基本面分析：投资者专家会议纪要

1、IntroductionGeoff Hi, good afternoon. This is Geoff Robinson from UBS Fundamental Analytics team. I have with me today Yiding Lu who worked with me in quite a lot of detail on this new note “ HYPERLINK /shared/d2WQDVJSS4 Whats in the price: Lifting the veil on a stock price”. We are going to spend 2

2、0-25 minutes just running through what we were thinking in that noteIll say up front we didnt release the model. The reason we didnt release the model is that we are trying to build a Whats Priced In model here that is scalable across maybe 2,500 stocks. There is still work to be done on our side to

3、 get that to the stage where it is completely robust across that universe, and we are going to keep testing it. Well keep publishing on it, and in time, hopefully were going to get there.But, Im going to turn you over to the deck now. The deck was sent to you on a couple of emails over the last coup

4、le of days. The last deck went out maybe an hour ago, so if you have that in front of you, it is going to be helpful.What does a stock price tell us?I am on slide 1: What does the stock price tell us? Thats the title of the slide. Frankly, not a lot. I mean, it tells you that the stock might cost yo

5、u $15 to buy but you get no real insight into that stock. Of course, you can calculate multiples and the like, but its not really giving us an underlying insight into the drivers. Thats what a Whats Priced In (WPI) model is trying to do. Its trying to provide a view on whats inside a stock price.The

6、re are various different ways to do this. Ive done this for years when Ive had my DCF. Ive sat down, Ive done my forecasting and Ive come out with a number that says the stock price should be X or Y. And then I did it in reverse. I plugged in the current stock price into the output and then tried to

7、 work backwards to get the DCF to support what that stock price is actually doing. I find that this was an incredibly useful technique, especially when Im just not really getting what the stock price is doing or the multiples doing.I remember looking at a company in APAC a couple of years back tradi

8、ng on very, very high multiples. I couldnt get my head around why the multiple was so high. So, what I tried to do is build the DCF model and then just backed out what the multiple was saying or what the stock price was. That just gave me an insight to the fact that it looked like massive momentum w

9、as needed in the revenue growth rates and the development in the margin needed to be quite aggressive just to even get close to what that stock price in the market was saying.But that technique still has human flaws. I mean, as human beings, we are prone to bias. I dont think anybody here can stand

10、and say were not biased, whether its conscious or unconscious. We react to recent events more strongly than we react to more historic events. We use our judgment; we use our skill. Our judgment and skill can be open to interpretation.What we felt is running reverse DCF had its flaws. And so, our WPI

11、 model, not the only one we could build, is really trying to provide us with an outside view on the stock and whats priced in it to compare that to and question what our inside view is saying. The model itself is largely automated. Its using historic fade rate data, high resolution consensus, a bit

12、of clever mathematics to imply what is going to happen into the longer run, in order to come out with a set of results, metrics, and diagnostics that we can then analyse in order to detect anomalies within what could be in the price.WPI model isnt the answer; its a view. Its a data-driven view that

13、hopefully is more abstract from the human biases that we can sometimes demonstrate when were doing the forecasting ourselves.Inside view vs outside viewThats really what we covered on slide 2 is this idea of whats an inside view and whats an outside view. Inside view as an analyst my job is to make

14、forecasts. Thats based on knowledge and experience. That can be flawed and we pick up on an excellent piece of work, and its a dated piece of work now by Daniel Kahneman. He published with Amos Tversky a piece of work in 1973 called, “On The Psychology of Prediction.” If you havent read that, stick

15、it into Google, download it, its there to pick it up. Have a read because its a fascinating piece of work. He went on to win the Nobel Prize.But the overriding conclusion of his work was that human beings are prone to bias, no surprise, but he was able to prove it. There is also the fact that the hu

16、man mind really isnt wired to look at the statistical data of distributions around it and factor that into his work. I mean, if I am a reasonably good analyst and Ive done some forecasting. It is difficult for me to sit down and say “Let me rip apart my own forecasts with a load of data.” Thats some

17、thing that doesnt sit particularly well in the human mind.Thats what an outside view is trying to do. Its trying to ignore the specifics of any situation and look at the big picture. Look at the data and calculate rather simplistic statistics just to figure out the likelihood of the outcomes that ar

18、e embedded in the data. Its looking at historical data, peer groups and consensus to try and draw out a view. I use that view just to challenge the inside view that Im developing.Thats really the overview. If Im honest, Yiding joined us earlier this year and his skill set, and that was brought into

19、our group, is very much data analytical. He comes from an AI background in computer science, and hes just brought a new blend and lens to our business. Hes been responsible for really taking the WPI model from where it was at the beginning of this year to the stage where we are able to actually exec

20、ute the model.Im going to pass it over to him and hes going to talk you through the process, a little bit of the mathematics behind it, so you can get an idea of what our secret sauce is, and you can maybe take that into your own work because this is something that we do talk to clients about on a f

21、airly regular basis. We do offer our advice, the pitfalls, the pragmatism that you have to show when you are building these scalable models. Because, if youre going to run something that is automated across 2,500 companies, there are assumptions that you do have to make and you do have to be pragmat

22、ic because dealing with that kind of structured data in an unstructured manner at times that can be quite tricky.Im going to pass over to Yiding now and lets see what he has to say.Our WPI processYiding Thanks, Geoff. If you turn now to slide 3, I just want to go through the high-level process of ho

23、w the WPI model works.It essentially is a two-stage DCF model using free cash flow to the firm. My apologies that the picture shows a three-stage DCF model. This is from one of our older models that we built where the third stage was basically the terminal value.Figure 1: High level WPI processSourc

24、e: UBSIf you have been following our work, we published about a year ago this note called, “ HYPERLINK /shared/d21neQmSFWbt Is your terminal value terminal?” where we talk about the pitfalls and the dangers of estimating a terminal value. Its really difficult to actually get a correct terminal valua

25、tion for a company, which is why in our WPI model, we extend our investment horizon over 150 years such that the terminal value of a company is negligible at the end of the investment horizon. Essentially, we can ignore the terminal value and we have a two-stage DCF model. We use DCF because its wid

26、ely used and is easily understood by analysts. But, you can use any model that you like and youre comfortable with.In the first stage in our DCF model, we take consensus data from Visible Alpha. Visible Alpha provides us with a high resolution taxonomy with specific line items so that we can build a

27、 good model and we can calculate the FCFF from the consensus data to know that how much value is created in the first stage.And then on the second stage we drive our FCFF by fading our return on invested capital (ROIC) curve. We assume that ROIC will reduce to weighted average cost of capital (WACC)

28、 over a long period of time as we think that companies cannot create value indefinitely. We adjust our fade rate in the second stage such that we can create a value thats equal to the current market value of the company. This Ill go into more details in our next slide.In the next slide, slide 4, we

29、discuss about how we fade our curve. As I mentioned previously, our FCFF generation is driven by a fade in our ROIC, and that we assume that ROIC would decay to WACC over the long run. We use a modified exponential curve to drive our decay and the modification we made is because we do not believe th

30、at all companies will reach maturity at the end of the visible period of five years, which is what we use in our model.Some companies could still be increasing their returns during the second fade period before it starts to decay, and so how we determine this is that we look at the shape of our retu

31、rn on invested capital curve during stage one:If your ROIC is increasing in stage one, we assume that the company has now reached maturity and hence your stock will continue to increase before it starts to decay in the second stage.If your ROIC is constant during the first stage, then we think that

32、your ROIC should remain constant before it starts to decay in the second stage.If your ROIC has already begun to reduce in the first stage that means the company has reached maturity and it should start to decay your ROIC immediately during the second stage.Figure 2: Modification made to decay curve

33、 to allow ROIC to increase in Stage 2Source: UBSGeoff Can I jump in there to add something? This is an evolution in our shaping of the second stage fade over the last, lets say, four or five months, I think.When we started this project over, we thought we were being terribly clever by using somethin

34、g called a sigmoid curve. Now a sigmoid curve has two key variables. One is a half-life, and a steepness coefficient. A half-life is how many years does it take the curve to descend 50% of its drop, and then the steepness is how much velocity we have through that midpoint.We thought this was terribl

35、y clever because we were able to talk about maybe the sustainability of competitive advantage because the longer your half-life was the more sustainable your competitive advantage was, and then your steepness coefficient allows you to think about well, how quickly could we lose that competitive adva

36、ntage. We really liked it but the downside of that curve is that it takes your starting point or the end of the visible period and it can only go down, with the basic equation.When we start to try and fit these curves across broader populations of stocks, we had situations where we could not actuall

37、y justify the stock price, mainly because you had situations where companies just werent mature by year five and there was still some growth left in that stock, according to what we were considering for a WPI perspective. Yiding came in and then developed this modified decay curve, looking at effect

38、ively not so much the fade rates but the implications of returns during the first stage of high resolution consensus period. Back to you, Yiding.Yiding As Geoff mentioned, the variable which we try to fit in our second stage is the fade rate, which is the speed that a companys ROIC decays. This rate

39、 of decay is determined by the sustainability of the competitive advantage of the company.If a company can sustain its competitive advantage, we think that its return can be sustainable. This means that its ROIC curve should decay slower. Sustainability of competitive advantage is basically determin

40、ed on a number of factors, such as the number of competitors you have, your barriers to entry, your product differentiation. But what our WPI model assumes is that this fade rate should be priced in by the market already so the stock price reflects the sustainability of the competitive advantage.Bas

41、ically a company that is able to sustain its competitive advantage has a lower fade rate, and this allows it to generate a higher level of free cash flow to the firm and thus justify a higher valuation. Hence, we iterate through different ROIC curves at different fade rates to find one that generate

42、s FCFF thats equal to the current market price of the company. Thats what we get from a WPI model.Figure 3: Lower fade rate supports a higher valuationSource: UBSApplying WPIIf you turn over to slide 5, whats the whole point of doing this Whats Priced In model? We want to generate insights. Based on

43、 WPI model, we can back out the growth rate or the margins and the value creation timeline of the company. How is my ROIC changing on a year-to-year basis? Is my revenue growing? How is my NOPAT changing? What are my EBIT margins? How much enterprise value am I generating during my visible period? H

44、ow much value am I generating in my second stage?Figure 4: Generating insights using WPI modelSource: UBSWe can compare these to our historical data to see whether there are any outliers. For example, if my company has been generating 10% EBIT margins throughout the last ten years, and my forecasts

45、show its going to generate 20% margin next year, does that make sense? Thats what we want from the WPI model. Its essentially a lens and it tells us “Focus on this area. It is disconnected from historical base rates or this is different from what peer groups are saying, maybe you should look at it.”

46、 Essentially thats what you want to get from our WPI model.We applied our WPI model to the Global Food Retail sector. If you turn over to slide 6, we applied it to 14 companies in the Food Retail sector. We used a range of WACCs between 5% and 9%. We used five different values, and this is based on

47、a discussion with our Food Retail analysts. This range is whats sensible.Figure 5: List of companies analysed using WPI modelSource: UBSWhy we want to use a range of WACCs instead of a single WACC is that our model is sensitive to the WACC value used because it is used to discount FCFF to its presen

48、t value. Its difficult to estimate a single point of WACC. If you use the CAPM model, sometimes its difficult to determine what is the beta, etc. that you should use.Geoff I mean its worth, maybe, letting the investors know the pain we went through when we did this because I had this idealistic situ

49、ation that all I would stick into this model would be a ticker, and then out would flow this beautiful WPI model.Were not a million a miles away from that but at one stage we did have a systematically-calculated WACC usingwe looked at various data providers, risk free rates, equity markets, risk pre

50、miums, weightings, etc. And quite frankly, on a systematic basis there were a material number of times that the WACC calculation generated absolute nonsense. Even if I were to be able to generate a WACC estimate that I felt comfortable with, WACC is in the eye of the beholder. Its yourrisk assessmen

51、t as an investor. Theres always going to be disagreement whether the number is 5%, 6% or 7% because we have different views.Thats why I think its an important decision on our part to not force a WACC number onto the use of this model or certainly the people reading this note, but to give you the sen

52、sitivity to be able to choose what you thought was appropriate. By using different WACCs of 5%, 6%, 7%, 8%, 9%, what the model actually does is it generates multiple second-stage fade curves according to that WACC calculation. You can then choose what you would like to do. You may disagree and highl

53、y likely to disagree with what I say and certainly when it comes down to the WACC.Sorry, Yiding, back to you.Yiding Thats all right. For the Food Retail sector, we used five different WACCs, 5%, 6%, 7%, all the way to 9%, and for each of the WACCs, we run WPI model for all the companies and we calcu

54、late optimal fade rate for each of the companies for each of the WACCs. On slide 7, we showed the results of what we run on the company. So, at 5%, 6%, 7%, 8%, and 9% WACC, what the optimal fade rates for the companies are.Figure 6: Cross-sectional analysis of the fade rate of Global Food Retail com

55、paniesSource: UBSWe highlighted basically the ones that are outliers compared to the rest of the peer group. If you look at figure 31, you see that Eurocash has a fade rate thats much higher than the rest of the peer group, regardless of which WACC value we are using. This tells us that we should go

56、 back to the consensus data, to the FCFF estimates for Eurocash and see whether it makes sense compared to the rest of the peer group. Were not saying that this FCFF from consensus data is wrong, its just that its not similar to the rest of the peer group. There might be a very good reason why Euroc

57、ash has a different set of FCFF, but this is basically providing withyou a focal point of saying this is what is different from the peer group, go back and review it.And its not just the consensus data you should review. You should also look at the assumptions that we used in the WPI model. For exam

58、ple, the WACC that we used, you might think the WACC should be outside of the range that we used. Or you think that its a different curve that you should decay your ROIC curve. But, thats up to the user. What we want to provide you with is the approach of how you can do this and where you should foc

59、us your effort on.So, this shows the cross-sectional analysis of the companies. What we also did is perform time series analysis for one of the companies. So, if you turn now to slide 8, we chose Kroger for our time series analysis. Kroger is a supermarket chain thats based in the US. We compared th

60、e WPI models output to ten years of historical data and also visible period for five different WACCs, so 5% to 9%.Figure 7: Time series analysis for KrogerSource: UBSWhat we noticed is that when you use the 5% WACC, the ROIC curve is decaying very quickly during the fade period because youre using a