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.
|Number of pages||19|
|Journal||Journal of Business Ethics|
|Publication status||Published - Jan 2018|
- Algorithmic trading
- Information leakage
- Market fairness
- Market quality