We examine factors that influence decisions by U.S. equity traders to execute a string of orders, in the same stock, in the same direction, around the same time. Order splitting is more likely to occur when traders submit larger-size orders and when market depth and trading activity are lower. Order splitters demand liquidity more and pay higher trading costs, but their overall performance is better. When controlling for execution time, split orders are more informative than single orders. Our results suggest that order splitting arises from a variety of factors, including informational differences, order and trader characteristics, and market conditions.
Topic: Market microstructure
Subtopics: Transactions costs · Market quality · Liquidity · Market making · Flash crash · Market structure
Using a version of the ITCH data set time stamped to the millisecond, O’Hara, Yao and Ye find that odd-lot trades are highly informed. However, NASDAQ reports trades based on the size of the resting limit order, creating a bias in the count of odd-lot trades. Using ITCH data from 2013, time stamped to the nanosecond, we find that roughly 50% of odd-lot trades are created by the resting limit order and are part of larger marketable orders. We show that odd-lot marketable orders are not more informed than round/mixed lot marketable orders.
The probability of informed trading (PIN) is used widely as a measure of information asymmetry. Relatively little work has appeared on how well PIN models fit empirical trade data. We reveal structural limitations in PIN models by examining their marginal distributions and dependence structures represented by copulas. We develop a distribution-free test of the goodness-of-fit of PIN models. Our results indicate that estimated PIN models have generally poor fit to actual trade data. These results suggest that researchers should be cautious when PIN estimates are plugged into empirical models as explanatory variables.
Regulators continue to debate whether high-frequency trading (HFT) is beneficial to market quality. Using Strongly Typed Genetic Programming (STGP) trading algorithm, we develop several artificial stock markets populated with HFT scalpers and strategic informed traders. We simulate real-life trading in the millisecond timeframe by applying STGP to real-time and historical data from Apple, Exxon Mobil, and Google. We observe that HFT scalpers front-run the order flow, resulting in damage to market quality and long-term investors. To mitigate these negative implications, we propose batch auctions every 30 milliseconds of trading.
The New York Stock Exchange’s Rule 80A attempted to de-link the futures and equity markets by limiting index arbitrage trades in the same direction as the last trade to reduce stock market volatility. Rule 80A leads to a small but statistically significant decline in intraday U.S. equity market volatility. In addition, the results are asymmetric: volatility is dampened more in a rising market than in a declining one. These results suggest that, to a limited basis, rule restrictions on trading can sufficiently de-link the futures and equity markets enough to reduce the transmission of volatility.
In this study we analyze dealer exit, survival, and competitive equilibrium in the NASDAQ Stock Market using data from a unique period that entails major changes in regulatory and competitive environments. We decompose the forces that affect dealer survival into market factors and dealer attributes. Market factors encompass those variables that affect the demand for and profitability of dealer services as a whole. Variation in survival probability across dealers results mainly from their competitive advantages in business strategies, information, quote aggressiveness, access to order flow, and economies of scale. On the whole, our results suggest that dealer markets exhibit a Darwinian survival of the fittest.
We compare the liquidity providing behavior of NASDAQ market makers in 2010 to their behavior in 2004. We examine how frequently market makers are at the inside quote, what market and stock specific factors influence market makers’ behavior, and the relation between market maker participation and intraday bid-ask spread patterns. We observe a decrease in the percent of the trading day dealers’ quote at the inside, a decline in the number of market makers, and a decrease in the influence market makers have on intraday spread patterns. The results suggest that the role of NASDAQ market makers declines over time.
During the Flash Crash on May 6, 2010, a short period of high stock market volatility, some stock prices declined to $0.01, while others increased to $100,000. Examining Intermarket Sweep Orders (ISOs) before, on, and after May 6, we find that ISO use is substantially higher on May 6. For those stocks whose prices fell the most, over 65% of the sell volume comes from ISOs. During the price recovery period for these stocks, about 53% of the buy volume comes from ISOs. We believe that the unusual behavior of ISOs contributed to the sudden drop and recovery of the market.
We investigate whether market makers with inventory concerns are compensated with subsequent monthly returns in the cross-section. We find a significant negative relation between order flows and monthly returns, “the order flow effect,” suggesting that market makers lower prices for stocks with sell order flows and demand a reward in the form of higher expected returns. Further, the order flow effect is stronger for high-volatility or high-volume stocks for which market makers have serious inventory concerns. Funding liquidity of market makers also affects the order flow effect. Finally, our finding is independent of existing regularities and robust to the decimalization.
This paper investigates the influence of information asymmetry on the cross-sectional variation of volume-return relation. We find that the dynamic volume-return relation within medium-size trades has the most significant response to the degree of information asymmetry. We also show that the effect of information asymmetry on the volume-return dynamics migrates to small-size trades in recent years, especially in larger stocks. These results are consistent with the notion that informed traders prefer medium-size trades and this preference has shifted to small-size trades. Our findings highlight the importance of incorporating informed traders’ trade-size decision in the examination of the dynamic volume-return relation.