A Wealth Creation Journal

Category: Talk

Takeaways from MiB podcast interview with Ed Thorp

I very much recommend listening to the podcast episode of Masters in Business with Ed Thorp, through iTunes or through this Bloomberg link. It might well be one of the greatest podcasts of 2017. Thorp talks about how much he liked working with Claude Shannon, the father of information theory (my favorite class in uni). Shannon helped Thorp to optimize his position sizing in blackjack, given the odds and the estimated edge calculated by Thorp’s own card counting system.


Some takeaways:

  1. When Thorp didn’t ace a chemistry competition because he did not bring along the right slide rule to the exam, he learned that there’s many different ways to fail in real life. Takeaway being that in investing one should think creatively about different downside scenarios.
  2. Thorp was asked whether managing money did not get scary when the stakes went higher. His response was that he gradually got used to new amounts of money and that the fundamental problem of investing does not change along with scale (although the type of successful investing changes because of scaling). I relate a lot to this argument that investors should always start small to get comfortable with managing money.  This could be one of the reasons why many of my friends that were not investing as students feel uncomfortable now with the higher amount of money that they accumulated throughout their careers as they did not “grow” into investing. For my personal portfolio, I started out with a small amount of savings 10 years ago and I am managing an amount almost two orders of magnitudes higher today. Compounding has the property to feel slow in the short-term, which is why I feel completely at ease managing this new order of magnitude. I also make abstraction of absolute money amounts as I agree with Buffett (and Thorp!) that I do not really need the money.
  3. Thorp, being both a great practitioner and scientist, likes to use the rule of 72.

 “Don’t confuse the cost of living with the standard of living.” – Buffett & Thorp

For those that are looking for position sizing literature, I recommend Thorp’s work on Kelly betting (Wikipedia). For example, this paper by Thorp (page 27-28) shows that in a world with uncertainty of Kelly parameters (i.e. not gambling but the real world), it is better to use fractional Kelly betting as one gets penalized twice in case one’s estimate of risk/reward is too good (once for extra risk and once for less growth). As I will explain below, the way I think about which fraction to choose is besides the point.

Limits of the Kelly criterion for investors

I think the use of full Kelly betting is very dangerous in the stock market as the parameters are (very) uncertain. Too many quants go bankrupt by applying full Kelly  when it turns out they had overconfidence in their ability to estimate the risk and reward parameters. This is what Nassim Taleb (a great fan of Thorp, he wrote the foreword to his biography) calls victims of the ludic fallacy.

How I use the Kelly criterion in practice

If (full) Kelly betting cannot be done in the real world with uncertainty of parameters, and we don’t know how to choose our fraction, you might ask why bother. I let some years pass to think about this (as I was not able to find someone that addresses this), and I think the point of fractional Kelly is that, although we never know which fraction to pick, we should try to do our relative position sizing between individual portfolio positions proportional to the expected value versus risk (or signal-to-noise for the information theory folks) of each pick.

For example, the way I use the criterion goes like this: if I estimate that position A has three times better risk/reward ratio than position B, it should be sized three times the size of B. This is what I call internal position sizing consistency, and I try applying this in my portfolio.

Lastly, even in a portfolio that is for example 20% cash and 80% invested with internal position sizing consistency, it is of course dangerous to trust one’s relative risk-reward estimates if some position sizes go beyond 2-5x of others and reach high absolute sizes in the portfolio, because of idiosyncratic risk.  Thus, position sizes should also be capped on an absolute basis (for the real Buffetts among us this is probably in the ballpark of 30-50% in exceptional circumstances, for the rest it might well be in the 15-20% range).

If we estimate that position A has three times better risk/reward ratio than position B, it should be sized three times the size of B. 

Remark 1

I follow Greenwood Investors letters with great interest. However, I find the following in their latest letter a bit distasteful:

At quarter-end, our ratio of reward-to-risk stood at 38.3x, which is marginally better than the 38.2x at the beginning of this year. – Q2 2017 letter Greenwood Investors

  • first of all, I find it absurd to report a difference in reward-to-risk of 38.3x Q2 vs 38.2x Q1, this looks like pseudo-accuracy to me
  • secondly, if reward-to-risk for the portfolio would really be so huge as 38x, it would take huge uncertainty on these parameters to justify not having a huge gross exposure like 500 – 2000% for a rational investor like Ed Thorp (and which I trust Greenwood doesn’t have), which kind of proves my first point.

Remark 2: W. Poundstone popularized the Kelly criterion in his book Fortune’s formula, easy read.


Til next time,




My favorite simple rules by Guy Spier

I admire the intellectual honesty of Guy Spier, author of The Education of a value investor. He practices what I would describe as low stress, minimal decision making, low turnover value investing from the Alps.

In his talk at Google he mentions simple but powerful rules he adheres to.

This is the list with my favorites in bold and great open questions in italic.

  1. Stop checking the stock price
  2. If someone tries to sell you something – don’t buy it
  3. Don’t talk to management
  4. Gather Investment Research in the right order
  5. Never buy or sell stocks when the market is open
  6. If a stock tumbles after you buy it, don’t sell it for two years
  7. Don’t talk about your current investments

TC comments

  1. Classic one: remove the noise, focus on signal
  2. Simple but very powerful: I use it in investing, life. In investing, I think this can be extended to shunning stocks that appear very often in the media,  battlefield stocks that lure you to take a stance etc. Attention breeds efficiency in general
  3. Obviously controversial. In any case, while talking to management one should always be acutely aware that CEO’s self-select for salesmanship and a great salesman doesn’t appear to sell something. I think most takeaways from management are more meta-knowledge (traits such as candidness, rationalism)
  4. I think this is a very powerful rule. I interpret this as “don’t change or make any decisions on trading while the market is open” because you’d most probably be using the availability bias against yourself looking at noise
  5. Don’t know if this is a good heuristic
  6. I guess that talking and summarizing your investment thesis makes you fall in love more with a stock. This rule is an interesting one. I interpret “talking” as broadcasting investment theses to random people/clients without a specific aim to get valuable thoughts or feedback. This would be a rule I consider using, given I update my investment thesis by writing it down.


I am really sorry, I will be unable to buy your product because you are selling it to me. – Guy Spier informing cold caller about rule number two


Key takeaway of Pabrai’s latest talk at Google

Here’s the link to Pabrai’s third talk at Google [Youtube].

Pabrai’s book The Dhandho Investor is worthwhile too.

I will be upfront: I’ll provide the reader with one key thing to remember, but also one Q&A answer to forget.

The Best Idea Fund

Charlie Munger coined a problem at his dinner party: Capital Group (large LA based fund manager) created The Best Ideas fund. This fund would collect one favorite stock per analyst in the fund. Munger mentioned that this fund underperformed the market significantly and asked guests as to why this might be.

The answer was

  • consistency bias: the idea that managers had spent the most research time on was typically their “best idea”. Human beings tend to selectively filter new information that confirms the first thing they belief to be true based on something they read or listened to.
  • the specialist problem: specialist analysts get biased toward their sector benchmarks. They do not select the best stock, but the best stock in the sector.
    • TC Comment: this compounds the insider view bias that Kahneman discovered. Man is naturally taking too much of an insider view already in general, and not enough benchmarking ideas to the outsider view, or base rates.

Why I think Pabrai’s thought on the general market valuation was confusing at best

In the Q&A Pabrai says (minute 57):

If interest rates stay low, for an extended period of time, then present valuations may be a bargain. [..] And of course we won’t know that, til we get to ’20 – ’24. And so, markets are discounting mechanisms, if markets had a crystal ball to tell us where interest rates were at 2020 or beyond, you could get to [the valuation] accordingly.

Of course we won’t know how interest rates for maturity X will change in the future, this is self-evident. What Pabrai seems to forget is that we do know what the expected future interest rate is for maturity X at future time T by backing out forward rates from the current term curve.

In short, I found it a bit stunning that Pabrai forgets to mention that we can use the market discounting mechanism today to find the market’s implied future interest rate for maturity X at future time T. The market does provide us with a crystal ball that gives us the expected interest rates in the future, or the ‘central scenario’, if you will.

It looks like Pabrai is not familiar with the elementary concept of forward rates (and neither is the Google audience!). For those that want to familiarize themselves with the concept, feel free to visit Wikipedia: forward rates, or take a Yale Introduction to Finance course by Robert Shiller: ‘Forward and Future Rates’.


Key takeaways from Jesse Felder’s podcast interview with Steven Bregman

I’ve been reading investors letters by Steven Bregman for years (Horizon Kinetics). I admire his original thinking as he highlights many effects of the long bull market in passive funds or indexation.

Although it is hard to think about where we are in the indexation bull market, I think it is very interesting to hear Mr. Bregman talk about what indexation means, and will mean, for stock-picking.

For those that are not familiar to the Horizon Kinetics letters, I encourage you to take a look at some of the idiosyncratic stock picks they did.

For that we need to get into the plumbing of indexation.

Key takeaways

  • float adjusted market cap: index ETF’s changed the simple market cap weighted rule when they became more popular. For example, benchmark owner S&P changed to float-adjusted weighting, which weighs every stock by the valuation of the float, or the piece of the business that is held by the general public
    • Implications
      • Insiders selling create mechanical (=motivated) buying pressure from index funds, while studies have shown that companies with less insider ownership underperform (I believe the sweet spot to be around 50% insider ownership in a regression)
      • Insiders buying or companies buying their own shares fast such as the uber cannibals make index funds motivated sellers, while these companies outperform
        • TC Comment: if we assume that the fundamental performance of these outperformance stays identical vis-à-vis a scenario where no index funds exist to create selling pressure, this means that, ceteris paribus, the insider and uber cannibals anomaly is expected to become bigger over time by virtue of a lower entry price
  • Automatic bid: as long as capital flows into index funds, stocks that are currently most weighted in passive funds (such as mega caps and FANGs) are expected to get more automatic bids. If not, they are subject to the marginal buyers (e.g. active investors supposedly not interested in FANG stocks at the current price levels)
    • Bregman compares anticipating the tech bubble versus the index bubble with a fishing boat observing incoming waves from a storm versus a fishing boat getting lifted by the overall higher water level from a tsunami, not very visible until it hits the shores
    • Passive indexation is not equally pervasive in the stock market. This imbalance exists not only because of the market-cap rule, but also because there are sector and country specific ETFs offerings that create differences depending on ETF buyers’ current preferences with respect to total stock supply
      • Geography
        • Bregman cites Norway as an under-indexed country.
      • Sector:
        • Bregman cites the shipping sector as an example of an under-indexed sector
      • TC Comment: while the reasons are not discussed (for shipping this could be because it is in a down cycle and unpopular right at the time that most money is flowing into indexation), I took this phenomenon as an input in my checklist item of “Why the mispricing might exist” before buying Wilh. Wilhelmsen Holding (WWIB) in Aug. ‘16 a Norwegian shipping company
      • Bregman mentions Siem Industries in Norway. Having looked into the company a bit we found there are only a couple of hundred shareholders for this >1B USD market cap company. Illiquidity is clearly a barrier for indexing.

Illiquidity in stocks, acting as a deterrent of index funds, and institutions in general, is known as a big driver of returns. I will do a separate post on the topic.



Takeaways from Joel Greenblatt’s Columbia lectures notes

I will summarize the notes of Joel Greenblatt’s  lectures in Columbia Business School back in ’05. His books are great material for any investor starter or otherwise. These quick reads excel in both great examples and clarity of teaching. I recommend both You can be a stock market genius and The Little book that still beats the market.

Some great lessons from his lectures:

Singular focus on normalized EV / EBIT and ROIC

We (Gotham Partners) know a little bit more than what I wrote in the book (The Magic Formula). But I figured if you could double people’s returns in stocks or close to triple the return in small stocks that was worthwile. We do look for these two things (high ROIC and high earnings yield), but instead of looking at last year’s earnings we use normalized earnings. Most people can’t figure that out. We can’t figure it out for most stocks, but for those stocks where we can figure it out, we are looking for companies with high returns on tangible capital on a normalized basis and high earnings yield based on normalized earnings. That is just very logical.


Problems with valuation shorting

My quibble with long/shorts – the guys who do special situations in shorts where it is a scam or the company will run out of money. I like those type of shorts though I am not particularly good at them. If you are doing valuation shorts then I don’t like that. That strategy blows up every seven or eight years – the shorts go up and the longs go down and that happens to every quant guy. I am not saying a long/short hedge fund doesn’t make sense. But I don’t value short term volatility because I take a three or four year view. Then why give up 2.5% a year in returns by shorting. I am not adding value. It doesn’t add value because I am losing 2.5% a year and I don’t care about volatility.

Break out no-growth value & value from growth

Simplify everything – what is it worth now if they just stopped growing? Then if they take some of their incremental dollars capital and buy stuff what kinds of incremental returns do I think they are going to get on that? So I break things into two pieces generally. [..]  I have to make an assumption of what will they do with that cash (on no-growth value).

Go check out the lecture notes here.


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