The difference between complex and complicated

Greenlight Capital, Third Point, Glenview Capital, Lone Pine Capital, Omega Advisors, Millennium Management and Point72. If you came across these names on a list, you’d probably think it was the agenda for a high-powered New York hedge fund conference. Actually, these are just some of the many hedge funds at the top of the share register of SunEdison as at June 30, 2014, just three months before the stock lost 80% of its value in a free-fall towards bankruptcy. Some of the funds had exited their positions by then, but some others were still holding the bag.

So how is it that so many intelligent and highly capable investment analysts got it so wrong on SunEdison? It is highly unlikely the true intrinsic value of the company could have been worth $1 one day and $0.20 three months later. I believe the answer to this question stems from the difference between complex and complicated – an important distinction for all investors to keep in mind.

Intelligent people are naturally drawn towards complicated problems by the intellectual challenges they pose, and hedge fund managers are no exception. Solving a complicated problem and reaping an awesome return also makes for impactful marketing, so there are powerful incentives for tackling complicated problems. However, the problem with complicated problems in the investment arena is that what usually appears complicated is in reality complex. A complicated problem may be hard to solve, but it is structured and can usually be solved by throwing more resources and/or computing power at it. When Sergey Brin, cofounder of Google, wrote an algorithm with several hundred million variables to crawl through and rank the entire Web in 1997, he was solving a complicated problem (“computing the eigenvector of a matrix”, as he described it). When investors analyse and value Google today as an investment, they are tackling a complex problem.

Complex problems are problems that not only contain numerous known unknowns, but also any number of unknown unknowns and interrelated factors that can’t be boiled down to rules and algorithms. Former professional poker player Annie Duke offers an apt analogy in her book “Thinking in Bets” – complicated problems are analogous to chess, while complex problems (e.g. investing) are analogous to poker. Chess is a complicated game that can take decades to master, but once mastered, a top ranked Grandmaster is unlikely to ever lose to a novice. The rules are set in stone, moves can be planned well in advance, and there is no luck involved. Poker players and investors, on the other hand, are constantly making decisions faced with incomplete information and limited feedback.

The first difficulty for investors is that no one is there to tell you (or remind you) that something is complex instead of complicated. As most readers can probably relate, if you spend long enough looking at a complex problem, you can start convincing yourself that it’s a complicated one. The human brain evolved on order and certainty and detests chaos and uncertainty. This explains why most investors find comfort in a confident, well-structured and intuitively reasoned investment thesis, even if it only pays lip service to the possibility and outcomes of being wrong.

Investors in SunEdison were confronted with complicated financial statements due to the company’s consolidation of majority-owned interests in and non-recourse debt of various public companies and solar projects. By parsing through and understanding these complicated financials better than anyone else, they convinced themselves that the market was mis-valuing the company. But of course, in a complex system, any number of things could go wrong that could send a debt-laden company reliant on financial engineering spiralling rapidly into bankruptcy.

Chart 1: SunEdison share price

The other difficulty for investors is that no one is there to offer feedback on the quality of your decisions. The market will give you an outcome – you make or lose money – but it won’t tell you whether the outcome was due to you being right or lucky (same as in poker). For an investor, decision quality should be more important than outcome, as over the long run, luck will eventually reverse. When a program bugs out or a chess grandmaster loses a match, we can usually go back to the code or the move list and identify objectively where a mistake was made. Ironically, when an investor buys stock in a company that files for bankruptcy shortly thereafter, there’s a good chance they can identify where their thesis was wrong or where they failed to anticipate complexity and learn some valuable lessons. The real risk is when investors make money off bad decisions. This can lull investors into believing they’ve “worked out” a complicated problem and to apply the same process to subsequent complex problems. Early Facebook investors generated superb returns, but applying the same playbook to Twitter would have led to prolonged disappointment.

The key takeaway for investors is that complexity should always be respected, and never conflated with complication despite what the thesaurus may say. The temptation is always to find conviction in an investment idea by framing complex systems as complicated problems that can be solved with some degree of certainty. To that end, Warren Buffet’s advice to stick to your circle of competence is a great remedy – your circle is the fuzzy and slightly porous boundary between your known unknowns and the great unknown unknowns. There is little correlation between investment returns and how complicated an investment thesis is. Therefore, investors interested in generating attractive risk-adjusted returns over a long time horizon are often better off reducing complexity rather than tackling the complicated.

DH5_2155Daniel Wu is a Research Analyst with Montaka Global Investments. To learn more about Montaka, please call +612 8046 5000.

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