Inventory Models and Inventory Effects

Traditional economic models of price-setting focus on call-auction markets in which all trading occurs simultaneously, at pre-established discrete times, with no market makers involved. Such models leave no role for any of the three sources of friction found in modern models of market liquidity: inventory, order-processing costs, and adverse selection. As market microstructure research has developed, researchers studying the links between market-maker inventories and liquidity have shed considerable light on how market makers (often modeled as dealers) resolve temporal imbalances in the continuous trading environment that characterizes most financial markets. In general, inventory models predict that market makers set ask prices above bid prices, that they lower their quotes when they have very large inventory positions, and that they may or may not change the magnitude of their quoted spreads as their inventory changes, depending on whether they are capital constrained or not. Models in which market makers face capital constraints also offer an explanation of flight to quality, in which the riskiest securities suffer the greatest liquidity declines. Multi-dealer inventory models predict that relative inventory positions give rise to interdealer trading and determine which dealers have the best (lowest ask or highest bid) quotes in a market. These predictions have been tested and largely borne out in empirical studies to date

Who Trades With Whom, and When?

This paper examines empirically how market participants meet on the NYSE to form trades. Pure floor trades, involving only floor brokers and the specialist, account for only 4% of trading volume in the average stock, while pure system trades, involving only orders submitted electronically, account for 50% and floor and system interaction trades account for 46% of trading volume in the average stock. Market quality analysis reveals that pure system trades involving automatic execution are the most informative, while floor- initiated interaction trades also have high information content. This study offers insight into how market design affects the interaction of liquidity supply and demand and resulting market quality

A Tale of Two Time Zones: The Impact of Substitutes on Cross-Listed Stock Liquidity

This article examines how the market quality of European cross-listed stocks is affected by the partial-day availability of close substitutes, i.e., shares of the same companies that are traded in their home markets but are not fully fungible with the cross-listed shares. Our findings suggest that narrower spreads and more competitive liquidity provision during overlapping trading hours reflect a significant impact from the availability of more substitutes in addition to the enhanced information environment and liquidity externalities when home markets are open. Our results also provide a richer picture of specialists’ intraday activities and offer new evidence of market integration

Short Sales, Long Sales, and the Lee-Ready Trade Classification Algorithm Revisited

Asquith, Oman, and Safaya (2010) conclude that short sales are often misclassified by the Lee-Ready algorithm. The algorithm identifies most short sales as buyer-initiated, whereas the authors posit that short sales should be overwhelmingly seller-initiated. Using order data to identify true trade initiator, we document that short sales are, in fact, predominantly buyer-initiated and that the Lee-Ready algorithm correctly classifies most of them. Misclassification rates for short and long sales are near zero at the daily level. At the trade level, misclassification rates are 31% using contemporaneous quotes and trades and decline to 21% when quotes are lagged one second

The Performance of Short-term Institutional Trades

Using a database of daily institutional trades, we document that a majority of short-term institutional trades lose money. In aggregate, over 23% of round-trip trades are held for less than three months, and the returns on these trades average -3.91% (non-annualized). These losses are pervasive across all types of stocks, with the lowest returns occurring in small stocks, value stocks, and low-momentum stocks. Short-term trades lose more in more volatile markets. Across funds, the worst short-term returns accrue to funds that do the most trading, and there is no evidence of persistent skill or disposition effect in short-term institutional trades

Trading System Upgrades and Short-Sale Bans: Uncoupling the Effects of Technology and Regulation

We examine the market quality effects of technology upgrades juxtaposed with short-sale bans. Between 2011 and 2013, the Spanish Stock Exchange introduced a smart trading platform (SIBE-Smart) and colocation to facilitate high-speed trading, and they also imposed two short-sale bans. We find that the SIBE-Smart introduction, which occurs between the two short-sale bans, leads to reduced market quality. The introduction of colocation, which occurs during the second short-sale ban, improves market liquidity although it does not attract additional high-speed trading. Our results highlight how the effects of latency-reducing infrastructure improvements depend on, and differ across, different regulatory regimes

Trading in the Presence of Short-Lived Private Information: Evidence from Analyst Recommendation Changes

We study how short-lived private information affects trading strategies and liquidity provision. Our empirical identification rests on information acquisition before analyst recommendations are publically announced. Consistent with theory, institutional investors who are likely to possess short-lived private information on average “buy the rumor and sell the news,” buying before analyst upgrades and selling when upgrades are announced. When we go beyond existing theory, we find that different classes of informed institutions differ in their profit-taking patterns, reflecting variations in their trading horizons and motives. The returns to holding private information are economically large. Individuals, who are unlikely to be informed early, buy on upgrade announcements but not before. Institutions that are not attentive to firm-specific news appear to suffer from a winner’s curse, emerging as de-facto liquidity providers to better-informed institutions. Placebo tests confirm that these trading patterns are unique to situations in which some investors have a short-lived informational advantage

Attention Effects in a High-Frequency World

How does limited attention affect stock prices in today’s computer-driven financial markets? We study this issue by re-examining the effects of limited attention using a dataset that separately identifies trades made by high-frequency traders (HFTs, or computers) versus those made by non-high-frequency traders (human decision-makers). We employ a set of six attention proxies to identify earnings announcements with low investor attention: announcements made on Fridays and on days with multiple earnings announcements, and announcements with slow analyst forecast adjustments, high news distraction, low EDGAR download volume, and low Google search volume. Across multiple attention proxies, we find that HFT trading improves the responsiveness of prices by increasing the short-horizon price response and reducing the long-term price drift following earnings surprises, diminishing the inefficiencies previously observed around low-attention announcements by 69% to 100%. We find that the price efficiency improvements are more closely tied to HFT liquidity demand than supply, suggesting that HFTs improve efficiency by processing and trading on the information in low-attention announcements

You Can’t Always Get What You Want: Trade-Size Clustering and Quantity Choice in Liquidity

This paper examines whether investors care more about trading their exact quantity demands at some times than at others. Using a new data set of foreign-exchange transactions, I find that customers trade more precise quantities at quarter-end, as evidenced by less trade-size clustering. Customers trade more odd lots and fewer round lots, while the number of trades and total volume are not significantly changed. I also find that the price impact of order flow is greater when customers care more about trading precise quantities. This work sheds new light on trade-size clustering and offers a potential explanation for time-series and cross-sectional variations in common liquidity measures

Institutional Ownership and Return Predictability Across Economically Unrelated Stocks

We document strong weekly lead-lag return predictability across stocks from different industries with no customer-supplier linkages (economically unrelated stocks). Between 1980 and 2010, the industry-neutral long-short hedge portfolio earns an average of over 19 basis points per week. This return predictability arises exclusively from pairs of stocks in which there are common institutional owners. This predictability is a new phenomenon which does not originate from the slow information diffusion underlying previously documented lead-lag effects, weekly reversals, momentum, nonsynchronous trading, or other known factors. Our findings suggest that institutional portfolio reallocations can induce return predictability among otherwise unrelated stocks