A. Summary of Model-Based Simulation Results
I summarize here the results from model-based simulations that examine why individual investors use limit orders. The Internet Appendix surveys the theory of order choice and provides a thorough discussion of these simulations.
I examine the order choices of both individual and institutional investors by comparing these investors' behavior to numerical solutions of the state-of-the-art dynamic limit order book model of Goettler et al. (2009). The financial asset in this model has both common and private components to its value. The private component of value generates an intrinsic motive for trade. Traders with low private values are traders who need to sell because of a negative liquidity shock. Also, because some traders want to trade for private reasons, other traders with no private value component (and no information) have an incentive to provide liquidity.
The model-based estimates indicate that information, patience, and private value component realizations affect not only unconditional limit order usage rates, but also each trader's sensitivity to the state of the market. For example, informed traders are more sensitive than uninformed traders to changes in market conditions and also use more market orders. Impatient traders and traders with extreme private value component realizations submit more market orders. Traders' sensitivity to market conditions, and in particular to those related to the bid-ask spread, increases significantly in the magnitude of the private value component.
In actual data both individuals and institutions respond to changes in market conditions in much the same way as the simulated traders. Investors are more likely to submit limit orders when the spread is wide or has just widened, when the bid-ask spread midpoint has moved away from the trader, and when the flow of market orders on the opposite side of the market has been high. A comparison of individual and institutional investors suggests that institutions are far more responsive to variation in the size of the bid-ask spread. By contrast, individuals and institutions respond quite similarly to changes in the other nonspread market conditions.
In simulations, traders who are uninformed, more patient, or whose private value component realizations are closer to zero use more limit orders. Of these three channels, the private value component has the strongest effect and variation in this dimension can generate a remarkably good match between the data and the simulations. If institutions tend to receive larger draws of the private value component, then differences in individual investors' and institutional investors' order usage rates and sensitivities to market conditions conform to the simulations. Variation in this dimension could explain, first, why individual investors use more limit orders in general and, second, why institutions are more sensitive to changes in the bid-ask spread.
The conclusion that the private component channel can explain institutions' and individuals' order choices is intriguing. The model does not need to bombard individual investors with extreme liquidity shocks to get them to trade. Instead, this result suggests that individual investors resemble uninformed traders with private value components close to zero. Such traders enter the market because they expect to gain from the liquidity demand of impatient investors with large private value component realizations. This characterization of individual investors is consistent with Kaniel et al.'s (2008b) conclusion that individual investors provide liquidity to meet institutional demand for immediacy.
B. Intraday Returns on Individuals' and Institutions' Limit and Market Orders
Table IX reports on average intraday returns associated with market orders and executed limit orders. I compute average returns separately based on who submitted the limit order (i.e., an individual or institution) and who traded against the limit order with a market order. The first block reports on trades for which individual investors submit both types of orders. The second block reports on trades for which an individual investor submits the limit order and an institution submits the market order, and so on.
Table IX. Intraday Returns in Transactions between Individual and Institutional Investors
|Limit Order Investor||Order Direction||Return from Transaction to the Close (%)|
|Market Order Investor|
|Individual Investor||Buy|| 250,006||0.115||2.90|| 385,216||−0.293||−5.14||−0.133||−2.84|
|Sell|| 239,729||0.508||10.50|| 352,440||−0.049||−1.22||0.176||4.42|
|Both|| 489,735||0.307||23.42|| 737,656||−0.177||−7.45||0.016||1.03|
|Institutional Investor||Buy|| 259,712||0.244||6.68|| 1,099,757||−0.020||−0.29||0.030||0.48|
I compute log-returns from the transaction price to the bid-ask spread midpoint at the end of the day. I reverse the signs for sell limit orders so that positive numbers indicate better performance. I compute t-values from stock-day-clustered residuals. Thus, these inferences are robust to correlations between similar orders on the same day in the same stock.
The returns in Table IX satisfy an important adding-up constraint: because there is a buyer and a seller in each trade, the average return is zero. Any deviation from zero indicates that there is a monetary transfer from one group of investors to another. Barber et al. (2010) examine a similar issue. They compute the total monetary transfer from one group of investors to another instead of just studying short-term trading costs.
Table IX shows that the average executed limit order earns 6.5 basis points on the day of the trade. The average market order loses the same amount. However, this average masks significant variation that depends on who placed the limit order and who submitted the market order. Executed limit orders perform better when they are hit by individual investors rather than by institutions. The difference in means, 36.1 bps compared to −3.8 bps, is economically impressive and statistically highly significant. This difference suggests that institutions' market orders are more informative than are individuals' market orders. This difference also holds for limit orders. Institutions' executed limit orders outperform individuals' executed limit orders no matter who is on the other side of the trade. Anand, Chakravarty, and Martell (2005) find a similar pattern in a U.S. data set.
Individual investors' executed limit orders outperform their market orders by a statistically and economically significant margin. While the average executed limit order earns 1.6 bps on the day of the trade, the average market order loses 36.1 bps. The main reason for this difference is the bid-ask spread. An executed limit order earns half of the bid-ask spread at the time of execution, but a market order pays this amount.
The performance differences in Table IX should be interpreted with caution. I only measure returns on executed orders, so there is no penalty for unexecuted limit orders. Nevertheless, I can conclude that individuals would have paid higher trading costs had they used more market orders. This conclusion is justified because investors submit more limit orders at times when bid-ask spreads are wide. Thus, replacing the average executed limit order with a market order would cost at least as much as the cost associated with the average market order.
The favorable intraday returns on executed limit orders go in the opposite direction from the long-run return results. Individuals are compensated up front for using limit orders. Although Table IX shows that the individual investors' average limit order loses significantly 1 day after the trade, this loss should be netted against the savings on the day of the trade. If I extend the return horizon in Table IX to the day after the trade, then the average return on a limit order falls from 1.6 to −16.3 bps while the average return on a market order increases from −36.1 to −26.3 bps.16 The standard errors for these day-and-a-half average returns are 4.2 and 2.2 bps, respectively.17 Hence, executed limit orders are ahead of the market orders, even at the end of the following day, by a statistically and economically significant 10 bps margin.
These intraday return results, together with the posttrade performance results, are similar to Kaniel et al.'s (2008b) finding for a U.S. data set, which shows that individual investors are compensated for their liquidity provision services. After the stock prices move against limit orders 1 day after the trade, limit orders gain ground in the following weeks. Only at longer horizons are these gains offset as either adverse selection or momentum begins to move stock prices against limit orders. The positive returns that individuals earn on their executed limit orders are also consistent with my characterization of individuals, based on Goettler et al.'s (2009) model, as uninformed traders with no intrinsic motive to trade.
Most studies on individual investors' performance ignore the same-day return to avoid any bid-ask spread effects. The results in Table IX suggest that because individuals often earn the bid-ask spread and because these initial profits are partially offset by lower returns in the days to come, performance studies should also include intraday returns.