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Investment Decision Log – User Manual

I have recently created a spreadsheet to record all of my investment decisions. The goal is to track decision quality and try to learn from mistakes and reinforce things that I am doing well. This is a long term project to improve my decision-making skills.

Decision-making is inherently statistical in nature. Hence the nature of this assessment should be statistical in nature too. With a sufficiently large sample size, the assessment of the decisions should start to become meaningful as the quality of my decisions will converge with share price performance.

I also included ideas that I have done some work on but not acted on….

Each decision will be given a rating based on two dimensions – 1) share price performance since decision; 2) facts that evolved since the decision to measure the soundness of decision logic e.g. I sold DTG because I think the risk of long term price competition is not sufficiently captured in the valuation.

The second dimension is still subject to my own judgement and hence the risk that it is not sufficiently well captured. The good thing is that I still have share price performance as an objective measure to capture things that clearly look out of place. For example, if the share price is down 90% while I claim the original decision is good then I need to have a very convincing explanation backed by strong evidence. I trust that I can be brutally honest to myself.

To assess the second dimension of decision making:

      1. Did what I predict to happen actually materialise?
      2. Based on the outcome of the events, was the original probabilistic assessment correct?
      3. Was luck involved in the magnitude of the outcome? (added to comments)

If the answer to all three is positive, then it is a good decision. If 1 and 2 are conflicting, need to explain why they are conflicting. Will still need to make a collective judgement. Also need to comment on the role of luck. For example, I expect a positive event to yield a 10% increase in share price but it went up 50% because of extraneous factors. Then luck was responsible to push up the magnitude of the return

There are five possible ratings for each decision:

      • G – Good Decision and Good Outcome
      • U – Good Decision and Bad Outcome
      • L – Bad Decision and Good Outcome
      • E – Bad Decision and Bad Outcome
      • X – Unable to evaluate decision quality regardless of the outcome

The goal is to prevent U decisions to discourage me from making the same decision in the future. Nor should I let L decisions to trick me into over-confidence. And allow for reinforcement by G decisions and learn from E decisions. Rating X is given to decisions where there is insufficient facts and time to evaluate the quality of the decision.

The assessment period for each decision depends on the nature of the underlying decision. For example a special situation investment decision depends on the outcome of a specific event. So even if I sold the position before the event crystallises and make profit on it, I must still wait for the outcome of the decision to determine the quality of my decision. On the other extreme, an investment in Games Workshop requires a longer time to evaluate because the fundamental investment thesis is a long term one. For example, Warhammer IP is a very good one requires continuous assessment. Hence each investment decision should be assigned to different assessment periods.

Ways to analyse my own decision:

      • Based on position size – big vs small – am I good at making big position decision vs small position decisions
      • Value of add and reduces
      • Decision by investment categories – General / Compounder / Workouts
      • The magnitude of mistake of omission
      • The decision over the lifetime of each investment
      • The decision that yield the best returns vs worst returns

Shortcomings of this decision log – it doesn’t capture a lot of passively made decisions such as to do nothing to an existing position when stock prices go up. This is something I need to think about how to capture better.

Links

  • Booking.com thesis (excellent overview) – [the 10th man BKNG thesis]
    • I concur the valuation is compelling but I think this is borderline too hard. In my mind there is a >70% probability BKNG stock is a home run. In the other case, BKNG loses its edge relatively quickly (measured in years while it trades around 20X earnings) as Google keeps innovating & lowering the user friction to book directly with hotels (or any other OTA bidder, aka make the bidding process for ads in the Hotel Module – which is one giant & very user-friendly meta search ad – much more competitive & hence expensive for BKNG). If the user experience becomes better (and hence the search process for hotels and travel starts) on Google, then the legacy moat of BKNG is in trouble. The post does not elaborate on potential further Google innovations such as Google Assistant sorting out a booking with a direct AI phone call to hotels, passing on the parameters the user was looking for originally (hence lowering friction to book directly & getting a birds’ eye view on). Stratechery [The Google Squeeze] focused more on the latest innovation at least.
    • Google makes available a direct booking API for larger chains to easily plug into. That will increasingly happen to smaller hotels too. BKNG has painful take rates of 15% on hotel revenue

Google Hotel Module is making auctions for customer attention more competitive. As the real estate of mobile phone is limited, competitors get only one shot for attention in this superior meta search tool. The highest bidder is featured on top.

  • Druckenmiller 2020 Outlook [Bloomberg]
    • general takeaway: long equities, commodities (though not energy), short long duration fixed income (he is basically long inflation)
      • maybe not read too much into it as he reverses positions frequently
    • last takeaway: bull on UK domestic economy: “never underestimate the common sense of the British people” – Thatcher via Druckenmiller. Stan is a brexiteer, biggest FX long is GBP & says UK domestic stocks are at low multiples.
  • Peter Lynch in [Barron’s ]

 

 

 

Information Flow – A Force of Nature

I have been going through old interviews by Meituan founder, Wang Xing. He is obsessed with the mechanism of information transmission in our society and technological advances that improves information transmission brings about drastic changes to how people interact and conduct their daily lives. And when people change their ways of conducting daily lives, it usually means formation of new businesses that take advantage of these changes. Incumbent businesses that rely on the older way of information transmission will be left in shatters.

Wang Xing likes to define information technology (IT) as technology that enables the flow of information. So in this sense, just like Internet and smartphones, older technologies such as printing press & books are also IT. In fact, three out of the four ancient Chinese inventions (papermaking, printing, gunpowder and compass) are about IT. Papermaking is about storage of information. Printing is about mass replication of information. Finally compass is about production of geospatial information.

Internet brought about a revolution in terms of information transmission. Not only does it lower the cost of information transmission, it also unlocks new ways to transmit information. For example, social networks built on top of Internet allows information to pass through the network of humans. We share news, products, services, personal experiences with our friends on social network. Direct to consumer businesses have taken advantage of this change. This is challenging the long-held belief that FMCG companies have impregnable moats.

Technology does not just reduce cost of information transmission. They also allow new types of information to be transmitted. For example smartphone allows the individual location information to be transmitted instantly and hence new businesses such as ride-hailing and food delivery platforms to emerge. Smartphones also allow companies like Meituan which is a marketplace for local services such as hairdressers, beauty salons and restaurants to market to potential audiences more efficiently than before. Meituan will show consumers nearby local services based on location information which are most relevant to the user.

The impact of information flow is even more nuanced than just allowing new business models to emerge. I believe that because the digital revolution has brought about reduced communication and management costs which has reduced the transaction costs within a firm. This partly explains why we are seeing companies becoming larger than ever. I don’t think this trend will reverse.

As investors, the appreciation of the magnitude of changes and the path of change can be very lucrative. For example Uber drivers have driven demand for vehicle hires and this demand for rental vehicle has driven fleet sales in Brazil. Since fleet sale is done at wholesale price, this meant that auto OEMs has to accept lower unit price and margin compression.

The increased digitalisation will transform the food industry in the next 10 years. The first step has been the arrival of the food delivery platform which brings food from the restaurants to the consumers. This can happen because the consumer side has been “digitalised” with smartphone. The next step is to digitalise the restaurant. Currently the bottleneck in terms of food delivery efficiency lies with waiting time at the restaurants. Digitalisation of the kitchen will allow the food delivery platform to predict cooking time much more accurately. Then it is about the logistics. Instead of delivering with clumsy human beings, the next step is to deliver with autonomous robots. Eventually, I believe that food production will be centralised and cost of food comes down dramatically. People will continue to go to restaurants for social experiences. But new flats might not have kitchens anymore.

After studying Wang Xing carefully, I am 100% with him that digitalisation will continue to change the society. And the next stage of change is mostly focused on the enterprise side. While Warren Buffett has derided “change” as detrimental to the investors, I think “change” can be quite lucrative if it is misunderstood by most.

Scribbles: KKR at the Goldman Sachs conference

  • 2X as much KKR investment professionals in RoW vs US.
  • 18 out of 22 investment strategy families less than 10 years old. Significant operating leverage on those (mostly) RoW people in young strategies
  • KKR Capital Markets: doing deals with third parties and being able to control the dealmaking and capture some fees, having a cap markets division is a competitive advantage that does not show up in typical alternative’s KPIs such as AUM, FPAUM etc
  • when KKR converted to C-corp, it stopped distributing 75% of distributable earnings. Today it is lower
    • Note I believe this is reason why KKR trades cheapish: the market prefers bird in the hand over compounding bird in the KKR stock bush, with supermajority voting stock etc.
  • KKR major player in Asia, biggest in terms of private equity funds, started in 2005
  • Most Asian investment professionals actually in India
  • Investing mostly in domestic consumption stories in Asia

 

Coming wave (next 1-2 yrs) of flagship fund raisings, chronologically:

  1. Asia PE fund
  2. Americas
  3. Infrastructure

 

Flagships will be additive to overall fundraising (last 3 yrs 90B of AUM raising without flagship wave, i.e. I believe only 1) and should be 30-40B USD in aggregate.  Everything else equal, 120B AUM raising in 3 years is possible (bull case environment maybe).

 

Odd lot split-off Danaher – NVST

If you buy 99 shares of Danaher through IBKR today at the US open, you’ll probably be able to tender these by the end of the business day (which is the deadline) for 5% more value in NVST shares. In two weeks you’ll get NVST shares delivered in your account.

Danaher is splitting off its 80% interest in NVST.

I am doing this unhedged, i.e. simply buying DHR and taking the volatility risk on NVST

Results may vary a lot, but 5% upside baked in is quite a high expected IRR for a few weeks of waiting.

 

 

 

 

Presenting a new blog post category: scribbles

The blog has been inactive for a while as the time hurdle to write down structured thoughts proves to be high.

From here we’ll post interesting notes, conference tidbits, thoughts in a more haphazard way under the post category “scribbles”.

We hope you’ll enjoy it.

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