So my biases are clear and fully disclosed, I should point out that I am a big fan of James Montier and his work, including his books and his “white papers” both when he was at SocGen and now that he is at GMO. Since I also try to be a deep value investor whose work is informed by behavioral finance, we share the same general approach. And since I met him this afternoon for the first and found him both friendly and engaging, I am obviously inclined to be an appreciative audience. James said that he was a bit nervous about his speech — The Flaws of Finance — because it is a new one, but he needn’t have been. My rough notes follow (with no guaranty of accuracy or comprehensiveness).
The focus: bad models. bad policies, bad incentives and bad behavior.
- If you give CAPM and VAR to monkeys, they’re going to create a financial crisis. Indeed, I think they just did.
- Remember that models are abstractions and don’t represent reality — they have clear weaknesses (low beta outperforms high beta).
- CAPM assumes risk is volatility and ignores liquidity and leverage (and that’s nuts).
- VAR — like a vest that is 95% bulletproof (fails when you need it).
- Bad models and bad assumptions tend to replace common sense.
- Graham — the more abstruse the maths, the more uncertain the results (“complexity to impress”).
- Derman and Wilmott (2008): The Modelers’ Hippocratic Oath.
- VAR — like asking children to grade their own schoolwork.
- Bad policies generate bad incentives.
- Experts tend to have the tendency of giving us “permission” to turn our brains off.
- Anchoring — give someone a number — any number — and s/he thinks it means something (even when it doesn’t).
- Narrow framing — risk means much more than volatility.
- Bad policies encourage bad behavior.
- Asymmetric policy to problems — cut rates; environment of low rates encourages investors to reach for yield (a cardinal sin of investing — following Keynes’s law, that demand creates its own supply); Buffett — “Never ask a barber if you need a haircut.”
- Commission-based loan originators’ loans failed 30% more often than salary-based loan originators; incentives create asymmetric responses.
- With a simple game, the higher the incentive, the worse people played.
- We tend to neglect risk.
- Leverage can’t make a bad investment good but can make a good investment bad.
Problems: Five impediments to recognizing predictable surprises:
- Over-optimism (hope is not a strategy).
- The illusion of control (Kahneman’s “planning fallacy”).
- Self-serving bias (confirmation bias).
- Myopia (lunch is not a long-range plan).
- Inattentional blindness (we don’t see what we don’t expect to see).
A Manifesto for Change
- The Modelers’ Oath (no points for elegance).
- More realistic models.
- More practical experience.
- Less complex math.
- Aim for robustness, not optimality.
- Define risk as the permanent impairment of capital.
- Be skeptical of alleged innovation (it’s usually simply out for leverage).
- Know the limits of your models (and don’t exploit them).
- Focus on the long-term.
- Study history, especially financial history.
- Don’t get bogged down in details (complexity).
- Look for predictable surprises (start with Minsky’s models).
- Central banks should lean against the wind.
- The markets are *not* efficient (science advances one funeral at a time).
- Capital adequacy should be contra-cyclical/
- Guard against regulatory capture and industry-based self-serving bias.
- Lessons from the 2008-09 crisis (not equity-based risk, but expensive equity-based risk — that has sown the seeds of our next crisis as we are now preaching “buy more government bonds”).
Investing is simple but not easy.
- James (and GMO) uses simple models, but it is hard to stick to them.
- The accounts they have the most confidence in are the accounts they are most likely to be fired on.
- Buy when the consensus is selling and vice versa (be contrarian).
- Chasing what is popular is a great business model but an unethical one.
- Sadly, regulators are no less susceptible to behavioral bias than anyone else (the bias blind-spot).
Nice notes. I also like the thinking of James Montier and the rest at GMO. That’s why there Benchmark-Free Allocation strategy (open through the Wells Fargo Absolute Return fund) is one of my 3 favorite mutual funds for navigating the market going forward
Thank you for the excellent notes — I was hoping you would post them. Montier’s articles are some of my favorites. “95% bulletproof” is a LOL line.
The professional investment community’s equating of volatility for risk is both ludicrous and an opportunity for value investors. Defining risk as permanent impairment of capital makes sense from a value investor’s perspective. However, knowing how the market improperly defines, quantifies and rewards “risk” is imperative if you want to take advantage of others’ misapprehension of risk. I love thinking of sound investments in the way I learned from Mohnish Pabrai’s book, “The Dhandho Investor”. While the books has some flaws, he describes good value investments as “heads I win; tails I don’t lose so much” propositions. As he puts more formally, look for low risk, high uncertainty opportunities. By this he means that the possibility of permanent loss of capital is low and muted — achieved by traditional value analysis (for example, liquidation analysis, or balance sheet and debt maturities analysis). And the outcome is uncertain, meaning highly variable. The markets are bad at pricing uncertainty, and therefore severely discount stocks whose outcome is uncertain even where most outcomes are favorable.
MSFT in 2011 was a good example of such an investment — it had (and continues to have) a rock-solid balance sheet, incredible earnings and cash-flow from at least five business lines. It is a predictable winner in at least a couple technologies of the future – such as cloud computing – due to its massive scale. Despite being widely held, the stock was unloved because whether its Windows franchise or Office franchise would survive another decade is unclear. Count the cash, as Berkowitz would say.
Besides the permanent loss of capital, another risk a value investor has to consider is the risk of underperformance. By this I don’t mean we should chase returns. Rather, while waiting for the market to recognize the low price of our investment relative to its value, our internal rate of return on capital invested is declining. So we need to choose between the universe of investments where we can start at a low price relative to intrinsic value. But within that universe, I have been a fan of guys like Whitney Tilson, who encourage us to identify the catalyst that will unlock the difference within a reasonable period of time. It would suck to buy a stock that is 50% undervalued, and have to wait 10 years for the market to recognize that fact.
Finally, on risk, I highly recommend Howard Marks’ writings on the subject, especially his January 2006 investor memo: http://www.oaktreecapital.com/MemoTree/Risk%2001_19_06.pdf
Thanks again for sharing with those who could not be there!
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Thanks for posting the summary. I have also learned a lot from James.
I have put together a resource page on all his writing I could find on the web. Here is the url: http://www.eurosharelab.com/james-montier-resource-page
I shall definitely be adding your post to it.
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Reblogged this on Финсовет – Инвестиции без жадности и страха and commented:
Стоимостной инвестор Джеймс Монтье — один из обязательных источников мудрости в моей диете.
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Everything is very open with a very clear explanation
of the challenges. It was definitely informative.
Your site is very useful. Thanks for sharing!
Does anyone has practical case studies displaying explicitly the application of Value investing concepts (like moats, Intrinsic value, Margin of safety) and yes the most important the calculation intrinsic value…with proper explanations to the same.
Thanks & Regards,
Graham and Dodd’s classic “Security Analysis” remains the standard in the field. Seth Klarman’s “Margin of Safety” is very difficult to come by, but I think that you could find it in PDF form on the web.
Thanks for reading and commenting, Ari.
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