WEBVTT - Mitch Zacks on Upward Earnings-Estimate Revisions 0:00:13.000 --> 0:00:16.200 Welcome to Inside Active, a podcast about active managers that 0:00:16.239 --> 0:00:19.279 goes beyond sound bites and headlines and looks deeper into 0:00:19.320 --> 0:00:24.640 their processes, challenges and philosophies and securitance selection. I'm David Cohne. 0:00:24.720 --> 0:00:28.400 I'd be Mutual fund and Active Research at Bloomberg Intelligence Today. 0:00:28.640 --> 0:00:31.600 My co host is Mike Casper, sector and small cap 0:00:31.640 --> 0:00:35.160 strategist at Bloomberg Intelligence. Mike, thank you for joining me today. 0:00:35.280 --> 0:00:35.920 Thank you, David. 0:00:36.479 --> 0:00:38.600 Well, you and Chris Kane put out a note last 0:00:38.680 --> 0:00:43.880 week talking about applying bi's proprietary b MVP multi factor 0:00:43.920 --> 0:00:47.879 methodology for the small cap Bloomberg two thousand. Can you 0:00:47.920 --> 0:00:51.000 give a brief overview of the methodology and how it's performed. 0:00:51.360 --> 0:00:52.960 Sure, So what we do is we start with the 0:00:53.000 --> 0:00:56.160 Bloomberg two thousand index. That's kind of roughly equivalent to 0:00:56.160 --> 0:00:58.720 the Russell two thousand. It's just mark a cap weighted though, 0:00:59.360 --> 0:01:01.800 so there are some slight differences on the edges. But 0:01:01.880 --> 0:01:03.720 what we do is we take that universe and we 0:01:03.720 --> 0:01:06.360 apply a profitability screen to it. It's awfully difficult to 0:01:06.600 --> 0:01:12.039 kind of make conclusions on fundamentals without having such a screen, 0:01:12.160 --> 0:01:14.560 given that about a third of stocks in the Russell 0:01:14.560 --> 0:01:17.760 two thousand or in small caps in general aren't profitable 0:01:18.040 --> 0:01:20.880 at the moment, So we take that profitability screen and 0:01:20.880 --> 0:01:23.120 that whittles down the universe a little bit further. I 0:01:23.200 --> 0:01:25.479 think as of September we were at about eleven hundred 0:01:25.520 --> 0:01:28.160 constituents left over from the two thousand. When we do that, 0:01:29.360 --> 0:01:32.559 and then we apply our momentum scores on a sector 0:01:32.560 --> 0:01:35.320 neutralized basis. All these factors are on a sector neutralized basis, 0:01:35.680 --> 0:01:38.800 but momentum the average of trailing six and twelve month 0:01:38.840 --> 0:01:41.760 total returns excluding the last two weeks. We apply our 0:01:41.800 --> 0:01:45.840 low volatility factor, our value factor, which four small caps, 0:01:45.840 --> 0:01:48.840 we just look at the trailing twelfth month sales to 0:01:48.880 --> 0:01:52.240 price ratio, and our profitability factor, which is the average 0:01:52.280 --> 0:01:56.000 of trailing twelve month ROE and ROIC, and then the 0:01:56.040 --> 0:02:00.360 top descal of those percentile ranks once we average those out, 0:02:01.160 --> 0:02:05.280 is the final b MVP small cap portfolio. Now a 0:02:05.280 --> 0:02:07.160 little bit of stats before I go into the back 0:02:07.160 --> 0:02:10.240 test result. That results in a little bit higher market 0:02:10.280 --> 0:02:14.520 cap weighting than the B two thousand. For example, so 0:02:14.560 --> 0:02:17.560 the Bloomberg MVP, the media market caps about two point 0:02:17.680 --> 0:02:23.320 eight billion. In that universe with the largest stock about 0:02:23.320 --> 0:02:26.040 eight and a half billion, small stock about two hundred 0:02:26.040 --> 0:02:28.000 and eighty seven million, So the market cap range of 0:02:28.000 --> 0:02:29.840 the B two thousand as a whole as much wider 0:02:30.120 --> 0:02:32.120 the same thing with the Russell two thousand. The media 0:02:32.160 --> 0:02:35.760 market caps about a billion. But onto results, the cumulat 0:02:35.800 --> 0:02:39.000 return for this thing is pretty solid. So back to 0:02:39.080 --> 0:02:42.160 nineteen ninety nine through September twenty twenty four, you're looking 0:02:42.160 --> 0:02:46.200 at percent significantly lower volatility than the Russell two. 0:02:46.040 --> 0:02:46.840 Thousand equal WEIGHTD. 0:02:47.320 --> 0:02:49.680 But what I really like to compa against is the 0:02:49.720 --> 0:02:53.360 only profitable bin in the B two thousand. That annualized 0:02:53.360 --> 0:02:55.400 return is about eleven point eight five percent, so we 0:02:55.440 --> 0:02:58.440 are generating some alpha over that bin using. 0:02:58.160 --> 0:03:01.560 This nice I think this is a great time to 0:03:01.639 --> 0:03:06.919 introduce our guest, Mitch Zach's. He's CEO and principal portfolio 0:03:07.040 --> 0:03:10.440 manager of Zach's Investment Management. Mitch, thank you for joining 0:03:10.520 --> 0:03:10.959 us today. 0:03:11.280 --> 0:03:13.000 David and Michael, it's a pleasure to be here. 0:03:13.080 --> 0:03:15.560 I'd like to start off, you know, just hearing more 0:03:15.600 --> 0:03:17.200 about your career background. 0:03:17.760 --> 0:03:20.760 Sure, I you know, went to school at Yale and 0:03:20.880 --> 0:03:25.520 I joined Lazard Freyer and Mergers and Acquisitions as an 0:03:25.560 --> 0:03:30.880 analyst I left the analyst program to join Zach's Investment Research. 0:03:31.840 --> 0:03:35.040 At the time, Zach's and we founded a division of 0:03:35.120 --> 0:03:38.840 Zach's Investment Research, which was a money management subsidiary, a 0:03:38.920 --> 0:03:43.160 wholly owned money management subsidiary. At the time I joined, 0:03:43.160 --> 0:03:47.600 we had roughly fifty million dollars in assets under management, 0:03:48.160 --> 0:03:52.240 and over the past twenty some odd years, you know, 0:03:52.960 --> 0:03:55.480 twenty odd plus years actually at this point in time, 0:03:56.160 --> 0:03:58.920 we've been able to grow the firm from around fifty 0:03:58.920 --> 0:04:01.920 million dollars in asset to a little bit over nineteen 0:04:01.920 --> 0:04:06.080 billion dollars in assets under management and model delivery and advisement. 0:04:06.920 --> 0:04:09.960 So you know, that's kind of my background. I've been 0:04:10.040 --> 0:04:16.000 working with quantitative equity portfolio strategies my entire career, and 0:04:16.080 --> 0:04:20.240 I've designed most of the asset management strategies that we 0:04:20.320 --> 0:04:25.160 implement at Zach's. Zach's Investment Management's a wholly owned subsidiary 0:04:25.760 --> 0:04:29.600 of Zach's Investment Research. Zach's Investment Research is the second 0:04:29.720 --> 0:04:33.480 largest provider of independent investment research in the country, and 0:04:33.560 --> 0:04:37.159 our contribution to finance was that we created the concept 0:04:37.480 --> 0:04:42.080 of the quarterly consensus earnings estimate before Za's came around. 0:04:42.160 --> 0:04:45.760 What people were doing was pretty much looking at how 0:04:45.839 --> 0:04:49.560 earnings evolved on a year over year basis, looking at 0:04:49.600 --> 0:04:52.760 how that earnings growth compared to sort of a trend 0:04:52.800 --> 0:04:56.760 line analysis based on historical earnings growth, and saying, well, 0:04:56.839 --> 0:05:00.200 earnings have been growing at you know, ten percent, in 0:05:00.240 --> 0:05:02.560 at twelve percent year over year earnings growth, it was 0:05:02.560 --> 0:05:05.440 a good quarter. What we started doing is we created 0:05:05.440 --> 0:05:09.560 this concept of the quarterly consensus earnings estimate, where we 0:05:09.600 --> 0:05:13.320 took to the cell side earnings estimates by all the 0:05:13.440 --> 0:05:18.040 analysts following these companies, and we created a consensus and 0:05:18.160 --> 0:05:21.200 then when a company would report earnings, it would be 0:05:21.240 --> 0:05:25.520 compared to what Wall Street expectations were. If the earnings 0:05:25.560 --> 0:05:28.919 were greater than Wall Street expectations, it would be deemed 0:05:28.960 --> 0:05:32.520 a earning surprise, and if they were less than Wall 0:05:32.520 --> 0:05:36.040 Street expectations, it would be a negative earning surprise. And 0:05:36.160 --> 0:05:39.240 we started to look at this data we created and 0:05:39.320 --> 0:05:42.440 started to say, well, how can we use this data 0:05:42.480 --> 0:05:46.880 to effectively manage money? And what we found to be 0:05:46.960 --> 0:05:50.200 the most important use of the data is not to 0:05:50.240 --> 0:05:54.359 look at the year over year earnings growth and instead 0:05:54.400 --> 0:05:57.800 to focus on how the earnings estimates are changing over 0:05:57.880 --> 0:06:00.560 time and what we found to be the case is 0:06:00.600 --> 0:06:06.120 that there's some degree, it's a reasonably strong degree given finance, 0:06:06.600 --> 0:06:11.440 you know, time series metrics of earning of serial correlation 0:06:12.120 --> 0:06:15.480 with earnings estimate revisions, and that became the basis of 0:06:15.520 --> 0:06:18.839 some of our proprietary models that we then started the 0:06:18.960 --> 0:06:20.200 asset management firm with. 0:06:21.480 --> 0:06:24.320 So you mentioned, you know, earnings revision. Can you, I 0:06:24.320 --> 0:06:27.520 guess speak to you know how that affects your investment philosophy? 0:06:27.600 --> 0:06:30.160 And you know, I'm thinking, actually, you know your investment 0:06:30.240 --> 0:06:33.000 process specifically for the ZACH small cap or fund. 0:06:33.320 --> 0:06:36.760 Sure, what we're What we found to be the case 0:06:37.240 --> 0:06:43.400 is that companies that have received upward earnings estimate revisions 0:06:43.400 --> 0:06:48.120 in the past are more likely to receive upward earnings 0:06:48.200 --> 0:06:52.440 estimate revisions in the future. And if you own a 0:06:52.680 --> 0:06:57.120 group of companies that have received upward earnings ESTEMA revisions 0:06:57.120 --> 0:07:00.760 in the past, you're roughly about sixty percent more likely 0:07:01.520 --> 0:07:04.760 to receive upward earning cestemer revisions in the immediate future. 0:07:05.200 --> 0:07:07.520 So the first thing I started to do is to 0:07:07.600 --> 0:07:09.880 try and say, well, what if we create a portfolio 0:07:10.280 --> 0:07:13.800 where we simply own all the companies that are receiving 0:07:13.880 --> 0:07:16.880 upward earning customer revisions, we hold them for a certain 0:07:16.880 --> 0:07:20.880 period of time, and then we rebalance the portfolio. And 0:07:21.000 --> 0:07:24.520 what happens is if you kind of apply that sort 0:07:24.600 --> 0:07:28.840 of very very structured approach, you tend to have a 0:07:28.960 --> 0:07:34.400 very high degree of turnover in the portfolio. Roughly sixty 0:07:35.360 --> 0:07:39.200 seventy percent of the companies that are receiving upward earning 0:07:39.200 --> 0:07:41.680 cestemer revisions at the beginning of the month are not 0:07:41.840 --> 0:07:45.360 receiving upward earning customary visions over the end of the month, 0:07:45.680 --> 0:07:47.680 So there tends to be a high degree of turnover. 0:07:48.120 --> 0:07:50.840 You also tend to get a very very high degree 0:07:50.840 --> 0:07:54.040 of sector concentration, so you not only see a high 0:07:54.040 --> 0:07:58.360 degree of turnover, you see a level of sector concentration 0:07:59.360 --> 0:08:04.000 that increases sort of the volatility and tracking error of 0:08:04.040 --> 0:08:07.840 the portfolio relative to the benchmark. So what we started 0:08:07.840 --> 0:08:10.440 to do is saying, well, how can we control the 0:08:10.480 --> 0:08:15.840 turnover and how can we control the sector exposure to 0:08:16.000 --> 0:08:21.120 generate a return that's more in keeping with the benchmark. 0:08:21.480 --> 0:08:25.160 And that's what I've been doing for many years, and 0:08:25.280 --> 0:08:29.320 in the ZAC small cap core process as a mutual 0:08:29.360 --> 0:08:34.520 fund CIX, which is a very small mutual fund which 0:08:34.559 --> 0:08:36.920 actually kind of helps us in terms of being able 0:08:36.960 --> 0:08:40.280 to move in and out of these positions. We're trying 0:08:40.320 --> 0:08:43.600 to control the turnover so that the turnover is around 0:08:43.600 --> 0:08:46.920 one hundred percent per year, and we're trying to control 0:08:46.960 --> 0:08:50.319 the tracking error as much as we can through optimization 0:08:51.240 --> 0:08:54.880 to try and keep the risk in line with our 0:08:54.960 --> 0:08:56.680 benchmark of the Russell two thousand. 0:08:56.760 --> 0:09:00.680 Basically, now I give my whole feel at the beginning 0:09:00.720 --> 0:09:03.720 of this about how profitable companies tend to outperform the 0:09:03.720 --> 0:09:07.920 egaloid to benchmark. It sounds like we're kind of arriving 0:09:07.920 --> 0:09:10.200 at similar things you with earning s revision mean with 0:09:10.200 --> 0:09:12.760 profitable stocks. When you look at earning servision, do you 0:09:12.800 --> 0:09:15.920 only select profitable companies or do you care if it's 0:09:15.960 --> 0:09:18.960 going from unprofitable to less unprofitable. 0:09:19.240 --> 0:09:23.079 What we're looking for are changes in the earnings estimate 0:09:23.200 --> 0:09:26.600 revision so that they're positive. So we're looking for sort 0:09:26.640 --> 0:09:30.920 of four factors, agreement to the extent to which multiple 0:09:30.960 --> 0:09:35.239 analysts are revising their earning estaments in the same direction, magnitude, 0:09:35.280 --> 0:09:39.400 the size of those estimate revisions upside, where the most 0:09:39.480 --> 0:09:43.080 accurate or recent earnings estaments are coming in relative to 0:09:43.120 --> 0:09:47.440 the consensus, and the earning surprise, the frequency and magnitude 0:09:47.760 --> 0:09:51.560 of earning surprises that have occurred in the process that 0:09:51.559 --> 0:09:55.720 we're implementing. We're using the Russell two thousand universe as 0:09:55.760 --> 0:10:00.120 the initial universe of selection, and we're adding to at 0:10:00.200 --> 0:10:03.720 any company that's in our current portfolio that may have 0:10:03.760 --> 0:10:06.000 been removed from the index at some point in time. 0:10:06.400 --> 0:10:09.440 So the union of those two sets is really our basis. 0:10:09.880 --> 0:10:12.920 What I will say is that companies that are getting 0:10:13.160 --> 0:10:17.480 negative earnings are a little bit less likely to get 0:10:17.520 --> 0:10:22.640 positive earnings estimate revisions because they're sometimes in some sort 0:10:22.679 --> 0:10:28.120 of growth phase where the management is spending money for 0:10:28.320 --> 0:10:33.520 some sort of potential you know, jackpot in the future, 0:10:33.559 --> 0:10:36.160 so to speak. So they might be a biotech company 0:10:36.200 --> 0:10:40.200 where the management has decided, hey, we're going to continue 0:10:40.200 --> 0:10:43.000 to spend money to engage in the drug development and 0:10:43.080 --> 0:10:44.920 our hope is that it gets approved at the end 0:10:45.000 --> 0:10:48.160 of the entire process. So generally, those companies that are 0:10:48.240 --> 0:10:50.960 kind of in the development phase that are based on 0:10:51.440 --> 0:10:55.120 massive capital expenditures to sort of capture a market will 0:10:55.640 --> 0:10:59.160 likely not get upward earning cestmer visions, And in some 0:10:59.200 --> 0:11:01.760 of those instances, it's possible they get downward esimate revisions, 0:11:02.040 --> 0:11:06.160 but they're getting closer to their sort of goals in 0:11:06.240 --> 0:11:09.679 terms of growth that the stock price will respond positively. 0:11:09.880 --> 0:11:13.520 So they are two separate ideas. One idea is, hey, 0:11:13.640 --> 0:11:17.079 can you look at companies that have positive earnings to 0:11:17.080 --> 0:11:21.440 do companies with positive earnings or positive expected earnings outperformed 0:11:21.440 --> 0:11:25.920 companies with negative earnings. And that has historically been the case, 0:11:26.280 --> 0:11:30.640 but that's been widely disseminated amongst managers. So I'm not 0:11:31.200 --> 0:11:34.559 positive that's going to be completely the case over time. 0:11:35.000 --> 0:11:37.840 I think it might be a little bit better instead 0:11:37.840 --> 0:11:40.560 of sort of throwing out you know, I guess you know, 0:11:40.640 --> 0:11:44.960 roughly thirty percent of the Russell two thousand to instead say, okay, 0:11:45.480 --> 0:11:48.080 are these companies? These companies are expected to lose money, 0:11:48.280 --> 0:11:53.080 but they expected to lose less money this quarter? Are 0:11:53.080 --> 0:11:56.360 they expected to lose less money over the last month 0:11:56.480 --> 0:12:00.560 because analysts are revising their earnings estimates upward. And what 0:12:00.640 --> 0:12:03.240 I've actually anecdotally found to be the case is companies 0:12:03.240 --> 0:12:08.320 that move from a nonprofitable sort of scenario where they're 0:12:08.320 --> 0:12:11.400 losing money to companies that are going to generate positive 0:12:11.679 --> 0:12:16.120 earnings tend to maybe not all the time, but have 0:12:16.160 --> 0:12:19.679 a potential for very very strong returns, and you don't 0:12:19.679 --> 0:12:21.920 want to necessarily remove those from the portfolio. 0:12:23.559 --> 0:12:27.439 That makes sense. You know, I'm familiar with Zach's investment research, 0:12:27.559 --> 0:12:30.480 and you know kind of you know Zach's rank and 0:12:30.559 --> 0:12:33.240 you know strong buy stocks. Does it kind of follow 0:12:33.400 --> 0:12:36.040 the portfolio kind of follow something similar where it only 0:12:36.040 --> 0:12:41.800 includes like strong buy or buy stocks according to those formulas. 0:12:42.040 --> 0:12:45.840 Yeah, in zsci X, which has been performing relatively well 0:12:46.200 --> 0:12:48.400 over the last you know, a couple of years. Here, 0:12:48.760 --> 0:12:52.360 what we're doing is we're combining an estimate revision model 0:12:52.960 --> 0:12:56.880 with a model that focuses on quality. We're using that 0:12:56.920 --> 0:12:59.520 sort of alpha model as an input to an optimization 0:12:59.640 --> 0:13:03.120 routine to generate a buy, a hold, and sell list 0:13:03.240 --> 0:13:07.680 that we then evaluate and pretty much implement. But there 0:13:07.800 --> 0:13:11.720 is a basis in the alpha model looking at companies 0:13:11.760 --> 0:13:15.720 that are likely to receive upward earnings estimate revisions in 0:13:15.720 --> 0:13:18.760 the future. But you have to remember that we again, 0:13:20.160 --> 0:13:24.920 the pure estimate revision universe has a high degree of turnover, 0:13:25.400 --> 0:13:27.360 and what we're trying to do is use earning sest 0:13:27.480 --> 0:13:31.079 revisions as an alpha source and then control the turnover 0:13:31.600 --> 0:13:35.080 and control the sector exposure. So the overall risk of 0:13:35.080 --> 0:13:39.280 the portfolio is in line with the Russell two thousand benchmark. 0:13:39.360 --> 0:13:41.640 So to answer the question, what we're doing is we're 0:13:41.760 --> 0:13:45.520 using earnings estimate revisions along with another model in a 0:13:45.640 --> 0:13:49.600 multi factor model, but we're using that as an input 0:13:49.720 --> 0:13:53.440 to optimization that then can identify companies. So it's a 0:13:53.480 --> 0:13:57.800 subset of that group that might be potential by candidates, 0:13:57.960 --> 0:14:00.840 and we might even go outside of that group if 0:14:00.880 --> 0:14:06.120 the optimization indicates that adding that position could conceivably reduce 0:14:06.200 --> 0:14:06.880 tracking error. 0:14:08.040 --> 0:14:11.960 Okay, so you know, if you're starting at this universe, 0:14:12.400 --> 0:14:14.200 you know, you know, I know the four factors of 0:14:14.320 --> 0:14:16.520 zach Ring. So that is, you know, something that's just 0:14:16.600 --> 0:14:20.200 kind of at the very beginning, is what you're looking at. 0:14:20.960 --> 0:14:23.760 Now, are there any sectors that you find particularly compelling 0:14:23.760 --> 0:14:26.720 at the moment or that are being flagged for overweights 0:14:26.800 --> 0:14:27.160 right now? 0:14:28.080 --> 0:14:31.640 What I've found to be the case over time is 0:14:31.680 --> 0:14:38.840 that sector risk control is less effective than looking at 0:14:39.080 --> 0:14:43.840 projected tracking error relative to a benchmark. So what happens 0:14:43.960 --> 0:14:48.640 is if you focus on sort of sector exposure, what 0:14:48.960 --> 0:14:52.520 tends to happen is there'll be companies that are classified correctly. 0:14:52.640 --> 0:14:55.600 They are companies that are misclassified based upon their sector. 0:14:56.000 --> 0:15:00.200 But there are companies that will be classified correctly based 0:15:00.280 --> 0:15:04.360 on their sector, but they will move like a company 0:15:04.400 --> 0:15:08.000 that is outside of that sector. So, for instance, right now, 0:15:08.040 --> 0:15:11.800 there are companies that are construction companies that are helping 0:15:12.000 --> 0:15:18.200 construct sort of cloud computing centers, helping with construct infrastructure 0:15:18.360 --> 0:15:23.040 on semiconductor equipment, manufacturing plants, and things of that sort, 0:15:23.560 --> 0:15:27.520 and these companies will be correctly classified as construction companies. 0:15:27.560 --> 0:15:31.240 They build things, they issue debt, they use the debt 0:15:31.320 --> 0:15:34.560 to buy to buy land to build things, and they 0:15:34.680 --> 0:15:38.360 sell the they sell their what their access or they 0:15:38.400 --> 0:15:41.680 sell their effective use to a company that needs to 0:15:41.760 --> 0:15:43.520 use that type of building or that type of source. 0:15:43.920 --> 0:15:48.000 But in terms of their contribution to risk, they behave 0:15:48.240 --> 0:15:52.960 much more like semiconductor companies than construction companies. So I 0:15:53.240 --> 0:15:57.200 tend not to try and think from a top down perspective. 0:15:57.720 --> 0:16:01.040 I tend to want to think from a bottom up perspective. 0:16:01.520 --> 0:16:04.280 And what we're looking for when we do do some 0:16:04.440 --> 0:16:08.600 qualitative oversight in some of our other strategies is we're 0:16:08.760 --> 0:16:12.280 looking for lack of crowding in the market. So we 0:16:12.480 --> 0:16:16.520 don't want to look for a individual company where there's 0:16:16.600 --> 0:16:22.360 a tremendous amount of information or focus from either the 0:16:22.440 --> 0:16:26.880 media or from investors on what the outcome of or 0:16:26.880 --> 0:16:30.240 how that company is evolving over time. But in terms 0:16:30.280 --> 0:16:32.280 of like if you had to pin me down in 0:16:32.360 --> 0:16:36.360 terms of sectors, I think industrials are looking relatively interesting 0:16:36.400 --> 0:16:37.320 at this point in time. 0:16:39.320 --> 0:16:42.720 Kind of want to move to a slightly different topic, actually, 0:16:42.760 --> 0:16:45.880 market caps something that I've kind of been looking at recently, 0:16:46.560 --> 0:16:49.760 where if you think of the traditional dollar value of 0:16:49.800 --> 0:16:52.920 what one considers a small cap, you know, up to 0:16:52.960 --> 0:16:55.640 about two billion. I've been noticing a lot of mutual 0:16:55.680 --> 0:17:00.120 funds holding much larger stocks. I know, for this particular one, 0:17:00.200 --> 0:17:03.280 I think about half the portfolios in midcaps, you know, 0:17:03.320 --> 0:17:06.040 which is, you know, much less than what I've seen. 0:17:06.119 --> 0:17:08.520 Some small cap funds are even holding large caps. So 0:17:08.560 --> 0:17:10.800 I'm just asking you know, for you is it Do 0:17:10.840 --> 0:17:13.560 you think this is due to small companies growing into 0:17:13.640 --> 0:17:17.720 midcaps or just they're being less available small cap companies. 0:17:18.480 --> 0:17:21.000 I think that if you look at the companies right 0:17:21.040 --> 0:17:24.120 now that are getting upward earning cestemer visions, they tend 0:17:24.119 --> 0:17:27.320 to be more mid cap companies. If you look at 0:17:27.400 --> 0:17:30.840 various quality metrics that we're combining with the estimate revisions, 0:17:31.200 --> 0:17:34.960 it also tends to bias more towards MidCap companies. And 0:17:35.040 --> 0:17:37.920 going back to I think Michael said, if you look 0:17:37.960 --> 0:17:42.639 at those companies that are having a negative earnings, I 0:17:42.760 --> 0:17:46.280 believe that they also correlate with small with much smaller 0:17:46.320 --> 0:17:51.360 cap companies. So we're agnostic in terms of capitalization as 0:17:51.440 --> 0:17:54.760 long as it is within the Russell two thousand universe 0:17:55.160 --> 0:17:57.919 or within our portfolio. But we are looking for companies 0:17:58.320 --> 0:18:01.320 are receiving upward earning cestemer revisions. We are looking for 0:18:01.400 --> 0:18:05.000 companies that have a reasonable score on our quality metric, 0:18:06.000 --> 0:18:08.359 and we are looking for companies that, when added to 0:18:08.400 --> 0:18:12.840 the portfolio, will conceivably provide us with the alpha score 0:18:13.200 --> 0:18:16.439 while reducing the tracking error relative to the benchmark. So 0:18:17.000 --> 0:18:19.960 you know, part of this is that you know, midcaps 0:18:20.000 --> 0:18:23.080 have been performing a little bit better than small caps. 0:18:23.520 --> 0:18:29.000 As you go down capitalization ranges, the number of companies 0:18:29.040 --> 0:18:32.920 that are not quote high quality or good companies tends 0:18:32.920 --> 0:18:35.680 to increase. So as you go down to sub five 0:18:35.760 --> 0:18:38.639 hundred million dollar market cap, as you go down to 0:18:38.760 --> 0:18:41.840 sub two hundred million dollar market cap, the number of 0:18:41.880 --> 0:18:46.040 companies that really shouldn't be public tends to increase. The 0:18:46.560 --> 0:18:49.800 number of companies that are kind of basically completely ignored 0:18:49.840 --> 0:18:54.400 by the market for usually a good reason also tend 0:18:54.480 --> 0:18:58.880 to increase. So if you start using quantitative factors that 0:18:59.040 --> 0:19:07.160 statistically have just gener returns, profitability, momentum, earnings, estimate revisions quality, 0:19:07.600 --> 0:19:10.719 that will always try and sort of push you away 0:19:11.720 --> 0:19:15.239 from smaller cap companies. You also have the case that 0:19:15.320 --> 0:19:19.359 with estimate revision based strategies there tends to be a 0:19:19.520 --> 0:19:24.240 correlation between higher market cap and the amount of analyst coverage. 0:19:24.280 --> 0:19:28.120 So the larger the market cap, not all the time, 0:19:28.160 --> 0:19:30.280 it really depends on the sector and how much in 0:19:30.359 --> 0:19:33.560 vogue that company is, but generally the larger the market cap, 0:19:33.800 --> 0:19:36.840 the more analyst coverage you have. So again I don't 0:19:36.840 --> 0:19:40.640 think it's a function of a qualitative decision. It's more 0:19:40.680 --> 0:19:43.720 a function of the result of the quantitative models we're 0:19:43.800 --> 0:19:47.199 using to try and generate returns. And my guess is 0:19:47.240 --> 0:19:49.520 in the process that was described at the top of 0:19:49.560 --> 0:19:51.760 the hour, there would also be a somewhat of a 0:19:51.800 --> 0:19:57.320 MidCap bias if you're using low volatility and price momentum 0:19:57.600 --> 0:19:59.960 and valuation metric to take a look at things based. 0:20:01.160 --> 0:20:06.280 Okay, do you incorporate valuation metrics at all when you're 0:20:06.320 --> 0:20:08.919 looking at, you know, quality companies, and. 0:20:08.840 --> 0:20:13.680 In this process we're somewhat valuation agnostic, so we're really 0:20:13.720 --> 0:20:18.000 focused on, well, if the company's receiving upward earning estament revisions, 0:20:18.440 --> 0:20:22.600 does adding that company to the portfolio potentially reduce the 0:20:22.640 --> 0:20:26.600 tracking error in other strategies that we runs. As I mentioned, 0:20:26.760 --> 0:20:29.240 we're currently managing a little bit over nineteen billion dollars. 0:20:29.920 --> 0:20:33.560 We do look at valuation as a primary factor that 0:20:33.600 --> 0:20:37.080 we're looking at. My experience has been that as you 0:20:37.520 --> 0:20:43.040 move lower in capitalization, the returns from sort of the 0:20:43.119 --> 0:20:48.320 smaller cap universe tend to accrue a little bit more 0:20:48.840 --> 0:20:53.920 towards technology and more towards some biotech companies because they're 0:20:54.000 --> 0:20:59.800 able to effectively compound over time, go from becoming a 0:21:00.000 --> 0:21:03.040 small cap company to a mid cap and maybe even 0:21:03.080 --> 0:21:07.960 potentially a small large cap company, and that compounded return 0:21:08.720 --> 0:21:12.440 compensates you for the losses that you have with all 0:21:12.440 --> 0:21:16.240 these other companies. So that if you look at valuation 0:21:16.600 --> 0:21:20.880 amongst very very small cap companies, you can run into 0:21:21.000 --> 0:21:24.679 a somewhat of evaluation trap where you find companies that 0:21:24.680 --> 0:21:29.000 are attractively valued and there might be an initial push 0:21:29.080 --> 0:21:32.760 upward as the price book ratio you know, reverts to 0:21:32.920 --> 0:21:37.840 historical means amongst historical levels amongst sort of bank stocks. 0:21:38.240 --> 0:21:43.280 But these companies don't have the ability to compound on 0:21:43.359 --> 0:21:48.640 an annualized basis at a high enough rate to compensate 0:21:48.720 --> 0:21:53.720 you for the smaller cap names that might not perform 0:21:53.800 --> 0:21:56.600 well or might be value traps. So what I found 0:21:56.640 --> 0:21:58.960 to be the case is that valuation metrics seem to 0:21:58.960 --> 0:22:02.880 make more sense and the larger cap that you moved towards, 0:22:03.200 --> 0:22:05.960 and as you move towards smaller cap names, you know, 0:22:06.000 --> 0:22:09.720 you have the traditional Fama French research which shows this 0:22:09.840 --> 0:22:14.800 anomaloust return amongst small cap value names, and I'm not 0:22:14.920 --> 0:22:18.160 quite sure if that has been picked over by the 0:22:18.200 --> 0:22:22.359 market with the amount of capital that's that's looking for 0:22:22.400 --> 0:22:25.399 these opportunities. So if you if you think about value stocks, 0:22:25.400 --> 0:22:28.800 and you're saying, well, do small cap value stocks necessarily 0:22:28.800 --> 0:22:32.080 always out reform small cap growth stocks? You're looking at 0:22:32.080 --> 0:22:35.439 this regression analysis that's saying, well, yes, small cap value 0:22:35.480 --> 0:22:39.560 gives you a bias, But it's possible. We've seen sort 0:22:39.560 --> 0:22:43.600 of a such a flood of capital into the small 0:22:43.640 --> 0:22:47.640 cap space that that thirty five dollars small cap company, 0:22:47.680 --> 0:22:50.160 or that twenty dollars small cap company is now worth 0:22:50.160 --> 0:22:52.159 twenty two dollars, or is now worth thirty six or 0:22:52.160 --> 0:22:55.560 thirty seven dollars, and that would eliminate the annualized return 0:22:55.880 --> 0:22:59.440 from owning those small cap value companies, And anecdotally, that's 0:22:59.440 --> 0:23:02.800 what I happening. So I think that as the market 0:23:02.880 --> 0:23:10.480 becomes more efficient, valuation as a metric sometimes loses some 0:23:10.560 --> 0:23:15.240 of its historical outperformance. And I'm much more comfortable owning 0:23:15.280 --> 0:23:19.320 companies that are receiving upward earning vest revisions and controlling 0:23:19.359 --> 0:23:22.200 the risk through tracking you a relative to the benchmark, 0:23:22.680 --> 0:23:26.280 looking at the PE multiple, looking at cash will multiples, 0:23:26.359 --> 0:23:28.240 just eyeing them to make sure they're in line with 0:23:28.280 --> 0:23:32.240 the benchmark in aggregate, than saying let's try and select 0:23:32.280 --> 0:23:36.800 those companies in the Russell two thousand that have the 0:23:36.840 --> 0:23:40.440 lowest p multiple relative to the industry group that they're 0:23:40.440 --> 0:23:40.919 trading in. 0:23:40.960 --> 0:23:44.560 Basically, So, we. 0:23:44.560 --> 0:23:46.800 Talked a little bit about profitability, and you mentioned that 0:23:46.920 --> 0:23:50.119 it's pretty well disseminated across the market that profitability in 0:23:50.119 --> 0:23:54.080 small caps works, right, And when we were breaking down 0:23:54.080 --> 0:23:57.240 our MVP model across the four factors. We notice that 0:23:57.280 --> 0:24:00.480 pretty much any naive factor that you want to look 0:24:00.520 --> 0:24:02.639 at for the long end of a factor was working 0:24:02.760 --> 0:24:04.480 very well over the long term. Do you think that's 0:24:04.720 --> 0:24:07.080 kind of more of a function of factor crowding given 0:24:07.119 --> 0:24:10.120 your commentary on profitability and everybody kind of be in there, 0:24:10.600 --> 0:24:12.520 or do you think it's more, you know, small caps 0:24:12.560 --> 0:24:14.320 are inefficient or something else. 0:24:16.160 --> 0:24:20.199 What I think is happening is that the nature of 0:24:20.280 --> 0:24:26.040 the small cap market is changing over time. So if 0:24:26.080 --> 0:24:29.080 you take a step back and you say, well, what 0:24:29.359 --> 0:24:33.760 type of company would be going public in the small 0:24:33.760 --> 0:24:38.520 cap space, it would be a company looking for capital 0:24:39.040 --> 0:24:43.080 where the capital is not available through bank financing, so 0:24:43.119 --> 0:24:46.960 they don't have any physical, tangible assets, they can't engage 0:24:46.960 --> 0:24:50.880 in any bank financing. They have an idea, they're going 0:24:50.920 --> 0:24:54.400 to create an online service, they're going to create a 0:24:54.640 --> 0:24:58.560