Just one week ago I wrote that I initiated a position in Aratana Therapeutics (NASDAQ:PETX). At the time stock was trading was trading at roughly a 1.2% discount to the price Elanco Animal Health (NYSE:ELAN) was willing to pay, and a CVR with a max payout of $0.25/share was thrown in the mix for free. With Elanco now trading at $30.77/share and Aratana at $4.63/share the spread is a negative 1.6% and the CVR is implicitly valued at $0.07/share. My rough, and perhaps optimistic guess, of its value was around $0.10/share so there is little reason to keep my Aratana position. Unfortunately I decided to not hedge with short position of Elanco, so while the spread went in the right direction, I didn’t make any money. But that’s not a bad result for a week like this!

Disclosure

No position in Aratana Therapeutics anymore

A question that people regularly ask me is what kind of books, blogs and other resources I would recommend. To tackle this question once and for all I thought the best idea was to write one or more blog posts about it, starting with books that I would recommend reading. What books to recommend is an easy question in my opinion. There are really a ton of good books out there, and I don’t think it matters too much which ones you read. Start with some of the well known classics and it’s really hard to go wrong.

After reading enough books on, for example, value investing you will see the content becoming more and more repetitious. They first book you read you might think, “Wow, that’s genius buying stocks at a discount to NCAV” while in the 10^th book you start thinking “Can’t this author tell me anything new?”. It’s not that the 10^th book is a worse one, but at some point you know the drill. So I have to admit that these days I rarely read investing books anymore, so quite possibly I’m not the most qualified person to give reading tips. But since you have been asking I’ll try anyway! All the books in the lists below are books I have read personally. If you think I’m missing something great, let me know in the comments.

Investment basics, philosophy

If you are totally new to investing these are the kinds of books that I think should be helpful to understand what (intelligent) investing is, what kind of attitude you should have with regards to your investments, and what kinds of stocks could be profitable investments.

• The Intelligent Investor I think this timeless classic is a must have on the reading list, and a good one as a first book to read.
• Margin of Safety Only buy it if you run a successful hedge fund and want to showoff with the books on your bookshelf. For the rest of us, you should be able to find the pdf-version somewhere online.
• The Manual of Ideas A way more modern text that incorporates many of the ideas of the previous books and some new ones.
• Common Stocks and Uncommon Profits and Other Writings There is more under the sun than just buying dirt cheap stocks.
• Essays of Warren Buffet At some point in your investing life you will probably get bored by hearing for the millionth time the same Buffett quotes, but if you are new to investing these are a must read. Instead of buying the book you can also go for the value option, and read all of Buffets shareholder letters on berkshirehathaway.com.

Psychology

Understanding psychology is extremely important if you want to make good decisions, yet at the same time it’s very hard to apply. There is a big difference between reading a book about psychology, and being able to avoid the many psychological pitfalls you read about. But reading these books is a good first step I think.

• Thinking Fast and Slow It’s not really an investment book, but a great book to understand the various heuristics and biases our brain has that can influence our decision making. An absolute must read in my opinion.
• Beyond Greed and Fear It’s the same topic, but this time a bit more tailored towards investing.

Applied accounting/investing/valuation

The border between books in this category and the investment basics category is arguably a bit vague, but these books are on average a bit more practical, maybe aiming at more specific subjects and heavy on examples and case studies.

• You Can Be a Stock Market Genius The title of the book is very cheesy, but this is really one of the best books ever written on special situations investing. Highly recommended!
• Security Analysis: Sixth Edition, Foreword by Warren Buffett Another classic that goes a bit more in-depth into the accounting and valuation. Don’t read this one as one of your first books though, probably a bit too tough and dry!
• Quality of Earnings Another text I highly recommend. Being able to spot weird things in financial statements is an invaluable skill.
• Financial Shenanigans A good addition to the book linked above.

Case studies/entertainment

Most investing books I read nowadays fall in this category. Individually you’re probably not going to learn a whole lot from each book, but most of these books contain interesting nuggets of wisdom, and perhaps more importantly, are just very good reads.

• Fooling Some of the People All of the Time Entertaining story of Einhorn’s fight against Allied Capital.
• Reminiscences of a Stock Operator Interesting story about trading and speculation in the early days of the stock market
• The Big Short One of the best stories about the 2008 financial crisis. The movie is surprisingly great as well. So if you are tired of reading, you can just watch it.
• When Genius Failed Good story about the spectacular failure of the Long Term Capital Management hedge fund.
• Red Notice Hedge fund manager turns into social justice warrior after the Russian state murders his lawyer. Book reads reads like thriller, something you don’t expect in the finance category. @Billbrowder is worth a follow on Twitter too.

Part of my bookshelf containing some of the books recommend above

Disclosure

All of the links in this post contain my Amazon affiliate code. If you buy something I will get a small percentage while you get the same price as always. Hopefully this post can make me rich!

It’s probably a sign of the times that every self respecting value investing blog has to have a post about bitcoin. And when you tell a random person that you invest the first question these days is if you own (or trade) bitcoins or not. So I guess “The Alpha Vulture” can’t stay behind, and share with the world what it thinks about bitcoin, whether you want to hear it or not. As the title of this post implies I’m skeptical about bitcoin, and most other software projects that are based on blockchain technology^[1].

To explain why I’m skeptical I’ll first try to explain in the simplest possible way what the whole fuss is about because I suspect that part of the reason why people are so enthusiastic about the blockchain is the fact that they don’t really understand the technology. If you read an explanation about bitcoin you hear cool terms like distributed trust (which is actually cool) and stuff you probably don’t understand without a computer science background (like hash functions). These things are actually not that hard to explain, but not really necessary to understand the key idea behind the blockchain. What you basically do is, instead of trusting security to a single party that (hopefully) has a smartly designed system you substitute that by a large amount of computational power provided by multiple parties. There is a lot more to it than that, but that is just noise and implementation details.

The fact that you don’t rely on a single party is great! I’m not going to dispute that.

The fact that the security is provided by a large amount of computational power is bad.

Just a large amount of computational power would actually not be bad. Chips always get faster and more efficient. But it’s not just a large amount, it’s a relative large amount. The security only works because the amount of computational power required to “take control of the network” is simply too expensive for a malicious third party to acquire (even temporary). So if chips get 10 times faster the computational power required to keep the network secure simply increases with the same factor. If chips get 100 times more energy efficient, the amount of energy required to keep the network secure doesn’t drop at all^[2].

Additionally, the amount of computational power required scales with the value of the transactions being done on the network. You need enough computational power to make it unattractive for an attacker to acquire even more computational power and take control of the network. How much computational power this exactly is, is quite an interesting theoretical question (I don’t know the answer). But for sure the amount is high. An attacker only needs a short moment to inflict huge and long-lasting damage on the network. They could for example double spend coins, hurting not only those who would receive them, but also causing a massive loss of trust in the system that would be longer lasting.

So why is this all so problematic? It’s simple: high computational power requirements translate into (relative) high transaction fees. And that’s a problem for a lot of the applications that have been proposed for the blockchain. Using bitcoin as a currency is the biggest obvious problem. Most banks for example process millions of transactions daily, and most of these transactions are almost free because running a nice secure sever that handles a million transaction a day isn’t a lot more expensive than one that handles just a few transactions^[3].

If you use bitcoin to speculate on the value of bitcoin transaction costs of a few dollar^[4] aren’t very problematic. If you use bitcoin like some sort of virtual gold slash store of value it’s not a real problem. If you use bitcoin for money laundering or other black market transactions it also not a problem^[5]. But for the first two to work out I think you need bitcoin to become a broadly used medium for transactions, and just betting on there being a large black market is a questionable proposition as well in my mind. The current hype is mostly driven by speculation, and for most real-world applications high transaction fees are a very serious obstacle. There are probably some niches where it makes sense, but I think broad adaptation of blockchain technology would in most cases be a step backwards. And I’m only looking at the financial angle here, not even mentioning the environmental impact it would have…

Disclosure

Author has zero blockchain related exposure^[6]

^{[1] I’m using the blockchain and bitcoin sort of interchangeably in this post. I know it’s not the same.}

^{[2] I doubt this is true. There is probably some complex relationship between both the cost of new chips, the energy requirement and perhaps even some other tangential factors.}

^{[3] At least not a factor million more expensive. Scaling performance is still hard. It is also going to be hard for bitcoin.}

^{[4] Average transaction fees are now already ~$4, and this is despite the fact that miners are currently also compensated by newly mined bitcoins. Currently miners get 12.5BTC for every block mined. With roughly 2,000 transactions per block and a current BTC price of ~$5500 one transaction actually costs almost $40.}

^{[5] The fact that all transactions are publicly stored in the blockchain might be though…}

^{[6] I used to own a couple of dozen bitcoins in 2011 or something like that back then when mining them on your own graphics card was still economical. Sold them between $20 and $30. Still better than buying two pizza’s for 10,000 BTC though.}

Regular readers of my blog have probably noticed that I use Google Sheets for almost everything. I use it to track the live net asset values of various companies, I built my valuation models in Google Sheets and everything is tied together in a master portfolio sheet (some if its functionality you can glimpse from my performance review posts) . A few years ago I wrote a short tutorial that explained how you can add realtime price information in Google Sheets for stocks that aren’t support by the standaard =GoogleFinance() function.

What I’ll be focusing on today is how you can pull data from multiple documents to create one sheet with a nice overview of everything. To make this easy it’s smart to standardize your documents a bit. What I do is that when I value a company there is always a sheet called “Thesis” that has a few standardized items at fixes positions, like this:

The price target links to a valuation model in a different sheet (but in the same document), the price (in this case) comes from the =GoogleFinance() function and the last update cell is useful to keep track if I have updated my valuation recently. As you can see here I should probably take a little bit of time soon to update my Pardee Resources valuation. If you make it a habit of doing this for everything your research you can make a nice portfolio sheet that tracks realtime how much upside every position has remaining. Part of my portfolio sheet looks like this:

For obvious reasons I have hidden the number of shares I own, and the value of every position, but you can see how I have an price target for every position that is updated automatically based on the latest price. To pull data from a different document in the sheet we can use the =ImportRange() command. It requires two arguments, one is the url of the sheet where you want to pull the data from, and the second one is the reference to the cell you want to pull the data from. So it would look something like this:

=IMPORTRANGE("https://docs.google.com/spreadsheets/d/blabla/edit#gid=123456789","thesis!C15")

After entering this function in a cell you initially get an error message. Hover with the mouse cursor above the error message, and you will see that a small pop-up that asks if you want to give your sheet access to the other sheet. Do this, and the data will appear :).

Combined with the option to get realtime prices (as discussed in my old tutorial) you have an incredible toolset to make a fancy spreadsheet.  Not only will this give a quick overview of which positions are perhaps becoming more or less attractive, it’s also easy to build a watchlist like this (I have done this as well). Everything that you researched, but didn’t buy can be put in that list together with positions that you have sold in the past.

One limitation to keep in mind is that Google isn’t happy if one sheet requires to much calls to outside sources, and by making one sheet dependent on tons of other sheets the number of calls can explode quickly. So you will have to try to not invoke to many =ImportHTML() and =ImportXML() functions in all the combined sheets in order not to break things.

Disclosure

Author is long the stuff in the sheet