“We built this company on pay per performance to begin with and its no secret that we did it with Google. Google and us have had a very good relationship together. We’ve both benefited tremendously.” -Glenn Fogel, Booking Holding CEO

Google is the gatekeeper to the internet. Once upon a time we had web “portals” that served as the “home page” where internet users started any exploration of the internet. But today it’s just Google. Whether you go to Google.com or type a search term into the URL bar of your desktop or smartphone browser, you are accessing the internet through Google.

We are a long-time shareholder of Google and have stated before that the company’s services “have become a required part of modern life, almost a form of oxygen for internet connected populations,” as well as claimed that “Google’s opening up of the collective knowledge of humanity to anyone with an internet connected device at no cost to the user can easily be seen as one of the most important social benefits ever created by any nonprofit or for-profit organization.”

But a lot of people just think of Google as a monster that devours profits that have historically accrued to other companies.

(Source: Robert Scott Lawrence)

Devouring other companies’ profits is not a bad thing. It is how capitalism is supposed to work. But if you are one of the companies whose profits are being devoured, you’ll take no comfort in this philosophical view. As Google shareholders, we are well aware of the company’s extremely powerful position and its ability to change the fortunes of other companies at a whim.

So it may seem irrational that one of our largest holdings is Booking Holdings, the leading online travel company operating in a space where Google has recently thrown its weight around. But while we agree that Google will continue to capture the largest portion of the online travel profit stream and grow their share, we think Booking is uniquely advantage within the publicly traded online travel peer group as being well positioned to dodge this threat.

Booking Holdings is an online travel agency (OTA). This means that it actually books travel related reservations. Expedia is the other big player in this space. The other main type of online travel company offers “metasearch.” Metasearch websites like Kayak or TripAdvisor are designed to search across multiple travel related sites to find the best deal and then earn ad revenue for sending users to those other sites to then book a reservation.

Both types of companies provide a type of service known as “vertical search,” which searches the internet, but within a limited domain of information. Vertical search services are not limited to traditional search engines. Amazon can be thought of as vertical search for shopping. More product searches start on Amazon then start at Google. While Google’s general search offering is outstanding, some categories of information can benefit from specialized search services. In certain niches, vertical search services from companies such as Yelp, Zillow, and LexisNexis have thrived despite Google’s dominance in general search.

But the thing about the travel-related vertical search industry is that it is big, big business. According to research by Skift, one of the leading travel industry research groups, Google’s travel related advertisements generate so much revenue that this one segment of Google business is more valuable than any of the standalone online travel companies. It is generally thought that Booking may well be Google’s single-biggest customer and that travel overall is one of Google’s biggest sources of advertising revenue.

But that’s not enough for Google. Like any good competitor, they aren’t happy being first. They want to win and win, and then win some more. They don’t just want to generate the most profits in the industry, they want to maximize profits, period.

One strategy Google has deployed to win a larger share of the travel industry profit pool is create their own vertical search products. Back in 2011, the company launched Google Flights, and today this search product is considered the gold standard for the vertical search category for finding airline tickets. Unlike general Google search, which returns a list of links to other websites, Google Flights allows you to search for flights using flight specific queries and returns results in a format that is most useful for comparing flights. This is the same service that other travel metasearch products offer.

Obviously travel metasearch companies think this is terrible! They were used to having the best product and capturing the ad revenue that airlines paid them when a potential customer clicked on a link to a flight. But Google Flights has been widely viewed by consumers as a fantastic innovation.

Scott’s Cheap Flights, is an online service that identifies when airlines put flights on sale or other deeply discounted airfares and alerts their subscribers. The service has been widely praised by many media organizations and I use it personally. And what does Scott’s Cheap Flights tell its subscribers is the very best tool for finding cheap flights? Google Flights.

But what have online travel companies said about Google Flights? They say Google is “increasing the ad load” and “degrading the user experience.” The implication of this point of view is that Google Flights is an ad product (which it is) and by putting it at the top of the search results they are pushing further down the page “organic” (ie. free unpaid links) search results that point to the travel companies’ own websites. Under this worldview, “paid” search results are “bad” for users and “organic/free” search results are what users are looking for.

But that’s wrong.

Google Flights is a paid search product, but it is also the best search product to find the best flight. Users aren’t being forced or tricked into using Google Flights simply because they have to scroll further down the page to see organic results. They are using Google Flights because it does a better job of getting them the information they seek.

But Google Flights isn’t a big deal to Booking Holdings, because the vast majority of Booking’s revenue comes from booking hotels. And very importantly, most of their hotel booking revenue comes from hotels in Europe and Asia, not the US, which we’ll explain the importance of a bit later.

About a year ago, Google rolled out Google Hotels. And guess what? It is a really good tool for searching for hotels.

Over the summer, Google began “optimizing” their travel products. Said more bluntly, they have been strategically updating their algorithm so that users are more likely to go through their paid travel search products.  When TripAdvisor, Expedia, and Booking reported their 3rd quarter results, the market did not like what it heard:

(Source: Bloomberg)

TripAdvisor and Expedia fell by 20%-30%. Booking declined as well, but the commentary from each company could not have been more different.

Here’s Skift’s analysis of what happened.

“The fact that Google is leveraging its dominance as a search engine into taking market share away from travel competitors is no longer even debatable. Expedia and TripAdvisor officials seem almost depressed about the whole thing and resigned to its impact.

“We believe our most significant challenge remains Google pushing its own hotel products in search results and siphoning off quality traffic that would otherwise find TripAdvisor via free links and generate high-margin revenue in our hotel click-based auction,” TripAdvisor stated.

Expedia Group CEO Mark Okerstrom in Bellevue, Washington, said Google is taking “more revenue per visitor, and I think it’s just the reality of where the world is in the Internet, and the importance of Google at the top of the funnel.”

Both Okerstrom and Kaufer complained that their organic, or free, links are ending up further down the page in Google search results as Google prioritizes its own travel businesses.”

But here’s what Booking Holding CEO Glenn Fogel said at RBC’s analyst conference a few weeks later:

“We built this company on pay per performance to begin with and it’s no secret that we did it with Google. Google and us have had a very good relationship together. We’ve both benefited tremendously.”

While all three of these companies are online travel businesses, there is something idiosyncratic about Booking Holding’s business model. And this distinction is the critical key to why Booking and Google should best be understood as engaging in a mutual beneficial relationship. Sure, they might both want to maximize the profits they each earn, even at the others’ expense. But their relationship is not a zero-sum game. They both can win over the long term, which is why at Ensemble we’re very comfortable being long in both stocks.

Key Distinction #1

Unlike TripAdvisor, which is primarily a metasearch site which earns advertising revenue by sending users to hotels or hotel OTAs to reserve a hotel room, Booking is primarily a hotel OTA. Google is in the search business. They don’t book reservations in either their Flight or Hotel product. For that matter, across Google’s entire search related business they don’t enable users to engage in transactions on their site for the most part. They are in the advertising business and they earn their profits by generating advertising revenue in exchange for sending users to other companies’ websites to engage in transactions.

So, Google Hotels is a direct competitor to TripAdvisor. However, the search results inside of Google Hotels includes hotel websites, but also Booking and Expedia listings offering the ability to reserve a hotel room that fits the search.

Key Distinction #2

Booking and Expedia are the two leading OTAs. But while Booking is mostly just hotels and mostly in Europe and Asia, Expedia has more exposure to airline tickets and far more exposure to the US.

Empty hotel rooms are like perishable inventory. If you don’t sell a hotel room one night, you can’t ever sell that room on that night again. This is why hotels are willing to pay OTAs a large commission for finding a traveler to book a room.

In the US, where Expedia is more exposed, the market is dominated by big branded hotel chains such as Marriott, Hyatt and Hilton. When a traveler heads to New York, rather than going to a metasearch or OTA website, they might just go right to Marriott or Hyatt or Hilton’s website. These companies have strong loyalty programs and are well known to customers. Their hotels tend to run with higher occupancy rates and so they aren’t particularly dependent on the incremental demand that OTAs can provide. Indeed, OTAs have had to negotiate much lower commission rates with the big brands.

But in Europe and Asia, where Booking is more focused, independent hotels are the norm. If a traveler is going to Milan or Bangkok or Paris, they are much more likely to stay at an independent hotel. These hotels don’t have loyalty programs or global brand recognition. They tend to operate with lower occupancy rates. With more “perishable inventory” going bad every night, independent hotels are much more dependent on OTAs and pay a much higher commission rate than branded hotels.

For independent hotels, Booking and other OTAs effectively act as outsourced digital marketing companies who are paid on commission. This is an incredibly valuable service for independent hotels who simply don’t have the resources or expertise to run global digital advertising programs.

This is why the hotel listings inside of Google Hotels will typically be direct links to Hyatt or Hilton or Marriott’s own website (bypassing the OTAs), but for independent hotels the links will typically be to Booking or other OTAs’ listing for the hotel.

Key Distinction #3

If TripAdvisor, Expedia and Booking all have their hotel listings inside of Google Hotels, why are Expedia and TripAdvisor moaning about it while Booking doesn’t seem to care?

Remember those “organic” or free links that TripAdvisor and Expedia are saying are being moved further down the page as Google “pushes” users into the Google Hotels product (or rather users choose to use Google Hotels because it is a better experience than general search)? That traffic is referred to as SEO (search engine optimization) traffic. TripAdvisor and Expedia have been free-riding on Google’s service while Booking generates its earnings stream from paid Google links.

This is the most important distinction of all.

While the companies’ do not disclose the amount of traffic they receive from organic vs paid links, the above analysis by Cowen & Company aligns with the companies own past comments and our analysis of the companies’ business models.

TripAdvisor and Expedia are dependent on Google’s free search results for the majority of their earnings. While Booking uses Google’s paid advertising search results to drive the vast majority of their earnings.

TripAdvisor and Expedia complain that Google is “increasing the ad load,” “degrading the user experience,” “pushing its own products,” and “siphoning off quality traffic.” But there is another way to think about what’s going on.

Here’s venture capitalist Chamath Palihapitiya speaking in his typically bombastic manner at the recent Phocuswright travel industry conference:

“If you are a business that thrives inside of this Google environment… you are f***ed. Ratably, tick for tick, every single person that feeds off of them, will see their profitability erode. That’s the unvarnished truth of what’s happening. If you are in the business of being a parasite on top of Google, your medium and long term prospects are terrible. You are an impaired company, even though you don’t know it.”

While Palihapitiya is prone to dramatic statements, venture capitalist Bill Gurley who is more balanced in his public comments retweeted Palihapitiya’s statement saying simply “this is accurate.”

Bottom Line

Booking and Google are not at war. Booking is one of Google’s biggest customers. Booking built its business model around paid links from Google. Google has found a way to offer metasearch for hotels in a way that offers a superior user experience and has the benefit of Google getting paid for sending users to other sites to book rooms. This is hugely disruptive to companies built on the idea that somehow they deserve to get free traffic from Google. But it is not disruptive at all for a company like Booking that has figured out how to pay Google even while it earns fantastic profits.

For more information about the positions owned by Ensemble Capital on behalf of clients as well as additional disclosure information related to this post, please CLICK HERE.

While we do not accept public comments on this blog for compliance reasons, we encourage readers to contact us with their thoughts.

Past performance is no guarantee of future results. All investments in securities carry risks, including the risk of losing one’s entire investment. The opinions expressed within this blog post are as of the date of publication and are provided for informational purposes only. Content will not be updated after publication and should not be considered current after the publication date. All opinions are subject to change without notice and due to changes in the market or economic conditions may not necessarily come to pass. Nothing contained herein should be construed as a comprehensive statement of the matters discussed, considered investment, financial, legal, or tax advice, or a recommendation to buy or sell any securities, and no investment decision should be made based solely on any information provided herein. Links to third party content are included for convenience only, we do not endorse, sponsor, or recommend any of the third parties or their websites and do not guarantee the adequacy of information contained within their websites. Please follow the link above for additional disclosure information.

This is Part 7 of our series on position sizing. PART 1PART 2PART 3PART 4, Part 5, Part 6.

Over the course of our multi-part series on position sizing, long-term readers will note that we’ve described a far more quantitative process than the more narrative driven descriptions we’ve offered for why we own the companies we do. This difference is driven by the type of decision making that informs what to own, versus the type of decision making needed to decide exactly when to place each trade.

In Part 4 of our series, we wrote:

“Annie Duke explained the need for decision makers operating under conditions of uncertainty and time pressures to align and reconcile what Kahneman called System One, or reflexive decision making, and System Two or deliberative decisions making.”

From Kahneman’s seminal book Thinking Fast and Slow:

“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. System 2 allocates attention to the effortful mental activities that demand it. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration… The highly diverse operations of System 2 have one feature in common: they require attention and are disrupted when attention is drawn away… [But] System 2 has some ability to change the way System 1 works, by programming the normally automatic functions.”

(Source: Ameritest)

We believe that System 2, deliberative decision making, is at the heart of analyzing companies. It is extremely time intensive to understand a business at a deep level, which is why we think (and the evidence strongly supports) that managing a focused portfolio of holdings is a key attribute of most successful fundamental, equity investing strategies.

But every day, the price of every position in our portfolio changes continuously. At the same time, news flow on our companies, their competitors, and the economy can influence our calculated fair values and our conviction ratings. In addition, we offer our investment strategy not only in the form of a mutual fund, but also as separately managed accounts, where cash inflows and outflows can impact the weight we have in each position we hold.

Compounding the need to make complex, continual judgments about specific buy/hold/sell decisions (the decision to do nothing and just hold is still a decision) is the fact that investors face significant cognitive biases. We believe that cognitive biases are more intense the closer you get to trade date. If you ask investors what they expect the market will do over long periods of time, they often will provide an estimate that is closely related to the historical base case of 8%-10% annualized returns. But under conditions of stress, investors’ time horizons shrink, causing them to make fundamentally mistaken forecasts due to a range of cognitive biases.

Recognizing that we need to make continual optimization decisions across multiple holdings and multiple portfolios and that these particular types of decisions are often clouded due to cognitive biases, we deconstructed our more standard, qualitative position sizing framework and designed the algorithm we’ve spent this series of posts describing.

This process builds on the research described in Roy Baumeister’s book Willpower, in which the author describes techniques to reconcile System 2 deliberative strategies you craft to reach your goals with the need to make in the moment decisions where System 1 reflexive processes take over.

“We often think of willpower in heroic terms, as a single act at a crucial moment in life… [But our] analysis of a large set of published and unpublished studies on people who scored high in self-control… [showed] the people with high self-control were distinguished by their behaviors that took place more or less automatically.”

In other words, by building an automatic position sizing formula, based on deep, deliberative thought to articulate what decisions we would want to make under all sorts of future conditions, we have created a system under which we can effortlessly, but relentlessly, stay true to our disciplined approach. At the same time, by handing off the cognitive load to an algorithm to decide things like “do we trim this stock at 6.5% or should we let it ride up to 7%?” or “do we buy now or after earnings?” we free up cognitive and attention resources to focus on deliberative System 2 processes that are key to uncovering alpha opportunities.

In many ways, the Ensemble Capital investment strategy is good old fashioned stock picking based on the qualitative judgments of human analysts. We use this process because we think it is superior to more quantitative approaches. But when it comes to tick by tick trade decisions and determining exactly how much of any stock we want to own in a portfolio, we think qualitative judgments become overwhelmed by cognitive biases.

We think we have a competitive advantage when it comes to assessing the long-term cash generation potential of companies. But when it comes to short term buy/hold/sell decisions in the context of rapidly moving prices and opportunity sets, we know that algorithms have humans beat and we’ll happily focus on designing and refining our algorithm instead. In doing so, we’ve made our best effort to follow Duke’s advice to “align and reconcile” System 1 and System 2 thinking.

Please click here for Part 6

Please read important disclosures HERE.

While we do not accept public comments on this blog for compliance reasons, we encourage readers to contact us with their thoughts.

Past performance is no guarantee of future results. All investments in securities carry risks, including the risk of losing one’s entire investment. The opinions expressed within this blog post are as of the date of publication and are provided for informational purposes only. Content will not be updated after publication and should not be considered current after the publication date. All opinions are subject to change without notice and due to changes in the market or economic conditions may not necessarily come to pass. Nothing contained herein should be construed as a comprehensive statement of the matters discussed, considered investment, financial, legal, or tax advice, or a recommendation to buy or sell any securities, and no investment decision should be made based solely on any information provided herein. Links to third party content are included for convenience only, we do not endorse, sponsor, or recommend any of the third parties or their websites and do not guarantee the adequacy of information contained within their websites. Please follow the link above for additional disclosure information.

This is Part 6 of our series on position sizing. PART 1PART 2PART 3PART 4PART 5

This is Part 6 of our series on position sizing. Part 1 provided insights into the number of stocks that should be held in a portfolio. Part 2 discussed the various stages of research and how that should be taken into consideration when building out your portfolio. Part 3 explained how conviction plays a role. Part 4 elaborates on conviction and how we turn a qualitative assessment into a quantitative input. And Part 5, brings it all together into a weighting system. We will now explore our framework around target drift, trading among companies within our portfolio, and portfolio turnover.

A stock drifting above the portfolio target is a result of that stock outperforming the portfolio. When this happens, we don’t want to be too quick to trim that stock back to target. According to the vast research on the momentum factor, “winning” stocks tend to continue winning in the near-term. In order to capture this additional source of return we allow companies to drift above their target. This process also makes our strategy more tax efficient by allowing more unrealized gains to accrued before we trim. Our process allows a stock to outperform the portfolio to the extent that its weighting in our portfolio exceeds our target weight by a few percentage points before we trim the position.

To simplify this example, we’ll use a static portfolio (i.e. the target/max not changing as the discount to fair value changes), rather than the dynamic portfolio we discussed in Part 5 . If you have a 20 equally weighted stock portfolio (Table 1) and all the stocks return 10% over the period, then their weighting is unchanged. In order for one of the holdings to have a ~3% higher weight than the initial target, it would need to outperform the rest of the portfolio by ~70%.

Table 1

Table 2

As we’ve discussed in our previous posts, our dynamic model adjusts the target weight and their corresponding max weight as the discount to fair value changes (i.e. as the price or fair value changes). This means that in practice, it doesn’t take ~70% (as described above) of outperformance before a trim is recommended. Instead in the absence of any increase in our assessment of a stock’s fair value, about 15-30% outperformance will typically cause us to trim the position. However, when a stock increases in value by 30% over a relatively short time frame, there is usually new fundamental information that may also increase our assessment of the stock’s fair value, which may cause us to defer trimming the position.

The difference between the target and the max weight is not the same amount throughout the weighting scheme. It gets larger as the target weight approaches and reaches 0%. We found that doing this reduces the portfolio turnover and is more consistent with the idea of us rating a stock as a “hold” (as opposed to a “buy” or “sell”). Here is an example of what this looks like:

To make sure that we’re fully invested in our best ideas (which as we know from the end of Part 5 is where a higher percent of our alpha is likely to come from), if there is a situation where a highly ranked company has underperformed the rest of the portfolio and is now underweight, and there is no new cash to invest, we’ll sell one of our lower ranked names to fund the purchase. We have a few conditions in order for this trade to occur. The funding source(s) must be above its current target and the improvement in the discount to fair value of the trade must be greater than 20%. This means that if stock A (which is underweight) has a current discount to fair value of 35% and stock B [the over-target funding source(s)] must have a discount to fair value of 15% or less. This 20% capture-premium is a threshold that we’ve deemed necessary to minimize turnover, but also keep the portfolio optimized. Importantly, we know that fair values are not precise calculations, but instead are the central point of a range of possible values. We believe that we can estimate fair values within a range of about +/-20% and so once a stock offers 20% more appreciation potential than another holding, we think the difference is statistically significant enough to make a trade.

As cash is available in the portfolio, then we’ll invest it according to the rank of the underweight stock (based on the matrix described in Part 5) as well as take into consideration our minimum trade size (i.e. we’re not going to make a trade to bring a stock from 4.9% to 5%).

Monitoring turnover is important for several reasons, but the typical way it’s calculated (the lesser of the sum of all securities sold or all securities purchased, divided by portfolio value) can be misleading in our view. We think this standard approach overly simplifies the analysis and might not reflect the true intentions of the manager. Someone from the outside might see a high turnover as the manager frequently changing their mind on portfolio companies, when in fact our view hasn’t changed. Rather, it’s the market’s view of the company that has changed.

We view turnover as being made of two types; internal and external. Internal turnover is when there is trading among portfolio holdings. We’ve recognized that some of our stocks have relatively stable fundamentals, but volatile stock prices. When this occurs, it provides excellent trading opportunities and allows us to shift those funds into higher expected return stocks. There are even times where we can totally exit a stock yet continue to follow it because we still believe it’s a great-but-expensive company, relative to other companies in our portfolio. In the normal view on turnover, an outsider might think we don’t like the company anymore, when in fact we do, but we have other opportunities that present a better potential return. Over time, the return potential of that company may come back into favor and we may buy it again.

External turnover on the other hand is driven by initiating coverage on new companies or us discontinuing coverage (with a final sale) of a company because we’ve lost confidence. In previous client conference calls (of which our next is scheduled for 1/9 @ 1:00pm PST) we discussed the reasons why we discontinue coverage of companies and gave a range of examples of companies we’ve exited over time.

In general, we estimate that about 15%-20% of our portfolio turnover comes from us changing our mind on a company, with the balance of our turnover being driven by external factors such as market and individual stock volatility.

Sean will conclude this series with Part 7, bringing it all together through the lens of behavioral finance and how a system like this helps remove our inherent biases.

Please click here for Part 5 and here for Part 7

Please read important disclosures HERE.

While we do not accept public comments on this blog for compliance reasons, we encourage readers to contact us with their thoughts.

Past performance is no guarantee of future results. All investments in securities carry risks, including the risk of losing one’s entire investment. The opinions expressed within this blog post are as of the date of publication and are provided for informational purposes only. Content will not be updated after publication and should not be considered current after the publication date. All opinions are subject to change without notice and due to changes in the market or economic conditions may not necessarily come to pass. Nothing contained herein should be construed as a comprehensive statement of the matters discussed, considered investment, financial, legal, or tax advice, or a recommendation to buy or sell any securities, and no investment decision should be made based solely on any information provided herein. Links to third party content are included for convenience only, we do not endorse, sponsor, or recommend any of the third parties or their websites and do not guarantee the adequacy of information contained within their websites. Please follow the link above for additional disclosure information.

This is Part 5 of our series on position sizing. Part 1, Part 2, Part 3, Part 4.

As Sean has discussed in Part 1, Burton Malkiel provided guidelines for how many stocks to own, and Modern Portfolio Theory provides a framework for portfolio construction under an assumption that markets are efficient, and risk is defined as stock price volatility. In contrast, the field of active equity portfolio management has not developed any widely-agreed upon approach to position sizing. In Part 4 we showed how we’re able to incorporate qualitative judgements into our quantitative portfolio construction process. We know that human judgement is required to make long-term forecasts about companies and yet we know that decision-making and forecasting is rife with cognitive biases.

One solution is to equal-weight all holdings, but we believe a better system is one that combines qualitative and quantitative factors to weight positions based on a combination of their return potential and the likelihood that a positive outcome occurs. This system converts our qualitative judgments into an algorithmic position-sizing framework. We’ve crafted a process that we believe retains most of the value of qualitative assessment while stripping out as much of the biases as possible.

This system is made up of our conviction (as discussed here and here) and our assessment of the company’s discount to fair value. In isolation, each of these components only tell part of the story. For example, say you have two companies that are available for investment and they come out to have the same discount to fair value. If you only looked at discount to fair value, you might weight them the same. But, if you had a lot more faith in one company’s management, moat, or your ability to forecast the company’s results, you’d want to weight one more than the other.

The merging of these factors creates a matrix with various targets throughout each intersection. Here is a sample illustration of this matrix:

There is also a third dimension to this weighting scheme – the research stage (as discussed here). This measures the extent of research we have completed on the company. Since we’re still learning about the company in the earlier stages, we apply an adjustment to the weighting scheme to take into consideration that there’s greater uncertainty around our conviction and discount to fair value related to information we may not yet know that you don’t know.

This creates a portfolio that is tilted toward our highest conviction and highest discount to fair value names. This strategy of “putting your money where your mouth is,” can pay off. Through some great work at AlphaTheory, they’ve shown that managers that apply a disciplined weighting process to their portfolio tend to more heavily weight securities that outperform. According to their research, “Top 5 positions went up an average of 12.1% while the portfolio as a whole went up 8.4% (for shorts, went down 8.4%).  That’s 50% better!”

We’re not advocating for a 5-stock portfolio. The prudent investor remembers that the first rule in managing money is to limit permanent loss of capital. Investors do not have perfect insight into what will happen with a company and its stock price, so holding a portfolio of 20-25 stocks helps mitigate that risk. We discussed this in more detail here.

And, while the framework is algorithmic, that doesn’t mean we blindly follow it. We monitor our model position sizes constantly and regularly update each company’s ratings. From time to time, our intuition conflicts with the output of our model. When this occurs, we dig deep into what is happening and why. Sometimes, for example, while our intuition might be telling us we shouldn’t be trimming a stock that the model is calling for, after further assessment we recognize that we’re just suffering from a bias of wanting to hold a “winning” stock and so we go with the model’s recommendation and trim the position. Occasionally, we recognize that our intuition is correctly recognizing that some aspect of our rating has gone stale and we update our conviction. This continual examination of our process can lead us to slightly edit our model from time to time. We know that our model is not perfect and so we’re open to refining it if the evidence is strong that we should make a change.

In the next post in this series we’ll review our process for letting stocks drift from target and how we decide when to trade between companies in the portfolio.

Please click here for Part 4 or here for Part 6

Please read important disclosures HERE.

While we do not accept public comments on this blog for compliance reasons, we encourage readers to contact us with their thoughts.

Past performance is no guarantee of future results. All investments in securities carry risks, including the risk of losing one’s entire investment. The opinions expressed within this blog post are as of the date of publication and are provided for informational purposes only. Content will not be updated after publication and should not be considered current after the publication date. All opinions are subject to change without notice and due to changes in the market or economic conditions may not necessarily come to pass. Nothing contained herein should be construed as a comprehensive statement of the matters discussed, considered investment, financial, legal, or tax advice, or a recommendation to buy or sell any securities, and no investment decision should be made based solely on any information provided herein. Links to third party content are included for convenience only, we do not endorse, sponsor, or recommend any of the third parties or their websites and do not guarantee the adequacy of information contained within their websites. Please follow the link above for additional disclosure information.

This is part 4 of our series on position sizing. Part 1, Part 2, Part 3.

In our last post, we explained why an investor’s conviction in a positive outcome – the probability of success – is just as important as how much potential upside a stock may have when you are trying to determine how much of a stock to own. In this post we’ll explain how we go about ranking the probability of success for companies in our long term oriented investment strategy and how this process drives our position sizing process.

In this post, we laid out a diagram of Ensemble Capital’s investment philosophy. In the diagram, we explained the three core areas we assess and the two key questions we seek to answer in each area.

  • Moat
    • How likely is it that the moat be intact in 10 years?
    • How likely is it that customers will value the product/services in 10 years?
  • Management
    • Does management understand and execute on creating economic value?
    • Does management thoughtfully weigh dividends, buybacks, M&A, and debt repayment?
  • Forecastable
    • Does the business lend itself to accurate forecasting of long-term outcomes?
    • Does our team have the domain knowledge to understand the business?

Each of these questions help us evaluate the degree to which we think we can adequately establish the fair value of a company. We rate each question on a scale of zero to three.

3: We are highly confident in a positive answer to this question. We believe that the odds of our positive answer proving to be correct over time are about 80% or better.

2: We are confident in a positive answer to this question. We believe that the odds of our positive answer proving to be correct over time are about 70% or better.

1: We believe it is more likely than not that a positive answer to this question is correct. We believe that the odds of our positive answer proving to be correct over time are about 60% or better.

0: We are not confident in a positive answer to this question. We think the odds that a positive answer proves to be correct over time are about 50% or worse.

We also assign a “global” conviction rating in which we rate our confidence on the more general question of how likely we think it is that “the fundamentals will play out as or better than expected” for the company in question. This last question provides an opportunity for us to intuitively assign our own weights to all of the tangible and intangible elements of a company being successful.

In general, our global rating is very similar to the average rating of the sub question. When there is a deviation, we dig into why we think the whole is better than the sum of the parts to determine if there are intangible, hard to quantify aspects of the business that are not captured by the sub ratings, but which we think correctly support our higher global rating. That analysis sometimes validates the deviation in our global rating, while other times we recognize that our intuitive weighting was being driven by a bias (such as maybe really liking the management team and thus intuitively underweighting an only mediocre moat or higher cyclicality than we like to see).

Finally, we average the ratings to establish a single, quantitative conviction score. To buy a company, it must have an average rating of at least 2. Our highest conviction names score near to a 3 (it is extremely rare for a company to be assigned a “perfect” 3 rating). If a company is scored as a zero on any question, it become uninvestable for us. Any 1 ratings require a discussion. If there are multiple 1 ratings, we typically won’t invest in the company even if the average rating is a 2.

We believe that judgement around the long term outlook for companies is an inherently qualitative decision making process. However, by converting our qualitative judgement into quantitative scores we create a process by which we can make decisions on exactly how much of a stock we want to own and precisely differentiate between our portfolio of holdings, all of which we like and have a strong level of conviction in.

Readers familiar with the work of decision making researchers Phil Tetlock, Nate Silver, Annie Duke, and Daniel Kahneman will notice their thinking has greatly influenced the system we’ve developed. Kahneman, for instance, discussed a decision making process for identifying military recruits who might have the talent to become officers that used specific attribute questions as well as a “global” rating. Tetlock has extensively documented the need for forecasters to quantify the probability of an event occurring as well as making sure that forecasts are internally consistent with other forecasts made about the same topic. Duke explained the need for decision makers operating under conditions of uncertainty and time pressures to align and reconcile what Kahneman called System One, or reflexive decision making, and System Two or deliberative decisions making. And Nate Silver elegantly described how Bayesian statistics demonstrates how best to update your assessment of probabilities as you obtain new information.

While the literature on investing extensively documents how to evaluate and value a company, and financial planning literature comprehensively analyzes asset allocation decisions, bizarrely there is very little literature on how to size investments within a portfolio. The literature on this subject is often based on managing short term volatility of stock prices, rather than managing long term risk of permanent impair of capital. But lucky for us, the science of decision making has been more robustly explored when it comes to forecasting political events (Tetlock/Silver), sports events (Silver), gambling (Duke) and then we have Kahneman who literally won a Nobel Prize for his psychological insights to economic theory, particularly in the areas of judgment and decision-making under uncertainty.

In our next post, we’ll share a sample position sizing matrix and then finish up this series with a discussion of how we decide when to trade between stocks in our portfolio and how new stocks end up in our portfolio.

Please click here for Part 3 or here for Part 5

Please read important disclosures HERE.

While we do not accept public comments on this blog for compliance reasons, we encourage readers to contact us with their thoughts.

Past performance is no guarantee of future results. All investments in securities carry risks, including the risk of losing one’s entire investment. The opinions expressed within this blog post are as of the date of publication and are provided for informational purposes only. Content will not be updated after publication and should not be considered current after the publication date. All opinions are subject to change without notice and due to changes in the market or economic conditions may not necessarily come to pass. Nothing contained herein should be construed as a comprehensive statement of the matters discussed, considered investment, financial, legal, or tax advice, or a recommendation to buy or sell any securities, and no investment decision should be made based solely on any information provided herein. Links to third party content are included for convenience only, we do not endorse, sponsor, or recommend any of the third parties or their websites and do not guarantee the adequacy of information contained within their websites. Please follow the link above for additional disclosure information.