Both fairness and efficiency are important considerations in market design and regulation, yet many regulators have neither defined nor measured these concepts. We develop an evidencebased policy framework in which these are both defined and measured using a series of empirical proxies. We then build a systems estimation model to examine the 2003–2011 explosive growth in algorithmic trading (AT) on the London Stock Exchange and NYSE Euronext Paris. Our results show that greater AT is associated with increased transactional efficiency and reduced information leakage in top quintile stocks. For less liquid stocks, manipulation at the close declines. We also document the tradeoff between reduced spreads and increased manipulation or information leakage following the introduction of MiFID1.
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See Foreword and Executive Summary of Objectives and Principles of Securities Regulation, May 2003, IOSCO which identifies the key objective of securities regulation as “ensuring that markets are fair, efficient and transparent (emphasis added)”. For discussions of why the third concept transparency is a means to an end rather than an end in itself, see Aitken and Leduc (2012).
The authors define an efficient market as “a market in which it is cheap to trade and the prices at which one is trading reflect all available information”. We define a fair market as “…a market in which prohibited trading behaviours are minimised”.
Dugast and Foucault (2014) develop a model of price reversals as speculators trade fast on false signals and then revert back after processing the signal.
See for example the statements of objectives on websites of the ASIC (Australia): a fair and efficient market characterised by integrity and transparency; the SEC (US): fair, orderly and efficient markets; the FSA (UK): efficient, orderly and fair markets; and the OSC (Canada): fair and efficient capital markets [emphases added].
The same goes for broker-client conflicts of interest where brokers are seldom required to add a condition code identifying whether the order/trade is as principal or as agent. This information became available in Australia from July 28, 2014, but there is a $1 m fine for providing access to the data outside a regulatory or exchange inquiry.
The founder of SIRCA and CEO of CMCRC is the first co-author on this paper. CMCRC is a research consortium of universities, bank/brokerage houses, national regulators and exchange operators worldwide. For more description on the Market Quality Dashboard go to https://www.youtube.com/watch?v=NegVKCHmsGw&list=UUnyU9-4WAduJhlYOGTHTf3w.
We acknowledge that fees for clearing and settlement (i.e. the explicit transaction costs) have become increasingly important in some of our research because their magnitude now exceeds that of the bid-ask spread in all the largest markets worldwide.
In June 2009, one of the principal MTFs facilitating AT, Chi-X, accelerated this gain in liquidity and transactional efficiency by sharply reducing settlement fees.
The estimated incidence of HFT varies widely across countries. Brogaard’s (2010) analysis of U.S equities finds that 60 % of all NASDAQ trades involve an HFT on at least one side. Using a 2009 sample for LSE, Jarnecic and Snape (2010) estimated that 40 % of trades include an HFT. In their 2010 response to the Committee of European Securities Regulators (CESR), the LSE estimated that HFT represented 33 % of total UK equities trading
The referee cautions quite appropriately that correlation is not causation in these studies. Later in the paper, we will perform exogeneity, strong instruments and other specification tests to address this issue.
NASDAQ designates access ports as HFT/AT based on the CTR, the order-to-trade ratio, zero inventory positions held overnight, speed attributable to co-location and stated intent. We do not employ as a proxy for HFT/AT the OTR adopted by Hendershott et al. (2011). Although it is tempting to suggest CTR might be an instrument for identifying OTR, the covariance between CTR and residual disturbances at the individual stock level such as state of the market regime shocks is surely non-zero. Hence, CTR would be endogenous and therefore invalid as an instrument.
European Parliament News Release—12 April, 2012.
Our research highlights the interrelationships between market fairness and transaction cost efficiency. The referee points out correctly that additional insight in future research can come from exploring the simultaneous effects on informational efficiency.
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The U.K. Treasury sponsored this research through their Foresight Project, The Future of Computer Trading in Financial Markets: An International Perspective, Government Office for Science, 2012. Aitken et al. (2012) provided our preliminary report. We wish to thank several Office of Science referees, seminar participants at the U.S. Securities Exchange Commission, the Australian Securities and Investments Commission, the Italian CONSOB, and the Six Swiss Exchange, as well as the CMCRC development team and SIRCA for providing the raw data. An anonymous referee provided exceptionally insightful perspectives.
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Aitken, M.J., Aspris, A., Foley, S. et al. Market Fairness: The Poor Country Cousin of Market Efficiency. J Bus Ethics 147, 5–23 (2018). https://doi.org/10.1007/s10551-015-2964-y