Tag Archives: Paul Wilmott

The Bionic Invisible Hand

Technology is omnipresent. The impacts of technology on markets and market structures are a topic of much debate recently. Some point to its influence to explain the lack of volatility in equity markets (ignoring this week’s wobble). Marko Kolanovic, a JPMorgan analyst, has been reported to have estimated that a mere 10% US equity market trading is now conducted by discretionary human traders.

The first wave of high frequency trading (HFT) brought about distortive practises by certain players such as front running and spoofing, as detailed in Michael Lewis’s bestselling exposé Flash Boys. Now HFT firms are struggling to wring profits from the incremental millisecond, as reported in this FT article, with 2017 revenues for HFT firms trading US stocks falling below $1 billion in 2017 from over $7 billion in 2009, according to the consultancy Tabb Group. According to Doug Duquette of Vertex Analytics “it has got to the point where the speed is so ubiquitous that there really isn’t much left to get”.

The focus now is on the impact of various rules-based automatic investment systems, ranging from exchange traded funds (ETFs) to computerised high-speed trading programs to new machine learning and artificial intelligence (AI) innovations. As Tom Watson said about HFT in 2011, these new technologies have the potential to give “Adam Smith’s invisible hand a bionic upgrade by making it better, stronger and faster like Steve Austin in the Six Million Dollar Man”.

As reported in another FT article, some experts estimate that computers are now generating around 50% to 70% of trading in equity markets, 60% of futures and more than 50% of treasuries. According to Morningstar, by year-end 2017 the total assets of actively managed funds stood at $11.4 trillion compared with $6.7 trillion for passive funds in the US.

Although the term “quant fund” covers a multitude of mutual and hedge fund strategies, assuming certain classifications are estimated to manage around $1 trillion in assets out of total assets under management (AUM) invested in mutual funds globally of over $40 trillion. It is believed that machine learning or AI only drives a small subset of quant funds’ trades although such systems are thought to be used as investment tools for developing strategies by an increasing number of investment professionals.

Before I delve into these issues further, I want to take a brief detour into the wonderful world of quantitative finance expert Paul Wilmott and his recent book, with David Orrell, called “The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets”. I am going to try to summarize the pertinent issues highlighted by the authors in the following sequence of my favourite quotes from the book:

“If anybody can flog an already sick horse to death, it is an economist.”

“Whenever a model becomes too popular, it influences the market and therefore tends to undermine the assumptions on which it was built.”

“Real price data tend to follow something closer to a power-law distribution and are characterized by extreme events and bursts of intense volatility…which are typical of complex systems that are operating at a state known as self-organized criticality…sometimes called the edge of chaos.”

“In quantitative finance, the weakest links are the models.”

“The only half decent, yet still toy, models in finance are the lognormal random walk models for those instruments whose level we don’t care about.”

“The more apparently realistic you make a model, the less useful it often becomes, and the complexity of the equations turns the model into a black box. The key then is to keep with simple models, but make sure that the model is capturing the key dynamics of the system, and only use it within its zone of validity.”

“The economy is not a machine, it is a living, organic system, and the numbers it produces have a complicated relationship with the underlying reality.”

“Calibration is a simple way of hiding model risk, you choose the parameters so that your model superficially appears to value everything correctly when really, it’s doing no such thing.”

“When their [quants] schemes, their quantitative seizing – cratered, the central banks stepped in to fill the hole with quantitative easing.”

“Bandwagons beget bubbles, and bubbles beget crashes.”

“Today, it is the risk that has been created by high speed algorithms, all based on similar models, all racing to be the first to do the same thing.”

“We have outsourced ethical judgments to the invisible hand, or increasingly to algorithms, with the result that our own ability to make ethical decisions in economic matters has atrophied.”

According to Morningstar’s annual fund flow report, flows into US mutual funds and ETFs reached a record $684.6 billion in 2017 due to massive inflows into passive funds. Among fund categories, the biggest winners were passive U.S. equity, international equity and taxable bond funds with each having inflows of more than $200 billion. “Indexing is no longer limited to U.S. equity and expanding into other asset classes” according to the Morningstar report.

click to enlarge

Paul Singer of Elliott hedge fund, known for its aggressive activism and distressed debt focus (famous for its Argentine debt battles), dramatically said “passive investing is in danger of devouring capitalism” and called it “a blob which is destructive to the growth-creating and consensus-building prospects of free market capitalism”.

In 2016, JP Morgan’s Nikolaos Panagirtzoglou stated that “the shift towards passive funds has the potential to concentrate investments to a few large products” and “this concentration potentially increases systemic risk making markets more susceptible to the flows of a few large passive products”. He further stated that “this shift exacerbates the market uptrend creating more protracted periods of low volatility and momentum” and that “when markets eventually reverse, the correction becomes deeper and volatility rises as money flows away from passive funds back towards active managers who tend to outperform in periods of weak market performance”.

The International Organization of Securities Commissions (IOSCO), proving that regulators are always late to the party (hopefully not too late), is to broaden its analysis on the ETF sector in 2018, beyond a previous review on liquidity management, to consider whether serious market distortions might occur due to the growth of ETFs, as per this FT article. Paul Andrews, a veteran US regulator and secretary general of IOSCO, called ETFs “financial engineering at its finest”, stated that “ETFs are [now] a critical piece of market infrastructure” and that “we are on autopilot in many respects with market capitalisation-weighted ETFs”.

Artemis Capital Management, in this report highlighted in my previous post, believe that “passive investing is now just a momentum play on liquidity” and that “large capital flows into stocks occur for no reason other than the fact that they are highly liquid members of an index”. Artemis believes that “active managers serve as a volatility buffer” and that if such a buffer is withdrawn then “there is no incremental seller to control overvaluation on the way up and no incremental buyer to stop a crash on the way down”.

Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader.

Machine learning uses statistical techniques to infer relationships between data. The artificial intelligence “agent” does not have an algorithm to tell it which relationships it should find but infers, or learns if you like, from the data using statistical analysis to revise its hypotheses. In supervised learning, the machine is presented with examples of input data together with the desired output. The AI agent works out a relationship between the two and uses this relationship to make predictions given further input data. Supervised learning techniques, such as Bayesian regression, are useful where firms have a flow of input data and would like to make predictions.

Unsupervised learning, in contrast, does without learning examples. The AI agent instead tries to find relationships between input data by itself. Unsupervised learning can be used for classification problems determining which data points are similar to each other. As an example of unsupervised learning, cluster analysis is a statistical technique whereby data or objects are classified into groups (clusters) that are similar to one another but different from data or objects in other clusters.

Firms like Bloomberg use cluster analysis in their liquidity assessment tool which aims to cluster bonds with sufficiently similar behaviour so their historical data can be shared and used to make general predictions for all bonds in that cluster. Naz Quadri of Bloomberg, with the wonderful title of head of quant engineering and research, said that “some applications of clustering were more useful than others” and that their analysis suggests “clustering is most useful, and results are more stable, when it is used with a structural market impact model”. Market impact models are widely used to minimise the effect of a firm’s own trading on market prices and are an example of machine learning in practise.

In November 2017, the Financial Stability Board (FSB) released a report called “Artificial Intelligence and Machine Learning in Financial Services”. In the report the FSB highlighted some of the current and potential use cases of AI and machine learning, as follows:

  • Financial institutions and vendors are using AI and machine learning methods to assess credit quality, to price and market insurance contracts, and to automate client interaction.
  • Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions.
  • Hedge funds, broker-dealers, and other firms are using AI and machine learning to find signals for higher (and uncorrelated) returns and optimise trading execution.
  • Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment, and fraud detection.

The FSB report states that “applications of AI and machine learning could result in new and unexpected forms of interconnectedness” and that “the lack of interpretability or ‘auditability’ of AI and machine learning methods has the potential to contribute to macro-level risk”. Worryingly they say that “many of the models that result from the use of AI or machine learning techniques are difficult or impossible to interpret” and that “many AI and machine learning developed models are being ‘trained’ in a period of low volatility”. As such “the models may not suggest optimal actions in a significant economic downturn or in a financial crisis, or the models may not suggest appropriate management of long-term risks” and “should there be widespread use of opaque models, it would likely result in unintended consequences”.

With increased use of machine learning and AI, we are seeing the potential rise of self-driving investment vehicles. Using self-driving cars as a metaphor, Artemis Capital highlights that “the fatal flaw is that your driving algorithm has never seen a mountain road” and that “as machines trade with against each other, self-reflexivity is amplified”. Others point out that machine learning in trading may involve machine learning algorithms learning the behaviour of other machine learning algorithms, in a regressive loop, all drawing on the same data and the same methodology. 13D Research opined that “when algorithms coexist in complex systems with subjectivity and unpredictability of human behaviour, unforeseen and destabilising downsides result”.

It is said that there is nothing magical about quant strategies. Quantitative investing is an approach for implementing investment strategies in an automated (or semi-automated) way. The key seems to be data, its quality and its uniqueness. A hypothesis is developed and tested and tested again against various themes to identify anomalies or inefficiencies. Jim Simons of Renaissance Technologies (called RenTec), one of the oldest and most successful quant funds, said that the “efficient market theory is correct in that there are no gross inefficiencies” but “we look at anomalies that may be small in size and brief in time. We make our forecast. Then, shortly thereafter, we re-evaluate the situation and revise our forecast and our portfolio. We do this all-day long. We’re always in and out and out and in. So we’re dependent on activity to make money“. Simons emphasised that RenTec “don’t start with models” but “we start with data” and “we don’t have any preconceived notions”. They “look for things that can be replicated thousands of times”.

The recently departed co-CEO Robert Mercer of RenTec [yes the Mercer who backs Breitbart which adds a scary political Big Brother surveillance angle to this story] has said “RenTec gets a trillion bytes of data a day, from newspapers, AP wire, all the trades, quotes, weather reports, energy reports, government reports, all with the goal of trying to figure out what’s going to be the price of something or other at every point in the future… The information we have today is a garbled version of what the price is going to be next week. People don’t really grasp how noisy the market is. It’s very hard to find information, but it is there, and in some cases it’s been there for a long long time. It’s very close to science’s needle in a haystack problem

Kumesh Aroomoogan of Accern recently said that “quant hedge funds are buying as much data as they can”. The so-called “alternative data” market was worth about $200 million in the US in 2017 and is expected to double in four years, according to research and consulting firm Tabb Group. The explosion of data that has and is becoming available in this technological revolution should keep the quants busy, for a while.

However, what’s scaring me is that these incredibly clever people will inevitably end up farming through the same data sets, coming to broadly similar conclusions, and the machines who have learned each other’s secrets will all start heading for the exits at the same time, in real time, in a mother of all quant flash crashes. That sounds too much like science fiction to ever happen though, right?

The imperfect art of climate change modelling

The completed Group I report from the 5th Intergovernmental Panel on Climate Change (IPCC) assessment was published in January (see previous post on summary report in September). One of the few definite statements made in the report was that “global mean temperatures will continue to rise over the 21st century if greenhouse gas (GHG) emissions continue unabat­ed”. How we measure the impact of such changes is therefore incredibly important. A recent article in the FT by Robin Harding on the topic which highlighted the shortcomings of models used to assess the impact of climate change therefore caught my attention.

The article referred to two academic papers, one by Robert Pindyck and another by Nicholas Stern, which contained damning criticism of models that integrate climate and economic models, so called integrated assessment models (IAM).

Pindyck states that “IAM based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading”. Stern also criticizes IAMs stating that “assumptions built into the economic modelling on growth, damages and risks, come close to assuming directly that the impacts and costs will be modest and close to excluding the possibility of catastrophic outcomes”.

These comments remind me of Paul Wilmott, the influential English quant, who included in his Modeller’s Hippocratic Oath the following: “I will remember that I didn’t make the world, and it doesn’t satisfy my equations” (see Quotes section of this website for more quotes on models).

In his paper, Pindyck characterised the IAMs currently used into 6 core components as the graphic below illustrates.

click to enlargeIntegrated Assessment Models

Pindyck highlights a number of the main elements of IAMs which involve a considerable amount of arbitrary choice, including climate sensitivity, the damage and social welfare (utility) functions. He cites important feedback loops in climates as difficult, if not impossible, to determine. Although there has been some good work in specific areas like agriculture, Pindyck is particularly critical on the damage functions, saying many are essentially made up. The final piece on social utility and the rate of time preference are essentially policy parameter which are open to political forces and therefore subject to considerable variability (& that’s a polite way of putting it).

The point about damage functions is an interesting one as these are also key determinants in the catastrophe vendor models widely used in the insurance sector. As a previous post on Florida highlighted, even these specific and commercially developed models result in varying outputs.

One example of IAMs directly influencing current policymakers is those used by the Interagency Working Group (IWG) which under the Obama administration is the entity that determines the social cost of carbon (SCC), defined as the net present damage done by emitting a marginal ton of CO2 equivalent (CO2e), used in regulating industries such as the petrochemical sector. Many IAMs are available (the sector even has its own journal – The Integrated Assessment Journal!) and the IWG relies on three of the oldest and most well know; the Dynamic Integrated Climate and Economy (DICE) model, the Policy Analysis of the Greenhouse Effect (PAGE) model, and the fun sounding Climate Framework for Uncertainty, Negotiation, and Distribution (FUND) model.

The first IWG paper in 2010 included an exhibit, reproduced below, summarizing the economic impact of raising temperatures based upon the 3 models.

click to enlargeClimate Change & Impact on GDP IWG SCC 2010

To be fair to the IWG, they do highlight that “underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and economic research characterizing these relationships”.

The IWG released an updated paper in 2013 whereby revised SCC estimates were presented based upon a number of amendments to the underlying models. Included in these changes are revisions to damage functions and to climate sensitivity assumptions. The results of the changes on average and 95th percentile SCC estimates, at varying discount rates (which are obviously key determinants to the SCC given the long term nature of the impacts), can be clearly seen in the graph below.

click to enlargeSocial Cost of Carbon IWG 2010 vrs 2013

Given the magnitude of the SCC changes, it is not surprising that critics of the charges, including vested interests such as petrochemical lobbyists, are highlighting the uncertainty in IAMs as a counter against the charges. The climate change deniers love any opportunity to discredit the science as they demonstrated so ably with the 4th IPCC assessment. The goal has to be to improve modelling as a risk management tool that results in sensible preventative measures. Pindyck emphasises that his criticisms should not be an excuse for inaction. He believes we should follow a risk management approach focused on the risk of catastrophe with models updated as more information emerges and uses the threat of nuclear oblivion during the Cold War as a parallel. He argues that “one can think of a GHG abatement policy as a form of insurance: society would be paying for a guarantee that a low-probability catastrophe will not occur (or is less likely)”. Stern too advises that our focus should be on potential extreme damage and that the economic community need to refocus and combine current insights where “an examination and modelling of ways in which disruption and decline can occur”.

Whilst I was looking into this subject, I took the time to look over the completed 5th assessment report from the IPCC. First, it is important to stress that the IPCC acknowledge the array of uncertainties in predicting climate change. They state the obvious in that “the nonlinear and chaotic nature of the climate system imposes natu­ral limits on the extent to which skilful predictions of climate statistics may be made”. They assert that the use of multiple scenarios and models is the best way we have for determining “a wide range of possible future evolutions of the Earth’s climate”. They also accept that “predicting socioeconomic development is arguably even more difficult than predicting the evolution of a physical system”.

The report uses a variety of terms in its findings which I summarised in a previous post and reproduce below.

click to enlargeIPCC uncertainty

Under the medium term prediction section (Chapter 11) which covers the period 2016 to 2035 relative to the reference period 1986 to 2005, a number of the notable predictions include:

  • The projected change in global mean surface air temperature will likely be in the range 0.3 to 0.7°C (medium confidence).
  • It is more likely than not that the mean global mean surface air temperature for the period 2016–2035 will be more than 1°C above the mean for 1850–1900, and very unlikely that it will be more than 1.5°C above the 1850–1900 mean (medium confidence).
  • Zonal mean precipitation will very likely increase in high and some of the mid-latitudes, and will more likely than not decrease in the subtropics. The frequency and intensity of heavy precipitation events over land will likely increase on average in the near term (this trend will not be apparent in all regions).
  • It is very likely that globally averaged surface and vertically averaged ocean temperatures will increase in the near term. It is likely that there will be increases in salinity in the tropical and (especially) subtropical Atlantic, and decreases in the western tropical Pacific over the next few decades.
  • In most land regions the frequency of warm days and warm nights will likely increase in the next decades, while that of cold days and cold nights will decrease.
  • There is low confidence in basin-scale projections of changes in the intensity and frequency of tropical cyclones (TCs) in all basins to the mid-21st century and there is low confidence in near-term projections for increased TC intensity in the North Atlantic.

The last bullet point is especially interesting for the insurance sector involved in providing property catastrophe protection. Graphically I have reproduced two interesting projections below (Note: no volcano activity is assumed).

click to enlargeIPCC temperature near term projections

Under the longer term projections in Chapter 12, the IPCC makes the definite statement that opened this post. It also states that it is virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean temper­atures increase and that, in the long term, global precipitation will increase with increased global mean surface temperature.

I don’t know about you but it seems to me a sensible course of action that we should be taking scenarios that the IPCC is predicting with virtual certainty and applying a risk management approach to how we can prepare for or counteract extremes as recommended by experts such as Pindyck and Stern.

The quote “it’s better to do something imperfectly than to do nothing perfectly” comes to mind. In this regard, for the sake of our children at the very least, we should embrace the imperfect art of climate change modelling and figure out how best to use them in getting things done.