Welcome to Inside Active, a podcast about active managers that goes beyond sound bites and headlines and looks deeper into their processes, challenges and philosophies and security selection. I'm David Cohne, I lead mutual fund and active research at Bloomberg Intelligence. Today my co host is Christopher kine, Us, quantitative strategist at Bloomberg Intelligence. Chris, thank you for joining me today.

Welcome to Inside Active, a podcast about active managers that goes beyond sound bites and headlines and looks deeper into their processes, challenges and philosophies and security selection. I'm David Cohne, I lead mutual fund and active research at Bloomberg Intelligence. Today my co host is Christopher kine, Us, quantitative strategist at Bloomberg Intelligence. Chris, thank you for joining me today.

Thank you so much for having me. David.

Thank you so much for having me. David.

So, you wrote a recent note about adding profitability to value stocks or the profitability factor. Since our conversation today will be focused on quality, I wonder if you could give us a sense of how adding that profitability factor to a standalone value strategy performs.

So, you wrote a recent note about adding profitability to value stocks or the profitability factor. Since our conversation today will be focused on quality, I wonder if you could give us a sense of how adding that profitability factor to a standalone value strategy performs.

Yeah. I'm a big fan of adding profitability to value. I mean just in empirically, you know, it seems to work better and it's very logical. So you know, just as a as a review, you know, value was the only factor that underperformed in twenty twenty four by our measurement. If you look at the long short factor, it was the only one that's down on the year, And if you only look at the long only legs, it was the only one that underperform the market. So it so just Q one value. Just cheap stocks returned about ten percent last year in the Russell one thousand as compared to about thirteen or so percent return for the equal weighted a Russell one thousand. If you added some profitability to that, that increased the returns to about fifteen to sixteen percent. So you know it, I'll perform a market if you just had that profitability component. To me, you know, it's very logical because you know, sometimes value stocks are valued for a reason. You know they have, you know, not good prospects and little chance for a turnaround. If you add a profitability filter to your value factor, you kind of filter out those value traps or you know, cheap companies that are cheap for a good reason. And that seems to work good both in the short term and the long term.

Yeah. I'm a big fan of adding profitability to value. I mean just in empirically, you know, it seems to work better and it's very logical. So you know, just as a as a review, you know, value was the only factor that underperformed in twenty twenty four by our measurement. If you look at the long short factor, it was the only one that's down on the year, And if you only look at the long only legs, it was the only one that underperform the market. So it so just Q one value. Just cheap stocks returned about ten percent last year in the Russell one thousand as compared to about thirteen or so percent return for the equal weighted a Russell one thousand. If you added some profitability to that, that increased the returns to about fifteen to sixteen percent. So you know it, I'll perform a market if you just had that profitability component. To me, you know, it's very logical because you know, sometimes value stocks are valued for a reason. You know they have, you know, not good prospects and little chance for a turnaround. If you add a profitability filter to your value factor, you kind of filter out those value traps or you know, cheap companies that are cheap for a good reason. And that seems to work good both in the short term and the long term.

That makes sense. So I think we'd like to introduce someone who's actually no stranger to quality, John Davey. John is founder, CEO, and CIO of Astoria Portfolio Advisors. John, thank you so much for joining us today.

That makes sense. So I think we'd like to introduce someone who's actually no stranger to quality, John Davey. John is founder, CEO, and CIO of Astoria Portfolio Advisors. John, thank you so much for joining us today.

Great great to be here, guys, thank you for having me on.

Great great to be here, guys, thank you for having me on.

So before we get into talking about your investment process, Can you tell us more about your start in the investment industry and what led you to start Astoria.

So before we get into talking about your investment process, Can you tell us more about your start in the investment industry and what led you to start Astoria.

Sure. So I began as a quantitative derivative analyst and Merrill Lynch's derivative research group in the late nineties. So it was an intern you know, basically spent my first ten years kind of doing index level research. Quant research, always at the index level, so that included like index futures and next options ETFs. You know, got popular in the late nineties and so ets were underneath our research group. So then did that for ten years, and then I became head of Morgan Stanley's ETF content for institutional clients. And you know, I had always liked macro research, asset allocation, and you know, always wanted to join the buy side and be an entrepreneur. So I felt, you know, here is a way to kind of do both kill two birds one stone, kind of joined the buyside and also to you know, be an entrepreneur.

Sure. So I began as a quantitative derivative analyst and Merrill Lynch's derivative research group in the late nineties. So it was an intern you know, basically spent my first ten years kind of doing index level research. Quant research, always at the index level, so that included like index futures and next options ETFs. You know, got popular in the late nineties and so ets were underneath our research group. So then did that for ten years, and then I became head of Morgan Stanley's ETF content for institutional clients. And you know, I had always liked macro research, asset allocation, and you know, always wanted to join the buy side and be an entrepreneur. So I felt, you know, here is a way to kind of do both kill two birds one stone, kind of joined the buyside and also to you know, be an entrepreneur.

So let's talk a little more about Astoria. How would you describe the Astoria investment philosophy.

So let's talk a little more about Astoria. How would you describe the Astoria investment philosophy.

Yeah, so we I would say it's it's a combination of there's kind of two verticals, like two business lines. One is like quantitative research, and the expression could be like an SMA, a quant SMA could be an ETF. And then the other business line is kind of multi asset ETF investing kind of asset allocation. So really I would say it's it's combining active and passive. That's kind of what we do, you know, at our firm. I would say our ETFs are systematically active. It's very rules based. Implementation is active like when we actually rebalance and how we trade, and I can spend a lot of time talking about that, but it's very kind of rules based, quantitative in nature. Our asset allocation is you know, I would say the idea there is that we want to use the business cycle, use earning valuations, use sentiment in order to kind of dictate whether or not we want to be ovoid on the way in NASA class, and then we use a tiny bit of liquid olts in order to kind of hedge our left tail risk. Once you hedge your left tail risk, which I know we're in a bull market, but we've had many crisises the last you know, seven eight years, so that ability to kind of of hedge left tail risk, I think is quite important and you know, just use like a little bit of liquid alts in order to kind of hedge that. So that's kind of like our two business lines. One is quant sma's ETFs, the other is multi ASCID ETF model portfolios.

Yeah, so we I would say it's it's a combination of there's kind of two verticals, like two business lines. One is like quantitative research, and the expression could be like an SMA, a quant SMA could be an ETF. And then the other business line is kind of multi asset ETF investing kind of asset allocation. So really I would say it's it's combining active and passive. That's kind of what we do, you know, at our firm. I would say our ETFs are systematically active. It's very rules based. Implementation is active like when we actually rebalance and how we trade, and I can spend a lot of time talking about that, but it's very kind of rules based, quantitative in nature. Our asset allocation is you know, I would say the idea there is that we want to use the business cycle, use earning valuations, use sentiment in order to kind of dictate whether or not we want to be ovoid on the way in NASA class, and then we use a tiny bit of liquid olts in order to kind of hedge our left tail risk. Once you hedge your left tail risk, which I know we're in a bull market, but we've had many crisises the last you know, seven eight years, so that ability to kind of of hedge left tail risk, I think is quite important and you know, just use like a little bit of liquid alts in order to kind of hedge that. So that's kind of like our two business lines. One is quant sma's ETFs, the other is multi ASCID ETF model portfolios.

Great. So if we want to, you know, focus in on the ETF specifically OROE the astoria US equal Weight Quality Kings ETF, is there a process you follow to narrow down a universe and then select the stocks for the portfolio.

Great. So if we want to, you know, focus in on the ETF specifically OROE the astoria US equal Weight Quality Kings ETF, is there a process you follow to narrow down a universe and then select the stocks for the portfolio.

Absolutely, so you know the idea there is that, you know, and for background, you know, we all worried about the concentration risk and the ND the CES. You know, ten stocks make up forty percent of the S and P five hundred and we launched are we back in August first of twenty twenty three, So and you know it takes like, you know, three six months to let's say, launch an ETF. So we were worried about concentration risk for a while, you know, and we looked at the available options out there and we just weren't quite comfortable. And I can spend some time talking about what makes our equo weight approach different, but essentially it's like a funnel, right, we start with like ten thousand stocks, we filter out, set out, we picked you know, large liquid you know, minimum free float, minimum market cap weights. Then we come up with like eight hundred investible stocks. And the idea with those eight hundred stocks is if we want to pick the hundred of you know, let's say the best in kind of that eight hundred, And what we do is, you know, we look at five different factors, so things like quality, valuation, growth, momentum, and dividend. And we want to so we'll weight our etf So fifty percent of the quant code is allocated to the quality factor, twenty percent to the dividend factor, twenty percent to the valuation factor, five percent to growth factor, and five percent to the momentum factor. And essentially these winds up being like high quality companies that are reasonably priced. You know, within each one of those five factors, there's like three, four or five fundamental ratios that express the factor of view. So in the case of like quality, like we're looking at companies with ro OE r O A, r O I C, dividing could be you know, the sustainability the dividend, the dispersion of the dividend. Valuations could be like, you know, things like PE price, the sales. The growth factor could be things like PEG ratios. The momentum factor could be like relative strength indicators and our premise. And you asked me before, David, like, what do we fundamentally believe in? You know, we definitely think that the more factors you can harvest in a portfolio, as long as you harvest in a low cost manner and as long as you stick with the factors like that historically has been able to give you higher risk adjuster returns and kind of put you higher up on the fishing frontier. So to summarize, you know, start with like ten thousand investable stocks, filter down to eight hundred of the ones that are the largest, most liquid, you know, good free float, and then pick a hundred of that eight hundred that have you know, strong roe, strong row, you know, strong quality metrics, that paid dividends, that are reasonably priced, that have a little bit of growth and momentum characteristics. It's great.

Absolutely, so you know the idea there is that, you know, and for background, you know, we all worried about the concentration risk and the ND the CES. You know, ten stocks make up forty percent of the S and P five hundred and we launched are we back in August first of twenty twenty three, So and you know it takes like, you know, three six months to let's say, launch an ETF. So we were worried about concentration risk for a while, you know, and we looked at the available options out there and we just weren't quite comfortable. And I can spend some time talking about what makes our equo weight approach different, but essentially it's like a funnel, right, we start with like ten thousand stocks, we filter out, set out, we picked you know, large liquid you know, minimum free float, minimum market cap weights. Then we come up with like eight hundred investible stocks. And the idea with those eight hundred stocks is if we want to pick the hundred of you know, let's say the best in kind of that eight hundred, And what we do is, you know, we look at five different factors, so things like quality, valuation, growth, momentum, and dividend. And we want to so we'll weight our etf So fifty percent of the quant code is allocated to the quality factor, twenty percent to the dividend factor, twenty percent to the valuation factor, five percent to growth factor, and five percent to the momentum factor. And essentially these winds up being like high quality companies that are reasonably priced. You know, within each one of those five factors, there's like three, four or five fundamental ratios that express the factor of view. So in the case of like quality, like we're looking at companies with ro OE r O A, r O I C, dividing could be you know, the sustainability the dividend, the dispersion of the dividend. Valuations could be like, you know, things like PE price, the sales. The growth factor could be things like PEG ratios. The momentum factor could be like relative strength indicators and our premise. And you asked me before, David, like, what do we fundamentally believe in? You know, we definitely think that the more factors you can harvest in a portfolio, as long as you harvest in a low cost manner and as long as you stick with the factors like that historically has been able to give you higher risk adjuster returns and kind of put you higher up on the fishing frontier. So to summarize, you know, start with like ten thousand investable stocks, filter down to eight hundred of the ones that are the largest, most liquid, you know, good free float, and then pick a hundred of that eight hundred that have you know, strong roe, strong row, you know, strong quality metrics, that paid dividends, that are reasonably priced, that have a little bit of growth and momentum characteristics. It's great.

So let's focus in on quality for a second. What is your research shown about the long term value of quality stocks.

So let's focus in on quality for a second. What is your research shown about the long term value of quality stocks.

Well, I liked what Chris said before. I mean, you know, valuing in beta or valuant quality you know definitely have very very low negative you know, very low correlations, if not negative. Right. So if again, if you believe in this concept of like how do I get higher? But on the fish frontier, how do I mix factors? You know, you're picking a factor quality that you know has pretty robust long term characteristics, and especially if you pair that with like value or you know, you know small cap stocks that that looks very very attractive to us for us at the end of the day, Like you know, quality are companies that you know are robust, they pay dividends, they have good earnings, good ROI. You know ro O, I c roa Roe. And you know it's it's this concept of like it's persistent, pervasive, robust. It works across sectors, across countries, across economic cycles, and you know there is historical out performance.

Well, I liked what Chris said before. I mean, you know, valuing in beta or valuant quality you know definitely have very very low negative you know, very low correlations, if not negative. Right. So if again, if you believe in this concept of like how do I get higher? But on the fish frontier, how do I mix factors? You know, you're picking a factor quality that you know has pretty robust long term characteristics, and especially if you pair that with like value or you know, you know small cap stocks that that looks very very attractive to us for us at the end of the day, Like you know, quality are companies that you know are robust, they pay dividends, they have good earnings, good ROI. You know ro O, I c roa Roe. And you know it's it's this concept of like it's persistent, pervasive, robust. It works across sectors, across countries, across economic cycles, and you know there is historical out performance.

Right.

Right.

So if you do some farm of French you know analytics, you'll see that high quality stocks have beaten low quality stocks and being the market you know, over the last you know, half a century, if not longer. I think their data set goes back to like nineteen sixty three. So that's what we're looking for. I mean, these could be you know companies that Microsoft, Google, you know Facebook, I mean a lot of them tend to be growth now, but you know it's brand hold names essentially. So that's that's how we think about it, you.

So if you do some farm of French you know analytics, you'll see that high quality stocks have beaten low quality stocks and being the market you know, over the last you know, half a century, if not longer. I think their data set goes back to like nineteen sixty three. So that's what we're looking for. I mean, these could be you know companies that Microsoft, Google, you know Facebook, I mean a lot of them tend to be growth now, but you know it's brand hold names essentially. So that's that's how we think about it, you.

Know, John, I I talked to a lot of clients in my role, and you know, when it comes to factor investing, you know, I feel like the standard factors, and you know, most people have a very good definition.

Know, John, I I talked to a lot of clients in my role, and you know, when it comes to factor investing, you know, I feel like the standard factors, and you know, most people have a very good definition.

While it might vary somewhat of these factors, right, things like value, things like momentum, things like low risk. Right, you might have a different look back for your for your momentum factor per se, but we all understand what momentum is. Quality seems to be the one where people have different differing definitions. It typically is a profitability metric. And then I've seen other things and you just mentioned some of them. But like when you think about a quality company, obviously you use profitability metrics like r O E r O. I see other than other factors saying within the quality bucket, like what other thing like do you look at like things like stability of earnings or cruels or anything like that when you're when you're kind of defining your quality factor.

While it might vary somewhat of these factors, right, things like value, things like momentum, things like low risk. Right, you might have a different look back for your for your momentum factor per se, but we all understand what momentum is. Quality seems to be the one where people have different differing definitions. It typically is a profitability metric. And then I've seen other things and you just mentioned some of them. But like when you think about a quality company, obviously you use profitability metrics like r O E r O. I see other than other factors saying within the quality bucket, like what other thing like do you look at like things like stability of earnings or cruels or anything like that when you're when you're kind of defining your quality factor.

Yeah, we do. I mean there's you know, there's about five that go within the quality bucket. But you know, as I try to emphasize, like our ro O E Tiff, not just high quality stocks, like we're picking high quality stocks that have you know, attractive valuations, that have good dividend pain ability and that all over and then have a little bit of growth the momentum too. But yeah, I mean in the end though, within the quality bucket it is skew towards ro O E, r O I, C ro O A. But I think the point that I'm trying to articulate and maybe I am, you know, I need to do better job. But you know, if you have a good return on equity of a good return on asset, you know you are doing other things well in the company, Like you're issuing dividends, you have good free cash flow, you don't use a lot of debt to equity. Your leverage ratios are you know, well or well allocated. Let's say. So it does at times, Chris like feel a little bit fuzzy because I think, like values just okay, you're a stock that has a broken story and you need to have something to come corrected. The sis factor. You know, small cap stock you're just you know, very small. You know, momentum is just you know, relative strength, you know, very technical factor. So quality kind of encapsulates a few other metrics beyond just you know, the return on equity, return on assets. You know, some index providers and some metail fishers will say, you know, debt to equity is a big component of it. Within you know, the quality definition. For us, it's not. We tend to use more kind of traditional metrics like r O A ro OE and r O C.

Yeah, we do. I mean there's you know, there's about five that go within the quality bucket. But you know, as I try to emphasize, like our ro O E Tiff, not just high quality stocks, like we're picking high quality stocks that have you know, attractive valuations, that have good dividend pain ability and that all over and then have a little bit of growth the momentum too. But yeah, I mean in the end though, within the quality bucket it is skew towards ro O E, r O I, C ro O A. But I think the point that I'm trying to articulate and maybe I am, you know, I need to do better job. But you know, if you have a good return on equity of a good return on asset, you know you are doing other things well in the company, Like you're issuing dividends, you have good free cash flow, you don't use a lot of debt to equity. Your leverage ratios are you know, well or well allocated. Let's say. So it does at times, Chris like feel a little bit fuzzy because I think, like values just okay, you're a stock that has a broken story and you need to have something to come corrected. The sis factor. You know, small cap stock you're just you know, very small. You know, momentum is just you know, relative strength, you know, very technical factor. So quality kind of encapsulates a few other metrics beyond just you know, the return on equity, return on assets. You know, some index providers and some metail fishers will say, you know, debt to equity is a big component of it. Within you know, the quality definition. For us, it's not. We tend to use more kind of traditional metrics like r O A ro OE and r O C.

Yeah, I totally agree with that. I mean, just based on my research. You know, like you said, it's quality is sometimes a fuzzy definition. But it seems very clear to me that profitability is the most important part of quality. And when you when you look at back tests of all these different things or crurals or stability of earnings or whatever, you know, the r O E, r O I C r O A back tests just perform much better. And I and you know, I think profitability is really the backbone equality in many respects. So another question I give from clients all the time, kind of leading you to water here. But you know, some some clients that might not be a sophisticated in fact or investing. They'll say things to me like, well, isn't quality just growth? Why don't you just use growth? Why don't why do you use quality in addition to growth or quality, you know, instead of growth? You know, what would you say that? Like, would you say, and you know, would you say, quality is like growth? How would you differentiate it? What would you say to that question?

Yeah, I totally agree with that. I mean, just based on my research. You know, like you said, it's quality is sometimes a fuzzy definition. But it seems very clear to me that profitability is the most important part of quality. And when you when you look at back tests of all these different things or crurals or stability of earnings or whatever, you know, the r O E, r O I C r O A back tests just perform much better. And I and you know, I think profitability is really the backbone equality in many respects. So another question I give from clients all the time, kind of leading you to water here. But you know, some some clients that might not be a sophisticated in fact or investing. They'll say things to me like, well, isn't quality just growth? Why don't you just use growth? Why don't why do you use quality in addition to growth or quality, you know, instead of growth? You know, what would you say that? Like, would you say, and you know, would you say, quality is like growth? How would you differentiate it? What would you say to that question?

So they're different. We happen to like combining both of them. We have an ETF G Triple Q that's kind of quality growth stocks. It's like fifty percent, you know, it's like half quality half growth. Let's say I would say, right now, we're in this kind of unusual period, Chris, where a lot of growth companies tend to also be in the quality bucket. It wasn't O is the case, you know, but because tech stocks have produced such strong returns and they have good you know, earnings revisions, they have good growth estimates, good PEG ratios, they also happen to have like strong ROE strong ro I C strong ro A, So they're in both buckets. But like you know, we use like these zis. We have like a white paper we wrote for our ro O E TF and we show them in the paper, like, Okay, what's historically the sharp ratio the standard deviation for quality momentum involve those size growth market divine il VALU and you see quality as the highest sharp ratio and it's got a pretty low standing deviation. So the standardiation and this has gone back you know, thirty years in MSA and the SEAS is about fourteen and a half. The growth factor has about seventeen and a half stand deviation, so much like almost like three standard deviation points higher. And they both have similar kegers. So growth stocks just happen to have a lower sharp ratio. But this is like thirty years worth of data, right, But I would say technically the answer is like they are different. You know, one has traditionally like strong roa ROI. You know that's equality, and the growth have you know, more like higher peg ratios earnings momentum. It's just the only recently in the last cycle where I would say there's a lot of like mix. You know, stocks are in both quality ETFs and the growth ETFs.

So they're different. We happen to like combining both of them. We have an ETF G Triple Q that's kind of quality growth stocks. It's like fifty percent, you know, it's like half quality half growth. Let's say I would say, right now, we're in this kind of unusual period, Chris, where a lot of growth companies tend to also be in the quality bucket. It wasn't O is the case, you know, but because tech stocks have produced such strong returns and they have good you know, earnings revisions, they have good growth estimates, good PEG ratios, they also happen to have like strong ROE strong ro I C strong ro A, So they're in both buckets. But like you know, we use like these zis. We have like a white paper we wrote for our ro O E TF and we show them in the paper, like, Okay, what's historically the sharp ratio the standard deviation for quality momentum involve those size growth market divine il VALU and you see quality as the highest sharp ratio and it's got a pretty low standing deviation. So the standardiation and this has gone back you know, thirty years in MSA and the SEAS is about fourteen and a half. The growth factor has about seventeen and a half stand deviation, so much like almost like three standard deviation points higher. And they both have similar kegers. So growth stocks just happen to have a lower sharp ratio. But this is like thirty years worth of data, right, But I would say technically the answer is like they are different. You know, one has traditionally like strong roa ROI. You know that's equality, and the growth have you know, more like higher peg ratios earnings momentum. It's just the only recently in the last cycle where I would say there's a lot of like mix. You know, stocks are in both quality ETFs and the growth ETFs.

Yeah, I found that same thing, and you know I've also found that you know, quality is much better, sharp ratio is much better risk adjuster returns, and like you know, I've seen that quality slaze profitability. It's definitely not the same as something like low risk, but it does overlap with like low risk, low beta lovall, you know, you typically get similar stocks in there, and that also has very strong risk adjuster returns. So the next question I wanted to ask you was something that's very interesting and something that I again talk to clients all the time about, and that is weighting your stocks right like right now. Just because of recency bias, it seems like any non market cap weight you know, it's it's lagging. You know, customers come to me and say, why would you eagually wait the stocks? Why would you have any other waiting scheme other than market cap waiting, because market cap waiting has worked so well very recently, and you know, I know that when you go back far in time, that's not the case. So I would love to know your thought process behind why you weight your stocks how you do, and maybe you know, you could dub to into maybe some you know, concerns you could potentially have with the market cap weighted indices how they're currently constructed.

Yeah, I found that same thing, and you know I've also found that you know, quality is much better, sharp ratio is much better risk adjuster returns, and like you know, I've seen that quality slaze profitability. It's definitely not the same as something like low risk, but it does overlap with like low risk, low beta lovall, you know, you typically get similar stocks in there, and that also has very strong risk adjuster returns. So the next question I wanted to ask you was something that's very interesting and something that I again talk to clients all the time about, and that is weighting your stocks right like right now. Just because of recency bias, it seems like any non market cap weight you know, it's it's lagging. You know, customers come to me and say, why would you eagually wait the stocks? Why would you have any other waiting scheme other than market cap waiting, because market cap waiting has worked so well very recently, and you know, I know that when you go back far in time, that's not the case. So I would love to know your thought process behind why you weight your stocks how you do, and maybe you know, you could dub to into maybe some you know, concerns you could potentially have with the market cap weighted indices how they're currently constructed.

I saw my career in two thousand and it was basically the Internet bubble erupted, and that was kind of like the the you know, the impetus for like Wisden Try got popular, Rob Barnett got popular, you know, AQR launch, And it was because Marke cap weight indities were flawed because you know, they had a lot of these tech companies. Some of them were profitable, some of them were not profitable, and really smart bait investment, I think became popular because the tech bubble and you know, unwinding in two thousand and one, two thousand and two, I feel like it's the same movie again, although the difference is now is that a lot of these companies are actually pretty profitable. It's just that they're just gargantuan size. I would say we whenever I've done quantitative like stock selection, you know here at Astoria or my prior companies, like we always generally equally weighted, and it's like a risk mitigation tool. You know, the more way you give one stock, the more you know risk you take in that one stock. You know, obviously now that there's a big passive bubble, a ETF bubble, and money keeps pouring into like spy, vu IVV and it's like this massive self fulfilling prophecy because you know, the cost sore low. It's you know, two basis points, three basis points. Whatever it is, it works, people buy it, more money comes in, they buy the same stocks. So I would say now more than ever. You know, it's important to tilt away. And we have like these decision trees, like when we run our multi asset ETF strategies, like what do you want to tilt the way towards? So I'll go that real quick. So basically, okay, like you tilted away from our cap. You can choose eco weight. That's one you know decision tree. But you have to be careful with equal weight because how are you equally waiting it? And we could spend like five minutes talking about that. Do you want to tell towards value? Okay? Value is a factor. That's very tough. It's very fickle. It works you know every ten years, maybe two three of the ten years work. So that's tough. Small caps, that's tough. You need a certain credit cycle, interest rate cycle for small caps to work. Then you have international. So the point is like, if you have concerns about market weighted ind ses, people tend to lean on equal weight. You have to be careful with equal weight. I think eqal weight you know now has been tough, but like over like twenty thirty years, Like you see the equal weight tensaple form mark cap it works better out of like a recession, so when you cut it up, you'll see that. Like when when you're like in this like economic slowdown, people buy more market cap weight, which I think is kind of this current period that we're in now. I sometimes hear people say, wow, small cap EQO weight's no longer going to work because companies stay in private. But I just think we're in this like really unusual bubble of passive indexation ETF flows and mark cap is just bid because just money keeps plowing into those products.

I saw my career in two thousand and it was basically the Internet bubble erupted, and that was kind of like the the you know, the impetus for like Wisden Try got popular, Rob Barnett got popular, you know, AQR launch, And it was because Marke cap weight indities were flawed because you know, they had a lot of these tech companies. Some of them were profitable, some of them were not profitable, and really smart bait investment, I think became popular because the tech bubble and you know, unwinding in two thousand and one, two thousand and two, I feel like it's the same movie again, although the difference is now is that a lot of these companies are actually pretty profitable. It's just that they're just gargantuan size. I would say we whenever I've done quantitative like stock selection, you know here at Astoria or my prior companies, like we always generally equally weighted, and it's like a risk mitigation tool. You know, the more way you give one stock, the more you know risk you take in that one stock. You know, obviously now that there's a big passive bubble, a ETF bubble, and money keeps pouring into like spy, vu IVV and it's like this massive self fulfilling prophecy because you know, the cost sore low. It's you know, two basis points, three basis points. Whatever it is, it works, people buy it, more money comes in, they buy the same stocks. So I would say now more than ever. You know, it's important to tilt away. And we have like these decision trees, like when we run our multi asset ETF strategies, like what do you want to tilt the way towards? So I'll go that real quick. So basically, okay, like you tilted away from our cap. You can choose eco weight. That's one you know decision tree. But you have to be careful with equal weight because how are you equally waiting it? And we could spend like five minutes talking about that. Do you want to tell towards value? Okay? Value is a factor. That's very tough. It's very fickle. It works you know every ten years, maybe two three of the ten years work. So that's tough. Small caps, that's tough. You need a certain credit cycle, interest rate cycle for small caps to work. Then you have international. So the point is like, if you have concerns about market weighted ind ses, people tend to lean on equal weight. You have to be careful with equal weight. I think eqal weight you know now has been tough, but like over like twenty thirty years, Like you see the equal weight tensaple form mark cap it works better out of like a recession, so when you cut it up, you'll see that. Like when when you're like in this like economic slowdown, people buy more market cap weight, which I think is kind of this current period that we're in now. I sometimes hear people say, wow, small cap EQO weight's no longer going to work because companies stay in private. But I just think we're in this like really unusual bubble of passive indexation ETF flows and mark cap is just bid because just money keeps plowing into those products.

Yep, yep. I found the exact same thing. So what about like you know, and I typically stick to equal weight as well. I feel like it gets you like ninety five percent of the way there. But like sometimes customers ask me, well, what about a more complicated scheme, Like what about some kind of inverse polatility waiting like Ray Dalio style, or like risk parity or maybe even more like me invariance optimization type waiting scheme, Like do you find any value in those overly complicated ones or do you think that's kind of adding more degrees of freedom to a model which is you know, generally bad.

Yep, yep. I found the exact same thing. So what about like you know, and I typically stick to equal weight as well. I feel like it gets you like ninety five percent of the way there. But like sometimes customers ask me, well, what about a more complicated scheme, Like what about some kind of inverse polatility waiting like Ray Dalio style, or like risk parity or maybe even more like me invariance optimization type waiting scheme, Like do you find any value in those overly complicated ones or do you think that's kind of adding more degrees of freedom to a model which is you know, generally bad.

I mean to be honest, Like it gets such a bad name, you know, smart beta. But this idea of like weighting based on like quant metrics, like the higher the quant rank in, the more way you give, Like we have some strategies where we do that. That to me makes sense and as like easier to explain if you deal with institutional investors, totally get it, you know, mean variance optimization inverse volatility, Like I think you can do that and get away with it. But in the financial advisor world that I live in, our world, like equal weight is enough tracking error versus the benchmark that and it's easy to explain. I think you just have to be careful with equally weight and like when you equally wait, you know, let's say the Russell one thousand index now you're talking about each stock is like thirteen bip weight. Let's say twelve bip weight. That's tough. If you equal wait s and P five hundred each stock is twenty BIPs. Wait, we just like the reason why we launched o OEI was because we weren't comfortable with SMP equal way to DTF because a, you know, there was only fifteen percent technology exposure, so we wanted to kind of optimize our ro oe E TF to match the S and P sector weight. So and then we pick only one hundred stocks. So now your marginal contribution and to risk and return is a lot higher when you once you equally weight one hundred stocks compared to like five hundred or thousand. And the other real extreme example is I put out a report called ten ETFs for twenty twenty five, and I put in there this xn tk ETF. It's an equal weighted tech ETF from Spiders and it's actually been the cues over the last three years. When we launched a report, and some people were surprised, but you know, it's got thirty five stocks. Each stock is three percent weight. And then it's not just like they pick stocks that have like strong sales and revenues, so it's a combination of like you know, smart beta, and then the mechanism to weight is like just a small concentrated portfolio thirty five stocks three percent weight, so just have to be careful with equal weight. Even though it's simplistic, it's easier to like talk to advisors about that. It's like, how are you doing it and how are you optimizing the sectors?

I mean to be honest, Like it gets such a bad name, you know, smart beta. But this idea of like weighting based on like quant metrics, like the higher the quant rank in, the more way you give, Like we have some strategies where we do that. That to me makes sense and as like easier to explain if you deal with institutional investors, totally get it, you know, mean variance optimization inverse volatility, Like I think you can do that and get away with it. But in the financial advisor world that I live in, our world, like equal weight is enough tracking error versus the benchmark that and it's easy to explain. I think you just have to be careful with equally weight and like when you equally wait, you know, let's say the Russell one thousand index now you're talking about each stock is like thirteen bip weight. Let's say twelve bip weight. That's tough. If you equal wait s and P five hundred each stock is twenty BIPs. Wait, we just like the reason why we launched o OEI was because we weren't comfortable with SMP equal way to DTF because a, you know, there was only fifteen percent technology exposure, so we wanted to kind of optimize our ro oe E TF to match the S and P sector weight. So and then we pick only one hundred stocks. So now your marginal contribution and to risk and return is a lot higher when you once you equally weight one hundred stocks compared to like five hundred or thousand. And the other real extreme example is I put out a report called ten ETFs for twenty twenty five, and I put in there this xn tk ETF. It's an equal weighted tech ETF from Spiders and it's actually been the cues over the last three years. When we launched a report, and some people were surprised, but you know, it's got thirty five stocks. Each stock is three percent weight. And then it's not just like they pick stocks that have like strong sales and revenues, so it's a combination of like you know, smart beta, and then the mechanism to weight is like just a small concentrated portfolio thirty five stocks three percent weight, so just have to be careful with equal weight. Even though it's simplistic, it's easier to like talk to advisors about that. It's like, how are you doing it and how are you optimizing the sectors?

So, actually you mentioned sectors, so I kind of want to touch on that a little bit. You know, we've talked about waiting stocks, but how do you determine overweighting or underweighting different sectors? Are you looking at more macro I guess component issues or so.

So, actually you mentioned sectors, so I kind of want to touch on that a little bit. You know, we've talked about waiting stocks, but how do you determine overweighting or underweighting different sectors? Are you looking at more macro I guess component issues or so.

In our quantitative stock selection SMAs and our ETFs, like we do track the benchmark sector weight, so we optimize against that. We want to have our stock selection provide the alpha and this concept that when we're designing it, you know, we're doing a multi factor you know, when we when we're just doing like multi asset investment in our ETF model portfolio business, you know, we will overweight on the weight and that's more based on macro top down research kind of like depending on you know, where we are in the earning cycle, the credit cycle, the inflation cycle. That's a much different game sort of say. But when we're talking about like just quantitative investing, we'd rather have our stock selection in our quont code, which you know, for background, we have five CFAs on staff. My colleague nixer Bone kind of oversees the quant side of it, and Ponkach Patel is kind of head of quant data science. I mean collectively these you know, the five CFAs, and we've got a lot of years experience building quant fulfillis like, we know that code is very powerful, but we just stay diligent. We stick to our process. We do have risk management risk management capabilities within that which I can talk about, but I think when it comes to like quantitative systematic investment, it's better to be like rules based, diligent and stick with the process.

In our quantitative stock selection SMAs and our ETFs, like we do track the benchmark sector weight, so we optimize against that. We want to have our stock selection provide the alpha and this concept that when we're designing it, you know, we're doing a multi factor you know, when we when we're just doing like multi asset investment in our ETF model portfolio business, you know, we will overweight on the weight and that's more based on macro top down research kind of like depending on you know, where we are in the earning cycle, the credit cycle, the inflation cycle. That's a much different game sort of say. But when we're talking about like just quantitative investing, we'd rather have our stock selection in our quont code, which you know, for background, we have five CFAs on staff. My colleague nixer Bone kind of oversees the quant side of it, and Ponkach Patel is kind of head of quant data science. I mean collectively these you know, the five CFAs, and we've got a lot of years experience building quant fulfillis like, we know that code is very powerful, but we just stay diligent. We stick to our process. We do have risk management risk management capabilities within that which I can talk about, but I think when it comes to like quantitative systematic investment, it's better to be like rules based, diligent and stick with the process.

You know, one of the things we do at BI that gets a lot of traction is monitoring the valuations of the factors. So we typically do is we'll look at meeting valuations of a long and short side of the factors, compare them to each other, right, and that would say like is a long and short factor cheap or expensive? Now I know that you said that your value is part of your overall process, but just thinking about quality in and of itself or really any other factor. To be honest, is there every scenario where like a certain factor gets too expensive historically, where you might decrease the weight to it. Like one of the things we've noticed recently is that profitability slash quality is very expensive. You can compare it to the market, you can compare it to low quality stocks. The ratio is very high historically when this stuff is very bit up. Would that have any influence on your the weighting of your factors?

You know, one of the things we do at BI that gets a lot of traction is monitoring the valuations of the factors. So we typically do is we'll look at meeting valuations of a long and short side of the factors, compare them to each other, right, and that would say like is a long and short factor cheap or expensive? Now I know that you said that your value is part of your overall process, but just thinking about quality in and of itself or really any other factor. To be honest, is there every scenario where like a certain factor gets too expensive historically, where you might decrease the weight to it. Like one of the things we've noticed recently is that profitability slash quality is very expensive. You can compare it to the market, you can compare it to low quality stocks. The ratio is very high historically when this stuff is very bit up. Would that have any influence on your the weighting of your factors?

It would, And that's where the power of being active and passive together and being kind of rules based, I think, is the benefit. So what I would say to you is like, Okay, right now, we've thought about this. In our quality to Froe, quality fact is only fifty percent, right, so again we've got twenty percent in like dividends, twenty percent in valuation. We would say if we got even more expensive, we would say, okay, take down the quality factor attribution in the ranking process down until it's say forty percent, because that you know, at the end of the day, like we do want to buy below intrinsic value. Way for it to achieve intrinsic value. Our ETF has like a seventeen p ratio versus like twenty two for SPY, So I feel like, you know, it's like quality at a reasonable price. But yeah, I think that's the flexibility that you can provide once you buy an active ETF or use an active manager. You know, if you just going out there buying like the quality TF qual, you know you're going to be stuck with high quality stocks, let's say, especially if it's MARKAP weighted, which that is you know for us, the minute you tilt away from mar Cap, you're going to get something that looks at a discount to the market. So you know, I would say the other main point to make here, Chris, is like each stock fights with one another in the quant ranking code, right, so if a stock got too expensive, you know eventually it's ranking would fall down. When we pick one hundred stocks in row, each stock fights one another, but we are picking top DEA SALSASL one, DEASTL two amongst you know, ten descals amongst twenty different valuation metrics. So if something gets very very expensive within the code, it'll de rank by itself.

It would, And that's where the power of being active and passive together and being kind of rules based, I think, is the benefit. So what I would say to you is like, Okay, right now, we've thought about this. In our quality to Froe, quality fact is only fifty percent, right, so again we've got twenty percent in like dividends, twenty percent in valuation. We would say if we got even more expensive, we would say, okay, take down the quality factor attribution in the ranking process down until it's say forty percent, because that you know, at the end of the day, like we do want to buy below intrinsic value. Way for it to achieve intrinsic value. Our ETF has like a seventeen p ratio versus like twenty two for SPY, So I feel like, you know, it's like quality at a reasonable price. But yeah, I think that's the flexibility that you can provide once you buy an active ETF or use an active manager. You know, if you just going out there buying like the quality TF qual, you know you're going to be stuck with high quality stocks, let's say, especially if it's MARKAP weighted, which that is you know for us, the minute you tilt away from mar Cap, you're going to get something that looks at a discount to the market. So you know, I would say the other main point to make here, Chris, is like each stock fights with one another in the quant ranking code, right, so if a stock got too expensive, you know eventually it's ranking would fall down. When we pick one hundred stocks in row, each stock fights one another, but we are picking top DEA SALSASL one, DEASTL two amongst you know, ten descals amongst twenty different valuation metrics. So if something gets very very expensive within the code, it'll de rank by itself.

Sort of saying I wanted to ask about selling positions. So I understand it's a quantitative portfolio, and you know, I know you're ranking the stocks. Is this done on a periodic basis or is there anything that would kind of trigger a cell, you know, in terms of rankings outside.

Sort of saying I wanted to ask about selling positions. So I understand it's a quantitative portfolio, and you know, I know you're ranking the stocks. Is this done on a periodic basis or is there anything that would kind of trigger a cell, you know, in terms of rankings outside.

Of that, So good question, I would say in anytime you equally wait, I think it's really important that you quarterly share change rebellance. Like we had this one stock sm CI that you know went up you know, eight hundred percent, So it's you know, each stock in our basket starts at one percent weight and then does drift up or down. So this then got up to like a three percent weight, and then you know, and then quarterly share a balance it you know, got trimmed down to like one percent. Our code is longer term in nature, so it's not going to pick up like accounting fraud. Let's say, so in the case of like app love and let's say when we launched our g triple QTF, it was like October first last year. Like in the case of like a Nazak one hundred, where like an annual rebellance, like you know, Nazek added app lovin in the December rebalance. I mean it was up like a thousand percent when they added it. You know, these are things that you can look for and be you know a little bit more strategic when you're active, let's say, but again, like it's not going to pick up like an accountant fraud story. So like if we had an instance where you know, a stock fell twenty percent, you know, overnight, you know, we would then do like the human bottoms up research and do like stock selection on that one stock to say, okay, you know, does this stock need to be kicked out? And I think that's a benefit of active. The extreme example I'll give you is like ten fifteen years ago, there was a stock in the FXI ETIF that like just stopped trading because of like a fraudulent thing, and it stayed in the FXIEYTF for like six months right before it's kicked out. So, like, you know, there's benefits of doing both right, being rules based, which is more passive, and then being you know, using like an human oversight and kind of be inactive. But outside of these very episodic periods, we generally don't have a lot of like intra quarter cell signals. Let's say, for ro O ETF. It is like an annual reconstitution process, but you know, every quarter we bring the shared change you know, to share way back to one percent and then we look at the quon code to see if anything materially deranked, and if it did materially de rank, like you know, a one to two decile became like an eight, you know, then we would like investigate further. So that is kind of like a risk management oversight.

Of that, So good question, I would say in anytime you equally wait, I think it's really important that you quarterly share change rebellance. Like we had this one stock sm CI that you know went up you know, eight hundred percent, So it's you know, each stock in our basket starts at one percent weight and then does drift up or down. So this then got up to like a three percent weight, and then you know, and then quarterly share a balance it you know, got trimmed down to like one percent. Our code is longer term in nature, so it's not going to pick up like accounting fraud. Let's say, so in the case of like app love and let's say when we launched our g triple QTF, it was like October first last year. Like in the case of like a Nazak one hundred, where like an annual rebellance, like you know, Nazek added app lovin in the December rebalance. I mean it was up like a thousand percent when they added it. You know, these are things that you can look for and be you know a little bit more strategic when you're active, let's say, but again, like it's not going to pick up like an accountant fraud story. So like if we had an instance where you know, a stock fell twenty percent, you know, overnight, you know, we would then do like the human bottoms up research and do like stock selection on that one stock to say, okay, you know, does this stock need to be kicked out? And I think that's a benefit of active. The extreme example I'll give you is like ten fifteen years ago, there was a stock in the FXI ETIF that like just stopped trading because of like a fraudulent thing, and it stayed in the FXIEYTF for like six months right before it's kicked out. So, like, you know, there's benefits of doing both right, being rules based, which is more passive, and then being you know, using like an human oversight and kind of be inactive. But outside of these very episodic periods, we generally don't have a lot of like intra quarter cell signals. Let's say, for ro O ETF. It is like an annual reconstitution process, but you know, every quarter we bring the shared change you know, to share way back to one percent and then we look at the quon code to see if anything materially deranked, and if it did materially de rank, like you know, a one to two decile became like an eight, you know, then we would like investigate further. So that is kind of like a risk management oversight.

Yeah, I'm just going to piggyback off that with the risk management. Like I know, I think about risk management is a couple of layers, right, It's like diversification is one layer and moving to equally wait does a lot for risk management in and of itself. And then you have individual position management like do you stop yourself out of positions which you kind of just spoke about. And then there's like I would call it like overall tactical risk management, Like, so, is there anything like that, like whether everybody a scenario where you think the whole market's going down or went below is two day moving average or something like that where you would like de risk the whole portfolio. Is there any kind of tactical overall signals like that or No?

Yeah, I'm just going to piggyback off that with the risk management. Like I know, I think about risk management is a couple of layers, right, It's like diversification is one layer and moving to equally wait does a lot for risk management in and of itself. And then you have individual position management like do you stop yourself out of positions which you kind of just spoke about. And then there's like I would call it like overall tactical risk management, Like, so, is there anything like that, like whether everybody a scenario where you think the whole market's going down or went below is two day moving average or something like that where you would like de risk the whole portfolio. Is there any kind of tactical overall signals like that or No?

Not in our SMAs and our ETF. So I mean in our other business multi asset you know, ETF investment, Like we do have the ability to kind of be much more flexible, which I can speak to. I would say, I just don't believe you can kind of time the market. So even if we were to de risk the portfolio, it would be like US trimming like expensive US docs to buy more value and or international and they're using like more liquid olds. But like we wouldn't go more than you know, let's say five ten percent versus like its benchmark, so we kind of have like guardrails in place. I think that the thing I would tell listener here is like, if you have a high conviction view, like you want to really de risk your portfolio, you know, you really have to have that kind of second line of thinking that like Howard Marx talks about, like what do you know that's not already in the price. And you know, the one instance where we really had this huge high conviction idea was like during the inflation scare in twenty twenty two, and that was just us you know, thinking about economics one O one, the M two velocity money, and we really did nail the inflation call, and we had some pretty significant performance versus benchmark, you know, past performance on it take a future result. But I think the last two years since that inflation scare, we've been mined in this like market cap you know, weighted bubble passive seven stocks driving most of the SMP. Not really sure what's going on with you know, the market from a policy standpoint, so you know, we don't make any of those bets now, we just haven't had that view. And then the only other point to really drive home is like, Okay, why we're active is like, and I do have high commction in this is that my first job was like derivative research like forecasts and ads and deletes to like the S and P and next to Russell and next And I mean I personally have a lot of friends that work with these hedgrophones at arbitrage in next flow. So I mean, let's say, you know, the cash Cow's ETF has thirty billion dollars. It's index space. You can basically know what stock's going to be added or deleted, and you know, you can like pre position yourself. I felt strongly that ANYTF that I was gonna put my name next to would not be passive, just because I wouldn't want the street to front run my order flow to like you know, arbitrage net asset value points off DTF, So what are what are I tf's like, we just don't rebalance on the day of you know, triple Witch, and let's say it's kind of like done you know, before or afterwards, and certainly could be intro day too.

Not in our SMAs and our ETF. So I mean in our other business multi asset you know, ETF investment, Like we do have the ability to kind of be much more flexible, which I can speak to. I would say, I just don't believe you can kind of time the market. So even if we were to de risk the portfolio, it would be like US trimming like expensive US docs to buy more value and or international and they're using like more liquid olds. But like we wouldn't go more than you know, let's say five ten percent versus like its benchmark, so we kind of have like guardrails in place. I think that the thing I would tell listener here is like, if you have a high conviction view, like you want to really de risk your portfolio, you know, you really have to have that kind of second line of thinking that like Howard Marx talks about, like what do you know that's not already in the price. And you know, the one instance where we really had this huge high conviction idea was like during the inflation scare in twenty twenty two, and that was just us you know, thinking about economics one O one, the M two velocity money, and we really did nail the inflation call, and we had some pretty significant performance versus benchmark, you know, past performance on it take a future result. But I think the last two years since that inflation scare, we've been mined in this like market cap you know, weighted bubble passive seven stocks driving most of the SMP. Not really sure what's going on with you know, the market from a policy standpoint, so you know, we don't make any of those bets now, we just haven't had that view. And then the only other point to really drive home is like, Okay, why we're active is like, and I do have high commction in this is that my first job was like derivative research like forecasts and ads and deletes to like the S and P and next to Russell and next And I mean I personally have a lot of friends that work with these hedgrophones at arbitrage in next flow. So I mean, let's say, you know, the cash Cow's ETF has thirty billion dollars. It's index space. You can basically know what stock's going to be added or deleted, and you know, you can like pre position yourself. I felt strongly that ANYTF that I was gonna put my name next to would not be passive, just because I wouldn't want the street to front run my order flow to like you know, arbitrage net asset value points off DTF, So what are what are I tf's like, we just don't rebalance on the day of you know, triple Witch, and let's say it's kind of like done you know, before or afterwards, and certainly could be intro day too.

So we have one more question. And actually it's funny you mentioned Howard Marks because my question was on books. I know his most important thing. Book is one of your favorites, might be your all time favorite. But it's curious what other, you know, favorite financial books.

So we have one more question. And actually it's funny you mentioned Howard Marks because my question was on books. I know his most important thing. Book is one of your favorites, might be your all time favorite. But it's curious what other, you know, favorite financial books.

You have, So I I definitely think, you know, Larry Sueddrow has written a lot of great books and and there's a very important concept that we definitely subscribe to, which is this idea that, like you know, when you combine in fact in a portfolio, you can get higher by in the first fal frontier. So and he's got research that goes back, you know, seventy years, fifty years. So basically he says, okay, in this one table, if you do a twenty five percent allocation towards Debta factor, the size factor, the value factor, and the momentum factor, the historical sharp ratios like point seventy four, and that's basically double any of the individual factor bets on either beta value size probably you know, Okay, So then he says, okay, instead of doing twenty five percent, do like twenty percent to each of those four factors. And then you add profitability as a fifth one, and you get a point nine to six sharp ratio. And then in the next iteration he's substitute quality for profitability, and now you got like a one point one sharp ratio. You know, when you combine you know, five factors, you had profitability, you sub out profitability for quality. You know, you're talking about almost triple the sharp ratio of any individual leg beta value size. And then you know, like if you he runs someone elsis on, like what's the underperformance odds over like a one year period, of three year period, five, ten, twenty year and the odds of you on the perform and are dramatically lower when you combine factors. And so his book, you know, like everything you want to know about fact investing. I maybe miss a title in the book, but that book is kind of seminal to us. You know, I'm a big fan of buffet. He just releases you know, newsletter this this weekend, and you know, kind of what he preaches, like buying quality businesses but just at a low price. I think that's really important to how we think about investing. So those are some of the top two or three that I would say resonate with me.

You have, So I I definitely think, you know, Larry Sueddrow has written a lot of great books and and there's a very important concept that we definitely subscribe to, which is this idea that, like you know, when you combine in fact in a portfolio, you can get higher by in the first fal frontier. So and he's got research that goes back, you know, seventy years, fifty years. So basically he says, okay, in this one table, if you do a twenty five percent allocation towards Debta factor, the size factor, the value factor, and the momentum factor, the historical sharp ratios like point seventy four, and that's basically double any of the individual factor bets on either beta value size probably you know, Okay, So then he says, okay, instead of doing twenty five percent, do like twenty percent to each of those four factors. And then you add profitability as a fifth one, and you get a point nine to six sharp ratio. And then in the next iteration he's substitute quality for profitability, and now you got like a one point one sharp ratio. You know, when you combine you know, five factors, you had profitability, you sub out profitability for quality. You know, you're talking about almost triple the sharp ratio of any individual leg beta value size. And then you know, like if you he runs someone elsis on, like what's the underperformance odds over like a one year period, of three year period, five, ten, twenty year and the odds of you on the perform and are dramatically lower when you combine factors. And so his book, you know, like everything you want to know about fact investing. I maybe miss a title in the book, but that book is kind of seminal to us. You know, I'm a big fan of buffet. He just releases you know, newsletter this this weekend, and you know, kind of what he preaches, like buying quality businesses but just at a low price. I think that's really important to how we think about investing. So those are some of the top two or three that I would say resonate with me.

Well, this is great, John, thank you again for joining us today.

Well, this is great, John, thank you again for joining us today.

Next, thanks guys and Chris, thank you for.

Next, thanks guys and Chris, thank you for.

Being my co host today. Thank you until our next episode. This is David Cohne with Inside Active.

Being my co host today. Thank you until our next episode. This is David Cohne with Inside Active.