Recently I was asked to contribute some ideas on the future of behavioural economics – these my v short thoughts

The past ten to fifteen years has seen an explosion in the development and use of behavioural economics (BE). One driver was clearly a dissatisfaction with the traditional economics concept of homo economicus or the “rational actor”.

My sense is that whilst BE has demonstrated a far better way to examine and explain economic activities, it really only looks at part of the issue. It pretty much concentrates on agent behaviour (whether individual or group) and has yet to develop into a wider understanding of overall activity. I think the “missing piece”, that will be worked on in the coming years, is network theory; and in particular applying ideas from Complexity Science.

So my “predictions/guesses” for the next twenty years (!!) are:

Some of the existing BE will challenged successfully by traditional economics – e.g. facile laundry lists of biases and loose talk about rationality will be seen to be unhelpful and a dead end.

Also the field of physics will put in a strong challenge to some BE ideas of irrationality – we are already seeing this with a battle developing around the application of ergodicity to challenge marginal utility. I have no idea how this will pan out.

Where I feel more confident is that lessons from biology (e.g. spread of medical viruses and the concept of super-spreaders) and in particular Complex Adaptive Systems will play an important part. In time whilst I think BE will retain a strong place in explaining the actions of agents, it will also be complemented by explanations of how these agents, act and react within networks. Importantly Complexity will show many network relationships are not linear and may well be inherently “unpredictable”. (This may be bad news for main stream economic commentators?).

Finally, a short example, let’s consider systemic risk in the global banking system. Currently there is massive regulatory oversight of individual financial institutions (the agents in the system), and a great deal of interest in BE about their choices regarding decision making. In future (work has already commenced by some central banks) there will be much closer analysis of the connectivity between institutions and the sometime hidden or only partially understood networks that may underlie the financial system. These ideas will also be useful in patterns of consumer behaviour, the role of “influencers” in markets….and perhaps an uncomfortable thought, this network analysis will show that much will remain unpredictable and may even be random.

So I think BE is only a partial answer – it has been v successful in uncovering agent motives and actions, but it will be Complexity Science that builds on it to help us having a more complete (or perhaps more accurately a less incomplete) understanding of economic activity.

Last night I was very lucky to see the first ever screening of the documentary The Divided Brain held at a private viewing at Ogilvy in London.
Hopefully this film will be broadcast in many countries soon – it’s a fascinating examination of the brain and its functions by leading academic & thinker Ian McGilchrist
Here is a link to the trailer
The Divided Brain
Also I very much recommend his book
The Master and His Emissary
https://www.amazon.co.uk/Master-His-Emissary-Divided-Western/dp/0300188374/

Major Speaking Themes for 2019

The Over-Connected World
The world is becoming more vulnerable as everything gets more connected. The Internet of Things even allows your phone to control your fridge! However, attacks on networks can flash around the world in seconds.
Are we living in a “Brittle New World” that is prone to sudden catastrophic risks? Can businesses survive an Electronic Hurricane?
What lessons can we learn from Complexity Science?

Evolution & Unpredictability
Evolutionary Biology suggests we are a mistake – the product of chance mutations. Can this offer lessons in risk taking & decision making?
What chance do we have of predicting the future?
Can evolution offer better ideas and ways to think and plan for the future?

The Big Data Dilemma
The current excitement regarding “Big Data” is everywhere – Is this misplaced?
The banks started crunching masses of data in the 90s and then applied sophisticated models for risk that proved disastrous. Is the rest of the commercial world about to make the same mistakes?
Do we risk making poor decisions because of “Model Mania” and misunderstanding the limitations of data?

Maybe it’s the political atmosphere or the still felt aftershocks of the credit crunch from ten years ago – but uncertainty still seems to be the watchword of the day.

Lots of clichés tumble out about the U word. We are told markets can’t stand it, in commerce firms sell solutions and boast how they can eradicate it; and for many religions certainty is their stock in trade, indeed they often promote their priests and prophets as Merchants of Certainty. Uncertainty must be banished if not eliminated.

One aspect of uncertainty that bears closer examination is how it relates to our progress through life and on the career ladder. I must say straightaway I dislike the term ladder, and think most people will find the better metaphor is a maze. We make good and bad decisions, lucky and unlucky turns in our careers and life; it’s not really a ladder we are on, more often happenstance and some stumbling along the way – just like a maze.

But as we post rationalise our previous decisions and actions we smooth out the wrong turns and bumps, attribute good luck to personal ability and try to remove bad luck from our memories. So the comforting image of the ladder redolent with a sure steady path is what we cling to. The now seemingly inevitable ascent is carefully plotted in the career history and our achievements gradually accumulate in neat order.

Now this creates a lovely feeling of control and certainty, almost as if our success was pre-ordained! Interestingly in the early stages of this climb, things are genuinely certain, from our early school days there are timetables and structure, exams and well mapped out decision points; this carries on into university though students tell themselves they are “breaking the rules”.

On entering work, again things are often pretty structured, corporate hierarchies, the demands of company values and mission statements all build the certainty cocoon. But somewhere up this ladder or more likely bumping around in the maze, things change. As we get promoted and take on more responsibility and decision making we find we are exposed to far more uncertainty. The rules based command & control structures don’t work so well in the messy slippery world of big decision making. New markets, new products, new regulations can all contribute to more of the U word.

So all that training in obeying rules and sticking to plans (as opposed to constant planning) can become redundant if not positively wrongheaded. As we climb further up we enter a void. Very different, and often totally new and unfamiliar skills and approaches are now needed, as many of the previous rules and structures fall away. This is a world where difficult questions are more important than simple answers, and are not addressed by past certainties and so called solutions.

It always fascinates me when someone has risen through the ranks of an organisation – they have somehow managed to switch from the certainty environment to the difficult slippery world of the unknown. This is a rare quality.

Wellington put it well when he said “All the business of war, and indeed all the business of life, is to endeavour to find out what you don’t know by what you do; that’s what I called ‘guessing what was at the other side of the hill.’”

Perhaps this an element in The Peter Principle – do we get promoted to the level where the uncertainties start to overwhelm us?

Here are three thinkers I enjoy and can recommend

Dave Trott
Sharp thinking and clear writing from one of the all time greats in advertising.
Blog LinkHere

Mark Blyth
Professor of Political Economy at Brown University.
Essential reading for those interested in what’s really going on behind economic data and politics.
His website
His regular podcast

David Miller
Equity fund manager, 30+ years veteran who writes about the big themes in financial markets.
Latest Blog

I must admit my heart sinks whenever I sit through a presentation that makes great use of slides with pictures of playing cards, chess pieces and pensive players, all used to reinforce the speaker’s message on strategy, or the way forward (sigh!) or the particular business mission they are espousing.

Anyone with an ounce of experience knows that business, risk and indeed life, is not a neatly packaged and defined game. In fact, it’s the exact opposite.

Consider some of the main features of chess; It has defined rules, is played in a totally linear fashion, has only one opponent and has a defined objective. Risk is almost the exact opposite of these conditions; rules are slippery and sometimes incomplete or non-existent, you are frequently up against multiple competitors, and whilst avoiding losses and trying to make worthwhile profits, objectives can change or be driven off course with little or no notice. And perhaps most devastating of all – risk is not linear, it doesn’t move around on nicely defined tramlines, indeed our attempts to build such structures to somehow contain risk is often the cause of huge problems and losses.

So why the game analogy and the players with furrowed brows? Well it’s a comfortable image, somehow we can become the clever player in a difficult game. You need skill and brains – and the presenter sells his ideas that will give you these if you follow his mantra. So yet again we are being sold what we most crave – certainty!

Certainty always sells – it is a desire very close to the human heart and mind. We feel uncomfortable with uncertainty, and to address that we try to build rules and structures we can monitor and measure, and tell ourselves we have a robust risk management regime.

Well up to a point Lord Copper – some areas of risk do lend themselves to such measures, but only if there are deep past data that behave in a predictable fashion in the future. A good example would be tide tables; from massive past data we can predict with a high level of confidence when tomorrows high tide will occur. In this very narrow example the game metaphor works, but in the slippery non-linear world of business risk such models can and do come unstuck.

In The Black Swan Nassim Taleb coined the term Ludic Fallacy to illustrate how misleading it can be to overuse games as a framework with which to consider risk. It is important that use much more agile thinking when tackling risk and uncertainty. It is frequently a world with few neat rules, lacking in past data and no amount of torturing that data will give rock solid certain guidance for the future.

One little rule of thumb I like to use, is to remember “The Map is Not the Territory”. However good your risk model it is always an approximation, an estimate built on assumptions and possibly insufficient data and can be riven with spurious accuracy and curve fitting.

Risk cannot always be solved like a puzzle, it can be more akin to riding a tiger.

Time to drop the Chessmen slides please!

This is an extract from “Two Speed World” which I wrote with Terry Lloyd and looks at the science of storytelling.

The late Professor David Ingvar a Swedish neurobiologist examined the scientific basis for storytelling. He found that a specific area of the brain, the frontal/prefrontal cortex, handled behaviour and knowledge along a timeline and it also handled action plans for future behaviour. Ingvar’s research demonstrated that damage in that area of the brain is found to result in an inability to foresee the consequences of one’s future behaviour. He concluded that the brain is ‘hardwired’ to do this and that plans are created instinctively every moment of our lives; planning for the immediate future, that day, that week and even years ahead. As these plans can be retained and recalled, Ingvar called them ‘memories of the future ‘.
We can illustrate this with a simple example of personal experience which we have all come across. Imagine you are taking up a new interest or perhaps sport, let’s say skiing. Before the new interest our minds had no particular focus or thoughts on the topic – but now suddenly we find there seems to be lots of magazine articles, perhaps special sales offers on ski equipment, and we notice more and more people seem to be talking about skiing! A coincidence or something weird is going on? In fact it’s neither, for as Ingvar’s research demonstrated we are now tuning our minds to potential future pathways and outcomes – in this case skiing. As a result we are building a memory of the future that centres around future skiing trips and adventures.
This activity is crucial – if we don’t open our minds to such pathways and planning we simply will not retain information on a given topic. Our brains are continually bombarded with a vast number of unordered stimuli such as sights, sounds and smells which cannot all be assimilated. All the input the brain receives is compared with previously constructed future memories and if there is no match it is discarded. In other words an unforeseen event cannot be seen. It goes straight over your head, or perhaps more literally doesn’t stick in the brain.
At the group or corporate level where there is obviously more than one brain involved, it is far harder to create a shared library of memories of the future. However the importance of rehearsing all likely possible futures is clearly a powerful tool, and this was recognised in the 1980s by Royal/Dutch Shell who use it as the technical basis for their planning technique of scenarios , as will be discussed later in this chapter.
In Chapter One we recounted the story of the Rainhill trials for the selection of the locomotive for the Liverpool & Manchester railway – curiously there is a coda to this story that fits in with Ingvar’s research. At the opening ceremony of the railway, which was the first in the world to have double tracks, the local Liverpool MP and former cabinet Minister William Huskisson was killed by one of the locos as he had left his carriage and was unaware of another train coming in the opposite direction. It was if he had no previous thoughts or memories of how railways would operate, and so had no intuitive understanding of the risks posed.

Two Speed World – Ashley & Lloyd

From my book Financial Speculation

One aspect of financial losses that is often overlooked is what we may term prospecting risk. Drilling for oil, mining for metals or for that matter producing theatre shows and films all have the same risk profile, and also have some lessons for financial investment.

Typically,prospecting risk has two main characteristics: first, the risk should be spread over a number of ventures, for example a number of theatre shows. This is just normal common sense and simple portfolio diversification, and helps guard against the fact that a high proportion of them are likely to be failures. Indeed in the film industry in any ten
projects it is likely that there will be five or six total failures, three or four that barely cover costs and hopefully one or two big successes (though not necessarily blockbusters) that cover all the costs of all the films plus a healthy profit margin. Second, the timing of where the successful 10% or 20% comes in the run of projects can be vital. This may seem irrelevant, after all if say you have divided your capital up into ten equal portions and allocated it accordingly to say ten drilling projects; it shouldn’t matter whether the pay dirt strike is first or last. True but there is a very large caveat. It is vital that you stick to only using the allocated capital for each project, if you overrun your resources you may run out at, say, drilling attempt number eight, and there is a chance that the big winner would have been number nine or ten. With these types of risks containing your costs is vital, and cost overruns are the nightmare of any mining prospector, movie producer or theatre impresario. The parallel in trading is our trading losses. If we fail to exercise sufficient discipline whilst experiencing losses there will be no chance to stay in the game to catch the big winner.

Unfortunately the world is full of romanticised stories of how down on their luck business heroes conquered all by betting their last cent on the one project. (The early oil prospecting experiences of J. Paul Getty make an interesting read in this respect.) But we only hear of the great successes while the silent majority of failures disappear into the land of losers anonymous. This survivorship bias plagues the investment world.
So the lesson of losses is learn to accept them as a normal part of the investment business, but be ruthless in containing them because if you don’t the market will be ruthless with you

With Christmas and the New Year hoving into view here are four books I have enjoyed and would recommend to read and to give as gifts

Reckoning with Risk: Learning to Live with Uncertainty by Gerd Gigerenzer

What is the Name of This Book? The Riddle of Dracula and Other Logical Puzzles by Raymond M. Smullyan

The Music Instinct: How Music Works and Why We Can’t Do Without It by Philip Ball

Misbehaving: The Making of Behavioural Economics by Richard H Thaler

From my book Financial Speculation

Francis Galton was the epitome of the wealthy upper class Englishman during the Victorian era, a polymath with a high degree of curiosity and a private income, he spent his entire life investigating and researching new ideas. His range of interests was diverse enough to encompass criminology, where he helped pioneer finger-printing techniques, weather patterns, where he devised the classification of cyclonic and anti-cyclonic weather systems; and he even conducted experiments to test the efficacy of prayer – though his results were not very encouraging on that front. And of more direct relevance to our interest in finance he spent a great deal of time looking at statistics and probability.

Whilst looking for ways to enliven his lectures on statistics Galton developed in the mid 1870’s a simple mechanical device he named a quincunx. The apparatus which he first demonstrated at the Royal Institution in London comprised a wooden box with a glass front and a funnel at the top. Metals balls of equal size and weight are dropped via the funnel to fall through a number of rows of pins spaced equally in the box. Each row was offset from the previous row so that the pins sat between the gaps of the row above. These pins then deflect each falling metal ball to the left or right with equal probability and at the bottom of the box each metal ball finished by falling into one of a number of compartments. After a number of metal balls are dropped through this device a pattern in the compartments below starts to emerge. The balls start to describe a binomial distribution which with a large number of rows approximates to our previous curve – the normal distribution.

With this device Galton sought to demonstrate that seemingly random events or facts do in fact tend to arrange themselves into a distribution. So it would appear that the distribution curve we examined earlier is a natural occurrence that appears even when seemingly random events take place. Galton went on to do a large number of experiments that looked to see if in fact distributions did appear in natural life. His researches conclusively proved that they do, and furthermore the outcomes were often quite close to the normal pattern described by the quincunx.

At this point we can depart from Francis Galton but use his ideas and clever box like device to look at financial derivatives. Derivatives have been around since finance began, some claim there is a reference in Aristotle to an option like instrument, and certainly by the Middle Ages very crude option like transactions were being executed. As we saw earlier with the story of Russell Sage, by the second half of the nineteenth century stock options were starting to emerge as a recognised, although specialist and niche, financial market. Of course options really took off with the publication of the Black-Scholes formula in 1973, which for the first time sought to accurately value options. The financial de-regulation of the late 1970’ and early 1980’s really boosted derivative trading and nowadays the global market has expanded massively. It is estimated by the latest (June 2008) Bank for International Settlements report to have an outstanding nominal value in excess of US$680 trillion. This is a truly eye watering number. To give you an idea of just how large – A trillion (being one million millions in modern usage) can be expressed in a number of ways – there are a trillion seconds in 31,710 years!

The Black-Scholes formula was just the start of a series of equations that sought to value and price options, and still remains one of the best known in the business, to calculate it, we need the following inputs:
1. The time to expiry of the instrument
2. The asset price; i.e. the stock, commodity or currency price
3. The strike price
4. The implied volatility of the instrument
5. The so called risk free interest rate – typically the yield on low risk short maturity government securities. E.g. 90 Day Treasury Notes

From these basic inputs we can get an option valuation, but it comes with a number of conditions and caveats, namely:

1. The asset price follows a log normal random walk
2. The risk free interest rate and volatility are known functions of time
3. No transaction costs in hedging portfolio
4. No dividends paid during the life of the option
5. No arbitrage possibilities
6. Continuous trading of underlying asset
7. Underlying asset can be sold short

A number of important problems strike one about these conditions; first and foremost an enormous assumption is being made that the underlying instrument is continuously traded, this of course ignores that most dangerous of foes – lack of liquidity. Secondly in the real world, brokerage, bid offer spreads, slippage (effectively the monetary cost of less than perfect liquidity) and taxes all loom large. In fact as we will see these charges can be quite punishing. So whilst Black-Scholes gives us the first serious approximation for pricing options risk it is hemmed in by a number of limitations. In fact it is probably true to say that it is more important to understand these limitations than to necessarily worry about the underlying maths. Risk assessment is not just about cold equations, the judgements we make about the softer more fuzzy elements of the decision process are often much more important. It is unlikely the maths alone will protect us – we have to know the context in which the result was calculated.

But let us go back to Mr. Galton’s box with its pins and metal balls, as it can produce a useful mental picture with which to consider and understand option pricing. Consider Chart Nine. The position of the funnel represents the current price of the underlying instrument; move it to the left to decrease the price; move it to the right to increase the price. Every row of pins is an increment in time, one day say, and the number of rows represents the time to maturity. The horizontal distance between neighbouring pins represents the volatility; moving the pins further apart increases volatility; moving them together decreases volatility. The figure shows the passage of one ball as it bumps down onto a pin and has to go either right or left, before falling to the next row. This represents the daily price movement of the underlying instrument and the movement of the option by one day towards maturity. At the bottom, the ball will drop into a box. The boxes are divided into two groups by the strike price. The boxes to the left hold winners for owners of put options; the boxes to the right hold winners for owners of call options. In each case, the boxes furthest from the strike price are the most valuable. Increase the strike price and there are more put winner boxes; decrease the strike price and there are more call winner boxes. When we drop a large number of balls, they finish up (expire) in the boxes distributed in the bell shaped curve that we met earlier. For the mathematically inclined, this requires us to deal in logarithms of prices, rather than the prices themselves, but ignoring this does not affect overall picture.

Many aspects of option behaviour can be understood from this model. In the example in the figure, the put option is ‘in the money’, while the call option is ‘out of the money’. You can see that adding more rows of pins (increasing the time to maturity) increases the number of winners that are far from the strike price, so generally increasing the value of the option. Increasing the distance between the pins (raising the volatility) has the same qualitative effect. The model also highlights the arbitrary nature of the underlying assumptions of the Black-Scholes formula. For example, why should all the pins be equi-distant?

 

Now in a way this is all just a parlour game it’s not meant to be a serious substitute for Black-Scholes or any of the myriad successor mathematical formulae for options and alike; but it does provide us once again with a quite vivid picture of option risk and a broad idea of how prices react in the three dimensional landscape of derivatives risk.