Who Supplies Liquidity, How And When?
University of Toulouse 1 – Toulouse School of Economics (TSE)
University of Toulouse 1
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IAE – Université de Toulouse 1 Capitole; Toulouse School of Economics
Who provides liquidity in modern, electronic limit order book, markets? While agency trading can be constrained by conflicts of interest and information asymmetry between customers and traders, prop traders are likely to be less constrained and thus better positioned to carry inventory risk. Moreover, while slow traders’ limit orders may be exposed to severe adverse selection, fast trading technology can improve traders’ ability to monitor the market and avoid being picked off. To shed light on these points, we rely on unique data from Euronext and the AMF, the French financial markets regulator, enabling us to observe the connectivity of traders to the market, and whether they are proprietary traders. We find that proprietary traders, be they fast or slow, provide liquidity with contrarian marketable orders, thus helping the market absorb shocks, even during a crisis, and they earn profits while doing so. Moreover, fast traders provide liquidity by leaving limit orders in the book. Yet, only prop traders can do so without making losses. This suggests that technology is not enough to overcome adverse selection; monitoring incentives are also needed.
Who Supplies Liquidity, How And When? – Introduction
In perfect markets, buyers and sellers immediately find each other and reap gains from trade at frictionless prices. Real markets, however, can fall short of delivering such welfare improvements, due to frictions.
Market frictions, indeed, can prevent final sellers from rapidly locating final buyers. In this context, intermediaries can provide liquidity to impatient sellers, by purchasing their assets and holding inventories, until they find final buyers. Such market-making services have been analyzed theoretically by Ho and Stoll (1981, 1983), Grossman and Miller (1988) and Weill (2007).2 What are the characteristics of intermediaries which enable them to supply liquidity? In fragmented markets, intermediation services can be provided by those agents with the best network linkages and the greatest search ability, which can be enhanced by high-frequency trading technology. Even if the market is centralized, delays can arise, reflecting that not all potential buyers and sellers are permanently monitoring the market, and that it takes time for investors to identify their trading needs, as analyzed theoretically by Biais, Hombert and Weill (2014). In this context, as in that of fragmented markets, Gromb and Vayanos (2002) show that arbitrageurs, able to take positions in different markets, can provide valuable liquidity. Market-makers, however, bear costs when holding inventories, eg because they are risk–averse and reluctant to carry unbalanced inventory positions, as analyzed theoretically by Ho and Stoll (1981), or because the principals of market-makers set position limits to discipline their agents. This suggests that the agents best placed to offer liquidity are those with the best inventory-holding ability, ie those with the greatest risk tolerance or the least acute agency problems. Because the aggregate inventory bearing capacity of market-makers is limited, however, liquidity shocks have a transient impact on prices, ie there are “limits to arbitrage,” and liquidity supply is profitable (Shleifer and Vishny (1997), and Gromb and Vayanos (2002, 2010, 2015)).
Another market friction that restricts liquidity is adverse selection. As first shown by Akerlof (1970), adverse selection can magnify the price impact of trades and even lead to market breakdown. As shown by Glosten and Milgrom (1985) and Kyle (1985), adverse selection leads market-makers to post relatively high ask prices, and relatively low bid prices. Here again, however, the question arises: which agents will play the role of market-makers, and why? Efficiency suggests that the intermediaries should be the agents who are best able to mitigate adverse selection. Such ability could reflect better market-monitoring technology, enabling intermediaries to cancel their orders before they are picked off. This, however, could worsen the adverse selection problem for other investors, with less efficient monitoring technologies. Adverse selection for these investors could be further amplified if intermediaries took advantage of their timely market information to hit stale quotes themselves.
Since the beginning of the century, three developments have made these questions highly topical. First, equity markets have converged towards an electronic limit order book structure, in which a large number of different financial institutions (not just designated market-makers) can provide liquidity by leaving limit orders in the book. Second, low latency technologies have become available, increasing, at a cost, the ability to monitor changes in market conditions and react rapidly to them.
Third, regulatory reforms before the crisis contributed to the fragmentation of markets and the development of high–frequency trading, while regulatory reforms after the crisis made proprietary trading more costly and complex for investment banks.3 How have these developments changed the economics of liquidity supply, and the gains from trades that can be reaped in financial markets? To shed light on these issues, we empirically analyze a new data set, with information about the orders and trades of different categories of members of Euronext, including proprietary traders and high-frequency traders.
Our data include a time-stamped record of all orders and trades (quantities and prices) on Euronext in French stocks during 2010. Our sample period brackets the Greek crisis of the summer of 2010, enabling us to analyze how liquidity supply compares between “normal” and crisis times. Our data also include anonymized member codes, and for each member, we know (i) the quality and speed of its connection to the market, and (ii) if its trades were 100% proprietary, 100% agency, or a mix of both. Using (i) we identify fast traders based on direct information about their technological investment, which contrasts with indirect identification, based on trading style. Because of its huge size, and also because of some technical characteristics of the Euronext market,4 this data set is difficult to handle. At this stage, we have analyzed 23 French stocks, including 10 large caps, nine mid-caps, and four small caps. The size of the corresponding data exceeds 7 tera–octets.
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