Wholesale market makers pay for retail options orders that must be executed on exchanges. Payment for order flow (PFOF) wholesalers compete via price improvement in exchange auctions. To attract retail orders, wholesalers run more auctions when their recent price improvement has been lower. However, auction price improvement lowers market maker revenues. Wholesalers earn revenues to pay PFOF in nonauction trades where their designated market maker status increases their execution priority. While some auctions produce substantial price improvement, most do not have multiple bidders offering meaningful price improvement. Overall, options market structure better promotes competition in auctions than in nonauctions.

We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model’s parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.

We examine the effects of high-frequency traders (HFTs) on liquidity using the September 2008 short sale-ban. To disentangle the separate impacts of short selling by HFTs and non-HFTs, we use an instrumental variables approach exploiting differences in the ban's cross-sectional impact on HFTs and non-HFTs. Non-HFTs’ short selling improves liquidity, as measured by bid-ask spreads. HFTs’ short selling has the opposite effect by adversely selecting limit orders, which can decrease liquidity supplier competition and reduce trading by non-HFTs. The results highlight that some HFTs’ activities are harmful to liquidity during the extremely volatile short-sale ban period.

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.

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.

Markets can give false impressions of liquidity and stability if failed attempts to trade are ignored. For collateralized loan obligations, we quantify this bias by estimating the total cost of immediacy (TCI) which incorporates failure rates and failure costs. TCI is substantially higher than the observed cost, 0.3–3.8% versus 0.04–0.12% across credit-quality tranches because trade failures are frequent, failure costs are large, and failure costs and rates are correlated. TCI is almost double the realized gains from trade for low-rated tranches. Overall, auction-based over-the-counter markets become illiquid and fragile, especially during stressful periods for low-rated assets.

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 hidden orders usage by algorithmic traders (ATs) and nonATs. ATs extensively use hidden orders but of smaller size than nonATs, who are the primary contributors to hidden volume. ATs’ relative share of hidden volume decreases with volatility, adverse selection costs, and the relative tick-size. Proprietary ATs (HFTs), who differ from agency ATs (AATs) in their information sets and potential gains from trade, hide orders to reduce competition for liquidity provision, whereas AATs use hidden orders to conceal information in their more informed orders and manage picking-off risk. Finally, superior technology provides limited benefit for hidden order execution.