Members of the NBER's Working Group on Market Microstructure met in Cambridge on May 11. Organizers Bruce Lehmann, University of California, San Diego, Andrew Lo, NBER and MIT, Matthew Spiegel, Yale University, and Avanidhar Subramanyam, University of California, Los Angeles, chose these papers to discuss:
Using proprietary data and an event unique in the history of financial markets, Anand and Weaver study the value that a specialist system adds vis-à-vis a multiple market maker system. Specifically, they analyze the "natural experiment" of the institution of a specialist system for equity options on the Chicago Board Options Exchange (CBOE) in the second half of 1999. The literature predicts a decrease in spreads and an increase in depth attributable to the change to a specialist system on the CBOE; their findings support these hypotheses. The changes are more pronounced for lower volume securities and smaller trades. There is also limited evidence that the market share of the CBOE increases in the period after the option class moves on to the specialist system, suggesting increased competitiveness for the CBOE. The authors also analyze the implications of the move arising from single listing of certain options and the lack of a national market system for options during the sample period.
Boulatov and Hackbarth analyze a continuous auction model of liquidity risk in asset markets with symmetrically informed agents. Buyers and sellers maximize their expected payoffs in the presence of liquidity shocks when they participate in an auction where different sellers have different reservation prices. The heterogeneous initial distribution of reservation prices across the agents may originate from different prior expectations of bargaining outcomes. The authors endogenously derive bargaining shares and optimal expected holding periods for markets with homogeneous and heterogeneous sellers where bidders encounter a tradeoff between the winners' curse and choosing to deal with higher reservation price sellers. For buyers and sellers solving a dynamic programming problem, the authors derive a set of partial differential equations for the optimal trading strategies of both types of agents. They then analyze how the optimal bargaining strategies lead to the steady-state equilibrium and show that this equilibrium is characterized by equal expected payoffs across the agents with different priors. After linearizing around the equilibrium, they perform a stability analysis and provide a closed-form solution for competing buyers and sellers. The optimal holding period in this case occurs because of the option of "early trading" when trading takes place before the equilibrium is established. In particular, this real option entails a tradeoff between selling to a distressed buyer versus the liquidity risk resulting from immediate disposal of the asset under unfavorable terms.
Besides its academic interest, the effect of institutional trading on the bid-ask spread is of interest to regulators and market makers. It is often (casually) argued that greater institutional participation results in increased volatility in the market. On the other hand, some argue that greater liquidity trading by institutions reduces spread. There is no direct empirical evidence and little theoretical knowledge to suggest a convincing relationship between institutional trading and spread. In this paper, Dey and Radhakrishna present some evidence on the nature and effect of institutional trading on spreads. They argue that institutional trading is not completely information driven; part of it is liquidity trading in nature. The authors find that information induced institutional trading increases the adverse selection component. However, large volume (liquidity) trading reduces the order processing costs. The net effect of institutional trading on spread is consistently negative. Moreover, institutional buys have differential information from sells. Institutional trades per se reduce spreads, but only sells increase the adverse selection component. Both effective and relative spreads impound the differential nature of institutional buys and sells.
The use of automated auctions to trade equities, derivatives, bonds, and foreign exchange has increased dramatically in recent years. Trading in automated auctions occurs through an electronic limit order book without the need for dealers. Automated auctions offer advantages of speed and simplicity, but depend on public limit orders for liquidity. To the extent that liquidity varies over time, it affects trading costs, volatility, and induces strategic behavior by traders. Time variation in liquidity is also of considerable importance because liquidity affects expected returns. Coppejans, Domowitz, and Madhavan use data from an automated futures market to analyze the dynamic relationship between market liquidity, returns, and volatility. They find that there is wide intertemporal variation in aggregate market liquidity, measured by the depth of the limit order book at a point in time. Discretionary traders trade in high liquidity periods, reinforcing the concentration of volume and liquidity at certain points in time. These results are consistent with models where liquidity is a factor in expected returns, but also suggest more complicated dynamics consonant with supply and demand imbalances in the market. While increases in liquidity substantially reduce volatility, volatility shocks reduce liquidity over the short run, impairing price efficiency. These effects dissipate quickly, however, and their magnitudes are small, indicating a high degree of market resiliency.
Jain analyzes the impact of various institutional features of stock exchanges on their performance in a unified framework. He assembles the institutional design features including organizational structure, trading mechanism, trade-execution system, transparency, degree of market fragmentation, age, and ownership for 51 major exchanges around the world. For these exchanges, representing over 90 percent of the world's market capitalization, their institutional features are linked with various performance measures, namely quoted bid-ask spreads, effective spreads, realized spreads, volatility, and trading turnover. Jain uses a simultaneous-system-of-equations model to explain linkages between the different measures of performance. He finds that hybrid systems have lower spreads and volatility than pure limit order systems, which in turn have lower spreads and volatility than pure dealership systems. Stock exchanges with bid-ask spreads have narrower tick sizes, competitive market makers, electronic limit order books, automatic execution of trades, centralized trading, and enforcement of insider trading laws. These results do not support theories that predict better liquidity for a monopolistic specialist system, or an electronic open limit order book with no dealers. Spreads are directly related to return volatility but inversely related to market capitalization on a global basis. The analysis has important policy implications for security lawmakers implementing fairness and transparency, companies seeking global listings, investors forming trading strategies, and stock exchanges altering their institutional design to increase competitiveness.
Stepping-ahead occurs when specialists trade at prices incrementally better than the best prices in the limit order book. Stepping ahead benefits market orders that receive a better price but it delays or prevents limit order executions. Edwards and Harris find that limit orders incur higher costs when the specialist steps ahead. As U.S. markets trade in decimals, market orders receive less price improvement, and the cost of stepping ahead decreases. Smaller ticks magnify the agency problems between specialists and the limit orders they represent. Specialists rarely step ahead of limit orders (less than 2 percent of the time), but they step ahead more often after the tick size changes from $1/8 to $1/16. The cost to individual limit orders is actually lower with smaller ticks, but since specialists step ahead more often, aggregate limit order costs have risen. More importantly, the price improvement benefit to market orders falls significantly. Market orders benefit on net with a $1/8 tick but this benefit is eliminated with $1/16 ticks.