Dragon Kings, Black Swans and Bubbles

"Dragon Kings" is a new term to me, and the subject on Monday evening of a presentation by Prof. Didier Sornette at an event given by PRMIA. Didier has been working on the diagnosis on financial markets bubbles, something that has been of interest to a lot of people over the past few years (see earlier post on bubble indices from RiskMinds and a follow up here).

Didier started his presentation by talking about extreme events and how many have defined different epochs in human history. He placed a worrying question mark over the European Sovereign Debt Crisis as to its place in history, and showed a pair of particularly alarming graphs of the "Perpetual Money Machine" of financial markets. One chart was a plot of savings and rate of profit for US, EU and Japan with profit rising, savings falling from about 1980 onwards, and a similar diverging one of consumption rising and wages falling in the US since 1980. Didier puts this down to finance allowing this increasing debt to occur and to perpetuate the "virtual" growth of wealth.

Corn, Obesity and Antibiotics - He put up one fascinating slide relating to positive feedback in complex systems and effectively the law of unintended consequencies. After World War II, the US Government wanted to ensure the US food supply and subsidized the production of corn. This resulted in over supply over for humans -> so the excess corn was fed to cattle -> who can't digest starch easily -> who developed e-coli infections -> which prompted the use of antibiotics in cattle -> which prompted antibiotics as growth promoters for food animals -> which resulted in cheap meat -> leading to non-sustainable meat protein consumption and under-consumption of vegetable protein. Whilst that is a lot of things to pull together, ultimately Didier suggested that the simple decision to subsidise corn had led to the current epidemic in obesity and the losing battle against bacterial infections.

Power Laws - He then touched briefly upon Power Law Distributions, which are observed in many natural phenomena (city size, earthquakes etc) and seem to explain the peaked mean and long-tails of distributions of finance far better than the traditional Lognormal distribution of traditional economic theory. (I need to catch up on some Mandelbrot I think). He explained that whilst many observations (city size for instance) fitted a power law, that the where observations that did not fit this distribution at all (in the cities example, many capital cities are much, much larger than a power law predicts). Didier then moved on to describe Black Swans, characterised as unknown unknowable events, occurring exogenously ("wrath of god" type events) and with one unique investment strategy in going long put options.

Didier said that Dragon-Kings were not Black Swans, but the major crises we have observed are "endogenous" (i.e. come from inside the system), do not conform to a power law distribution and:

• can be diagnosed in advanced
• can be quantified
• have (some) predictability

Diagnosing Bubbles - In terms of diagnosing Dragon Kings, Didier listed the following criteria that we should be aware of (later confirmed as a very useful and practical list by one of the risk managers in the panel):

• Slower recovery from perturbations
• Increasing (or decreasing) autocorrelation
• Increasing (or decreasing) cross-correlation with external driving
• Increasing variance
• Flickering and stochastic resonance
• Increased spatial coherence
• Degree of endogeneity/reflexivity
• Finite-time singularities

Didier finished his talk by describing the current work that he and ETH are doing with real and ever-larger datasets to test whether bubbles can be detected before they end, and whether the prediction of the timing of their end can be improved. So in summary, Didier's work on Dragon Kings involves the behaviour of complex systems, how the major events in these systems come from inside (e.g. the flash crash), how positive feedback and system self-configuration/organisation can produce statistical behaviour well beyond that predicted by power law distributions and certainly beyond that predicted by traditional equilibrium-based economic theory. Didier mentioned how the search for returns was producing more leverage and an ever more connected economy and financial markets system, and how this interconnectedness was unhealthy from a systemic risk point of view, particularly if overlayed by homogenous regulation forcing everyone towards the same investment and risk management approaches (see Riskminds post for some early concerns on this and more recent ideas from Baruch College)

Panel-Debate - The panel debate following was interesting. As mentioned, one of the risk managers confirmed the above statistical behaviours as useful in predicting that the markets were unstable, and that to detect such behaviours across many markets and asset classes was an early warning sign of potential crisis that could be acted upon. I thought a good point was made about the market post crash, in that the market's behaviour has changed now that many big risk takers were eliminated in the recent crash (backtesters beware!). It seems Bloomberg are also looking at some regime switching models in this area, so worth looking out for what they are up to. Another panelist was talking about the need to link the investigations across asset class and markets, and emphasised the role of leverage in crisis events. One of the quants on the panel put forward a good analogy for "endogenous" vs. "exogenous" impacts on systems (comparing Dragon King events to Black Swans), and I paraphrase this somewhat to add some drama to the end of this post, but here goes: "when a man is pushed off a cliff then how far he falls is not determined by the size of the push, it is determined by the size of the cliff he is standing on". 

 

 

Data Visualisation from the FT

Data visualisation has always been an interesting subject in financial markets, one that seems to always have been talked about about as the next big thing in finance, but one that always seems to fail to meet expectations (of visualisation software vendors mostly...). I went along to an event put on by the FT today about what they term "infographics", set in the Vanderbilt Hall at Grand Central Station New York:

One of my first experiences of data visualisation was showing a partner company, Visual Numerix (VNI), around the Bankers Trust 's London trading floor in 1995. The VNI folks were talking grandly about visualising a "golden corn field of trading oportunities, with the wind of market change forcing the blades of corn to change in size and orientation" - whilst maybe they had been under the influence of illegal substances when dreaming up this description, their disappointment was palpable at trading screen after trading screen full of spreadsheets containing "numbers". Sure there was some charting being used, but mostly and understandably the traders were very focussed on the numbers of the deal that they were about to do (or had just done).

I guess this theme ultimately continues today to a large extent, although given the (media hyped) "explosion of data", visualisation is a useful technique for filtering down a large (er, can I use the word "big"?) data problem to get at the data you really want to work with (quick plug - the next version of our TimeScape product includes graphical heatmaps for looking for data exceptions, statistical anomolies and trading opportunities, which confirms Xenomorph buys into at least this aspect of the "filtering" benefits of visualisation).

Coming back to the presentation, Gillian Tett of the FT said at the event today that "infographics" is cutting edge technology - not sure I would agree although given the location some of the images were very good, like this one representing the stock pile of cash that major corporations have been hoarding (i.e. not spending) over recent years:

There was also some "interactive" aspects to the display where by stepping on part of the hall floor changed the graphic displayed. Biggest problem the FT had with this was persuading anyone to step into the middle of the floor to use it (more of an English reaction to such a request, so the reticience from New Yorker's surprised me):

Videos from the presentation can be found at http://ftgraphicworld.ft.com/ and the journalist involved, David McCandless is worth a listen to for the different ways he looks at data both on the FT site but also in a TED presentation.

Taleb and Model Fragility - NYU-Poly

I went along to spend a day in Brooklyn yesterday at NYU-Poly, now the engineering school of NYU containing the Department of Finance and Risk Engineering. The event was called the "The Post Crisis World of Finance" was sponsored by Capco.

First up was Nassim Taleb (he of Black Swan fame). His presentation was entitled "A Simple Heuristic to Assess Tail Exposure and Model Error". First time I had seen Nassim talk and like many of us he was an interesting mix of seeming nervousness and confidence whilst presenting. He started by saying that given the success and apparent accessibility to the public of his Black Swan book, he had a deficit to make up in unreadability in this presentation and his future books.

Nassim recommenced his on-going battle with proponents of Value at Risk (see earlier posts on VaR) and economists in general. He said that economics continues to be marred by the lack of any stochastic component within the models that most economists use and develop. He restated his view that economists change the world to fit their choice of model, rather than the other way round. He mentioned "The Bed of Procrustes" from Greek mythology in which a man who made his visitors fit his bed to perfection by either stretching them or cutting their limbs (good analogy but also good plug for his latest book too I guess)

He categorized the most common errors in economic models as follows:

1. Linear risks/errors - these were rare but show themselves early in testing
2. Missing variables - rare and usually gave rise to small effects (as an aside he mentioned that good models should not have too many variables)
3. Missing 2nd order effects - very common, harder to detect and potentially very harmful

He gave a few real-life examples of 3 above such as a 10% increase in traffic on the roads could result in doubling journey times whilst a 10% reduction would deliver very little benefit. He targeted Heathrow airport in London, saying that landing there was an exercise in understanding a convex function in which you never arrive 2 hours early, but arriving 2 hours later than scheduled was relatively common.

He described the effects of convexity firstly in "English" (his words):

"Don't try to cross a river that is on average 4ft deep"

and secondly in "French" (again his words - maybe a dig at Anglo-Saxon mathematical comprehension or in praise of French mathematics/mathematicians? Probably both?):

"A convex function of an average is not the average of a convex function"

Nassim then progressed to show the fragility of VaR models and their sensitivity to estimates of volatility. He showed that a 10% estimate error in volatility could produce a massive error in VaR level calculated. His arguments here on model fragility reflected a lot of what he had proposed a while back on the conversion of debt to equity in order to reduce the fragility of the world's economy (see post).

His heuristic measure mentioned in the title was then described which is to peturb some input variable such as volatility by say 15%, 20% and 25%. If the 20% result is much worse than the average of the 15 and 25 ones then you have a fragile system and should be very wary of the results and conclusions you draw from your model. He acknowledged that this was only a heuristic but said that with complex systems/models a simple heuristic like this was both pragmatic and insightful. Overall he gave a very entertaining talk with something of practical value at the end.

Investment risk not rewarded

Interesting article from the FT, Reward for risk seems to be a chimera, effectively saying that more risky (volatile) equities do not necessarily provide higher returns than less risky equities. I like the suggestion that the reason for this is that "hope springs eternal" and investors buy more volatile stocks (pushing up price) in the hope of higher returns. However, as yet another illustration of the law of unintended consequences, the article goes on to suggest that choosing a benchmark index to outperform and limitations on borrowing imposed by investment mandates may both be driving this effect, are interesting and challenging ideas for investment managers.

 

RiskMinds 2010 - Day 2 - Perceptions of Risk

Very interesting presentation by David Spiegelhalter of Cambridge University on "Perceptions of Risk - Communicating Risks and Deeper Uncertainties In Words, Numbers & Pictures". David is the Winton Professor for the Public Understanding of Risk, Department of Mathematics at Cambridge University and is involved with the website understandinguncertainty.org.

David started by saying that when communicating risk what is needed is a user-friendly, easy to understand unit of risk. One example he gave was a one in a million change of death, which he termed a "micromort". He said that on average around 50 people a day die of unnatural causes in England and Wales every year, so this means that with a population of 50 million a person's exposure in England and Wales is 1 micromort. He then compared various means of transport and the distance that would need to be travelled to reach a 1 micromort measure:

• Walking - 12 miles
• Cycling - 20 miles
• Car - 217 miles
• Motor Bike - 6 miles

So I guess those of you with motor bikes should take note above!

He emphasised that also peoples reaction to risk is very interesting, and gave the example of a UK health official recently sacked for suggesting that the hobby (addiction?) to horse riding (he termed it "equesy") was just as risky as taking the drug ecstasy - statistically what the official said stacked up but it was simply not culturally acceptable to suggest such an association. No great advertisement for the NHS, but there are around 3753 deaths a year with an average of around 135,000 people in hospital at any one time. This works out at around 75 micromorts if you are in hospital which is around twice the level faced by troops in Afghanistan!

Obviously the above has a number of biases, but David was trying to illustrate how to compare risks and how people are not used to assessing them objectively. In particular, given a choice between probabilities of 1 in 10, 1 in 100 and 1 in 1000, around a quarter of the public would choose 1 in 1000 as the highest probability given it contains the "highest" number.

Whilst the above refers to the denominator with a constant numerator, given the choice between drawing from a bowl containing 1 sweet and 8 marbles and another bowl containing 5 sweets and 45 marbles, 53% of people choose the one containing 5 sweets (because it contains "the most" chances of getting a sweet).

David went on to test the audience on a few trivia questions using what he termed a "quadratic scoring" scale that asked the participant to select a multi-choice answer but to also associate a level of confidence with it. If right and confident the marks given would be high, but if wrong and confident the penalty mark would be much larger. He said that such scoring often produced interesting results and changed people's views, often with young men doing worse (testosterone not being good for risk seemingly!).

He showed how probability density seemed to better understood if represented by density of ink rather than the usual bell curves etc. He suggested that results should come with some more warning of how reliable they are to stop simple acceptance of the numbers reported as "truth". He described how risk is measurable whereas uncertainty is not, which led to the inevitable references to the wisdom (?) of Donald Rumsfeld.

Good fun talk with some great points to make - how humans (and bank management boards?) understand risk is interesting and to some extent surprising (see earlier post for a different slant on human perception of maths). Obviously it is accepted now that simple VAR measures are not enough, but even with the move towards scenario based methods then how to produce a simple but meaningful summary of risk for management is still challenging.

A French Slant on Valuation

Last Thursday, I went along to an event organized by the Club Finance Innovation on the topic of “Independent valuations for the buy-side: expectations, challenges and solutions”.

The event was held at the Palais Brongniart in Paris, which, for those who don’t know (like me till Thursday), was built in the years 1807-1826 by the architect Brongniart by order of Napoleone Bonaparte, who wanted the building to permanently host the Paris stock exchange.

Speakers at the roundtable were:

• Eric Benhamou, CEO  Pricing Partners
• Francis Cornut, Président  DeriveXperts
• Jean-Marc Eber, Président LexiFi
• Patrick Hénaff, Associated Professor at the University of Bretagne (see model validation paper for additional background)
• Claude Martini, CEO  Zeliade Systems

The event focussed on the role of the buy-side in financial markets, looking in particular at the concept of independent valuations and how this has taken an important role after the financial downturn.  However, all the speakers agreed that remains a large gap between the sell-side and buy-side in terms of competences and expertise in the field of independent valuations. The buy-side lacks the systems for a better understanding of financial products and should align itself to the best practices of the sell-side and bigger hedge funds.

The roundtable was started by Francis Cornut of DeriveXperts, who gave the audience a definition of independent valuation. Whilst valuation could be defined as the “set of data and models used to explain the result of a valuation”, Cornut highlighted how the difficulty is in saying what independent means; there is in fact a general confusion on what this concept represents: internal confusion, for example between the front office and risk control department of an institution, but also external confusion, when valuations are done by third-parties.

Cornut provided three criteria that an independent valuation should respect:

• Autonomy, which should be both technical and financial;
• Credibility and transparency;
• Ethics, i.e.: being able to resist to market/commercial pressure and deliver a valuation which is free from external influences/opinions.

Independent valuations are the way forward for a better understanding of complex, structured financial products. Cornut advocated the need for financial parties (clients, regulators, users and providers) to invest more and understand the importance of independent valuations, which will ultimately improve risk management.

Jean-Marc Eber, President LexiFi, agreed that the ultimate objective of independent valuations is to allow financial institutions to better understand the market. To accomplish this, Eber pointed to the fact that when we speak about services to clients, we should first think of what are their real needs. The bigger umbrella of “buy-side” implies in fact different needs and there is often a contradiction on what regulators want: on one side, having independent valuations provided by independent third parties; on the other side, independent valuations really mean that internal users/staff do understand what there is underline the products that a company have.In the same way, we don’t just need to value products but also measure their risk and periodically  re-value them.It is important, in fact, to have the whole picture of the product being evaluated in order to make the buy-side more competitive.

Another point on which the speakers agreed is traceability: as Eber said, financial products don’t exist just as they are, but they go under transformation and change several times. Therefore, the market needs to follow the products across its life cycle till its maturity stage and this pose a technology challenge, in providing scenario analysis for compliance and keeping track of the audit trail.

At the question, ‘what has the crisis changed’ panellists answered:

Eber: the crisis showed the need to be more competent and technical to avoid risk. He highlighted the need to understand the product and its underlying. Many speak of having a central repository for OTCs, obligations, etc but this needs more thinking from the regulators and the financial markets. Moreover, the markets should focus more on quality data and transparency.

Eric Benhamou, CEO pricing Partners, sees an evolution of the market as the crisis showed underestimated risks which are now being taken in consideration.

Claude Martini, CEO Zeliade, advocated the need for financial markets to implement best practices for product valuations: buy-side should apply the same practices already adopted by the sell-side and verify the hypotheses, price and risk related to a financial product.  

Cornut admitted  things have changed since 2005, when they launched DerivExperts and nobody seemed to be interested in independent valuations. People would ask what value they would get from an investment in independent valuations: yes, regulators are happy but what’s the benefit for me?

This is changing now that financial institutions know that a deeper understanding of financial products increases their ability to push the products to their clients. The speech I enjoyed the most was from Patrick Hénaff, associated professor at the University of Bretagne and formerly Global Head of Quantitative Analysis - Commodites at Merrill Lynch / Bank of America.

He took a more academic approach and contested the fact that having two prices to confront is thought to reduce the incertitude on the product but highlighting as this is not always the case. I found interesting his idea of giving a product price with a confidence interval or a ‘toxic index’ which would represent the incertitude about the product and reproduce the model risk which may originate from it.

We speak too often about the risk associated to complex products but Hénaff, explained how the risk exists even on simpler products, for example the calculation of VAR on a given stock positioning. A stock is extremely volatile and we can’t know its trend; providing a confidence interval is therefore crucial. What is new instead, it is the interest that many are showing in assigning a price to a determinate risk, whilst before model risk was considered a mere operational risk coming out from the calculation process. Today, a good valuation of the risk associated to a product can result in less regulatory capital used to cover the risk and as such it is gaining much more interest from the market.

Henaff describes two approaches currently taken from academic research on valuations:

1) Adoption of statistic simulation in order to identify the risk deriving from an incorrect calibration of the model. This consists in taking historical data and test the model, through simulations and scenarios, in order to measure the risk associated in choosing a model instead of another;)

2) Have more quality data. Lack of quality data implies that models chosen are inaccurate as it is difficult to identify exactly what model we should be using to price a product.

 

Model risk, which as said above was before considered  an operational risk, now becomes of extremely importance as it can free up capital. Hénaff suggested that is key to find for model risk the equivalent of the VAR for market risk, a normalized measure. He also spoke about the concept of a “Model validation protocol”, giving the example of what happens in the pharmaceutical and biologic sectors: before launching a new pill into the market, this is tested several times.

Whilst in finance products are just given with their final valuation, the pharmaceutical sector provides a “protocol” which describes the calculations, analysis and processes used in order to get to the final value and their systems are organized to provide a report which would show all the deeper detail. To reduce risk, valuations should be a pre-trade process and not a post-trade.

This week, the A-Team group published a valuations benchmarking study which shows how buy-side institutions are turning more and more often to third-parties valuations, driven mainly by risk management, regulations and client needs. Many of the institutions interviewed also admitted that they will increase their spending in technology to automate and improve the pricing process, as well as the data source integration and the workflow.

This is in line on what has been said at the event I attended and confirmed by the technology representatives speaking at the roundtable.

I would like to end with what Hénaff said: there can’t be a truly independent valuation without transparency of the protocols used to get to that value.

Well, Rome wasn’t built in a day (and as it is my city we’re speaking about, I can say there is still much to build, but let’s not get into this!) but there is a great debate going on, meaning that financial institutions are aware of the necessity to take a step forward. Much is being said about the need for more transparency and a better understanding of complex, structured financial products and still there is a lot to debate.  Easier said than done I guess but, as Napoleon would say, victory belongs to the most persevering!

RiskMinds - VaR as simple as chartism?

Interesting panel debate at RiskMinds Wednesday morning, entitled "Sophisticated Complex Models vs. Crude Robust Risk Measures".

Riccardo Rebonato of RBS started off the debate in (untypically?) controversial style by saying that he thinks that the risk management models (mostly VaR) used in financial markets are peculiar. Peculiar in that coming from a physics background he is used to models that have "causal" links between inputs and outputs, whereas VaR is based simply on the P&L distribution of a portfolio i.e. all the information is contained in the data itself. Riccardo said the obvious analogy was with chartism, where decisions are made on the observed market data itself without any reference to external (exogenous) factors at all (perhaps he should have a discussion on endogenous risk with Jean-Phillippe Bouchard at Quant Invest). Riccardo suggested that in the range of models from those that are "over specified" with two many inputs to those in "reduced form", then VaR was far too much at the reduced form end.

In response to Riccardo's proposal that risk models should involve more causal ("factor") effects, Andreas Gottschling of Deutshe Bank countered with the quote from Harry S. Truman "Give me a one-handed economist! All my economists say, On the one hand on the other.". To which Riccardo acknowledged that maybe Economists and Econometrics were less suited to trading/analyst reports (e.g. give me a single view of what the prospects/returns will be) and more suited to risk management (e.g. give me a range of scenarios with supporting assumptions for each).

Chris Finger of RiskMetrics moved on to put forward an argument for standardisation of risk reporting, saying that it was impossible to say what methodology was behind the VaR numbers disclosed by major financial institutions. He proposed that risk reporting needed to be standardised and obligatory, but emphasised that risk management should not standardised. Paul Shotton of UBS agreed, saying that whilst micro-prudential risk of Pillar I had decreased risk on an individual institution level, it had increased systematic (macro) level risk and this was an area of failure for the regulators. On this the panel agreed, echoing a lot of what Avinash Persaud said in proposing the more diversity of risk management was highly desirable.

On standardisation, Riccardo noted that many banks had switched from using 10-day to adjusting up a 1-day VaR, and as a result presenting a less risky picture to analysts and regulators, regardless of how risky the "tail" of each institutions' P&L distribution is. Riccardo also proposed that there should be "constructive ambiguity" over what is asked of the banks by the regulators - put another way he suggested the regulators should come up with the "curriculum" for risk but not the "questions", as definitive questions encourage arbitrage.

Andreas then brought the debate back to its title, and put forward that maybe VaR should be replaced by simpler measures such as limits on notional traded. Paul suggested that VaR was only good for simpler products and portfolios, under "normal" market conditions. He said that he had been an advocate of more stress testing for a long time as a complimentary approach to VaR, but also combined with the simpler approach of limits.

It was an interesting debate, particularly with Riccardo's proposal on VaR being too simple a measure based on statistics, and wanting a more "causal" model to be developed. Using the example of June 2007, Riccardo said that everyone knew something big was about to happen but this was not reflected in VaR calculations since they are statistically based and inherently backwards-looking and not predictive. The lack of prediction is a very valid point, but putting forward a counter-view, then I get the argument about economists giving a range of outcomes, but surely these should be fed into the scenario engine rather than trying to develop econometric models of relationships between market variables. Econometric models are just as vunerable as any other to the mis-behaviour of markets (anyone seen a stable correlation lately?).

A few of the other risk managers there expressed other views, from the more buy-side folks who were more comfortable with factor-based modelling, to risk managers who said that VaR was already "structural" with explicit relationships between valuations and interest rate inputs for example. It would be good to understand more of Riccardo's ideas on this, since it appeals from making risk a more "forward-looking" process but I find it difficult to quite grasp what "causal" model you can have of markets that is itself robust to changes in market behaviour.

RiskMinds - The Failure of Risk Models

Avinash Persaud of Intelligence Capital gave the opening talk of the morning at RiskMinds (see first of set of posts from last year here) and put forward a lot of the very good ideas that he has contributed to in the recent Warwick Commission Report. Main points that Avinash made:

• Regulators were admirably quick in working out where past regulation had gone wrong in focussing too much on micro (individual institution) rather than macro (whole market)/systematic risk.
• The regulators then came out with promising papers on counter cyclical regulation and other positive ideas.
• These new ideas do not win votes however and do not satisfy the public's desire to punish someone - Avinash called this the "Bad Apple" policy, with "bad bankers, bad products, bad jurisdictions" being the perceived guilty parties.
• All past crises have resulted in demands for three things: i) more risk management; ii) more regulation; and iii) more transparency.
• These are fine as demands but evidently do not prevent financial crises.
• Avinash recalled his work back at JPMorgan in the early 90's when the 4:15 report was produced for Sam Weill, which eventually led to VAR reporting becoming widespread.
• He then fast forwarded to the Asian crisis of 97 where he saw the failings of VAR (or rather its widespread use) first hand with all players using VAR which when volatility increased caused an increase in VAR causing JPM (and all) to sell causing markets to fall, increasing vol causing more selling, increasing correlation and leading to what is called the "loss spiral".
• In light of the recent crisis, Avinash said the public perception is that bankers created a load of toxic bombs (products), through them at an unsuspecting public and ran away...
• ...and in his opinion the reality is that banks created a load of toxic bombs and ran straight towards them i.e. this was a failure of risk management where bankers did not understand the risks they were buying and selling.
• He then took us back to the 1950's and the formation of modern portfolio theory with Markowitz and Danzig working at the RAND Corporation.
• At that time banks and insurers were still separate, with FX and capital controls still in place meaning that not only could the "efficient frontier" of investment portfolios be observed but it could also be acted upon.
• Now everyone has the same information everyone can observe the efficient frontier of investment opportunities but cannot exploit or act upon it, since usually everyone moves in (the "herd") and the value observed is changed by this crowded participation in the market. Here he seems to be echoing a lot of what Bob Litterman said at QuantInvest last week over the "crowded trade" and that the barriers to market knowledge and our ability to act on this knowledge have been lowered forever.
• Avinash put forward that many of the models we use today assume the statistical independence of decision making process whereas the reality is that the market is homogenous (everyone is thinking/acting the same) and hence these models are invalid in this "crowded" context.
• In light of this, the problem of risk management is not about exogenous risk (risks from outside the market, from Black Swan events to normal distributions) but more about endogenous risk i.e. peoples behaviours upon seeing opportunities cause strategic risks. (Interesting given Jean-Phillippe Bouchard at QuantInvest commenting on what makes prices move). Put another way, behaviour is the issue not the financial instruments themselves.
• Avinash proposes that risk capacity (the ability of an institution to absorb a particular type of risk) shoudl be thought through more fully, with for example insurance and pension institutions with long-term liabilities having a much greater capacity to absorb liquidity risk than banks, and banks with short term funding being a better position to manage a loan book.
• He pointed out that regulation that uses market prices to protect us against movements in market prices is doomed to failure before it starts.
• Booms occur due to some perceived "paradigm shift" technolgy leading to dramatically improved risk/return ratios - he cited things such as cars, electricity, rail, dotcom and the mantra from those involved that "This time it is different..." (see "bubble" post from last year)
• Avinash thinks the regulators are significantly to blame for the last crisis since they themselves said the latest financial innovations in credit derivatives were making us safer through sharing out risk in the system.
• He said that there is no theory for making a complex system "safe" as a whole and that the regulators did not/do not "get" this idea.
• Diversity of approach and risks in a large systems (macro financial markets) is our only current defence and regulatory "best practice" has driven conformity not diversity in the market, making systemic risks higher not lower.
• So the regulators are themselves creating a homogenised market.
• In terms of solutions, he proposes that risk and audit committees need separating so that risk management does not become a "tick box" exercise.
• He further proposes that the risk management function is given some capital so that it can place hedges at a macro level for institution (i.e. looking at the resulting risk when divisional risks have been aggregated) - here is proposing moving to risk "management" as opposed to the much more common risk "reporting" found in many institutions.
• One risk management indicator idea he proposed was to put a portfolio management model together that was linked to VAR in order to see where the "herd" is moving to (e.g. low vol, high return Asian markets of the past etc) and to move or hedge against this.
• He is concerned that applying Basel II regulation to the Insurance industry with Solvency II will mean that all players will be dancing to same VAR tune which will introduce more risk as more institutions are forced to react in the same way to market movements and volatility.
• On the same lines, Credit Rating Agency regulation will create barriers to changes in ratings methodology in response to endogenous market risk, again meaning that everyone will be forced to behave and act in the same ways.
• He summarised that "endogenous risk" (movements in the market caused by the market) and not statistical distributions that are the key issue and diversity is the only solution.

Entertaining speaker with some interesting ideas that fly in the face of much of what is being done by the regulators today, and generally well received by many of the risk managers present. Behavioural finance and the "crowded trade" (i.e. everyone doing the same thing in the market causing movements within the market) seem to be key themes occuring in a lot of what academics and practitioners have said on risk management recently. Now what to do about it? Not sure that less (not more) regulation will find many fans at the moment...answers on a postcard please!

Maths to Money - Quantitative Investment

I attended the Quant Invest 2009 event for the first time last week in Paris. The event is unsurprisingly about quantitative investment strategies, but with an institutional asset manager and hedge fund focus - so not so much about ultra-high frequency trading (although some present) but more about using quantitative techniques to manage medium/longer-term investment decisions and applied portfolio theory. A few highlights below that I found interesting:

• Pierre Guilleman of Swiss Life Asset Management gave an interesting 1/2 day workshop entitled "A random walk through models":
• He is a strong supporter of the need to understand more about the data and statistical assumptions upon which any quant investment model is based and how these fit with the desired investment objectives (similar to the Modeler's Manifesto)
• He made the point that good models can sometimes be almost annoyingly simple, and cited the example by a Professor Fair of Yale who had determined that US elections were predictable based on simple parameters such as past results, inflation and gdp and that policy did not seem to be a key factor at all - annoying for the politicians anyway! 
• Pierre seems very concerned that the Solvency II regulation applied to Life Institutions will negatively influence the investment policies of many institutions - applying sell-side risk measures like VAR to the insurance industry will drive a more short-term approach to investment. He strongly believes that VAR applied to his industry should have an expected return parameter introduced to fit with longer term investment horizons of 10 to 25 years.
• Bob Litterman of Goldman Sachs Asset Management opened the first "official" day of the conference:
• Bob put forward his "scientific" approach to investment modelling going through the stages of hypothesis, test and implement. He warned against overconfidence in investment (apparently 70% of us think we are "above average"...) and impulsiveness (quick impulsiveness test: "if a bat costs $1 more than the ball, and the bat and ball together cost $1.10 then how much does the bat cost?...") 
• He said that the failure of quantitative investment models in 2007 needed to be understood given the success of quant models over past decades. In particular he thought that quant investment became the "crowded trade" of 2007 with every hedge fund having a quant investment strategy. In terms of why this became a "crowded trade" Bob thinks that the barriers to entry into quant investment (particularly technology) have lowered significantly recently.  
• He noted that factor-based investment opportunities decay quicker than they used to due to increased competition - implying the need for a more dynamic and opportunistic investment approach.  
• GSAM are now looking at new markets and new investment instruments, trying to find areas of market disruption but without following what others are doing in the market.  
• He pointed out the conflict between investors wanting more transparency over what is done for them, against the need to be more proprietary about the investment models developed.  
• Next there was a talk on regulation from the French regulator that was dull, dull, dull both in terms of content and presentation style (when will regulators actually prepare well for the talks they give?)
• Panel debate was also pretty average, with the word "alpha" being used too much in my view - asset managers of a certain type seem to hide behind this word as an opaque "magic wand" to justify what they do.
• Jean-Phillippe Bouchard of Capital Fund Management did a great talk called "Why do Prices Move?". Some points from the talk:
• He started off with a reminder about the Efficient Markets Hypothesis (EMH) and how it says that crashes and market movements are caused by events outside (endogenous to) the market such as news, events etc.
• He then said this was not born out in the data, where extreme jumps in prices were only related to news only 5% of the time.
• Volatility looks like a long memory process with clustering of vol over time - similar to behaviour in complex systems
• The sign of order flow is predictable but the price movement is not, with only 1% of daily order volume accounting for price movements over 5%
• Even very liquid stocks have low immediate liquidity, meaning that price movements can play out over many hours and days as liquidity is sought to "play-out" some change in fundamental price levels.
• Joseph Masri of the Canadian Pension Plan Investment Board then did a good talk on Risk Management:
• Jo said that sell-side risk was easier to deal with in some ways since it involved fewer strategies in high volumes, and hence could be better resourced.
• Buy-side quantitative risk was harder due to its reliance onsell-side research and risk tools, the outsourcing of credit assessment to the credit rating agencies, the loss of Bear and Lehman's having caused the buy-side to have to do more risk management itself (and through third parties) rather than rely on the sell side risk management tools.
• He said that sell-side risk models are a good start for an asset manager, but need to be adapted to give both absolute and relative risk (to a benchmark fund for instance). All models are no substitute for risk governance.
• He described the cross over from risk methods: VAR, stress testing, factor-based and their applicability to market risk, credit and counterparty risk.
• Like Pierre he was not a fan of 1 or 10 day trading VAR being applied to investment managers since this risk measure was not suitable for long term investment in his view.
• On stress testing he said this needed to be top down (using historical events etc) as well as bottom up from knowing the detail of strategy/portfolio.
• In terms of challenges in risk management he said that VAR needed more stress testing to cope with the fat tails effect in markets, that liquidity risk both of counterparties and of illiquid products was vital and the importance of stress testing (he mentioned reverse stress testing) plus also the feedback (crowding effects) of having similar investment strategies to others in the market.
• Dale Gray of the IMF gave a very interesting talk on how he and Bob Merton have been applying the contingent claims model of a company (looking at equity in terms of option payoffs for shareholders and bondholders) to whole economies:
• He said that some of his work was being applied to produce a model for the pricing of the implicit guarantees offered by governments to banks
• He said these models were also applicable to macro-prudential risk
• Very interesting talk, and if he really has something of macro-level risk then this is great relative to the wooly approach by the regulators so far

There were some other good talks from Danielle Bernardi on Behavioural Finance, Martin Martens on Fixed Income Quant Investment, Vassilios Papathanakos on Stochastic Portfolio Theory (seemed to be a "holy grail" of investment model, giving good returns even in the crisis - begs the question why he is telling everyone about it?), Claudio Albanese on unified derivative pricing/calibration across all markets (again another "holy grail" worth more investigation) and Terry Lyons on speeding up monte carlo simulations.

Overall a good conference although the quality of the asset managers present seemed very digital from those who really seemed to know what they talking about to those who plainly did not (in my limited view!). Along this line of thought, I think it be good to test whether there is an inverse relationship between the quality of the asset manager and the amount of times they use the word "alpha" to explain what they are doing...

It's in the news...

I went along to the Forum on News Analytics over in Canary Wharf on Monday evening, organised by Professor Gautam Mitra from OptiRisk / Carisma at Brunel University. We seem to be in the early days of transforming news articles into quantifiable/machine-readable data so that it can be processed automatically/systematically in trading and risk management. It was a good event with both vendors and practititioners attending so was reasonably balanced between vendor hype and the current state of market practice.

As background on what is meant by news analytics data, then for example you might count the number of news articles about a particular company and look at whether the quantity of news articles might be a predictor of some change in the company's stock price or volatility. Moving on from this simple approach (assuming that you are clever enough to be certain about what news is about what company), then you can then move towards assessing whether the news is negative, neutral or positive in sentiment about a company/stock.

The context here is about having the capability to automatically process/analyse any kind of text-based news story, not just those from research analysts that might be nicely tagged with such quantifiers of sentiment (see http://www.rixml.org/ on xml standards for analyst data). The way in which the meaning of the text is "quantified" uses some form of Natural Language Processing.

The event started with a brief talk by Dan di Bartolemeo of Northfield Information Services. I hadn't heard of him or his company before (maybe I should pay more attention!) but he seemed a very solid speaker with strong academic and practical background in investment management and modelling. He referenced a few academic papers (available via their web site) on news analytics, and how news analytics and implied volatility could provide better estimates of future volatility than implied volatility alone. He also made some good points about how investment "models" are calibrated to history and how such models need to adapt to "today" - he put it as "how are things different now from the past?" and put forward the idea of a framework for assessing and potentially modifying a model to respond to the "now" situation. He also suggested that the market can react very differently to "expected news" (having a range of investment "what ifs" planned for a known earnings announcement) as opposed to unexpected information (we are back into the realms of the Black Swan and the ultimate in uncertainty wisdom from Donald Runsfeld)

Armando Gonzalez of RavenPack then began by explaining how RavenPack had become involved in applying text analysis to finance (it seems the subject has its origins, like a lot of things, in the military). RavenPack seem to be highest profile quantified news vendor at the moment, and whilst Armando is obviously biassed towards pushing the concept that money can be made by adding quantified news data to trading models, he said that not many firms are as yet systematically processing news and most people are relying upon manual interpretation of the news they buy/use. Some of the studies Ravenpack have on market news and prices are very interesting, showing how a news event can take up to 20 mins before the market settles on a new "fair" price level for a stock. Additionally, and maybe an interesting reflection on human behaviour, was that in bull markets there are usually twice as many positive stories about companies than negative, but strikingly in a bear market there was still almost equal amounts of positive and negative news - so humans are basically optimists! (or delusional, or just plain greedy...take your pick!)

Mark Vreijling of Semlab followed Armando and suggested that a lot of their sales prospects understandably desire "proof" of the benefits of adding quantified news to trading, but this was a little ironic since most financial institutions have been paying to receive "raw" news for years, presumably because they perceive beneift from it. Mark also mentioned that the application of quantified news to risk management was a new but growing area for him and his colleagues.

Gurvinder Brar of Macquarie then went into some of the practicallities of quantifying and using news in automated trading. He suggested that you need to understand what is really "news" (containing information on something that has just happened) and what is merely an news "article" (like a "feature" in a magazine etc). Assessing relevance of news was also difficult and he added that setting a hierarchy of what kind of events are important to your trading was a key step in dealing with news data. Fundamentally he suggested that why wait for five days for analysts to publish their assessment of a market or company-specific event when you could react to the event in near real-time.

The event then went into "panel" mode where the following points came out:

• Dan thought that a real challenge was integrating quantified news with all of the other relevant datasets (market data, but also reference data etc)
• Armando picked up on Dan's point by giving the example news about Gillette which at one point was about Gillette the company but then on acquisition became news about the Gillette "brand" which became a part of Proctor and Gamble.
• Dan said that a key problem with processing news was also understanding what news was simply ignored by the news wires i.e. we know what is being talked about, but what could have been talked about, why was it ignored and is it (even so) relevant to trading?
• Mark and Armando said that the "context" for the news story was vital and that market expectations can turn many "negative" news stories into positive outcomes for trading e.g. the market likes bad news when it is not as "bad" as everyone thought.
• Dan made a very interesting point about trading in terms of categorising trades as "want to" trades and "have to" trades. He gave the example of a trade being observed that seemingly has no news associated/prompting it - so does this mean the trade is occuring because somebody "has to" make the trade (a fund facing an welcome client redemption for example?) or because there has been some information leak to a market participant and such a participant "wants to" make a trade before the news becomes available to the market as a whole.
• I think all of the panel members then collectively hesitated before answering the next question from the audience, with Microsoft having one of their "text search" R&D team (think Bing...) asking about news categorisation and quantification.
• Dan also mentioned something that I have only recently become more aware of, which is that apart from major markets in the US, most exchanges world-wide do not publish whether a trade was a "buy" or "sell" trade (they just publish the price and transaction size). Obviously knowing the direction of the trade would be useful to any trading model, and Dan referred to this as wanting to know the "signed volume".
• A member of the audience then asked whether most quantified news had been based on just the English language and the concensus was that most was based on English, but Natural Language Processing can be trained in other languages relatively easily. A few members of the panel pointed out that all languages change, even English, requiring constant retraining, and also that certain languages, countries and cultures added further complication to the recognition process.
• The next question asked was whether the panel could outline the major areas that quantified news is applied in - the answer included intraday (but not quite real-time) trading, algorithmic execution, lower frequency portofolio rebalancing and in compliance/risk/market abuse detection.
• A good debate ensued about whether "news" was provided by the official newswires or by the web itself. The panel (and audience) concensus seemed to favour the premise the news wires are the source of news and the web is a reflection/regurgitation of this news. That said, Gurvinder of Macquarie gave the nice counter example of the analysts/news wires not making much of the new Apple iPod, when looking at the web it was possible to see that the public were in contrast very enthusiastic about it.

Overall an interesting event. I think the application of "quantified news" to risk management is interesting - maths and financial theory is very interesting but markets are driven by people's behaviour and if "quantified news" can help us understand this better it has to help in avoiding (some!) of the future problems to be faced in the market.

Tibco buys Insightful...

...it must be summer (maybe not in the UK according to the weather?), seems like I missed this but Tibco has just finalised its purchase of Insightful, the makers of the S-Plus statistical package. A release from Insightful explaining the deal can be found by clicking here. Not something that strikes me as an immediate "that makes obvious sense" but not a negative either, so let's see...