Robert H. Battalio

Order submission strategies, liquidity supply, and trading in pennies on the New York Stock Exchange

Abstract We use NYSE system order data to conduct a controlled experiment examining changes in trader behavior, displayed liquidity supply, and execution quality around the reduction in the minimum price variation to

ASSESSING INDIVIDUAL RISK ATTITUDES USING FIELD DATA FROM LOTTERY GAMES

0.01. Although traders do not substantially reduce their use of traditional limit orders in favor of market orders or non-displayed orders, they do decrease limit order size and cancel limit orders more frequently after decimals than before. These changes in order submission strategy appear to result in less displayed liquidity throughout the limit order book more than 15 cents from the quote midpoint. This reduction in displayed liquidity, however, does not manifest itself in poor execution quality. Even for large system orders, traditional execution quality is not worse with decimals than with fractions.

SOES Trading and Market Volatility

We use information from the television game show with the highest guaranteed average payoff in the United States, Hoosier Millionaire, to analyze risktaking in a high-stakes experiment. We characterize gambling decisions under alternative assumptions about contestant behavior and preferences, and derive testable restrictions on individual risk attitudes based on this characterization. We then use an extensive sample of gambling decisions to estimate distributions of risk-aversion parameters consistent with the theoretical restrictions and revealed preferences. We find that although most contestants display risk-averse preferences, the extent of the risk aversion implied by our estimates varies substantially with the stakes involved in the different decisions.

All else equal?: a multidimensional analysis of retail, market order execution quality ☆

The National Association of Security Dealers alleges that professional-trader use of the Small Order Execution System (SOES) causes greater security price volatility. We document bidirectional Granger causality between a proxy for professional SOES trading (the frequency of maximum-sized SOES trades) and a measure of stock price volatility. We find that high levels of volatility precede high levels of maximum-sized SOES trades, suggesting that volatility causes more frequent large SOES trades. Likewise, over a one-minute time interval, high levels of maximum-sized SOES trades cause high volatility. Over longer periods, however, intense maximum-sized SOES trading causes lower volatility. Interpreted in conjunction with Harris and Schultz (1997), these results suggest that high levels of maximum-sized SOES trades lead to more efficient price discovery. In light of these results, we believe that efforts to eliminate SOES based on volatility considerations are unwarranted.

Depth improvement and adjusted price improvement on the New York stock exchange

Abstract The extant execution quality literature generally suggests that brokers routing orders away from the NYSE might not fulfill their fiduciary best execution responsibility. This conclusion is drawn by comparing execution prices across trading venues and presumes that other execution-quality characteristics are equivalent. Using order audit-trail data, we find evidence that retail market orders obtain better trade prices on the NYSE but faster executions, more depth improvement, and order-flow payment at Trimark Securities, a Nasdaq dealer. Thus, non-price dimensions of execution quality are not equivalent across trading venues. Furthermore, considering order flow payments, brokers obtain better net prices with Trimark. If brokers pass enough of these payments through to investors in the form of lower commissions and/or better services, then investors also obtain better net prices with Trimark. Our results suggest that it may be misleading to evaluate execution quality or to base policy decisions on comparisons focusing on only execution prices.

The potential for clientele pricing when making markets in financial securities

Abstract Traditional price improvement improperly assesses large orders’ execution quality by ignoring additional liquidity depth-exceeding orders receive at the quoted price and viewing orders that “walk the book” as “disimproved”. Ignoring this additional liquidity is particularly problematic when assessing execution quality in markets with significant non-displayed liquidity. To correct this deficiency, we modify the price benchmark used to determine whether an order is price improved by making the benchmark a function of the orders size relative to the quoted depth. We document that the differences between conventional price improvement and our measure, adjusted price improvement, can be dramatic and show that the difference depends on trading volume, stock price, and volatility.

To Pay or Be Paid? The Impact of Taker Fees and Order Flow Inducements on Trading Costs in U.S. Options Markets

Abstract Benveniste et al. J. Financial Econom. 32 (1992) 61–86 argue that repeatedly dealing with the same brokers allows market makers to know when brokers exploit private information. This suggests that broker identity may allow market makers to differentiate between customers when pricing market-making services even when market makers can provide separate quoted prices for each order size. Estimates of a major Nasdaq dealers gross trading revenue vary substantively among routing brokers after controlling for order size. Furthermore, these differences exhibit a degree of stability over time. This suggests that market makers may effectively enforce clientele pricing schedules in a world where security prices are quoted without a minimum price variation and the limit order book is available to all market participants.

Post‐Earnings Announcement Drift: Bounds on Profitability for the Marginal Investor

Consistent with prior literature, we find that average relative effective spreads are higher for venues that pay for order flow (PFOF) than for venues utilizing the maker-taker (MT) model. This relation becomes more nuanced when liquidity fees are incorporated into liquidity cost measures. For the majority of options, PFOF venues offer lower average liquidity costs net of taker fees. Net liquidity costs for the high-priced options, however, are lower for MT venues. Overall, our results suggest that the inclusion of fees and rebates can rationalize the routing of most, but not all, marketable orders to PFOF venues.

Unrecognized Odd Lot Liquidity Supply: A Hidden Trading Cost for High Priced Stocks

The persistence of the post‐earnings announcement drift (PEAD) leads many to believe that trading barriers prevent investors from eliminating it. We examine two factors that have not been adequately addressed by the literature: the exact timing of earnings announcements and liquidity costs. Under a wide range of timing and cost assumptions, our results leave little doubt that over our sample period the PEAD was highly profitable after trading costs. An additional incremental investor could have earned hedged‐portfolio returns of at least 14% per year after trading costs. Over our sample period, investors did indeed leave money on the table.

Exchange Entrances, Mergers and the Evolution of Order Flow on NASDAQ 1993-2010

Current National Market System rules do not recognize odd lots in the protected intermarket quote. Thus, liquidity demanders can receive worse prices than they would receive if odd lots were protected. The effect of ignoring odd lots is magnified because off-exchange trades (over one-third of total volume) benchmark executions against the protected quote. The authors identify time intervals with an unprotected odd lot limit order at a price better than the protected quote and examine trades during those intervals for 10 high-priced stocks during one week in 2015. They find over 406,000 intervals, representing 37% of sample stock trading time, in which an odd lot order betters the protected quote. Examining trades within these intervals, they find nearly 55,000 cases in which the trade price is worse than the odd lot price. In total, the price disimprovement in their 10 stocks is