to pay on the March, June, September, December. Those are generally the biggest months for dividends. And then, February is actually the lowest month, because it's the shortest month out of calendar days. And traditionally, not a lot of companies pay dividends then. So, you'll see a little bit of fluctuations, but it's a monthly pay.
Yeah. So here's the million dollar question, right? Is your, you have an advisor that already has a existing diversified model portfolio. And just listening to you talk and what it's trying to accomplish, like it could theoretically, in my opinion, be something where you could run an SMA on kind of your entire portfolio and just have it be a standalone. But you and I both know that that's not how the average investment advisor or RIA is kind of running their book. They like a diversified mixed of assets. So if you're sitting down with an advisor, where are you suggesting that this exposure sits and kind of how much exposure? I know that's a loaded question, but kind of where would you put it and how would you advise them to use it?
Yeah. So we have actually gotten the most traction with retirement planning focused advisors, which is no surprise because our ETF is extremely well suited for inflation risk because of the potential for capital appreciation, as well as dividends growing over time and the sequence of returns, which is reducing volatility. So it does very well there. It's, an all in one dynamically rebalanced portfolio. I, of course, would use it for a hundred percent. But that being said, it's pretty good, I think, as a portfolio within retirement income portfolios and then allowing advisors to, put in different exposures, different assets on top. So I think, part of the, part of the art of this is how much income do you actually need for retirement, right? Whereas I think someone with,
A hundred million dollars retiring is very different than someone with one million dollars retiring. So if you are a one million dollar retiree, this can make up, all, if not most of it. And then maybe you add in some different asset exposure to either one further reduced volatility or gain some level of additional capital gains. If you're a hundred million dollar portfolio, I would still think this is a good part of your holdings, but maybe you want something that has, more kind of capital appreciation bent to it. So you can leave your heirs or, your charitable foundation with more money. It's actually worth discussing this with both a pension fund, as well as three endowments at the moment. It's also well used on the,
Institutional space. The second place that we've talked a lot with people about is what they call a courtesy account. I think a lot of advisors have, accounts that just don't meet their minimum thresholds, but they don't want to turn away solely because, those accounts have, relationship value or, they're a growing account. So this is a good place to, as an alternative for, someone who is new to investing, who's accumulating assets, who doesn't understand the amount of personal risk tolerance that they have to perhaps put in 50,000, 100,000, get a sense of their market feel. And then one, either later scale that into a strategy or, transform this into something else. Now, the third place is really for moderate and
Kind of conservative allocation portfolios. Whereas, if you use a moderate or conservative allocation portfolio, you have some level of stocks and bonds being mixed together. So this kind of does that and we rebalance it for you and takes away an additional layer of either, operational hassle or reducing fees from not having to pay multiple vendors
For essentially the same exposure. Yeah, I should have asked this earlier. I apologize. I didn't. Is the ETF the only way to access the strategy or are you guys working kind of outside the ETF providing, model portfolios or SMAs? Is that part of your business?
It is. We actually have a partnership with a company called Corus. We run our SMAs through their platform. So they've been an excellent partner for us. They have tax loss harvesting. So we have definitely those capabilities at the moment.
That's great. So I want to ask the question just because you're a new issuer. what was that decision like to jump into what we call like the ETF Thunderdome? there's thousands of them now. And how are you going about kind of getting the word out and telling your story? Yeah. So it was, we're starting and, we started this as
Two people that have, we're not traditional asset managers. So we came into this really with an intellectual property approach and we started this ETF actually at zero. We didn't, there was no seed, there was very little, if any kind of pre-marketing or socialization. We just really want to do this as a proof of concept for our business. So yeah, it's, it's an extremely competitive field. We think our product is differentiated, but at the same time, being first time issue or being a new issuer, there is just a level of, I think, wall climbing you have to do. So I think it's been, it's been a certainly very strong learning process for us. In the beginning, I think we were a little bit over-focused on certain marketing strategies that may have not,
Or that we were too early for. We've actually gotten a lot of traction just using the, using this as a retirement planning portfolio, engaging with people that are either individual retirees or retirement-focused advisors. And I think that's where this type of product is. It's a low volatility income generating product. It's gotten a good reception with, smaller endowments and also, also defined kind of contribution plans. That's where it is. We also, as a core part of our business, we're planning to release analytics. So people will be able to access our optimization and our tools as a software package shortly on our website, which is going to be, hopefully we have that out right around the 4th of July.
That's great. I'll definitely get in there and check it out. Speaking of your website and Yang, I really appreciate your time with us today. So where can people learn more about your company and where can people learn more about the ETF?
Yeah. So the, it's, we have a website redirect by the search. It's www.vwietf.com. It's got the landing page and the information for our ETF, our fact sheets on there. The prospectus is on there. Everything that you would like is on there. We have our research and insights linked. And our main corporate page is archindices.com. And that's where you're going to find our tools. You can also reach out to us. there's a contact page on both of our websites to reach out. So I'm on LinkedIn, I'm on Twitter. So any way people want to engage, we're more than happy to do so.
Well, again, thank you so much for your time. I appreciate you being with me today. Thank you so much for having me. It's been a pleasure. Thank you.
Daily Market Intelligence
The Signal
Brad Roth's daily market brief — systematic signals, ETF positioning, and what the data is actually showing.
Subscribe Free →