The economics of new forms of enterprise – current debates from a policy perspective

Jonathan Cave

Outline

• The economics of NFEs• Challenges to conventional policy

economics• Some policy challenges and debates

The economics of NFEsConventional policy makes many assumptions about business e.g.– Stable corporate structures to organise activity, hold liability– Financially-intermediated separation of ownership and control– Efficiency of market mechanisms (or regulation to fix holes)– Transferrable (monetised) store of value as primary motivation– Differentiated roles and competences rooted in trained skills– It is good to

• Minimise burdens and costs• Maximise connectivity, speed and complexity• Drive innovation and growth• Encourage risk-taking/mean-variance risk aversion and positive discounting

The teaching of this economics in business and policy schools and its application in a complex world refutes itselfThis is made worse by crisis mentality and herd behaviourBut it creates windows of opportunity at all levels

Economics and policy“Practical men who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back”

How economics drives policyCreating policy imperatives – “it’s the economy, stupid”Framing issues and options– Incentives – why do people do what they do?– A common basis of comparison – adding Apples ™ and Oranges ™ – The animating force of the market – societal infrastructure,

primordial network, measuring stick and channel of influence– Evidence of what works – empirical and experimental– Regulatory and other policy instruments– Impact assessment and evaluation

Business,MarketModels

Policy

EconomicTheory Administrations

Business Citizens

Research

Building blocksLevels:– Micro – Rationality (and psychology and sociology)– Meso – Firms, households, sectors (and networks)– Macro – Countries, blocs and aggregate measures– ‘Scale-free’ results – each level looks like the othersInteractions:– Competition, collusion and conflict – Game theory

• Players, strategies, preferences (payoffs), information• Non-cooperative, bargaining, cooperative• Mechanism design – from contracts to auctions

PolicyBasis – market failure (to do what?):– Allocational efficiency – can everyone be made better off?– Technical efficiency – can we produce more of everything?– Dynamic efficiency – including growth, recovery and innovation– Equity– Delivery of external and public societal benefitsMechanism: – selection (who plays) – incentives (what they do)Tools– Property rights (to allow trade and investment)– Ex ante restrictions (licenses, standards) and Ex post rules (conduct or

outcome-based)– Taxes and subsidies– Contests

Internet challenges to the standard modelEconomic policy assumes that things are done for moneyRationality and meaningful consent may not be reliableNon-human actorsHerding and contagionInformation goods involve access as much as ownershipTwo-sided (platform) marketsNetwork externalities - tipping, excess volatility or inertiaComplex systems behaviourNew stuff: IoT, Cloud, Big DataRapid dynamics and varying sizes and forms of enterpriseNew forms of entrepreneurial activity

Public goods and the value of informationStandard theory assumes exclusive and transferrable property rightsA few exceptions are recognised (in red):– Externality determines how we aggregate costs and benefits to decide what is

efficient – Permission determines whether we can use markets to determine efficient

outcome, organise production and access, and pay the costs

Permission to access: Externality

Owner User Perfectly negative (rivalrous) Zero(non-rival)

Positive(‘network’)

Y Y Ordinary good Club good Voluntary provision

N Y Congestion good Pure public good Commons

Y N Liability Fees Charity

N N Environmental goods

Value of informationCommon sense assures information is valuable – we can always ignore it, right?Consider the game of Prisoners’ Dilemma – Each person can choose between selfish and cooperative behaviour e.g. selfishness is traffic shaping (S) and

cooperation is net neutrality (N)– Selfishness helps the person less than it hurts the other, regardless of what the other does– There is a unique individually rational equilibrium – selfishness all round– But all the other outcomes are collectively rational (Pareto optimal) – it is not possible to make anyone better

off without hurting someone else

Now suppose that either strategy could be cooperative (shaping may help traffic types to cluster together; net neutrality may treat unequal parties equally)– Suppose that player 1 (row) knows which is which, and player 2 observes 1’s move– If the informed player (1) uses this information, the result is always the inefficient equilibrium– If the informed player randomises, so does the other; not perfect, but better than before– So the information has negative value to both players!

S N S N S N

S 3,3 0,4 S 1,1 4,0 S 2,2 2,2

N 4,0 1,1 N 0,4 3,3 N 2,2 2,2Shaping is

cooperativeNeutrality is cooperative

Random

Policy 1: Digital Single MarketPolicy context: Europe 2020 strategy– A policy chapeau – many initiatives, e.g.

• Future Internet PPP• Horizon 2020• Flagships• EFSI

– Digital Agenda for Europe – now Digital Single Market

The challenge of the Internet– Jurisdictional problems – regional, national, EU, global– Market definition and regulatory traction– Regulators not set up for Internet, with different cultures– Conflicting economic objectives – competition, competitiveness,

GDP, employment…– Tension between harmonisation and comparative advantage

The challenge of Grands Projects

Economic case for the Digital Single Market*Massive potential for Europe– 315 million daily Internet users– €415 billion in additional GDP/year– Substantial (unknown) contributions to employment)Europe’s digital market is not her own :– 42% of online economic activity lies within Member State borders; only 4% is cross-border.

• 15% of consumers bought cross-border; 44% bought domestically; Cross-border competition could save them up to €11.7 Billion per year

• 7% of SMEs managed significant cross-border sales – average extra cost of €9000 per year; uniform rules would increase proportion to at least 57%; VAT compliance adds €5000/country

• Shipping costs are a barrier for 90% of e-shoppers and 62% of companies• 52% of attempted cross-border orders within the EU are geo-blocked

– 54% of online economic activity involves US-based servicesFramework conditions are also lagging– Data protection reforms stalled, Safe Harbour is sunk, TTIP risks– Patchy penetration of fast broadband (22.5%) and 4G 59%-15% (rural)New opportunities– Cloud data storage (20%-40% in next 6 years)– Data analytics could save top 100 mfgs €415 billion, raise GDP growth by 1.9%

* Data from Eurostat and Digital Agenda Scorecard

Policy case for the Digital Single Market

• Elaborate and mature Single Market vision• Good examples e.g. Estonian eGovernment, German start-up

scene, UK fintech ecosystem, HD broadband (e.g. Ie, Be, Sw, Nl)• Perceived inequalities and inequities

Starting points:

• Consumer access to services• Market access by start-ups• Barriers to innovation (e.g. sharing economy, P2P services)• Lack of digital-by-default services and Internet-ready regulation• Small differences increasingly important

Barriers:

• Pan-European access to subscription services, FRAND IP protection• An end to locational discrimination in pricing and conditions• Unified or harmonised consumer rights• Effective, certain, innovation-friendly data protection framework• Improved access to public and private online services

Consumers:

• Single point of registration, once-only reporting• Competition rules that work for data-driven economy• Tax reform to address BEPS, VAT issues, fit digital business models• SaMBA default exemptions for growing/new businesses, REFIT• Interoperability standards, unified set of wholesale access products• Open Data Charter

Entrepreneurs:

Other Digital Agenda for Europe policiesInnovation Union – Targets human capital, finance, patenting costs, regulations and procedures, standards, strategic public procurement, fragmentation– Large variations, faltering progress (scorecard leaders, followers)

– Dimensions of innovation

European Fund for Strategic Investments – Regulatory and structural reforms to improve investment climate– European Investment Advisory Hub to channel finance to real economy– Supporting higher-risk financingAll owned by different players, and subject to internal and external shocksAll rely on others’ participation, but create unique risksEach relies on uncertain (and potentially inconsistent) economic modelling

4

Policy 2: Net neutrality

What could be wrong with neutrality?The separability of the transport layer – a bit is a bit?The race to zero (rating)Two-sided markets and walled gardens– Competition in the market or competition for the market– Is it all about content delivery?

Dimensions of performance and discrimination– Congestion externalities and crowding types – who interferes with

whom?– The indirect value of a subscriber base – cream-skimming and

sludge-passing– Quality of experience – latency, jitter and relative speeds

Net Neutrality 2: the necessity and efficiency of discriminationOften, platforms or infrastructures have large fixed costs– Allocationally efficient marginal-cost pricing will not cover them– Any feasible single price regime will generate welfare loss– Have to price according to inverse elasticity of demand (Ramsey)How to ensure the right kind of differentiation?Secondary question: who should have market power?How to balance regional and global interests, value capture and creation?Future-proofing the rulesIn diagrams, cost = area under MC plus fixed cost (shaded rectangle); revenue = sum of unshaded rectangles

Quantity

Pric

e

D

MC

AC

Monopoly

Quantity

Pric

e

D

MC

AC

Quantity

Pric

e

D

MC

AC

Monop

Inefficient single-price Efficient multi-price – includes subsidies

Extreme case: market would not existwithout discrimination

Policy 3: Regulatory ImpactsThe burdens of regulation fall on all businesses, but particularly on– Small and micro businesses– Non-standard businesses– Businesses with complex geographic and sectoral activities

• EU Smart regulation– applied to all types of EU intervention; (expenditure, regulatory including soft law)– “evaluate first” principle– REFIT (Regulatory Fitness and Performance Programme) reviewing entire stock of

EU legislation - identify burdens, inconsistencies, gaps or ineffective measures to ensure “a simple, clear, stable and predictable regulatory framework for businesses, workers and citizens” –seeks light-touch, smart rule-making in context.

– Ex post evaluation to ensure policy as well as (financial) programme learning.Treatment of small businesses – UK as model– Targets – RPC statutory role under Small Business, Enterprise and Employment Act 2015 as independent body

to “verify the assessments of economic impact in respect of all qualifying regulatory provisions within the business impact target (the target), and to verify the regulatory provisions that qualify and do not qualify for the target”

– To consider Civil Society Impacts– To assess ‘qualifying’ regulatory actions– Presumption of SaMBA exemption for small and micro businesses

Policy 4: Competitiveness and growthCompetitiveness is not the same as competition– Incubators vs. boot camps vs. Darwinian sandpits– The importance of failure– Building market share, IP, experience, organisational capitalCreative destruction - incumbents vs. new entrantsNew forms of enterprise– Classifying and understanding them; guiding their development– Size matters – but indirectly– Networked and transitory affiliations; rules assume corporates and growthFinance– Not just the amount, but the modalities– Competition regulation and financial regulationStructure (SCP)– Understanding market networks– Macroprudential regulation– Ecosystem servicesNew industrial policy and Better Regulation

Policy 5: Competition policyWhat is a market?Balancing competition with cooperation, minimising predation and collusionNew forms of enterprise compete in different waysStandards and self-regulation – participation of NFEsFrom promoting competition to promoting efficiencyNudging the self-organisation of markets and productsAvoiding capture and foreclosureBalancing public needs and private incentives

Policy 6: Privacy and securityEconomic or fundamental rightAdequacy of legal rolesThird-party monetisation and the two-way value of personal informationThe Right to be Forgotten – or remembered?Privacy of information or of action?The draft General Privacy RegulationSafe Harbour type arrangements – the EU-US Privacy ShieldSecurity concerns – and the effective placement of liability

Policy 6: Financial tradingNon-human actors, quants and complex systemsThe rule of algorithms (e.g. Gaussian Copula)The interaction of technical and financial efficiencyBehavioural responses and feedback loopsSeeing what is happening“The regulators had all the data; the investment houses had all the brains”

Regulating the cloud: more, less or different regulation and competing agendas

IntroductionThe cloud is: – a fad; – a metaphor; – a critical phase in complex ICT system development; – A microcosm for issues of Internet regulation– dead [choose all that apply]

Cloud-like things challenge regulation:– Unique issues– Existing issues made harder (or easier)

It shares this characteristic with “Internet regulation”Not regulatory convergence, but a regulatory networkRewards new models and approaches

Operational definition of the cloud

5-3-4 ……You know the rest

The cloud is already regulatedTechnical: standards; interoperability; QoS; security…Economic: general competition consumer protection; IPRSocial: privacy; content; liability(?)Sector-specific: finance, health, transport, servicesUp- and down-stream regulation: cloud, cloud-based and cloud-enhanced services

Why should it be regulated?Issues unique to the cloud (few)Issues made harder or easier by the cloudA convenient point of intervention – or at least discussionA natural platform for self- and co-regulationA ‘model’ for atomistic and dynamic competition (the cloud version of the app ecosystem)

Can it be regulated – and by whom?

Indirect relationships and regulatory tractionImplementation problems (e.g. jurisdiction)Conflation of regulatory and stakeholder agendas – a hard problem for regulatory designLimitations of existing statutory duties and powersNeed to assemble networked governance ecosystem to parallel – or to invade - cloud

How to frame the problemMap regulators’ statutory remit and toolsAssess problem:– Whose problem is it– Is it due to or changed by the cloud– Context:

• Can it be fixed without damaging other things• Will it go away by itself

Identify or develop instruments– Evidence and evaluation – finding the problem and fixing blame– Crafting and implementing a remedy– Changing behaviour

Monitoring and enforcement

Selection framework: legitimate interests

Citizen interests– Access to critical telecommunications services– Participation in society– Citizen protection

Consumer interests– Benefits of competition– Consumer protection– Consumer empowerment

Selection framework: specific duties

Ensuring the optimal use of the electro-magnetic spectrumEnsuring that a wide range of electronic communications services – including high speed data services – is available throughout the UKEnsuring a wide range of TV and radio services of high quality and wide appealMaintaining plurality in the provision of broadcastingApplying adequate protection for audiences against offensive or harmful materialApplying adequate protection for audiences against unfairness or the infringement of privacyCitizen and consumer interests

Screening testIs there citizen harm?Is there consumer harm?Is ‘the market’ likely to mitigate or eliminate this harm?Does it fit within regulator’s remit?Is new intervention/power required?Note: regulatory duties are not the same as policy objectivesNot the same for all nations (esp. within EU)

Citizen and consumer issues

Issues DescriptionCitizen har

m

Benefits of co

mpetition

Consumer

protecti

on

Cons

umer

empowermen

t

Existi

ng regulati

on

Marke

t soluti

on?

Remi

t

Consumer switching and mobility Consumers may be tied to cloud service providers by limited portability of data and applications, restricted interoperability, lack of information N I

Copyright and IPR i) Questions over whether Cloud Service Providers may be liable for actions of users and hosted service providers; ii) Questions over ownership of user-generated content; iii) Concerns that content-matching services may serve to legitimise unauthorised content copying N I

Unfair and potentially anti-competitive contract terms

Standard form SLAs are efficient for large numbers of consumers but prevent bargaining. This raises switching costs and can lead to lock-in, damage competition or promote potentially anticompetitive market segmentation. More serious for cloud because providers ‘have’ user data. ?

Security, reliability, resilience capacityConcerns: a) transparency of cloud providers’ practices; b) security and reliability of services; c) data loss and unauthorised release - even when not illegal, this can create significant harm, especially when data owners are not aware; d) Data security includes e.g. infrastructure resilience (continuity), authentication.

? ? E

Crime Users of cloud-hosted services may face an increased risk of a range of criminal threats: Identity, Data theft; Fraud; Malicious system, processing or data interference; Data loss or unauthorised release. N E

Privacy Privacy may be weakened by indirect relationships with cloud-hosted service providers who hold personal data, limited visibility, obsolete legal roles, privacy-invasive technologies and business models. ? I

Communications as a Service (CaaS) Integrated or converged video, voice and data communications and associated services that overlap with existing regulated communications but are not limited to communications service providers. Policy issue: whether/how to regulate them. ? D

Advertising and marketing Consumers may not be fully informed of what they sign up to; may see repeat of problems with e.g. broadband or mobile. Providers may be unable to certify, deliver or even inform consumers about the services they expect and those they receive.

Cloud as a utility (including risk of market foreclosure)

Cloud computing share technical, economic and societal features with other utilities (e.g. scale economies, universal service potential). Policy issues: potential monopolistic foreclosure, social case for Universal Service (quality, affordability, open access) regulation N E

Implementation issues

Issues DescriptionCitizen har

m

Benefits of co

mpetition

Consumer

protecti

on

Cons

umer

empowermen

t

Existi

ng regulati

on

Marke

t soluti

on?

Remi

t

Location and jurisdiction Locations are hard to verify and constantly changing; this raises consumer protection and jurisdictional issues. ? D

Locus of control Difficult to identify points of leverage for effective intervention; existing regulation could be undermined by cloud-hosted alternatives that operate outside the regulatory sphere. Also, control is different in different architectures and deployment models. ? D, I,

E

Consumer information, transparency of CSP practices

Linked to mobility, but important to other interests that depend on consumer choice, e.g. identity management, which is central to consumer choice and protection; if individuals cannot know or verify the identities of those with whom they transact, it may be hard to enforce rights Includes adverse impacts of unfair cloud contracts and advertising and marketing practices.

? I

Complexity of the cloud The cloud's inherent complexity and adaptability challenges conventional regulation. D

Certification and other self- and co-regulation

There are currently a variety of such ’market-provided' ways to address a range of concerns; may require national monitoring, and/or enforcement and/or be multiple, inconsistent, ineffective, costly, unmanageable, or anticompetitive. Y I

Cloud neutrality Cloud should be OS, hardware, software. neutral; but this may be restricted for purposes of efficiency. As with net Neutrality, it is an empirical question whether non-neutrality is harmful and whether harmful non-neutrality can be countered. N I

Consumer/SME similarities, regulatory heritage and convergence

In the cloud, both SMEs and users are potential cloud-hosted service providers therefore consumer harms thus occur in both B2B and B2C. A related issue is the 'fit' of communications, privacy. regulation with the cloud environment.

Potentially applies across the board D

Trust Security (esp. for firms, including SMEs) and privacy issues may reduce trust in cloud-hosted services, affecting their uptake; alternatively they may lead to to “privacy/security as a service” innovations N E

Modelling challengesComplex adaptive systemProtean N-sided marketSalience vs. realityMonetisation and participation rightsTechnical issues: capacity management, privacy and security as a service, data access vs. processingChallenging sectors: computerised/HF financial trading, health diagnostics, shared innovation, content sharing, supply chain data repositories

Three scenarios: dimensionsSovereignty over cloud regulation: national, international, market/self-regulationLocus of power in cloud services: Telcos (current EU situation), Google/Amazon (current US situation), Hypervisors (Vmware, MS/Citrix)Balance of cloud deployment from regulatory perspective: public (consumer and citizen harms incl. privacy), private/enterprise (competition), hybrid Architecture of (future) Internet: cloud as niche/overlay on current architecture or cloud-centric architecture (as in NEBULA).

Summary of scenarios Cloud-max Nothing new Another critical infrastructure

Sovereignty: Market/self-reg. National National/international

Power: Google/Amazon Telcos Hypervisors

Deployment: Hybrid Public clouds Private/enterprise (provider responsibility)

Architecture Cloud-centric Niche/overlay Niche/overlay

Cloud centricCloud develops under existing reg. frameworkIncreasingly central to Internet architecture and governance Emergent issues tend to be managed through a combination of market solutions and self-regulationMarket and governance power lie with dominant B2C providers of global Internet Dominant mode of computation, data storage and information-intensive communication and transactionCould drive re-examination of regulators’ duties and mandate; in short run, most issues are out of scope

Nothing newContinues trend as currently experienced in UKRegulatory sovereignty according to existing mandates (some ad-hoc additions)May create race to bottom amid contradictory requirements (especially at pan European level), as national regulatory agencies strive to attract profitable data centres.Dominant model is public (B2C, SME) cloud; dominant players are telcosExisting regimes continue in sub-optimal and fragmented fashion:– telecommunications regulators adopt consumer protection perspective– data protection authorities looking into how cloud service providers address privacy and data

protection obligations – security and law enforcement agencies provide guidance on managing cloud risks

A new critical infrastructureCurrently, unlikely due to perceived security weaknesses (confidentiality, availability and data integrity) and regulatory interestPrivate clouds are becoming more central, esp. in highly-regulated sectors subject to margin squeeze Another reason is expansion of Big Data AnalyticsRegulatory sovereignty will be increasingly joined up within and among nationsCritical core primarily provided as private/enterprise clouds; with the regulatory protection afforded by their centrality, they can compete successfully for citizen and consumer business as well. This reinforces the status of cloud service providers.

ConclusionsThe cloud is primarily useful as a metaphor, and a means of raising challenges:– Definitions– Policy linkage (economic growth, finance,…)– Realigned roles and responsibilities– Sandbox for n-sided market, app ecosystem issuesThe technical issues may need prior resolution– QoS– Self-organised complexity– Demand smoothing…

Algoarchy – the rise of formulÆ and machines

06 and 13 March, 2015 41

I. Debt derivatives and the Gaussian CopulaThe Internet connection• Like the Internet itself, the simplicity of these formulae

opened participation to all sorts of players• Lines of information and accountability blurred• Models that interpreted market data developed hidden

bias and error• Trading happened over the Internet, using big data

analytics to which fast and stupid models were applied• Systemic behaviour became harder to predict as individual

elements became simpler.06 and 13 March, 2015 42

Gaussian copulas – the formula that killed Wall Street?

In the US, 2007/2008 marked the bursting of a Housing BubbleThis triggered a major recession; people looked for scapegoats.Initially, blame was fixed on major financial institutions (Bear Sterns, Goldman Sachs, AIG, etc.)Later, the finger was pointed at the formulas they used to assess investment riskChief among these was David X. Li’s Gaussian Copula formula

Collateralized Debt ObligationsA CDO is a structured asset-backed security (ABS) whose value and payments come from an underlying portfolio of fixed-income assets: bonds; loans; credit default swaps (CDSs) and mortgage-backed securitiesThe first CDO was issued in 1987; they became steadily more popular starting in the late 1990s until the mid 2000sThe same period saw the growth of the CDSThe CDO offers different tranches of security– “Senior” tranche - paid first, most secure, most expensive– lowest (subordinate/equity) tranches are riskiest but cheapest– Investors have ultimate credit risk exposure to underlying entities, so banks used

CDOs to transfer risk from themselves to investors

CDOs, 2On each tranche, investor has “attachment percentage” and a “detachment percentage” - when the total percentage loss of the entities in the CDO reaches: – The attachment percentage, investors start to

lose money (not get paid fully)– The detachment percentage, investors won’t

get paid at all

CDO Example– Tranche 1 (equity tranche) = 0% - 5%– Tranche 2 = 5% - 15%– Tranche 3 = 15% - 30%– Tranche 4 (senior tranche) = 30% - 70%

If CDO has 3% loss, Tranche 1 (the equity tranche) will absorb that loss; other investors unaffected.If CDO has 35% loss, Tranche 1 and 2 gets no payment, Tranche 3 loses most of its payment; Tranche 4 unaffected

Credit default swaps (CDSs)Like an insurance policy that pays off in the event of defaultUnlike an insurance party in that it does not (necessarily) involve the original debtorNo limit to the amount of CDS that can be written on a single “underlying” credit.– Every underlying gets a certain amount of “basis points”

(representing .01%)– These depend on stability/riskiness of underlying– The riskier the underlying, the higher the basis points.

• Reflects market perception of default risk over riskless rate; like percentage odds that underlying will default before maturity

Gaussian copulaPurported to model correlation between default of two obligations (or the entities that control them) without using historical default data.Instead, formula used CDS pricing data (initially had less than 10 years’ of observations)Implicit assumption: CDS market was able correctly to price the default risk correctly on the underlying assetsA copula is used in statistics to couple behaviour of two or more variables and determine if they are correlatedWith so many underlying entities in CDOs and portfolio/index CDSs, copula seemed idealLi’s Gaussian formula was the only copula used in practice

The formula itself

– T = time period– and are the probabilities of A and B not defaulting

during T using inverse of standard normal cumulative distribution function (cdf)

– (the copula) couples individual probabilities associated with A and B to come up with a single number, using standard bivariate normal cdf of correlation coefficient γ

– = probability of both groups A and B defaulting within T

ResponseFinancial industry embraced it, and used it to create and sell unprecedented amounts of “AAA-rated” securitiesThis was easy: no need to examine (or even identify) underlying entities, just use one numberIf underlying entities were believed to be uncorrelated, the perceived risk of a CDO built of these CDSs was near 0, especially in senior trancheBanks began combining all kinds of risky underlyings; if they did not appear correlated, CDO was highly ratedMarkets grew rapidly:– CDS – from $921B end ‘01 to $62T by end ’07– CDO – from $275B in ‘00 to $4.7T by end ‘06

ImpactsUsed to be good practice to diversify underlyingsWith copula, a group of (apparently) uncorrelated home loans (say) could be advertised as a safe asset, because you’d ‘never’ lose everything (in the senior tranche)Banks started to sell riskier CDOs; they also made riskier loans (because they could lay off the risk)– Exacerbated by government pressure to make more loans– Sound familiar?

When the initial growth occurred, underlying (house prices) was increasing rapidly, meaning prices reinforced impression of low and uncorrelated default riskBy the time the bubble burst, this misleading price record was ‘set in stone’ – By the time defaults showed up, it was too late: AAA CDOs became worthless

What was wrong?Underlying correlation assumptions defeated by derivative cross-linkingModel intended for analysis, not decision-makingFundamentals not understood by model usersCertainly not scalableConspiracy of optimism lasted 6-7 years – more ‘good history’ to reinforce belief in modelMaybe we can do better now with – Network models– Big data to identify high-dimensional correlations– Avoids even possibility of ‘one formula to rule them all’

II. High-frequency and computer-based trading

Outline: High frequency and computer-based trading

What are CBT and HFT?Financial stability and CBTImpact on liquidity, price efficiency/discovery and transaction costsThe technology of CBT/HFT

What are Computer-based and high-frequency trading?

• Look at stability as a source of confidence in capital markets (as a store of wealth, source in ‘real’ investments, etc.)

• Fluctuations are always expected, but large, unexpected/inexplicable changes can impair the investment mechanism, erode confidence, and undermine financial stability.

• Example: 6/5/2010 “Flash Crash” • US equity market dropped 600 points in 5 minutes• Destroyed $800bn of value• Regained almost all losses within 30 minutes but• Led to several months of outflows from retail mutual funds.

• Mechanisms that may lead to instability:1. Nonlinear sensitivities to change2. incomplete information3. “endogenous” risks based on feedback loops.

Computer-based/high-frequency trading

• The internal chains of cause and effect producing endogenous risk create positive feedbacks that

• Amplify detrimental interactions among management processes

• Can even be worsened by risk-management systems• Can be driven by

• Changes in market volume or volatility• Market news• Delays in distributing reference data.

4. Social instability - normalisation of deviance (unexpected and risky events come to be seen as ever more normal, until disaster)

5. Network topology determines the stability and the flow of information and trades, hence overall system stability

Why is CBT/HFT different?interactions take place at a pace where human intervention could not prevent themgiven this, computer based (mechanical) trading is almost obligatory, with all its system-wide uncertainties Information asymmetries become more acute (and different) than in the pastthe source of liquidity has changed to computer based and high-frequency trading, which has implications for its robustness under stressLatency and other technical characteristics matter enormously

Varieties of CBT/HFTCan trade on an agency or proprietary basisMay adopt liquidity-consuming (aggressive) or liquidity-supplying (passive) trading stylesMay engage in uninformed or informed trading

Computer-based/high-frequency trading

• The balance between “Computer Trading” and “Human Intervention” has yet to be determined

• Further complexity arises from the divergent evolution of different markets

• But the base drivers remain largely common

• The pace of evolution means the answers to the questions arising must be in 2021 terms if they are to be delivered!

Balance between “Computer Trading” and “Human Intervention” has yet to be determined – black vs grey (vs white?)

2005 2006 2007 2008 2009 20101%

6% 9%

21%29%

38%

20%26%

35%

52%

61%56%

Europe

“Computer Trading” = - Mode of trading stocks, bonds, forex, derivatives and

commodities with the “use of machine”1

- Includes e-trading, programme trading, automated trading and their subsets algo-trading and HFT

Trades on or off exchange (eg ECNs, MFTs). Currently no regulation specific to computer trading – but watch this space Growth has been significant- Overall volumes trebled between 2004-2009- 70% NASDAQ trades are HFT- >30% UK equity trades are HFT- Asia Pacific picking up speed , from 15% to18% of total

But levelled off in Europe and US in 2010- Maturity phase?- Pause for breath/technology?- Calm before the (flash crash) storm?

“Curate’s Egg” debate continues- Greater volume => liquidity => stability, price transparency?- Greater price competition => added market depth => decreased

volatility?- 1987, 2010 Flash Crash

Percentage of institutional flow traded using low touch channels

2004 2005 2006 2007

21%

31%

53%

63%

*Percentage of Equity trading From HFT6

Further complexity arises from the divergent evolution of different markets

Factors: existing market structures, regulation, competition and trader needs have all affected the transition to electronic tradingEquity markets

US: many electronic trading venues, relatively few traditional exchangesEurope: electronic trading generally incorporated within its many traditional exchangesBoth: fairly straightforward and cost-effective to introduce computer trading (liquidity, homogeneity)

Foreign exchange markets41% of interbank trading in major currencies (BIS triennial survey)

Fixed income marketsSlower migration vs. equities – strong legacy of telephone dealing

Commodities marketsOverall low penetration, perhaps owing to wide differences between traded contracts on different exchanges

Derivatives marketsAlmost entirely electronic in US and Europe.

Foreign exchange market turnover by execution 2010

Total

Electronic Methods

But in the quest for speed and volume, the base drivers remain largely common

Increase of Trading Volumes

Algo volumes and speeds =>greater capacity(and reduced average trade size)

Intense Regulatory Scrutiny New legislation, e.g. Dodd-Frank, Consumer Protection Act,is expected to:• increase exchange activity • drive competition /new

business• “improve” technology and

products

SEC developing policies toaddress disparities betweencompeting exchanges

Decreasing Latency

Co-location eliminates geographicand some system constraints

Hardware acceleration furtherimproves Timeframe

Overall step changes in system speed(-> milli -> micro -> nano)on both exchange and trader side

Direct Market Access

Exchanges opening up beyond “traditional” approaches andnumbers/types of participants

As a result of

recent/potential trends….

Transparency?

Price /cost efficiency?

Tightening /universal

regulations?

Liquidity always

greater?

Volatility always

reduced?

# players – bulge or breadth?

New products/ extended services?

Changing competitive dynamics/

barriers

Impact on Exchanges? Geography?

Technological arms race

Tipping points quickly established i.e. must be in it to win it

Frog or prince?

Increased “local” activity?

Traders want first callExchanges want volume

Prices? Counterparties?Business models?

Operational risk? .

First issue: Financial stability and computer based trading

No evidence that HFT/AT has increased volatility

A number of empirical studies support this.

For example: 1) Jovanovic & Menkveld (2011) compare the

volatility of Dutch and Belgian stocks before and after the entry of one HFT firm in the Dutch stocks at Chi-X & Euronext.

They find that the relative volatility of Dutch stocks declines slightly.

2) Linton (2011): “The period 2008/2009 was one of great macroeconomic uncertainty, which resulted in a big increase in volatility. This fundamental volatility has since decreased”

However, in specific circumstances, a key type of mechanism can lead to significant instability in financial markets with computer based trading (CBT): self-reinforcing feedback loops can amplify internal risks and lead to undesired interactions and outcomes.

For example: the portfolio-insurance-led market decline of 1987.

Hedge feedback loop

Feedback loop 1 - hedge

May 6 2010 (I):

Feedback loop 2: risk

May 6 2010 (II):

Volume feedback loop

Feedback loop 3: volume

Feedback loop 4: shallowness

Feedback loop 5: quote delay

Feedback loop 6: Systemic divergence

Oliver Linton, Cambridge University

Second issue: Impact on liquidity, price efficiency/discovery and transaction costs

Improved liquidity?1) Hendershott, Jones, and Menkveld (2011)

use the phased automation of the NYSE to measure the effect of algorithmic trading on liquidity. They found that algorithmic trading improves liquidity and enhances the informativeness of quotes.

2) Payne (2011): Bid ask spreads declined while book depth has increased.

46

81

01

21

41

6

Jan 2009 Oct 2009 Jul 2010 Apr 2011

FTSE 100 mean spread (b.p.) FTSE 100 median spread (b.p.)

LSE order book best spreads in FTSE 100 stocks (Basis Points)

1000

020

000

3000

040

000

5000

060

000

Jan 2009 Oct 2009 Jul 2010 Apr 2011

FTSE 100 mean depth (GBP) FTSE 100 median depth (GBP)

LSE order book depth available at the best quotes, (GBP) FTSE 100 stocks

3) Menkveld (2011): general improvement in liquidity supply (bid-ask spread and depth at the best quotes)

4) Linton (2011) looks at time series evidence from FTSE All Share index and individual stocks. He finds that after 2008 illiquidity increased considerably (although still much lower than in 2000). Illiquidity has since come down from this short term high.

Illiquidity

Better price efficiency and discovery

Some empirical support e.g. – Castura et al. (2010) investigate trends

in variance ratios on the Russell 1000 and 2000 stocks over the period 2006 to 2010.50 They show that efficiency has improved over time.

• Hendershott, Jones, and Menkveld (2011): more algorithmic trading leads to more efficient price quotes.

• Brogaard investigated 120 Nasdaq stocks. He estimates that in the absence of HFTs, a trade of 1,000 shares would cause the price to move an additional $.056. He argues HFT contributes more to price discovery than do non-HFT activity.

Linton (2011) provides evidence based on daily UK equity data (FTSE Allshare). He computes variance ratio tests and measures of linear predictability (inefficiency) for each year from 2000-2010.

He finds no trend in efficiency in the UK market, whether good or bad.

Falling transactions costsAngel et al. (2010) show that average retail commissions in the USA have decreased between 2003 and 2010, a period relevant for inferring the effects of computer trading.

Effect of entrythe entry of Chi-X into the market for Dutch index stocks in 2007/2008 had an immediate and substantial effect on trading fees for investors through:

– the lower fees that Chi-X charged – the consequent reduction in fees that

Euronext offered. [Menkveld (2011)]

Technology

(1) Speed has always mattered to traders

– early birds, worms

(2) Technology has always increased speeds & reduced delays

– horses, pigeons, telegraphs, tickers, telephones, internet

(3) In the past decade, trading technology has gone superhuman

– We’re not in Kansas any more, Toto

Three things about trading and technology

Three major technology shifts that are happening now(1)Using custom silicon to “bypass the PC”

– field programmable gate arrays (FPGAs), software defined silicon

(2) From multi-core to many-core• my GPU trumps your CPU

(3) Cloud computing & remotely hosted services (e.g. EC3)

– Pay-as-you-go access to supercomputers

Three major consequences:Depopulation of the trading floors

– One computer, one man, one dog

Lowering of barriers to entry, rise of new hubs

– New global exchanges built from BRICs

Extreme failures of risky technology via normalisation of deviance

– “Challenger, go at throttle-up”

Failures in risky technology, normalization of deviance

19971984 2005

Why The Failures?

http://www.nanex.net/StrangeDays/06082011.html

What the Dickens?

?????????

EU indices: unexplained slump in index futures on 27/12/10Euro Stoxx and DAX: only -2% thanks to circuits breakers

CAC40: -4% in less than 3 min, -3% in a few seconds

Unexplained mini-flash crashes – e.g. in single stocks despite volatility bands/circuit breakers

(1) Map

Understand the system that we have, its network & dynamics

(2) Manage

Develop policies that are suited to the map, keep the map updated

(3) Modify

Alter the network to make it less risky, more resilient

Rethinking the Financial Network, Redux…

Dist

ribut

ed

tech

nolo

gies

Closed systems Open systems

Cent

ralis

ed

tech

nolo

gies

• Globalisation advances[Global economic growth]

• Tech-literate new trading generation[Education, Investor profile]

• Competition leads to technology innovation, pressure on traditional trading margins[Competion/lower costs, Business model innovation]

• Interlinked global exchanges, single exchange view[Market structure]

• Competing trading (‘TradeStation’) and clearing (‘ClearPal’) components[Social media/networks, Settlement/execution]

• Competing world powers and economic models[Geopolitics]

• Companies in much of Asia owned by state and financial elite[Concentration of capital]

• In Asia, retail trading of derivatives and other synthetics explodes – copied in the West[Democratisation]

• New instruments and online exchanges for company financing[Asset classes, Technology]

• Investment in systemic risk surveillance[Risk management]

S1

S2S3

S4

• International institutions play an increasing role in governing economic and political systems[Politics/geopolitics]

• Rebalancing of capital and trading volumes to emerging markets[Emerging economy]

• Regional exchanges dominate, interconnected in a carefully regulated system[Market structure]

• Responding to low beta margins, more macro strategies lead to correlation, lower volumes[Asset classes, information homogeneity]

Possible Scenarios

• Economic systems worldwide retrench in the face of severe challenges[Global economic growth]

• Pressure on exchanges, trading firms, leads to consolidation, rebundling, monopolies[Market structure]

• Proliferation of synthetic products with opaque structures, responding to demand for ‘safe returns’[Greater use of synthetics, Financial engineering]

• HFT grows. Churning. Copycat strategies[Competition, lower costs]

• Endogenous feedback loops create risk[Risk management]

Policy Options1. Harmonised circuit breakers2. Different types of circuit breakers3. Minimum obligations for market makers4. Tick sizes5. Central Limit Order Book (CLOB) vs. Exchange order books6. Real time surveillance7. Market abuse surveillance8. Maker-taker fees9. Minimum resting times10.Order preference11.Continuous market vs. randomised stop auctions12.Algorithmic regulation13.Internalisation

14.Priority rules