Fintech Financial Stability and Regulation Franklin Allen Imperial

Fintech, Financial Stability and Regulation Franklin Allen Imperial College London IWSFAS Conference Cass Business School City University London 10 September 2018

The outline of the lecture • Credit scoring • Internet based banking and investment services • Peer-to-peer platforms • Cryptocurrencies • Initial Coin Offerings (ICOs) 2

The outline of the lecture (cont. ) • Settlement systems using blockchain • High frequency trading • Quantitative investment strategies • Fintech and financial inclusion • Regulation of Fintech 3

Credit scoring • Widely used in consumer and small business finance • Standard systems • Possible developments using big data and machine learning techniques – Fuster, Goldsmith-Pinkham, Ramadorai and Walther (2018) and Berg, Burg, Gombovic and Puri (2018) • Financial stability issues – example of subprime loans 4

Internet based banking and investment services • One of the most important aspects of Fintech is the increased use of the internet for the provision of online banking and investment services • Carbo-Valverde, Cuadras-Solas and Rodriguez-Fernandez (2018)* study how customers migrate to the using internet based services • Alibaba’s Alipay, Tencent’s We. Chat and Baidu’s equivalent have become payment methods of choice and these companies banking services have rapidly expanded 5

Internet based banking and investment services (cont. ) • Klus, Lohwasser, Hornuff and Schwienbacher (2018)* consider the interactions of banks with internet firms in the provision of financial services • Key issue is how regulators view banks versus tech firms as providers of financial services 6

Peer-to-peer platforms • There has been a large increase in peer-to-peer lending platforms in recent years • Franks, Serrano-Velarde and Sussman (2018) consider the operations of Funding Circle, a peer-to-business lending platform for British SME companies • Braggion, Manconi and Zhu (2018)* consider the role of peer -to-peer Chinese lending platform Renren Dai in helping people overcome LTV regulation while Lin and Pusiainen (2018)* consider the role of trust • How should peer-to-peer platforms be regulated both from a consumer protection and a macroprudential aspect? 7

Cryptocurrencies 8

Cryptocurrencies (cont. ) • Bitcoin was originally designed to be a medium of exchange that operated outside the control of any government • In practice it has become a speculative asset • There is a rapidly growing literature on cryptocurrencies – see, e. g. , Kroeger and Sarkar (2016)*, Pagnotta and Buraschi (2018) and many others • One of the major problems is the amount of electricity used 9

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Cryptocurrencies (cont. ) • Significant developments of blockchain technology such as Ethereum – open up whole new sets of possibilities such as “smart contracts” – see e. g. Tinn (2018) • Many regulatory issues • Is privacy good or bad? • Consumer protection issues • Systemic risk issue 11

Initial Coin Offerings (ICOs) • ICOs are a rapidly growing form of corporate finance – in 2017 there were 18 a month and total of $3. 7 billion raised for the year while up to July 2018 there were 99 a month and $17 billion had been raised (Economist, 1 September 2018) • There a whole range of interesting questions associated with ICOs including the fact that they can represent a new form of corporation, e. g. , Streamr • Zetzsche, Buckley, Arner and Fohr (2017) give the following classification 12

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Initial Coin Offerings (ICOs) (cont. ) • ICOs raise a whole set of interesting regulation issues • Role of the “White Paper” – they take many different forms but usually describe the nature of the technology being funded and the uses the technology can be put to • Often ICOs are structured to avoid regulation – in 51% of Zetzsche et al. ’s (2017) sample investors from certain countries were excluded from participation while 80% have no mention of the regulatory status of the ICO • Switzerland is a frequent venue for ICOs 14

Settlement systems using blockchain • One of the most frequently mentioned applications for blockchain technology is to automate back office settlement and other functions – according to the Economist 1 September 2018 issue, Santander has said that adopting blockchains could save the banking industry $20 billion a year in back office costs • Benos, Garratt and Gurrola-Perez (2018)* consider the role of distributed ledger technology for settlement • An important issue is how long the adoption of this technology may take 15

High frequency trading • Around 50 -60% of trading volume in the U. S. is by high frequency traders • On May 6, 2010 over 20, 000 trades across more than 300 securities were executed at prices more than 60% away from their values just moments before. Many were executed at prices of a $0. 01 or less, or as high as $100, 000, before prices of those securities returned to their “pre-crash” levels - see Kirilenko, Kyle, Mehrdad and Tuzun (2017) • How should high frequency trading be regulated? 16

Fintech and quantitative investment strategies • In recent years there has been considerable hype concerning the use of sophisticated computer based techniques for quantitative investment strategies • Many people argue this is the future of investment management • But we have been here before, e. g. , Allen and Karjalainen (1999) • Empirical evidence suggests quantitative strategies can work well in stable environments but are often caught out and work very poorly when there is a crisis or some other unexpected event 17

Do computers help - why do quantitative investment strategies produce alpha? • Perhaps the most important aspect of quantitative investment strategies for both buyers and sellers of the products is to understand if and how they produce excess returns • Are they simply reallocating returns across states so they look good most of the time but melt down occasionally? • Are there missing risk factors? • Are there market imperfections that the strategy is helping to get around and the increased efficiency is where the returns are coming from? 18

The challenge of market efficiency • Most of the data and theoretical and empirical tools that are used in the design of quantitative strategies are commonly available • If new money making techniques are found why doesn’t competition quickly eliminate any excess returns? • The key issue for buyers of such products is to understand the answer to this question and for sellers it is to explain it without giving away proprietary secrets 19

Reallocating returns across states • One useful way to think about quantitative investment strategies is in terms of complete markets • In this case we can think of trading state by state • One way to generate apparent excess returns is to use the complete markets to transfer returns from very low probability states to high probability states • It is important for investors to check this is not what is going on with derivative-based strategies or sophisticated dynamic trading strategies – this can potentially be done with stress tests, for example 20

Missing risk factors • Another reason for apparent excess returns is that there are risk factors that are not modelled properly so it seems there is an excess return but in fact there is not – e. g. , simple sorting strategies based on Fama and French may suffer from this • Financial crises are another possible form of risk of this type - they are low probability but very high impact events that either do not occur in the data sets or occur very infrequently • Another example is if a new strategy appears promising and is simultaneously adopted by many investment firms so the environment changes and when they all try to exit together, prices change in unexpected ways – in other words, risk may be endogenous 21

Market imperfections • One of the most plausible rationales for excess returns is that the strategies involved help reduce market imperfections in one way or another • Some of the most important ways imperfections can be overcome with quantitative strategies • Reduce transaction costs • Improve diversification • Carry trades 22

Fintech and financial inclusion • One area where Fintech has made considerable inroads is in terms of improving financial inclusion • Kenya provides a particularly interesting example of the role of Fintech and financial inclusion • M-pesa • Equity bank (Allen, Carletti, Cull, Qian, Senbet and Valenzuela (2017)) • Sha’ban, Girardone and Sarkisyan (2018)* consider what explains differences in financial inclusion across countries 23

Regulation of Fintech • Key issue is to implement regulation to avoid systemic risks, protect consumers, and improve efficiency withour discouraging the innovation inherent in many Fintech developments • Switzerland provides a good example of a regulatory system that encourages Fintech • The UK’s “Regulatory sandbox” approach is also helpful for allowing Fintech innovations 24

Concluding remarks • Fintech is a widely used term but it is not entirely clear to many people what it means – we have provided a taxonomy of some of the areas covered • These developments provide may opportunities and some will transform the landscape of finance • At this stage, it is not fully which of the developments will be the most transformative • The role of regulation will be key 25

References * next to a reference indicates it is part of the 2018 IWSFAS Conference. Allen, F. , E. Carletti, R. Cull, J. Qian, L. Senbet and P. Valenzuela (2017). “Improving Access to Banking: Evidence from Kenya, ” SSRN working paper. Allen, F. and R. Karjalainen (1999). “Using Genetic Algorithms to Find Technical Trading Rules, ” Journal of Financial Economics 51, 245 -271. Berg, T. , V. Burg, A. Gombovic and M. Puri (2018). “On the Rise of Fin. Techs – Credit Scoring using Digital Footprints, ” working paper, Duke University. Franks, J. , N. Serrano-Velarde and O. Sussman (2018). “Marketplace Lending, Information Aggregation, and Liquidity, ” working paper, London Business School. Fuster, A. , P. Goldsmith-Pinkham, T. Ramadorai and A. Walther (2018). “Predictably Unequal? The Effects of Machine Learning on Credit Markets, ” SSRN working paper. Kirilenko, A. Kyle, S. Mehrdad and T. Tuzun (2017). “The Flash Crash: High‐Frequency Trading in an Electronic Market, ” Journal of Finance 72, 967 -998. Pagnotta, E. and A. Buraschi (2018). “An Equilibrium Valuation of Bitcoin and Decentralized Network Assets, ” SSRN working paper. Tinn, K. (2017). “Blockchain and the Future of Optimal Financing Contracts, ” SSRN working paper. Zetzsche, D. , R. Buckley, D. Arner and L. Fohr (2017). “The ICO Gold Rush: It’s a scam, it’s a bubble, it’s a super challenge for regulators, ” University of Luxemburg Law Working Paper 2017011. 26