Investing Journal

I recently installed Windows 7 64-bit on my laptop, and found myself unable to install a driver for the Canon Pixma iP4200, which I have shared from my 32-bit version of Windows 7.  Normally you can do this by installing multiple drivers on the host machine by pointing Windows at the .inf file for the other version of Windows you want to use.  Well, Canon drivers don’t come with an .inf file.  So I contacted them, and these are the instructions, which are really easy and should work with any printer.  They’re unintuitive, so I thought I’d post them.

1. Install the driver on your client computer as if it were local.
2. In Devices and Printers, right click the printer and click on “Printer Properties” (not “Properties”).
3. On the Ports tab, add a new local port with the path to the printer. (\\Server name\Printer name).

That’s it.  Any friends in Windows want to make that more automated?

The Gorilla Game: An Investor’s Guide to Picking Winners in High Technology – by Geoffrey Moore

I just finished the 2-3 hour abridged cassette tape book, The Gorilla Game.  The premise of the book is that there is an easily identifiable pattern that technology industries follow that can make you a lot of money if you invest following the rules outlined in the book.  The rules are remarkably simple and low maintenance.  But the amazing thing to me, is that it’s a book about technology, written 10 years ago, that still seems relevant.  It’s not a strategy I plan to follow, but it’s still interesting.  I don’t particularly recommend the book, but it’s short.  I can lend you my tapes.

Principles:

• The technology industry is characterized by discontinuous innovation – new business segments periodically appear that are not evolutions of a prior technology, and are often started by new companies.
• When a new business segment is forming, the stock market frequently underestimates both the growth rate of the industry, and the length of the growth period.
• He names phases a Bowling Alley, and a Tornado phase. In the first, the business segment is slowly getting integrated by customers that are typically most tech savvy.  In the Tornado phase, almost everyone realizes that the technology will be useful.
• There are 2 classes of business segments:
• Enabling Technologies:  Winner-take-all segments like pc processors.
• Software Applications: Since you can get vendor-lock-in, there may be several small players that have success in this area.
• There are 3 types of companies:
• Gorillas – dominant leader in a segment. (such as Intel)
• Chimp – challenger to the gorilla.  (typically doomed to fail)
• Monkey – follower of the standards set by the leader.  (AMD)

This is the process:

1. Scan publications and news for business segments that may enter a tornado phase.
2. For Software Applications, buy before a gorilla emerges, because even chimps may be profitable.  For enabling technologies, wait until the tornado phase starts.  Buy stocks in all companies that may become the gorilla.
3. Track market share quarterly and consolidate holdings in the gorilla.

I’m not sure how well this will apply in a more mature technology market, where it feels like hardware is built on more standards, and software is increasingly built on advertizing, making it unclear if they will profit.  It all sounds reasonable, but I wonder if I’ll ever identify a business segment that I think will enter a tornado phase, and if I do, will the companies be public (which seems rare recently).  Anyway, the book convinced me that I should hold on to my Apple stock for longer.  I think the market is still under appreciating the growth potential for Apple, even after rising 70% since my Apple analysis post from late last year.

Note, my book notes are probably more for my own benefit than yours.  A 1-page summary just can’t do any book justice.

Predictably Irrational: The Hidden Forces that Shape our Decisions – by Dan Ariely

I finished listening to the audio CD of this book and really enjoyed it.  I wouldn’t say I particularly learning anything useful, but it was interesting to hear all the studies that prove just how irrational people are.

Dan cites study after study that prove:

1. We make decisions by comparing outcomes, rather than weighing factors correctly.  For example, if people are offered 2 similar items, and 1 different item, but 1 of the similar items is clearly better, people will choose the better of the 2 similar items.  Apparently it’s because they can effectively prove they’re not getting the worst of the 3.  This is why selling a new item for a much higher price can actually improve sales of other items, that now look like great deals.

2. People choose free things irrationally.  When something is offered for free, we often choose it, even when paying for something better would be a more rational choice.  He did an experiment where people were offered a Hershey Kiss for 1 cent, or a Lindt chocolate for 30 cents, people chose the Lindt.  But when the price of each was lowered by 1 cent, people chose the Kiss.  I think we can overcome this by adding $1 (or so) to the price of each item.  Would I rather use an ad-funded, naggy version of an app for $1, or buy the full version for $11?

3. People overvalue what the own.  When people are offered to sell some expensive tickets they won, they wanted really high prices for them.  Yet when they asked people who didn’t win what they’d pay, they wouldn’t pay nearly so much.  (He tells the story really well about Duke basketball tickets).

4. People like to leave options (doors) open, but that distracts us from our main goal.  He tells a story about people clicking on doors in a game to get a high score, and even though people didn’t need the door to do well, they tried to prevent it from disappearing.

5. We are happy to do socially beneficial things, but not when we are paid to do them.  Apparently there are market norms, and social norms.  It’s not good to pay someone to take you to the airport for example, because they stop thinking of the good their doing, and start thinking of if it is worth the money.  A non-monetary gift would be better in such a situation.

6. It appears that most people are willing to cheat in a scientific study, but likelihood of being caught isn’t much of a deterrent.  If the person is rewarded in non-cash, they’re much more likely to cheat.  He did an experiment where Cokes in college commons areas disappeared, but dollar bills did not.

Anyway, I liked it, and recommend it lightly, just don’t expect it to be life-changing. :)

Note, my book notes are probably more for my own benefit than yours.  A 1-page summary just can’t do any book justice.

More Than You Know: Finding Financial Wisdom in Unconventional Places by Michael Mauboussin

I finished reading More Than You Know this week on a relaxing vacation in Denver.  The core premise of the book is to teach us about things that are loosely related to investing, but generally no investing strategy.  It is divided into 4 sections: 1. Investment Philosophy, 2. Psychology of Investing, 3. Innovation and Competitive Strategy, and 4. Science and Complexity Theory.

I’ll list some notes preceded by the chapter number they’re from.  The chapters are quite independent.

1. Robert Rubin’s Harvard graduation speech: There is no certainty.  Decisions are a matter of weighing probabilities.  Judge your decisions on process, not only results.

3. Separate probability and magnitude.  Too many people focus on probability. Keep good focus.

5. Risk: unknown outcome; known distribution. (roulette).
    Uncertainty: unknown outcome; unknown distribution.

9. Six psychological tendencies that encourage a positive response: 1. Reciprocation; 2. Commitment & Consistency; 3. Social validation; 4. Personal Liking; 5. Authority; 6. Scarcity.

10. Two important, overlooked properties of the stock market:
    a. Nonlinear: cause and effect are not neatly linked.
    b. Nonstationary: statistical properties change over time.
This means that our mental experiential system is poor at making decisions.

14. Value is created by organizing the world’s resources.  Today more people are inventing instructions (which organize them) and less are executing them (because they’re automated).  Thus the importance of education to keep up with standard of living increases.

15. Businesses in a free market are similar to synapses (connections in the brain).  In young industries many businesses form, and then they disappear.  At one time there were over 75 companies in the US Auto industry.  One investment idea is to look at survivors at the end of a bust period.  Like solid internet companies in 2002, or maybe solid banks at the end of this crisis?

16. There’s an interesting idea about how industries start somewhat slow, and then have a rapid growth at which point investors tend to expect the growth to continue for extended periods.  Basically you should buy leading companies just before growth ramps up, and sell after a lot of growth happens and it becomes unrealistic.  I want to read the book Gorilla Game which is related.

17. The company leadership cycle is getting shorter.  Microsoft is bucking the trend by staying in the lead for so long.  Can it last?

18. Strategy as Simple Rules.

20. PE ratio affected by: taxes, inflation, earning composition (tangible assets), equity-risk premium.  The author thinks the PE ratio is likely to remain high.

22. “Intelligence is all about making a guess that discovers some new underlying order.”
Need to read this and this.

26. Low-probability, high-impact events are the biggest problem in finance.

27,28. You frequently can’t link cause and effect in the stock market.

30. Companies that reach the Fortune 50 often stall in their growth. High growth companies fall within realm of chance. See paper.

In general I liked this book because it broadened my viewpoints, but I’m not sure there’s anything necessarily great about it.  I lightly recommend it.

The Little Book that Builds Wealth: The Knockout Formula for Finding Great Investments by Pat Dorsey

I just finished this short book, and it was pretty good.  A long article on moats, or competitive advantages would have probably sufficed.  It was probably a good exercise to think about them for at least a few hours and go through some exercises.  Here are my notes on the book about how to follow his process.

Companies fall into 1 of 3 categories:

1. Wide Moat
2. Narrow Moat
3. No Moat

Questions to ask to identify the size of a company’s moat:

1. Has the firm historically generated solid returns on capital? (morningstar.com - Key Ratios)
2. Does the firm have one or more of the competitive advantages listed below?
3. How strong is the competitive advantage? Is it likely to last?

Competitive Advantages:

1. High Switching Costs - business services typically are great here. Consumer services are frequently poor.
2. Network Economics - great distribution network (visa), monopoly-like companies (eBay).
3. Low-Cost Production - better process (Dell) (temporary?), location, asset. size advantage (ups).
4. Intangible Assets - brands (premium, not popularity), patents, regulatory licenses.

Drivers for valuation:

1. Risk
2. Return on capital (will be >15% if the company has a moat)
3. Competitive advantage
4. Growth

He likes the price to cash-flow ratio for determining a fair value.

Only sell when one of the following conditions have been met:

1. Did I make a mistake?
2. Has the company changed for the worse?
3. Is there a better place for my money?
4. Has the stock become too large a position?

Super Crunchers: Why Thinking-By-Numbers Is the New Way to be Smart by Ian Ayres

I recently finished the unabridged audio CD of this book.  Despite a few college courses, I know little about statistics, and this book helped me think about how I should apply it more in my career.  This book doesn't need to be this long, and it doesn't really have any deep discussions, or how-to ideas--it's more like a motivational book for using statistics to make decisions.  Personally, I think that's what a lot of managers need to read.

I enjoyed learning about how law experts and doctors should be able to use statistics more in their professions, and it made me think of a way I should use them in mine.  The book tells how regression models were set up to predict wine based on rain and temperature, and decisions of the Supreme Court by knowing only a few facts about the case.  The models do way better than the experts in the area.  I hope to be able to encourage my team to use a bug priority calculator rather than the current gut-based approach.

In general, I enjoyed it, but you may just get good value out of it by pondering the following summary from an Amazon reviewer.  It doesn’t do it justice, but it’s a basic outline of the book:

(1) Mathematical regression models generated from large datasets often generate better predictions than human experts, and they provide supporting information on the predictive weight and reliability of each explanatory variable.
(2) Well-crafted experiments using randomized trials and control groups provide good market research and behavioral analysis results.
(3) Technological advances - the Internet, massive data storage devices, rapid computation, broadband telecommunication - are making it possible to share more sources of information and create ever-larger databases for analysis.
(4) Today's companies engage in multiple forms of market research by creating and using large databases and large-scale randomized trials.
(5) Many phenomena conform to normal distributions in which 95% of the population will be found within two standard deviations of the mean, the 5% balance generally divided evenly in the two tails.