Six Books for Summer

With the days getting longer and summer hoving into view here are six books about decision making and risk taking that I think will stimulate and entertain.

Against the Gods: The Remarkable Story of Risk by Peter Bernstein

The Mystery of Overend and Gurney: Adventures in the Victorian Financial Underworld by Geoffrey Elliott

The Wiki Man by Rory Sutherland

Why Information Grows: The Evolution Order from Atoms to Economies by Cesar Hidalgo

Anti-Fragile: Things that Gain from Disorder by Nassim Nicholas Taleb

Butterfly Economics: by Paul Ormerod



Brittle New World

It is commonplace to observe that we live in an “ever more connected” world, but are we reaching a point where we are exposed to serious risks by being over-connected?

The history of human progress illustrates how many of our greatest breakthroughs have been through advances in communication and the desire to link up, whether as individuals or organisations. From printing to the telegraph, to flying and the ability to transmit data around the globe in a flash – all have advanced trade, wealth and a general feeling of progress. But now we seem to be on the cusp of a myriad of technologies that will massively increase our connectivity, and also with the potential for serious risks if not disasters.

In little more than 30 years the internet has grown from a small exclusive network to an all pervading channel for much of our lives. For the first time in human history we can now connect to a vast global network – already half the world’s population is on the net, and this will only increase. At first this connectivity brought the usual risks associated with communication; operational failures, exposure to potential fraud and the need to invest heavily in new technology, business systems and knowledgable staff. But now the risk profile has subtly changed, the network itself, whilst a carrier of great efficiency and potential wealth, is also a risk itself.

The simple diagram below shows the danger of “over-connectivity”. Here we can see as connections increase in a network, it becomes more resilient (moving from randomness to organisation) however diminishing marginal returns eventually kick in and the network becomes stressed, brittle and ultimately prone to complete collapse. We can think of this a bit like have too many dominoes to closely together – so if one domino should fall?


For those involved in risk taking and decision making, the study of Complex Systems science is going to be an important element in understanding and mitigating these future risks. Complexity looks at networks and their agents and crucially seeks to understand the flows around the network and how small changes can have very large effects. Many of our new business networks whether electronic systems, management systems or actual infrastructure (eg power supply) are now at risk of moving from stable resilience to being stressed and vulnerable. Whether it be risks of an “electronic hurricane” from cyber attacks or the unthinking connections being rolled out in the “Internet of Things” we need to be aware of the consequences.

So what to do?

Policymakers, military planners, business people and individuals need to have a much clearer idea of networks and their inherent weaknesses, and their own exposures to networks that might be overly connected or overly optimised. Far too many organisations have no deep understanding of their own networks let alone those of others to which they are exposed. This is particularly true where firms have merged and there is no end to end complete analysis of the business. Network resilience testing should be the core of operational risk management and using the new analysis techniques that are coming from biology and physics via Complex Systems science.

Sensible mediation can be pretty straightforward, It really is a case of building in gaps and circuit breakers into any system…these come however with a cost (in both monetary and short term efficiency terms) but are essential to avoid heightened vulnerabilities. Whether we are planning large complex networks or happily hooking up our fridge to the net this whole area of network resilience  is becoming of vital importance.

Suggested Further Reading

The Tangled World: Understanding Human Connections, Networks and Complexity   Gerald Ashley & Terry Lloyd     Harriman House 2011

Positive Linking: How Networks Can Revolutionise the World   Paul Ormerod Faber & Faber 2013

Risk Management – What Really Matters

This a summary of a keynote speech I gave to a conference of African Central bankers in Johannesburg, looking at what really matters in risk management.

A central question when developing a risk management strategy has to be: “Do you manage risk or just measure it?” Ironically, risk management could be said to have been one of the major contributors to the financial crisis, through the concept that financiers believed they could take on additional risk because their risk management systems had become so much more sophisticated. The more mathematical or formulaic approach to risk management has simply proven that models can never replace personal judgment.

The key to risk management is risk ‘identification’ which proposes that instead of using ever more mathematical concepts commonplace in finance one should rather look at what finance can learn from other disciplines. While diversification is a central theme of any reserves management programme, regulators may have given insufficient consideration to the fact that globalisation, hitherto typically seen as a positive factor had in fact produced some important negative factors such as unexpected convergences. These potentially include convergence of regulators and rating agencies, the commoditisation of information systems and of information providers. De facto, this results in not as much diversification occurring as central banks or fund managers had anticipated.

It results in narrowed thinking – if everyone uses the same analytics and systems, they are probably all coming up with the same trading strategies which has the effect of increasing risk when users think they are lessening it. It brings about unexpected correlations across unrelated markets. In effect, it reinforces the ‘herd instinct’ at critical times of crisis, when it is least wanted. In fact, growing regulation tends to worsen this factor: new Basel rules may simply be bringing about new levels of herd mentality and potentially increasing volatility rather than reducing it.

What experience has taught is that as connectivity increases systems become more resilient – but only up to an optimum point. As things become too connected (as today) the resilience tends to decrease in the absence of ‘fire breaks’ in systems. This in turn increases turbulence and volatility. The serious systemic risks of such a highly connected world are often invisible and unexpected. Each new crash is completely unheralded and all the vigilance in the world has hitherto not been able to prevent them. What it requires is a close look at all the linkages in the system, otherwise known as Complexity Science – thinking outside the box at linkages and the various networks organisations have.

The answer may be for risk management to become less formulaic and more judgmental. Central banks establishing risk management systems should by all means employ the traditional tools always used – but not omit personal judgment. African central banks may benefit from the mistakes made by their colleagues in developed countries – but one rule to learn well as don’t over-rely on tools.

Chartists Economists & Gurus – Part Two

A second extract from Financial Speculation

This time a couple easy (but fraudulent) prediction scams!

One quite well known prediction scam, and nowadays totally illegal, is recounted in John Allen Paulos’s excellent book Innumeracy. There are a number of versions of the idea, with Paulos illustrating the idea with stock index prediction. The following version is similar, but with a slightly stronger marketing message as the punter is shown supposed predictive genius across a number of stocks, rather than just a single index.

The would-be expert starts a stock-market commentary business and sends out free predictions to an initial group of say one thousand investors. The commentary tells one half that Stock A will rise in the next month, and the other half that it will fall. After a month the ‘losing’ five hundred are dropped from the mail shot, and the process is repeated to the group that received last months’ correct prediction. Now one half (two hundred and fifty potential punters) are told Stock B will go up and the other half that it will fall. After a few rounds of predictions you can demonstrate to a core number of potential subscribers that you have a 100% percent record, across a number of totally different stocks and stretching out over a number of months! In fact the initial mail shot could be to say ten thousand recipients, after six months, and six correct market calls in a row you would have around one hundred and fifty souls who think you can see every stock-market turn! Of course once subscribers pay up you are faced with trying to predict as successfully (likely to be impossible) or making a run for South America.

Another similar idea, and equally fraudulent, is to set up a service predicting the sex of unborn children. Simply place an advertisement in the national press advertising your service, asking for a fee and (this is the rather unpleasant part of the idea) a small urine sample. In your marketing and advertising blurb you stress that the methods used are ground-breaking, and of course guaranteed. (Everyone loves a ‘guarantee’ and rarely stops to question its value.) If for some inexplicable reason the system fails to predict the sex of the unborn child correctly the customer gets their money back. As all marketing men know, the two most powerful phrases in any business are New and Free. So you have a new product, and whilst it’s not free, it does have a money back guarantee if the service fails to make a correct prediction. So how do you make this work? Easy – Just tell every customer that the child will be a boy – around 50% of your ‘predictions’ will be correct! The twist here is of course is that you are only predicting a known outcome, but the marketing and packaging make it look like you are offering a genuine service.

Chartists Economists & Gurus

Given all the recent controversy about financial forecasting and predictions about the economy; here is an extract from Chapter Six of my book Financial Speculation.

“One inch forward lies darkness” Japanese Proverb

None of us know the future, but of course in financial markets little else is talked about. Will the latest economic figures be good? Is it true the central bank will intervene overnight? What are the chances of a take-over bid? Why is the market lower, when we expect good news next week? What will happen next? This insatiable demand for prognostication has led to a huge industry of analysts, commentators, pundits, experts and alike. In every financial market there is a veritable travelling circus of performers that seek to persuade us that they can guess, surmise and predict absolutely anything and everything – in return for a modest fee.

Much of what is said and written about in financial markets is often dreadful rubbish, but that of course doesn’t matter. As we all know, the truth doesn’t count in finance, it’s what everybody else believes that is important. As a result there are tons of amusing and bizarre stories about how rumours and misunderstandings some times grip markets. Efficient Market types usually label such behaviour as ‘noise’ and that over time random and ill-judged market moves are soon corrected. Certainly much of the ‘noise’ is created by market commentators themselves, who feed the never ending demand for information, opinion and that ultimate Grail – What will happen next? Once again we can see that the rise of cheap global communication has been a major component in this activity. The sheer omnipotence of computers, e-mail, 24 hour business television etc., has caused avalanches of comment, analysis and opinion to rain down on the marketplace. The market is full of information – but of course knowledge is much more rarely seen.

It is against this background that the financial soothsayers operate. Theirs is a business with almost infinite demand, and a client base that is usually very forgiving of their often poor predictive records. There are many different groups to choose from, they have differing methods and techniques, and frequently approach their work with almost religious fervour. Often however, the only thing that truly unites them is their barely concealed hostility towards one another. Again for the sake of good order and to try and maintain some sanity when discussing these would-be financial Merlins, let’s just remind ourselves; none of us know the future.

Chartists – Selling Maps of the Future

Charts have been around for ages – certainly since the days of Charles Dow, the American journalist and publisher who in the late 19th century, was a co-founder of the Dow Jones Index and created The Wall Street Journal. Dow recorded closing prices of thirty major industrial stocks, created the world’s first stock index and then plotted various line graphs of industry sectors, and then over a period of time created a predictive idea called Dow Theory. From this point on the chartists were in business with a vengeance. Bar charts, point and figure charts, market timing cycles, percentage moves, swing charts; a whole plethora of inventive ideas appeared, all long before the computer. In the Orient the Japanese can lay claim to their own system, Candlestick Charts; that have a history going back to the 17th Century rice market; they have also dreamt up the Kagi, Ichimoku (a particularly bizarre format) and Renko charting systems.

If one thinks about it, charts are a curious investment tool; they can only perfectly map the past. Unlike naval charts or say road maps they record a journey that cannot be revisited. Of course a stock can trade at a previous price again, but the time frame will have changed, and the precise price action is nearly always different. So financial charts cannot be used in the same way as road maps, perhaps in a way they are more like a diary of the past events that faithfully records where a share price, or commodity has been; but in truth can give no definitive answer as to the future. You cannot use a stock chart to plan your future investment journey in the same way as you might use a road map to plan a touring holiday of France, though of course many market players seem to adopt this approach. Many chartists would bridle at such a notion; this is to misrepresent their craft, their skill they claim is in the interpretation of these past moves and postulating likely scenarios going forward. In one sense they do have history on their side; financial markets do seem to repeat the same cycles and the same general patterns of events, certainly at a macro level. But the key is can charts do the same on the micro level?

Nowadays of course charting is big business, and has exploded in popularity with the advent of the personal computer. Every cheap and cheerful internet broking system offers free charts, and charting tools. Of course some market participants had been hand drawing charts long before computers, but only a dedicated band of players could be bothered to do all the donkey work. As a result most of the data that was plotted was daily information; usually the open, high low and closing prices for the stock, index, commodity, or currency that was being charted. Occasionally trading volumes were also included, and amongst futures traders the open interest data was also plotted. Now of course computing power and graphics allows almost limitless time slicing. Some people create one minute bar charts, or perhaps divide the day up into unusual time periods, all as a way to try and reveal the coming the moves in the market. Charts have also developed into technical analysis, which seeks to create and analyse essentially secondary or derivative information about the market price. This can range from simple moving averages, through a whole gamut of mathematical indicators, to some of the latest ideas involving fractals, artificial intelligence and chaos theory. In fact some have extended the art (could it be called a science?) to make predictions about time in the markets as well as prices. Not content with predicting the Y axis on the graph, many claim to know when on the X axis things will happen. This seems to be prediction on a heroic scale; and of course the punters lap it up. Imagine knowing where the price will move to, and when!


Seven Books

On occasions I am asked which books are worth reading or are my favourites.

These questions are of course impossible to answer and any attempts are extremely subjective; but that said I think the ones below are fascinating and open up lots of ideas and thoughts.

For some Christmas & New Year reading they are all well worth considering.

John Allen Paulos   Innumeracy – Mathematical Illiteracy and its Consequences. Penguin. 1997

Paul Ormerod   Butterfly Economics. Faber & Faber. London 1998

George Cooper   The Origin of Financial Crises – Central Banks, Credit Bubbles & the Efficient Market Fallacy. Harriman House 2008

Eric D Beinhocker   The Origin of Wealth. Random House 2006

Philip Ball   Critical Mass – How One Thing Leads to Another. Arrow Books 2005

John Kenneth Galbraith   A Short History of Financial Euphoria. Penguin 1990.

Paul Ormerod   Why Most Things Fail: Evolution, Extinction & Economics Faber & Faber 2005

Messes Problems and Puzzles

The world is full of uncertainty, information is often blurred, and facts can be incomplete or sometimes deliberately hidden by the competition. So how can we plan successfully for the future when faced with such conditions?
We can characterise many decisions in life as messy, indeed much of business life can be described as a mess; this is not a statement of the current state of the commercial climate, but in fact a way of describing the decision landscape that firms operate in.
Running a business is one of the most practical of all skills, and things always seem more difficult than any theory suggests, and just like warfare there is a constant fog that seems to make decision making so much harder. Theoretical and so called correct solutions don’t always work, and in some cases can make things worse. Naturally business managers blame the solution or strategy employed but could in fact the real problem be that we have failed to correctly define the issue faced by the firm?
Many challenges can be described as a Mess. I am using the term Mess in particular way. In the 1970’s the decision analyst Russell Ackoff developed a rather neat way of describing complex problems. He defined them as Messes, Problems and Puzzles, each with their own characteristics and crucially each needing different tools and approaches to tackle them.
A Mess can be defined as a complex issue, critically one that doesn’t have a well defined form or structure. When you are in mess it is very hard to even know the true nature of the problems you face. Most very large issues in the world start out as messes. An example may be defence policy after the Cold War has ended, the issues to be faced are numerous and complex; money, new threats, new technologies, foreign policy implications etc. In business good examples of messes include; national energy policy (what part will be state owned, what will the role be of the private sector, and how and if they can raise the necessary capital, not to mention a wide variety of stakeholders from consumers, to shareholders through to special interest and lobbying groups).
In Ackoff’s hierarchy the next level is a Problem, here the issue does have a defined structure or form, and as result there are a number of potential solutions, but not one clear cut way to solve it. Problems have a number of defined variables and it is possible to know something of how they interact and shape things. A good example of a problem might be weather forecasting or trying to predict who will win the next election.
The final group are Puzzles – these are well defined issues with a solid structure around them and have a specific solution that someone can work out. There are myriad of these of course – and we encounter and solve many of them on a day to day basis.
Interestingly Ackoff’s research ties in with another area – that being the study of risk taking and decision making by both groups and individuals. Research shows humans crave certainty and as much control over events as they can muster, indeed this may be particularly true of business leaders etc…”Give me the facts, get the experts in….I want a solution now!” This hard wired human desire for certainty has an unfortunate by-product in Ackoff’s hierarchy of issues; as we tend to demand solutions, so we have a bias to treating most issues as puzzles and occasionally as problems.
Whereas the actual situation may be a mess, or at best a problem, and certainly not one with a defined solution that can be achieved. The desire for the correct solution, and a short cut answer, blinds us to the fact we haven’t even correctly categorised or properly thought through the challenge or issue we face.
Michael Pidd in his book “Tools for Thinking” (1996) neatly described it as
“One of the greatest mistakes that can be made when dealing with a mess is to carve off part of the mess, treat it as a problem and then solve it as a puzzle – ignoring its links with other aspects of the mess”.
Indeed one could go as far as to say this is the biggest trap that decision makers fall into – the overwhelming desire for a definitive answer can lead to very bad decisions and outcomes.
Risk taking and decision making will always be filled with uncertainties, but careful analysis of the nature of the issues encountered and then correctly applying the appropriate tools should and can transform the way we plan for the future.

Finance – Complex Not Complicated

It is a well-worn cliché that financial markets are simple but not easy – the implication being that they are relatively straightforward to explain but devilishly difficult to predict. Perhaps a more subtle approach would be to say that the markets are complex but not complicated. This isn’t playing with words these terms are chosen carefully and have precise meaning.
By way of explanation let’s look at the opposite case. A watch is complicated but not complex. When we open it up there is a whole heap of complicated gears and springs etc. but its outcome or objective is simplicity itself – it just tells the time. Now when we look at markets the machinery is pretty simple (buy/sell, borrow/lend, now/later pretty much covers everything) but the outcomes are complex because they are not readily predictable and certainly not stable or certain. This is the world of Complex Adaptive Systems and network theory. Drawing on ideas from both physics and biology, complexity science is developing new ways of thinking about networks and the agents within them. Needless to say, work is now being done on looking at the markets as networks (pretty obvious really) and the nature of the agents operating in the networks (anything but rational I hear you say).
One example is the concept of “Super Spreaders”, these are highly connected agents or nodes in a system (the term comes from research into the spread of viruses) and how we can analyse their impact on the network. The clever part of complexity is the analysis can point out connections that hitherto remained hidden, or their vital place in a network was overlooked. A good example of this may have been Lehman Brothers, a middling size firm in terms of capital, but with a disproportionate relationship in the markets due to its vital role as a prime broker. Did the regulators understand the super spreader status of Lehmans and the possibility of near catastrophic consequences that they unleashed by letting the firm go to the wall? In the post Lehman’s world – Central Bankers are now looking at Complexity Science – with the Bank of England for example producing papers and research on its relevance to systemic risk.
In the past ten years we have seen the growth of behavioural finance as a way of trying to explain away seemingly irrational market antics. But it looks increasingly as if that is only part of the story – the behavioural side may explain the motives of the agents but to truly understand markets we need to uncover the complexity within the networks.

So it’s time to start researching the implications of complexity in the markets. Some keywords to start with would include: Complex Adaptive Systems, Scale Free Networks, Emergence and Super Spreaders. If you don’t, you can be certain the competition will be soon.