Economics – Page 10 – We Know Zero

The Phoenix has landed

14. October 2013 · Write a comment · Categories: Birmingham, Cartoons, Economics, Regulation

Mention the Phoenix Four in Birmingham and you are likely to get a strong reaction. Most people knew someone who worked at MG Rover’s Longbridge plant, and many local families supplied workers for generation after generation. A huge rally brought tens of thousands onto the streets in 2000 when BMW put MG Rover up for sale, protesting against what had appeared at the time to be the most likely outcome of Alchemy Partners buying it and turning it into a low volume car manufacturer with only 2,000-3,000 of the 6,500 jobs there remaining. So there was jubilation when the ‘Phoenix Four’ group of businessmen (John Towers, John Edwards, Peter Beale and Nick Stephenson) stepped in to take the business off BMW’s hands for £10 with a further £500 million accompanying the business from BMW to sweeten the deal. By 2005 all the jobs had been lost.

A Government inquiry into Phoenix Venture Holdings (PVH – the Four’s company) reported in 2009 that The Four had managed to extract £42 million in salaries and pensions by this time. The inquiry spent 4 years and £16 million getting to grips with the convoluted machinations by which this was achieved. No criminal charges resulted. The Four were not even disqualified from being company directors. Instead, in 2011, they belatedly agreed voluntarily not to serve as directors for 3 (Edwards), 5 (Towers and Stephenson) and 6 (Beale) years respectively.

In January last year, the Executive Counsel to the Financial Reporting Council (FRC) finally turned to the advice The Four had been receiving during the whole saga, from Deloitte and specifically their head of UK corporate practice Maghsoud Einollahi, alleging that their conduct fell short of the standards reasonably expected of them in relation to Project Platinum (the project to put a deal together) and Project Aircraft (the specific deal to transfer MG Rover Group’s (MGRG’s) accumulated tax losses to a subsidiary of PVH). The tribunal ruled on these allegations last month. It makes entertaining reading unless you happen to be a former MG Rover employee.

One of the issues was that Deloitte had muddied the waters about who they were representing (MGRG or PVH) in order to mask a massive conflict of interest. As the tribunal states:

If the identity of the client is not known it is not possible to identify and consider whether there is any conflict existing or potential. That is the real importance of identifying the client. Here the client was known to the Respondents (ie Deloitte and Einollahi) a substantial time before the final existence of a letter of engagement and nothing was done about it.

The Phoenix Four were always the client. Deloitte were at all times acting on their behalf. We know too that the Respondents were represented at an MG Rover Group Limited Board Meeting and made a presentation to the Board thus suggesting that they were acting for MG Rover and not the Phoenix Four.

But my favourite bit is the extract of Einollahi’s testimony on who his client was:

Q: (reading his previous testimony) “…you did not think you had a client…”
A: (Pause) I think that is fair, that I didn’t believe I contractually had a client.
Q: Exactly
A: But
Q: And the problem is the one that I have alluded to already, that you would be holding yourself out to third parties as acting for, in this case, the group (ie MG Rover)
A: (Nods)

Following this Pinteresque dialogue, the tribunal moved on to Deloitte’s fee of £7.5 million. Part of the defence case had been that £7.5 million was not a very large fee within the context of Deloitte’s annual fee income, that contingency fees (ie which were paid only if a given result was achieved) were common and that clients were not prepared to accept different arrangements. The tribunal was not impressed:

It seems to us that Mr Einollahi would charge a contingency fee of a size he thought that he would be paid by the client without considering whether it was appropriate or not. Again when he gave evidence he was cross-examined and we refer to one question and answer.
Q: …you did not like to negotiate fees downward?
A: I didn’t – I didn’t act for people who wanted to negotiate my fees downward. I didn’t need to.

The tribunal concluded:

He wanted that fee of £7.5 million and realised that his best prospects of achieving that fee were by a deal between the Phoenix Four and HBOS rather than between MGRG and First National Finance or MGRG and HBOS

Project Aircraft, the scheme involving moving around MG Rover tax losses, had been attempted before under the title Project Salt/Slag and rejected by the Inland Revenue. Aircraft succeeded where Slag failed largely because the Revenue believed this time that MG Rover would benefit from the profits generated by the scheme.

Mr Towers said “frankly, for us, what mattered was there was a possibility here of creating cash, additional cash for the group and most particularly, for the cash-consuming part of the group which was the car company”. Mr Beale’s evidence was to the effect that MGRG benefited from the transaction because “it gave the group additional cash reserves which it could lend to MG Rover as and when required”. The Inspectors (from the Government inquiry) said at Chapter XI paragraph 17 “in practice, much of the money which the group generated from Project Aircraft was used to fund a payment to the Guernsey Trust”. (The beneficiaries of which included Messrs Beale, Edwards, Stephenson and Towers.) The Inspectors continued “immediately before Barclays Bank made its £121 million loan (which also paid off a previous loan and some other creditors), PVH had credit balances on its bank accounts totalling £2,184,083. The loan increased the credit balances to £14,736,629, enabling the company on 26 June 2002, without having received any money from any outside source in the interim, to pay £7,905,125 to the Guernsey trust (as well as paying £2,261,875 to Deloitte in respect of fees for Project Aircraft). No payment was made by PVH to MGRG at this stage, or in fact at any time before November 2003.

The tribunal continued:

Mr Einollahi undoubtedly played a significant part in Project Aircraft. He must have been aware, and admits that he was so aware, that the Phoenix Four were on holiday in Portugal in 2001 and while on holiday agreed between themselves to pay themselves very substantial bonuses. They in fact paid themselves collectively about £7 million after the conclusion of the Project Aircraft transaction. These sums came essentially from assets of MGRG and were used to make these very substantial payments to the Phoenix Four. They received the whole of the proceeds and MGRG received none.

In conclusion, the tribunal said:

They (ie Deloitte and Einollahi) placed their own interests ahead of that of the public and compromised their own objectivity. This was a flagrant disregard of the professional standards expected and required and was in each individual case, and of its own, serious misconduct.

The Executive Counsel, who had made the complaints, asked for a severe reprimand and a fine of between £15 million and £20 million. They also requested that Einollahi be excluded from membership of the Institute of Chartered Accountants in England and wales (ICAEW) for 6 months and fined an amount based on an assessment of his financial resources. Deloitte suggested instead that the fine should only be £1 million and Einollahi should not be fined at all.

At this point, in my view, the tribunal lost its way a little. They decided on a severe reprimand and a fine of £14 million for Deloitte. This was calculated as follows:

We have assessed the financial gain from the fees attributable to both Project Platinum and Project Aircraft with a deduction for the total amount of recorded costs against these projects. We have added interest at 1% over base rate to deny Deloitte any financial gain from the misconduct.

This raises an interesting question about what calculations other firms might make in the future about the chances of ending up in a tribunal like this and the likely consequences against the rewards of the deals themselves. If worst case scenario is that they won’t make a profit, I remain unconvinced that this will prove much of a deterrent.

They added:

We have borne very much in mind that Deloitte is not insured against the imposition of a fine and has undertaken to indemnify Mr Einollahi against any fine imposed upon him.

It is heartwarming to see them looking after their errant employee in this way, but their insurance arrangements should be of no interest to anyone.

Einollahi himself was excluded for 3 years rather than the 6 months requested by the Counsel, but only because he was not prepared to voluntarily relinquish his practising certificate. He also refused to cooperate with the assessment of his financial resources, leading to the tribunal to put a bit of a finger in the air and opt for a fine of £250,000.

So what now? The tribunal made much of the public interest in the hearings:

It was particularly important in the case of both Project Platinum and Project Aircraft that the public interest be considered because of the concern of inter alia the Government, employees, other employers, particularly in the West Midlands, creditors and the general public about the continuation of large scale car manufacturing in the West Midlands.

The importance of considering the public interest is further emphasised because both the Projects resulted in very large sums of money that might have been utilised for the benefit of the MG Rover Group in the running of its business instead, being used for the benefit of individuals, including the Phoenix Four.

But what is the public interest? My assumption would have been that it must primarily be about the portion of the general public which was most damaged by all this, namely the MG Rover workers who lost their jobs and their communities. The local MP, Richard Burden, agrees. The Trust Fund for former MG Rover workers, which John Towers had at one point said would have over £50 million in it, was finally wound up earlier this year when the £23,000 actually available was donated by the workers to a local hospice.

The £14.25 million awarded in fines would normally go to the Consultative Committee of Accounting Bodies (CCAB), an umbrella group for several professional bodies, which pays the costs of FRC disciplinary cases. However in this case the costs of the proceedings of just under £4 million have already been charged to Deloitte on top. Is the case for meeting the costs of future disciplined accountants really greater than the public interest in making some contribution to the communities that the FRC’s members have facilitated into the ground?

There will be some time to make this decision in. Depressingly, Deloitte and Einollahi filed formal notice on 1 October that they are appealing the decision, as indeed they have contested everything that wasn’t nailed down throughout the process. Their joint statement read as follows:

“We recognise the general desire to move on from this case but do not agree with the main conclusions of the tribunal which we feel could create significant uncertainty for individual members and member firms of the ICAEW.”

After all, if it ever became accepted that consultants had any responsibility to the most vulnerable people affected by their less-than-professional manoeuvrings, where might it end? There is no time limit on the tribunal member hearing the appeal to make a decision on whether an appeal can go forward.

Enough is enough. Deloitte should do the right thing and drop their appeal now.

Faced with Philip Booth’s free market fanaticism, I’d rather be Frank

07. October 2013 · Write a comment · Categories: Economics, Regulation, Risk

This was a letter sent to The Actuary on 12 September, but which they chose to publish neither in the magazine nor on the website.

Dear Sir

In response to the interview with Philip Booth in the September issue, I would just like to point out that the banks did not know during the 2007 financial crisis that they would be bailed out. The day before Alistair Darling announced a £500 billion rescue package in October 2008, shares in RBS fell by almost 40 per cent to a 15-year low, HBOS fell by over 40 per cent, Lloyds TSB dropped by 13 per cent and Barclays by 9 per cent precisely because a bailout was not assumed.

As regards how prudently financial institutions behaved before the changes in insurance regulation in the 1970s and 1980s, I would prefer to listen to the views of someone who was actually there. Frank Redington, in his submission to the Institute of Actuaries in 1981 entitled The Flock and the Sheep and Other Essays, says:

“We have no means now of telling how the profession would have emerged from what would have been the only real test of its collective character which it has had to endure in the last 100 years. When the curtain fell in 1939 the profession was not cutting a very brave figure. Valuation bases were too weak, the rate of bonus was some £5 too high and new business was being sold on prospects which were not achieved until 18 years later. A few reputable offices had their backs to the wall.

“The outcome, if the war had not interrupted the story, would probably have taught us a valuable lesson. As it is we have to conclude regretfully that the profession had not – and, I am sure, has not – learned how to live with its salemen’s promises. To put it another way, we are driving a powerful car but have not yet proved our ability to handle the brakes.”

Regulation was inevitable. The problem which remains is that financial institutions in many cases are too complicated in their current form to regulate effectively. As Robert Reich, former US Secretary of Labor, puts it when arguing for the need to split Wall Street banks, they are “too big to fail, too big to jail, too big to curtail”!

Yours faithfully
Nick Foster

Boomers versus Y and Z

10. September 2013 · Write a comment · Categories: Economics

When the GCSE results came out the other week I had a special reason to be interested as my daughter Polly was one of the anxious students waiting for them. As it turned out, she did very well, but I ended up listening to news coverage of the event which perhaps ordinarily I would have missed.

And how obnoxious it was! Despite a reassuring increase in students taking the more difficult subjects, and pass rates at all grades statistically no different from the previous year (nearly every media outlet seemed to report a drop, once again the direction deemed more important than the amount). Unless the numbers go up every year, apparently, none of us are happy.

When the steam (or hot air at least) had run out of these criticisms, people of my age seemed to be queuing up to appear on radio stations to tell today’s students that what they lacked was something called “grit”. We need to introduce a GCSE in Grit, they cried.

Grit. Really? This is the generation which has not had the grit to adequately tackle any issue which threatened the immediate earnings of the already rich and powerful, like climate change for instance, or a just tax system, either globally or even nationally.

But they are right in a way, because a generational war has been declared and the sooner today’s students wake up to this the better. We are not all in it together. The labels of “baby boomer” or Generation X, Y and Z are there to put us into economic camps (definitions vary, but I, at 50, am somewhere on the boomer-X boundary apparently, my children are Y and Z, or both Z, depending on the point someone is trying to squeeze out of the data). When they stumble out into the job market, today’s students risk having insufficient qualifications (because even when the numbers do all go up, employers cry “grade inflation” and pull up the drawbridge even further) for anything but a McJob, on zero hours contracts, or nothing at all, subject to youth curfews, ASBOs and acoustic dispersal devices. If they are lucky enough to be graduates they will have, in addition, a loan of at least £40,000 to repay. If they want to rent somewhere to live, they will be victim to an insufficiently regulated private rental market. If they want to buy, they are highly vulnerable to a property bubble being inflated for all its worth by George Osborne. It would be hard not to conclude that the rest of society had declared war on them while they were preparing for their gritless exams.

Meanwhile, the sense of entitlement amongst the boomers is frequently drowning out any other voices. Low interest rates are bad because they “attack” pensioners’ savings, and make annuities more expensive for those about to become pensioners. However this is just special pleading for one generational group. Low interest rates are good for making the Government’s money go further, and for spending on other priorities than the boomers.

Similarly high inflation is bad news if you’re a pensioner, and if your pension, as most are where the pensioner had a choice, is not inflation-linked. However, provided it is accompanied by earnings and economic growth, ultimately it is how a deficit burden, both private and public, is going to be shrunk most effectively.

The last thing Ys and Zs need is another 50-something lecturing them on what to do, but my plea would be that they don’t let these arguments be lost by default. The battle lines have been drawn. And I know which side I’m on.

Land mines and bank bailouts

28. August 2013 · Write a comment · Categories: Cartoons, Economics, Prediction, Regulation, Risk

A man is sentenced to 7 years in prison for selling bomb detectors which had no hope of detecting bombs. The contrast with the fate of those who have continued to sell complex mathematical models to both large financial institutions and their regulators over 20 years, which have no hope of protecting them from massive losses at the precise point when they are required, is illuminating.

The devices made by Gary Bolton were simply boxes with handles and antennae. The “black boxes” used by banks and insurers to determine their worst loss in a 1 in 200 probability scenario (the Value at Risk or “VaR” approach) are instead filled with mathematical models primed with rather a lot of assumptions.

The prosecution said Gary Bolton sold his boxes for up to £10,000 each, claiming they could detect explosives. Towers Watson’s RiskAgility (the dominant model in the UK insurance market) by contrast is difficult to price, as it is “bespoke” for each client. However, according to Insurance ERM magazine in October 2011, for Igloo, their other financial modelling platform, “software solutions range from £50,000 to £500,000 but there is no upper limit as you can keep adding to your solution”.

Gary Bolton’s prosecutors claimed that “soldiers, police officers, customs officers and many others put their trust in a device which worked no better than random chance”. Similar things could be said about bankers during 2008 about a device which worked worse the further the financial variables being modelled strayed from the normal distribution.

As he passed sentence, Judge Richard Hone QC described the equipment as “useless” and “dross” and said Bolton had damaged the reputation of British trade abroad. By contrast, despite a brief consideration of alternatives to the VaR approach by the Basel Committee on Banking Supervision in 2012, it remains firmly in place as the statutory measure of solvency for both banks and insurers.

The court was told Bolton knew the devices – which were also alleged to be able to detect drugs, tobacco, ivory and cash – did not work, but continued to supply them to be sold to overseas businesses. In Value at Risk: Any Lessons from the Crash of Long-Term Capital Management (LTCM)? Mete Feridun of Loughborough University in Spring 2005 set out to analyse the failure of the Long Term Capital Management (LTCM) hedge fund in 1998 from a risk management perspective, aiming at deriving implications for the managers of financial institutions and for the regulating authorities. This study concluded that the LTCM’s failure could be attributed primarily to its VaR system, which failed to estimate the fund’s potential risk exposure correctly. Many other studies agreed.

“You were determined to bolster the illusion that the devices worked and you knew there was a spurious science to produce that end,” Judge Hone said to Bolton. This brings to mind the actions of Philippe Jorion, Professor of Finance at the Graduate School of Management at the University of California at Irvine, who, by the winter of 2009 was already proclaiming that “VaR itself was not the culprit, however. Rather it was the way this risk management tool was employed.” He also helpfully pointed out that LTCM were very profitable in 2005 and 2006. He and others have been muddying the waters ever since.

“They had a random detection rate. They were useless.” concluded Judge Hone. Whereas VaR had a protective effect only within what were regarded as “possible” market environments, ie something similar to what had been seen before during relatively calm market conditions. In fact, VaR became less helpful the more people adopted it, as everyone using it ended up with similar trading positions, which they then attempted to exit at the same time. This meant that buyers could not be found when they were needed and the positions of the hapless VaR customers tanked even further.

Gary Bolton’s jurors concluded that, if you sell people a box that tells them they are safe when they are not, it is morally reprehensible. I think I agree with them.

Earthquakes and Equities

10. August 2013 · Write a comment · Categories: Economics, Prediction

Plotting the frequency of earthquakes higher than a given magnitude on a logarithmic scale gives a straightish line that suggests we might expect a 9.2 earthquake every 100 years or so somewhere in the world and a 9.3 or 9.4 every 200 years or so (the Tohoku earthquake which led to the Fukushima disaster was 9.0). Such a distribution is known as a power-law distribution, which gives more room for action at the extreme ends than the more familiar bell-shaped normal distribution, which gives much lower probabilities for extreme events.

Similarly, plotting the annual frequency of one day falls in the FTSE All Share index higher than a given percentage on a logarithmic scale also (as you can see below) gives a straightish line, indicating that equity movements may also follow a power-law distribution, rather than the normal distribution (or log normal, where the logarithms are assumed to have a normal distribution) they are often modelled with.
However the similarity ends there, because of course earthquakes normally do most of their damage in one place and on the one day, rather than in the subsequent aftershocks (although there have been exceptions to this: in The Signal and the Noise, Nate Silver cites a series of earthquakes on the Missouri-Tennessee border between December 1811 and February 1812 of magnitude 8.2, 8.2, 8.1 and 8.3 respectively). On the other hand, large equity market falls often form part of a sustained trend (eg the FTSE All Share lost 49% of its value between 11 June 2007 and 2 March 2009) with regional if not global impacts, which is why insurers and other financial institutions which regularly carry out stress testing on their financial positions tend to concern themselves with longer term falls in markets, often focusing on annual movements.

How you measure it obviously depends on the data you have. My dataset on earthquakes spans nearly 50 years, whereas my dataset for one day equity falls only starts on 31 December 1984, which was the earliest date from which I could easily get daily closing prices. However, as the Institute of Actuaries’ Benchmarking Stochastic Models Working Party report on Modelling Extreme Market Events pointed out in 2008, the worst one-year stock market loss in UK recorded history was from the end of November 1973 to the end of November 1974, when the UK market (measured on a total return basis) fell by 54%. So, if you were using 50 years of one year falls rather than 28.5 years of one day falls, a fall of 54% then became a 1 in 50 year event, but it would become a 1 in 1,000 year event if you had the whole millennium of data.

On the other hand, if your dataset is 38 years or less (like mine) it doesn’t include a 54% annual fall at all. Does this mean that you should try and get the largest dataset you can when deciding on where your risks are? After all, Big Data is what you need. The more data you base your assumptions on the better, right?

Well not necessarily. As we can already see from the November 1973 example, a lot of data where nothing very much happens may swamp the data from the important moments in a dataset. For instance, if I exclude the 12 biggest one day movements (positive and negative) from my 28.5 year dataset, I get a FTSE All Share closing price on the 18 July 2013 of 4,494 rather than 3,513, ie 28% higher.

Also, using more data only makes sense if that data is all describing the same thing. But what if the market has fundamentally changed in the last 5 years? What if the market is changing all the time and no two time periods are really comparable? If you believe this you should probably only use the most recent data, because the annual frequency of one day falls of all percentages appears to be on the rise. For one day falls of at least 2%, the annual frequency from the last 5 years is over twice that for the whole 28.5 year dataset (see graph above). For one day falls of at least 5%, the last 5 years have three times the annual frequency of the whole dataset. The number of instances of one day falls over 5.3% drop off sharply so it becomes more difficult to draw comparisons at the extreme end, but the slope of the 5 year data does appear to be significantly less steep than for the other datasets, ie expected frequencies of one day falls at the higher levels would also be considerably higher based on the most recent data.

Do the last 5 years represent a permanent change to markets or are they an anomaly? There are continual changes to the ways markets operate which might suggest that the markets we have now may be different in some fundamental way. One such change is the growth of the use of models that take an average return figure and an assumption about volatility and from there construct a whole probability distribution (disturbingly frequently the normal or log normal distribution) of returns to guide decisions. Use of these models has led to much more confidence in predictions than in past times (after all, the print outs from these models don’t look like the fingers in the air they actually are) and much riskier behaviour as a result (particularly, as Pablo Triana shows in his book Lecturing Birds on Flying, when traders are not using the models institutional investors assume they are in determining asset prices). Riskier behaviour with respect to how much capital to set aside and how much can be safely borrowed for instance, all due to too much confidence in our models and the Big Data they work off.

Because that is what has really changed. Ultimately markets are just places where we human beings buy and sell things, and we probably haven’t evolved all that much since the first piece of flint or obsidian was traded in the stone age. But our misplaced confidence in our ability to model and predict the behaviour of markets is very much a modern phenomenon.

Just turning the handle on your Big Data will not tell you how big the risks you know about are. And of course it will tell you nothing at all about the risks you don’t yet know about. So venture carefully in the financial landscape. A lot of that map you have in front of you is make-believe.

Needles and Pins 31 July 2013

31. July 2013 · Write a comment · Categories: Actuarial, Cartoons, Demographics, Economics, Pensions, Prediction

Uncertainty and its discontents

20. June 2013 · Write a comment · Categories: Economics, Prediction, Regulation

There’s certainly a great deal of uncertainty about.

In Nate Silver’s book, The Signal and the Noise, there is a chapter on climate change (which has come in for some criticism – see Michael Mann’s blog on this) which contains a diagram on uncertainty supposedly sketched for him on a cocktail napkin by Gavin Schmidt. It occurred to me that this pattern of uncertainty at different timescales was more generally applicable (it describes very well, for instance, the different types of uncertainty in any projections of future mortality rates). In particular, I think it provides a good framework for considering the current arguments about economic growth, debt and austerity. Some of these arguments look to be at cross-purposes because they are focused on different timeframes.

In the short term, looking less than 10 years ahead, initial condition uncertainty dominates. This means that in the short term we do not really understand what is currently going on (all our knowledge is to some extent historical) and trends which might seem obvious in a few years are anything but now. Politics operates in this sphere (long term thinking tends to look two parliaments ahead at most, ie 10 years). However, the market traders who by their activities move the markets and market indices on which we tend to base our forecasts and our economic policies are also working in the short term, the very short term (ie less than 3 months to close off a position and be able to compare your performance with your peers), even if they are trading in long term investments.

So both the politics and economics is very short term in its focus, and this is therefore where the debate about growth and austerity tends to be waged. The Austerians (which include the UK Government) claim to believe that debt deters growth, and that cutting spending in real terms is the only possible Plan A policy option. The Keynesians believe that, in a recession, and when interest rates cannot go any lower, demand can only be revived by Government spending. This argument is now well rehearsed, and is in my view shifting towards the Keynesians, but in the meantime austerian policies (with all the economic destruction they inevitably cause) continue in the UK.

However, there are other groups seemingly supportive of the UK Government’s position in this argument for altogether different reasons. Nassim Nicholas Taleb argues that high levels of debt increase an economy’s fragility to the inevitable large devastating economic events which will happen in the future and which we cannot predict in advance. He therefore dismisses the Keynesians as fragilistas, ie people who transfer more fragility onto the rest of us by their influence on policy. These concerns are focused on the structural uncertainty which is always with us and is difficult to reduce significantly. It is therefore important to reduce (or, if possible, reverse) your fragility to it.

At the longer term end are the groups who believe that we need to restrict future economic growth voluntarily before it is done for us, catastrophically rapidly, by a planet whose limits in many areas may now be very close to being reached. They are therefore implacably opposed to any policy which aims to promote economic growth. These concerns are focused where there are many possible future scenarios (ie scenario uncertainty), some of which involve apocalyptic levels of resource depletion and land degradation.

These different groups are tackling different problems. I do not believe that those concerned with the structural fragility of the economy really believe that the people paying for the restructure should be the disabled or those on minimum wage. Similarly, there is a big difference between encouraging people to consume less and move to more sustainable lifestyles and recalibrating down what is meant by a subsistence level of existence for those already there.

We do need to worry about too big to fail. Our economy houses too many institutions which appear to be too large to regulate effectively. We do need to reduce levels of debt when economic activity has returned to more normal levels. We will need to restructure our economy entirely for it to make long-term sense in a world where long term limits to growth seem inevitable. But none of these are our immediate concern. We need to save the economy first.

Skin in the right game

11. June 2013 · Write a comment · Categories: Actuarial, Economics, Prediction, Regulation

In my previous post, I looked at some of the reasons why we are so useless at making economic predictions, and some of the ideas for what might be done about it. One of the key problems, raised by Nassim Nicholas Taleb most recently in his book Antifragile, is the absence of skin in the game, ie forecasters having something to lose if their forecasts are wildly off.

But what if all forecasters had to have something to lose before they were allowed to make forecasts? What if every IMF or OBR forecast came with a bill if it was seriously adrift? What if you knew whenever you read a forecast in a newspaper or on a television screen that the person making that forecast had invested something in their belief in their own forecast?

Betting on events dates back at least to the 16^th century, but prediction markets (also known as predictive markets, information markets, decision markets, idea futures, event derivatives, or virtual markets) have developed most strongly over the last 25 years or so (the University of Iowa launched its first electronic market as an experiment in 1988). Intrade, which describes itself as the world’s leading prediction market, is now a little smaller than it was following the news just before Christmas last year that it would no longer let Americans trade on its site. It had been sued by the US regulator of the commodities derivative markets for breaking a commitment not to allow trading on the constituents of those markets.

A paper on prediction markets earlier this year called Prediction Markets for Economic Forecasting by Snowberg, Wolfers and Zitzewitz suggests there are 3 main types:

· Winner takes all. If the event you have bet on happens you win. If not, you lose your stake. Intrade is this type of prediction market: you pay a proportion of £10 a share based on the average probability of the event happening (according to the market participants) and get £10 back if it happens, nothing if it doesn’t. The Iowa Electronic Market current offerings, on congressional elections and US federal monetary policy, are also winner takes all. The price of the bet at any time should reflect the market’s view of the probability of the event happening.

· Index. The amount paid out is unknown but tied to the variable you are betting on, eg the number of seats won by a party in a particular parliamentary election, or the value of an index at close of business on a particular date. The price of the bet at any time should reflect the market’s view of the expected value of the outcome.

· Spread betting. Most commonly found on sports betting sites, the amount bet and the amount paid out are fixed, but the event that leads to a pay out (eg number of goals scored in a match more than x) changes until the numbers of buys and sells match (a “buy” in this example is betting the number of goals will be above x, a “sell” is betting the number of goals will be below y, a number less than x. The spread, on which the betting site makes its money, is the difference between x and y. This could equally be applied to the values of an index at a particular date (eg Spreadex offer just such bets on several major share indices as well as currency exchange rates). Depending on the relationship between the pay out and the bet, the value of the spread points at any time should reflect the market’s view of a particular point in the probability distribution of the event, eg if the pay out is twice the bet, this would be the median (ie a 50% chance of the outcome being higher or lower).

As we saw in my previous post, currently economic predictions are largely blown off course by either:

Over-confidence in the information used to make them; or
The difficulty in standing out against a market which is making everyone a lot of money (buying has limited downside, selling limited upside); or (another possibility I haven’t mentioned before)
Bias in individual “expert” judgements, eg those with reputations at stake may want to keep their assumptions somewhere in the middle of the pack rather than standing out most of the time as this is less risky (hence the obsession with “benchmarking” assumptions in the actuarial world for instance).

Prediction markets can help with all of these problems:

Having to bet on your opinion should cause you to weigh the evidence backing it more carefully. Also, once a market is established and attracting a lot of bets, the range of evidence on which opinions are being based should expand. Prediction markets also appear to be quite difficult to manipulate or arbitrage.
Prediction markets can respond very rapidly to changes in the information available. As an example, within 25 minutes of Donald Rumsfeld’s former chief of staff tweeting about the death of Osama Bin Laden, the market view of the probability of this event on a prediction market rose from 7% to 99%.
Betting can take place anonymously. So, although the betting site knows who you are, no one else does, and the data from the voting therefore gets out into the public domain without any individual being accused of talking down a market or risking their reputation.

For these reasons, amongst others, forecast accuracy for established prediction markets might be expected to outperform that of professional forecasters. The paper of Wolfers et al suggests that this is the case.

There are still problems. The markets need to be popular to be much use for predictions, so the questions need to be interesting enough for mass participation. Secondly, a market could theoretically be undermined (although not necessarily to the detriment of its predictive ability) by traders with inside information. However, there are quite a few safeguards in place against this type of activity. Intrade, for instance, requires a photo ID and two proofs of address before it registers anyone to trade on their site. And Spreadex are regulated by the Financial Conduct Authority. A third problem is referred to as the “longshot bias”, which is observed on all types of betting markets. People tend to over-bet on events with long odds against them and under-bet on events which are fairly likely (which explains the narrowing of the odds as the starting gun approached on that horse you bet on just because of its name in the Grand National a couple of months ago). This is a problem of the winner takes all type of market, seemingly related to behavioural factors around the difference between how we view winning and losing, and it is difficult to see how it could be avoided completely. Care may need to be taken therefore when interpreting prediction markets on events which are seen as having fairly low probabilities.

But overall, prediction markets would seem to offer a way of significantly improving economic predictions, so why not make them compulsory for people who want to make such forecasts? By putting a cost on such predictions (a minimum bet could be set based on the size of the organisation making it), it would remove the casual forecasting we currently see too much of, and encourage people to review their beliefs rather more carefully. It would also ensure that the markets were popular enough to be effective. It may be that economic forecasting will always be far from perfect, but this seems a good place to start if we want, in Nate Silver’s words, to be “less and less and less wrong”.

A tax on economists? Not at all. But it might mean that we all have skin in the right game.

Skin in the wrong game

10. June 2013 · Write a comment · Categories: Economics, Prediction

The main reason I set up this website was to draw more attention to the fact that we are fairly useless at making economic predictions. The graph I use as a logo is just one of many comparisons of economic projections with reality which could be shown to demonstrate just how useless. The problem is that there is then a bit of a void when we are looking for ways to support economic decision making. Nassim Nicholas Taleb and Nate Silver are, in different ways, exploring ways to fill this void.

Taleb, in his book Antifragile, links the fragility of the economic system, amongst other things, to an absence of “skin in the game”, ie something to lose if things do not go to plan. Something is fragile if it is vulnerable to volatility, robust if it is relatively unaffected by it, and antifragile if it profits from volatility. If decision makers gain an upside when things go right, but are effectively bailed out from the downside when they don’t by others who then suffer the downside instead, they will have no real incentive to be sufficiently careful about the decisions they take. This makes the chance of a downside for everyone else consequently greater. Taleb refers to people in this position, championing risky strategies (or, more often, strategies where the risks are not really known) without facing the risks personally, as “fragilistas”. He suggests instead a system of “nonpredictive decision making under uncertainty” on the basis that “it is far easier to figure out if something is fragile than to predict the occurrence of an event that may harm it”.

Silver, in his book The Signal and the Noise, suggests the main reason why economic predictions are routinely so awful, even when the forecasters are trying to be accurate rather than making a splash, is that the events forecasters are considering are often out of sample, ie the historical data they are considering to make their forecasts do not include the circumstances currently being faced, but that the forecasters are confident enough to make predictions despite this. This explains, for instance, the failure in the US to predict the housing crash (there had never been such a boom before), the failure of the ratings agencies to understand how risky mortgage-backed securities were (they were in new more complex forms) and the failure to predict that the housing market would take the rest of the economy down with it (much more trading betting on house price rises that did not materialise than had ever been seen before).

Silver cites the economist Terence Odean of the University of California at Berkeley, whose paper Do Investors Trade Too Much shows that equity traders trade excessively in the sense that their returns are, on average, reduced through trading (due to transaction costs), and suggests that this is partly due to overconfidence in the constant stream of information in the media drawing their attention from one possible model to another. This effect can be modelled to show that markets behave irrationally in the presence of overconfident decision making, even when decision-makers are otherwise completely rational.

However, when we are looking at the calls made by traders there are other forces at work that make things even worse. Silver’s book advances the theory that it is not so much an absence of skin in the game that leads traders to continue betting on rising markets when bubbles have started to develop, but skin in the wrong game (at least from the point of view of the rest of us fragile people). He gives the example of a trader looking a year ahead to make a call on whether the market will crash or not. If he buys and the market rises everyone is happy. If he buys and the market crashes, he will be in the same boat as everyone else and will probably keep his job if not his bonus. If he sells and the market crashes he will be seen as a genius, but the tangible rewards of this beyond his current position might not be life changing. However, if he sells and the market rises he may never work in the industry again.

For a trader who wants to keep his job and remain popular with his colleagues, selling therefore has limited upside and unlimited downside, to analyse it in a Talebian way. Buying on the other hand has potentially unlimited upside while the music is playing and limited downside when it stops. So these traders do have skin in the game, which keeps them buying even when they can see the iceberg approaching, and does not particularly reward accuracy in forecasting. It’s just a different game to the one the rest of us are playing.

For these amongst many other reasons, economic forecasting (Silver differentiates forecasting – meaning planning under conditions of uncertainty – from predicting – meaning calling the future correctly) is unlikely ever to be very accurate. But whereas Taleb believes that planning for the future must be nonpredictive to avoid making suckers of us all, Silver believes there may be some scope for improvement. This brings us to the idea of prediction markets and their ability to introduce some of the right skin in the right game, which I will discuss in my next post.

DA = CB

23. May 2013 · Write a comment · Categories: Actuarial, Defined Ambition, Economics, Pensions, Regulation

Steve Webb, the pensions minister, thinks we only have 12 months to save DB but that, in its current form, it might be like trying to apply electrodes to a corpse. Unfortunately his prescription – Defined Ambition (DA) – is still very much undefined and therefore, as yet, unambitious.

Number of members of private sector occupational pension schemes: by membership type and benefit structure, 2004-11

Source: Office of National Statistics

The graph above shows how dramatic the decline of DB active membership (ie members still accruing benefits in defined benefit schemes which provide a pension defined in advance, where the balance of funding is committed to by the employer in nearly all cases) has been in recent years. It also shows, contrary to some reports, that there has been no advance in DC active membership (ie defined contribution schemes where only the contributions are defined in advance and final benefits are at the mercy of financial markets and annuity rates). It just hasn’t fallen much. In fact, if all of the DC active members had instead been offered DB active membership, the number of DB active members would still have fallen.

So it is a crisis and it appears to be those who are opting for no pension scheme at all who are really growing in number. The auto-enrolment programme starting to be rolled out across the country will have an impact, after all if you keep asking the question and don’t take no for an answer you will attract customers – just ask the banks who were selling PPI cover.

But I wonder if the crowd avoiding pensions of any sort up until now might perhaps have more wisdom than those trying to pile them into schemes whether they want to or not. Because DC has to date been a very poor offer for most, with very low levels of contributions. The latest survey by the ONS of households between 2008 and 2010 where the primary earners are between 50 and 64 revealed that median pension savings in DB schemes were equivalent to around six times those in DC schemes. And the minimum contributions under auto-enrolment of 8% of qualifying earnings from all sources with all risks staying with the member is unlikely to change this massive inequality quickly if at all.

If you have very little money, and the pension option means that your pension contributions are likely to be bounced around by the markets for a few decades before dribbling out in whatever exchange the insurance companies are prepared to give you, is it irrational to think that you might want to keep some access to your savings along the way? The following graph suggests most people don’t think so.

Breakdown of aggregate saving, where household head is aged 50 to 64: by deciles and components, 2008/10

Source: Office of National Statistics

This graph suggests that people do save for a pension where they can, but if there is not much to go round, they also want some more liquid savings. The problem is not that they are not saving for a pension, it is that they have no assets at all.

So what is to be done? Clearly campaigning for a living wage needs to continue and be intensified, and reductions to benefits are going to make the problem worse. But fiddling around with marginally different forms of DC arrangements for decades will also be disastrous. Think not just a few naked pensioners on the beach as we had before the Pension Protection Fund (PPF) came in for DB members. Think armies of them with a genuine grievance against a society that did this to them. And what will have been done to them is to suggest that by paying 4% of their salary into a pension scheme, they have somehow safeguarded their future. Good employers are not going to want to be associated with scenes (or schemes) like this.

DC contributions need to be much higher while they remain so risky, which is why DB schemes target asset levels much higher than their best estimate of the cost in most cases, but clearly DB levels are too high for nearly all employers. There is not much time, as Steve Webb says, so let’s stop messing around and pick an alternative.

I vote for cash balance (CB). There are many different sorts but the feature they all have in common is a defined cash sum available at retirement which members can then take in a combination of lump sum, annuity and drawdown (ie keeping the sum in the scheme and drawing income from it as needed). It means that the bumping around by the markets is taken on the chin by your employer not you, but only until retirement (the type of risk employers are used to managing in their businesses anyway), and the risk of you living longer (reflected in lower annuity rates) when you get to retirement is your problem. It seems reasonable to me. Whoever thought that an employer should be concerned with how long you are going to live (unless they were the mafia)? Good employers could also offer a broking service for annuity purchase to avoid the problem of pensioners not shopping around adequately.

There are a few of these in existence already, although only 8,000 members in total benefit from them so far. In the case of Morrisons, the guarantee is 16% of salary a year, uprated in line with CPI. This is one of the current minimum levels to be accepted as an auto-enrolment plan. Alternatively you could drop to 8% a year, but uprate it by CPI plus 3.5% pa. Either would be a huge improvement for someone with limited means to relying on what 8% of earnings pa might amount to in 40 years’ time, and unable to take the risk that the answer is not much.

But the first step is to establish CB as what is meant by DA and that will need Government support to work. I propose:

CB to be promoted as one of the main options for an auto-enrolment scheme, equivalent to the 8% minimum but without total risk transfer to the employee.
Develop a colour coding scheme for a combination of benefit level and risk transfer, with DC at minimum auto-enrolment at the red end, minimum CB at amber running through green to the equivalent of a public sector DB scheme or better as (NHS) blue.
Sort out the PPF position on CB. They currently treat them as full DB schemes. Scale down PPF levies to reflect the lower level of risk that they present to the PPF.
Simplify the pensions legislation around CB to reflect the fact that the scheme’s responsibility for managing risk ends at retirement.

And we really need to start now!

We Know Zero