Exchange-traded funds (ETFs) and FinTech: Market efficiency and systemic risk

Jay Cullen


Exchange-traded funds (ETFs) are by common consensus amongst the most innovative financial products to have been introduced to financial markets over the past few decades. Indeed, they have disrupted investment patterns to the point that passive fund management rivals active fund management in scale. ETFs’ share of passive fund assets has reached 40% and continues to grow, whilst passive management vehicles by at least one estimate controls over 50% of the US stock market.[1] ETF assets in the United States will reach around $5 trillion by the end of 2020, with this number predicted to hit $50 trillion by

2030. Significant growth is also expected in European ETF markets.

The advent of the ETF sector has coincided with the nascent adoption in financial markets of a variety of FinTech applications. These technologies exhibit considerable heterogeneity, yet a number interact at multiple levels with passive investment products. Robo-advisory services and algorithmic trading technologies, for example, have introduced channels which heighten trading speed and opportunities, by removing human intermediation from the investment process and allowing rapid turnover of securities. These markets have also grown extraordinarily in recent years, with at least SI.5 trillion employed in algorithmic trading strategies alone. Implicitly, such technologies enjoy synergies with the ETF market, which is characterised by passively managed, low-cost financial products requiring minimal human operational intervention. This also means that technological developments may create feedback effects between FinTech applications and the ETF market. Indeed, ETF functionality depends upon algorithmic traders and arbitrage between ETFs and related futures, options and underlying securities.

Taken together, these technologies represent a paradigmatic shift in the operation of financial markets. Growth has been so rapid in the respective sectors that the risk calculus attendant to their potential impacts is changing. As Omarova has noted, these technologies share features with many other financial innovations of the last 30 years, whereby products arc designed to synthesise economic interests and scale up transaction volume(s).[2] Conceptually, Omarova’s framework of pooling, layering, acceleration and compression may be applied to a variety of financial products and techniques, including ETFs and FinTech applications. ETFs in general, for example, pool and layer tradable assets through agglomerating securities within an index or exchange, which allows for the acceleration of investments tracking relevant benchmarks. Algorithmic trading and robo-advice share some of these accelerant properties, by providing ways in which to scale up financial asset trading.

These technologies also mark a qualitative shift in the function and interpretation of information in financial markets. Whilst trading becomes quicker, nimbler and more voluminous under passive investment strategies augmented by automation, the role of information also changes in subtle ways. Specifically, under these conditions, financial market prices will become increasingly self-referential; that is to say, investors in markets will no longer make investment decisions based upon information gathered by themselves or others which is, as shall be explained, a hallmark of the efficient markets hypothesis (EMH), but through acting on the trading of a smaller and smaller group of active traders. In such circumstances, misinformed trades by active traders in the markets may not be countered by other informed investors; rather, they would trigger more misinformed automated trades. This, in turn, may undermine the price formation mechanism, especially if the composition of views amongst informed traders is skewed by trading biases favouring a particular direction, thereby compounding informational gaps in the market.

In generating supplemental trading activity, robo-advice and algorithmic trading also raise financial stability concerns regarding possible herding behaviour. The Global Financial Crisis (GFC) demonstrated the rapidity with which panic-selling became contagious across global markets as liquidity in many short-term debt and structured finance markets evaporated amid widespread herding. Other recent market crashes have also been attributed to information problems, most notably the stock market collapse of 2002. Importantly, the disruptive impact of certain automated trading further reduces the incentive for information gathering, as these algorithmically driven vehicles free-ride on research by active traders and use their resources and speed to front-run their trades. One effect of these developments is a reduction in the informativeness of financial market pricing, which, if persistent enough, may cause systemic instability.

Importantly, although these informational gaps have been discussed before, they have not been linked explicitly to the FinTech revolution. As FinTech’s footprint expands in a similar manner to ETFs interesting questions arise concerning interactions with passive investment structures. Regulators have highlighted the potential financial stability benefits beyond a reduction in intermediation costs from the widespread adoption of FinTech. These benefits include decentralisation and diversification, heightened transparency and improved access to, and convenience of, financial services for retail and business market participants.[3] On the surface, these purported benefits are not particularly related to financial stability. Indeed, financial market products which share these features have in the past been extolled as financial stability enhancing, an assumption which often turned out to be erroneous.'' Rather, the increased automation and programmatic systems in trading arising from expanded use of FinTech applications have the potential to exacerbate herding behaviour and investment correlations in financial markets, in particular when compounded by the prevalence of passive investment structures engaged in rudimentary momentum trades.

The framework I rely upon in this chapter builds on the classic analysis of securities market efficiency mechanisms by Gilson and Kraakman, which is arguably the most influential law and economics exposition of this topic in the field. I depart from their analysis, however, by suggesting that the aggregative information function of ETFs, and the increasing prevalence of their use by investors, combined with the increasing financial flows facilitated by certain financial technologies, pose a unique challenge to regulators in maintaining financial stability. Whilst central banks have become more accustomed to preventing such episodes from spilling over into systemic crises, the threat posed by passive investment strategies remains potent and is growing. Given these risks, the anticipated widespread adoption of robo-advice and automated trading, and the explosion in investment in the ETF asset class over the last decade by retail investors, pension funds and other institutions, increased regulatory scrutiny of their contribution to market instability is justified.

  • [1] J Cox, ‘Passive Investing Automatically Tracking Indexes Now Controls Nearly Half the US Stock Market’(14 March 2019) 2 C Reinicke, ‘The ETF Market Will Hit $50 trillion by 2030, Bank of America Says’ (13 December 2019) 3 K Lamont, ‘4 Key Trends in the European ETF Market’ (22 January 2020) https://www.morningstar. 4 R Wigglesworth, ‘Volatility: How ‘Algos’ Changed the Rhythm of the Market’ Financial Times (9 January 2019) 142-1 le9-a581-4ff78404524e.
  • [2] S T Omarova, ‘New Tech v New Deal: Fintech as a Systemic Phenomenon’ (2019) 36 Yale J on Reg 735. 2 ‘The technique of combining multiple financial assets with certain shared characteristics’. Omarova (n 5) 762. 3 ‘The technique of synthesizing financial assets in a manner that creates a chain of hierarchically linked claims, so that the performance of each new asset “layer” is determined by reference to the combined performance of pooled financial assets underlying it’ Omarova (n 5) 763. 4 ‘[This] occurs whenever the speed of transacting is increased (the velocity of trading), thus allowing more trades to be executed (the volume of trading)’ Omarova (n 5) 764-765. 5 ‘The technique of aggregating and compacting risk exposures and obligations associated with multiple trades in a manner that de facto transforms them into a single economic transaction’ Omarova (n 5) 765-766. 6 Omarova (n 5) 787. 7 Omarova (n 5) 788-789. 8 See G Gorton and A Metrick, ‘Securitized Banking and the Run on Repo’ (2012) 104 J Fin Econ 425. 9 R Shiller, Irrational Exuberance (3rd edn, Princeton University Press 2016).
  • [3] Financial Stability Board, ‘Financial Stability Implications from FinTech: Supervisory and Regulatory Issues that Merit Authorities’ Attention’ (June 2017) R270617.pdf. 2 N Gennaioli, A Shleifer and R W Vishny, ‘Neglected Risks, Financial Innovation and Financial Fragility’ (2012) 104 J Fin Econ 452. 3 R J Gilson and R H Kraakman, 'The Mechanisms of Market Efficiency’ (1984) 70 Virginia L Rev 549. 4 H Markowitz, ‘Portfolio Selection’ (1952) 7 J Fin 77.
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