We study price pressures, i.e., deviations from the efficient price due to risk-averse intermediaries supplying liquidity to asynchronously arriving investors. Empirically, New York Stock Exchange intermediary data reveals economically large price pressures, 0.49% on average with a half life of 0.92 days. Theoretically, a simple dynamic inventory model captures an intermediary׳s use of price pressure to mean-revert inventory. She trades off revenue loss due to price pressure against price risk associated with staying in a nonzero inventory state. The closed-form solution identifies the intermediary׳s risk aversion and the investors׳ private value distribution from the observed time series patterns of prices and inventories. These parameters imply a relative social cost due to price pressure, a deviation from constrained Pareto efficiency, of approximately 10% of the cost of immediacy.

Responding to a September 2002 regulatory enforcement, the Island electronic communications network stopped displaying its limit order book in the three most active exchange-traded funds (ETFs) where it was the dominant venue. Island’s share of trading activity and price discovery fell, fragmenting the market. ETF prices adjust more slowly when Island goes dark, and there is substantial price discovery movement from ETFs to the futures market. Trading costs increase on Island and decrease off Island, with higher trading costs overall. When Island later redisplays its orders, market quality improves, with transparency and the reduction in fragmentation both playing important roles.

This paper examines the choice of trading venue by dealers in U.S. Treasury securities to determine when services provided by human intermediaries are difficult to replicate in fully automated trading systems. When Treasury securities go “off the run” their trading volume drops by more than 90%. This decline in trading volume allows us to test whether intermediaries' knowledge of the market and its participants can uncover hidden liquidity and facilitate better matching of customer orders in less active markets. Consistent with this hypothesis, the market share of electronic intermediaries falls from 81% to 12% when securities go off the run.

We show short selling in corporate bonds forecasts future bond returns. Short selling predicts bond returns where private information is more likely, in high-yield bonds, particularly after Lehman Brothers’ collapse of 2008. Short selling predicts returns following both high and low past bond returns. This, together with short selling increasing following past buying order imbalances, suggests short sellers trade against price pressures as well as trade on information. Short selling predicts bond returns both in the individual bonds that are shorted and in other bonds by the same issuer. Past stock returns and short selling in stocks predict bond returns but do not eliminate bond short selling predicting bond returns. Bond short selling does not predict the issuer’s stock returns. These results show bond short sellers contribute to efficient bond prices and that short sellers’ information flows from stocks to bonds but not from bonds to stocks.

The capital asset pricing model (CAPM) performs poorly overall, as market risk (beta) is weakly related to 24-h returns. This is because stock prices behave very differently with respect to their sensitivity to beta when markets are open for trading versus when they are closed. Stock returns are positively related to beta overnight, whereas returns are negatively related to beta during the trading day. These day-night relations hold for beta-sorted portfolios and individual stocks in the US and internationally as well as for industry and book-to-market portfolios and cash flow and discount rate beta-sorted portfolios. In addition to the change in slope of returns with respect to beta, the implied risk-free rate differs significantly between night and day. Consistent with this, returns on US Treasury futures differ significantly between night and day.

This paper combines daily buy and sell institutional trading volume with all news announcements from Reuters. Using institutional order flow (buy volume minus sell volume) we find a variety of evidence that institutions are informed. Institutional trading volume predicts the occurrence of news announcements. Institutional order flow predicts (i) the sentiment of the news; (ii) the stock market reaction on news announcement days; (iii) the stock market reaction on crisis news days; and (iv) earnings announcement surprises. These results suggest that significant price discovery related to news stories occurs through institutional trading prior to the news announcement date.

Over‐the‐counter (OTC) markets dominate trading in many asset classes. Will electronic trading displace traditional OTC “voice” trading? Can electronic and voice systems coexist? What types of securities and trades are best suited for electronic trading? We study these questions by focusing on an innovation in electronic trading technology that enables investors to simultaneously search many bond dealers. We show that periodic one‐sided electronic auctions are a viable and important source of liquidity even in inactively traded instruments. These mechanisms are a natural compromise between bilateral search in OTC markets and continuous double auctions in electronic limit order books.

This paper explores the competition between two trading venues, Electronic Communication Networks (ECNs) and Nasdaq market makers. ECNs offer the advantages of anonymity and speed of execution, which attract informed traders. Thus, trades are more likely to occur on ECNs when information asymmetry is greater and when trading volume and stock‐return volatility are high. ECN trades have greater permanent price impacts and more private information is revealed through ECN trades than though market‐maker trades. However, ECN trades have higher ex ante trading costs because market makers can preference or internalize the less informed trades and offer them better executions.

This paper studies the interaction between dealer markets and a relatively new form of exchange, passive crossing networks, where buyers and sellers trade directly with one another. We find that the crossing network is characterized by both positive (‘liquidity’) and negative (‘crowding’) externalities, and we analyze the effects of its introduction on the dealer market. Traders who use the dealer market as a ‘market of last resort’ can induce dealers to widen their spread and can lead to more efficient subsequent prices, but traders who only use the crossing network can provide a counterbalancing effect by reducing adverse selection and inventory holding costs.