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We analyze the contribution to price discovery of market and limit orders by high-frequency traders (HFTs) and non-HFTs. While market orders have a larger individual price impact, limit orders are far more numerous. This results in price discovery occurring predominantly through limit orders. HFTs submit the bulk of limit orders and these limit orders provide most of the price discovery. Submissions of limit orders and their contribution to price discovery fall with volatility due to changes in HFTs' behavior. Consistent with adverse selection arising from faster reactions to public information, HFTs' informational advantage is partially explained by public information.

We examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.

Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.

We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs' liquidity supplying orders are adversely selected. The direction of HFTs' trading predicts price changes over short horizons measured in seconds. The direction of HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.

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 examines liquidity externalities by analyzing trading costs after hours. There is less than 1/20 as many trades per unit time after hours as during the trading day. The reduced trading activity results in substantially higher trading costs: quoted and effective spreads are three to four times larger than during the trading day. The higher spreads reflect greater adverse selection and order persistence, but not higher dealer profits. Because liquidity provision remains competitive after hours, the greater adverse selection and higher trading costs provide a direct measure of the magnitude of the liquidity externalities generated during the trading day.

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.

We show that the consolidation of orders is important for producing efficient prices, especially during times of high liquidity demand. The NYSE's centralized opening call market performs better than Nasdaq's decentralized opening process on typical trading days. The NYSE is much better than Nasdaq on witching days when index arbitrage activity subjects S&P 500 stocks to large, predictable, and mostly informationlessorder flow around quarterly futures contract expirations. Nasdaq opening price efficiency improves to NYSE levels once Nasdaq initiates a consolidated opening call in November 2004, but prices on the decentralized Nasdaq remain less efficient at other times of day.

We examine the network of trading relationships between insurers and dealers in the over-the-counter (OTC) corporate bond market. Regulatory data show that one-third of insurers use a single dealer, whereas other insurers have large dealer networks. Execution prices are nonmonotone in network size, initially declining with more dealers but increasing once networks exceed 20 dealers. A model of decentralized trade in which insurers trade off the benefits of repeat business and faster execution quantitatively fits the distribution of insurers' network size and explains the price-network size relationship. Counterfactual analysis shows that regulations to unbundle trade and nontrade services can decrease welfare.

We provide evidence on market structure and the cost of raising capital by examining changes in market structure in U.S. equity markets. Only the Order Handling Rules (OHR) of the Nasdaq, the one reform that reduced institutional trading costs, lowered the cost of raising capital. Using a difference-in-differences framework relative to the New York Stock Exchange (NYSE) that exploits the OHR’s staggered implementation, we find that the OHR reduced the underpricing of seasoned equity offerings by 1–2 percentage points compared with a pre-OHR average of 3.6%. The effect is the largest in stocks with the largest reduction in institutional trading costs after the OHR.