Nick Dewhirst explains the meanings behind three fashionable phrases - 'black swans', 'perfect storms' and 'data mining' - and asks whether there is any profit in them
Black swans, perfect storms and data mining are three fashionable phrases that describe the devastating impact of highly improbable events very elegantly, but what are they, and how can investors cope with them most advantageously?
What are they?
The black swan is an example used by philosopher Nicholas Taleb to illustrate the problem of proving that something does not exist.
To prove that something can exist, it is merely necessary to find a single example, but to prove that something cannot exist, it would be necessary to have a comprehensive database. That would be infinitely large, and even then it would only include past data, not possible future data.
Swans illustrate the problem well in that all the swans that had ever been seen in Europe were white. It was only when explorers travelled to the other side of the world that the first black swans were spotted in Australia.
The perfect storm is an excuse quoted by fund managers when the statistically proven investment process used to market their funds proves to be very far from perfect in practice.
Typically, they will blame poor performance on some development that could not be predicted, when in truth it was a development that would have diluted the predicted performance had it been included in their process, so making it harder to market their funds.
Data mining refers to conclusions drawn from part of a database, rather than the whole. Typically, this is done to economise on the effort of processing all possible data, by taking the most accessible part.
While the standard defence against erroneous conclusions is random sampling, the greatest mistakes occur when important elements are not even in the database.
How can investors profit?
Black swans are unpredictable, so the correct responses are to avoid leverage and employ diversification.
Those who are cash rather than margin buyers have the choice of sweating out an unpredictable event if they believe in the self-correcting powers of capitalism, but those who have leveraged their portfolios have no choice. They must find additional collateral when their lenders demand it, but often they cannot.
Diversification dilutes the impact of an unpredictable event in part of a portfolio on the whole. Low correlations are the accepted statistical technique for assessing the effectiveness of diversification, but often in a stockmarket panic, all assets fall in unison, because investors fear contagion and sell what they can rather than what they should. The answer is to sweat it out, because the good ones will decline only temporarily.
Black swans differ from perfect storms in that the former are unpredictable, whereas the latter are merely unpredicted, because the odds were considered to be statistically insignificant.
This happens every time any particular investment game blows up and every time there is the excuse that nobody could predict it, when the truth is that it was predictable but few succeeded in predicting it.
Here are the classic cases.
- Switch charts were a popular early form of arbitrage at the beginning of the 1970s, until the BP/Burmah trade exploded when Burmah went bust at the bottom in 1974.
- Guided commodity programmes had excellent theoretical back histories at the beginning of the 1980s, until inflation was defeated and volatility declined.
- Programme insurance became increasingly popular as a low-cost way of protecting against market setbacks by placing stop-loss orders through the futures market, so avoiding the cost of buying put options, until Wall Street fell 20% on 20 October 1987.
- Genius failed when Long Term Capital Management collapsed in October 1998 because its Nobel Prize winners assumed that comparable investments would converge when they suddenly diverged everywhere simultaneously.
- Last August, it became fashionable to use quantitative trading systems to exploit the myriad tiny transient arbitrage opportunities faster than any human could do. Sadly, forced liquidation by the least successful players set off a chain reaction of liquidation.
The explanation lies in the difference between the physical and social sciences. Scientists studying the physical world do not affect it, but those studying society do, because they are part of it. Nowhere is this effect stronger than the stockmarket, because nowhere else are the financial rewards of learning so great.
Thus, any investment game that has been widely learned by stockmarket players is at risk of exploding if all try to exist at the same time. The answer lies in applying contrarian investment strategies. While the majority may earn small, short-term profits before risking catastrophic loss, this ensures the opposite.
Data mining differs from perfect storms in that the odds are statistically significant, but this was not apparent because the necessary data was not incorporated in the database. Thus, institutional investors may deploy great analytic skills, but if they are all poring over the same databases, they will probably come to similar conclusions.
If they all use Bloomberg, Datastream, Factset and I/B/E/S and market their virility in terms of how many company managements each meets, then the key question in selecting a fund manager is: what do they do that others do not do?
- Nick Dewhirst is CEO of www.investors-routemap.co.uk.
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