Altered Carbon: A New Fund Order Beyond COVID-19?

Altered Carbon: A New Fund Order Beyond COVID-19?

Compared to the actual frontier of Finance, by modern standards the long-standing opportunity set for hedge fund managers and “Alternatives” was all, well, rather predictable, a bit stagnant. From bond curve steepening and flattening, bond risk stripping, venture capital, private equity, private debt, equity arbitrage, risk premia stripping, carry-trades, commodities, convertibles, real estate, esoteric assets and a range of sleep inducing long/short, derivative strategies and structures. Even first generation statistical arbitrage (‘StatArb’). All a little analogue. Yawn.

Fast forward, beset by a global pandemic with systemic consequences, rapidly changing technological and environmental forces. Alternatives becomes exciting; (perhaps scary even). Global forces are creating an almost unrecognisable array of new assets, strategies and risk premia. Like an increasing number of movies and series like ‘Ghost in the Shell’, ‘Altered Carbon’, ‘Bladerunner’ and ‘The Matrix’ obsessed on the separation of physical and digital selves; Alternativesfind themselves occupying new categories or sleeves. Technology is the enabler but to access we first must deal with cognitive dissonance.

Ray Kurzweil first wrote on the cognitive dissonance between “intuitive linear” views of technological change (eg. Moore’s Law) and the actual “exponential” rate of change. Dissonance lies at the very heart of the human condition. Recognise then that “Dissonance” is bad. It’s a by-product of group think, as much as of individual biases. It is the negative decision process linked to peer pressure, resistance, herding and the opposite of synergy; it is the old fund order.

Human biases can infiltrate investment screens, buy decisions, media and also AI programs designed to automate some or all of your research process. That would be counter-productive. Such biases could be introduced by the programmer or emerge as the consequence of unseen latent biases held in different programs. This is all uncertain and so the question turns to how to prevent or manage dissonance arising? Hod Lipson, professor at Columbia first suggested “machine teaching”, the most simple example is the self-learning that occurs between two autonomous but interacting programs that then react to one another. They learn to adapt or one program teaches the other.

Eg. Imagine the first program is written with quantitative biases like 3 year stock earnings; it wants to pick companies that rank highest, while a second program has a qualitative bias to companies with higher Environmental Social Governance (ESG) merits. The two programs advocate different companies and first; rank, cross reference matches, list exceptions and continue to rank until a compromise on the final recommended buy list is found.

James Surowieckim asserts four conditions apply for crowd teaching to synthesise his ‘collective wisdom’: “(a) Cognitive diversity; (b) independence of opinion; (c) decentralisation of experience; (d) suitable mechanisms of aggregation." It is the last condition where data storage offers huge scalability. Here machine intelligence programs are then free to incubate, teach, learn, test and morph inside silo synthetic markets (digital replicas) before going live.

Welcome then to the New Fund Order.

Your machine-teaching platform could exploit sleeves like;

Organic ‘rules based’ factors: SmartBeta has been around since the 1970s even if not called as such. From the likes of Eugene Fama and Stephen Ross the concept of style factors and arbitrage pricing theory helped create Alternative strategies that diverged from the earlier and not-so-Modern Portfolio Theory. Noting that style and economic sensitivities have given rise to healthy and profitable risk premia industry in Alternatives; criticisms from the likes of GMO’s James Montier to paraphrase ‘SmartBeta is little more than old snake oil in new bottles’ seems warranted. One of the recent criticisms of rules-based investing is that the factor trend signals are subject to decay over time. They are not immutable. They can disappear, become crowded or even arbitraged away. Organic rules allow learning programs to evolve (drift) to respond to that decay. An example might be for an absolute value signal (Graham) that drifts to a modern value signal (Buffet) or even to a relative value (Greenblatt) signal as markets move.

Untraditional factor mining: Once described as ‘anomalies’; we now understand information theory, information cascades and multi-lateral correlation and causality far more than before. Mining involves looking at non traditional data sets, at scale, to identify patterns and correlations. For example Payrolls in one part of the market may have a relationship to discretionary consumer confidence in another. Spoiler alert: the list of variables and permutations are infinite and will typically involve complex relationships and multicollinearity. The coronavirus outbreak too will create both seemingly anomalistic and new economic pairings.

Digital disruption: The rate of digital disruption is both broad and unpredictable. Anecdotally we can all identify examples of digital disruption (Amazon), obsolescence (high street Retail) but not necessarily the rate of change. That impedes traditional analysis. It has an amorphous effect upon different industries at different rates, magnitudes and timescales. Using big data to model the rate of disruption can produce new cash flow models that sit outside conventional buy and sell side analysis.

Quantitative Complexity Management (QCM): Finance is complex. The COVID-19 coronavirus outbreak has ably demonstrated that. In any system interconnectednesscreates risk (lack of resilience) and opportunity. Observing market, company or economic complexity optically allows for greater diversification, better risk analysis and hence valuation.

Derivatives trading arbitrage: In effect such strategies are capturing and stripping the arbitrage actions of other traders and companies. Arbitrage will emerge between unmatched contracts, and margin accounts, not obvious to traditional traders. This could create unconventional trades without reference to the underlying.

Real Estate ‘Stat-Arbitrage’: assessing build rate, electricity usage, sustainability, occupancy and finance structures belying property and infrastructure developments (eg. ADAC loans) could allow strategies to arbitrage the values and developments of Real Estate projects and the companies financing and invested therein. The current stress in property funds and Retail property is a case in point.

Climate: Catastrophe securities and strategies will expand and evolve well beyond today’s Cat Bonds in order to de-risk climatic effects from global warming. Climate risk will become much more tradeable. Whether investors link shorter-term existential risks like pandemics with longer-term existential risks like viruses is unclear but there will be modelling opportunities ahead.

Water under-supply and over-supply data: From floods and rising sea levels to desertification and Forrest fires; establishing an equilibrium price for water, upon which to arbitrage, may become one of the great questions for future finance.

Traded Life Finance: Always on the fringe of morale acceptability; pandemics change the face of longevity underwriting and with it the value of traded life instruments based upon. Expect a resurgence of life trading from employers, life insurance companies and big Pharmas.

Carbon trading: Carbon looks set to be a huge opportunity as companies step up their TCFD carbon reporting in order to comply with tighter climate requirements.

Airspace: As mega-cities grow upwards the value of the airspace above will completely hack the traditional land price model.

Stock-lending arbitrage: the stock lending market is a daily activity that provides liquidity to cover derivatives trading. The rate of stock lending and liquidity will vary across the market at different times and this may create opportunistic strategies to step in or capitalise on illiquidity.

Social Media data: Putting aside Cambridge Analytica and data restrictive legislation, the value of user data will provide new data points for years to come. These will be scraped, washed and strategised with increasing effectiveness.

Inflation trading: How to benefit from stubborn disinflationary cycles or unexpected stagflation spikes? Stripping variances within inflationary measures and inflation baskets will evolve us well beyond the use of currencies, rate differentials, swaps and forward rate agreements as blunt proxies.

Individual arbitrage: There are literally millions, if not billions, of investors locked-up in pooled investment vehicles around the world. If all investors eventually diverged from their vast open-ended structures into individual risk pools and pathways then the scope of meeting those needs, to cross-match them and ultimately arbitrage their behaviour becomes huge.

We’re only just getting started, others might include;

•    Nano-trading and exchange arbitrage

•    Biometrics and Facial recognition data

•    Liquidity arbitrage

•    Exchange Traded Fund creation/redemption data

•    Political polling arbitrage

•    Geo-tagging and GPRS data

•    Dark web data

It is no coincidence that the largest asset managers are prioritising investment into Alternatives units, where asset management meets science fiction. Beyond COVID-19; the far reaching economic and technological effects will create many new alternative strategies at the fringe of our imagination.

JB Beckett, Author ‘New Fund Order’, NED

References;

James Surowieckim https://www.academia.edu/6686789/THE_WISDOM_OF_CROWDS_by_James_Surowiecki_Review

Hod Lipson http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.7011&rep=rep1&type=pdf

Cambridge Analytica and Facebook scandal https://www.icmrindia.org/casestudies/catalogue/Business%20Ethics/BECG160.htm

Ray Kurzweil https://www.kurzweilai.net/ask-ray-a-little-thought-experiment-on-cognitive-functions

QCM: https://www.optimumcomplexity.com/research/cm-what-it-is-not

Eugene Fama https://tevgeniou.github.io/EquityRiskFactors/bibliography/FiveFactor.pdf

Stephen Ross https://gersteinfisherfunds.com/wp-content/uploads/2015/05/The-Arbitrage-Pricing-Theory-Approach-to-Strategic-Portfolio-Planning.pdf

James Montier https://ftalphaville-cdn.ft.com/wp-content/uploads/2013/12/JamesMontierDec.pdf

Rob Arnott on factor decay https://www.researchaffiliates.com/en_us/publications/articles/710-alices-adventures-in-factorland-three-blunders-that-plague-factor-investing.html