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Chaoshin Chiao Zi-Mei Wang Shiau-Yuan Tong

The Determinants of Order Cancellation: Evidence from an Emerging Order-Driven Market. Chaoshin Chiao Zi-Mei Wang Shiau-Yuan Tong. Liquidity under Stress. The chart by Goldstein and Kavajecz (2001)

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Chaoshin Chiao Zi-Mei Wang Shiau-Yuan Tong

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  1. The Determinants of Order Cancellation: Evidence from an Emerging Order-Driven Market Chaoshin Chiao Zi-Mei Wang Shiau-Yuan Tong

  2. Liquidity under Stress The chart by Goldstein and Kavajecz (2001) depicts the average cumulative limit order book depth for the 100 stocks in their sample each half-hour over the period Friday, 10/24/97 at Noon through the close Wednesday, 10/29/97. 2013/3/7 2

  3. Given the importance of liquidity, numerous relevant studies investigate issues on intraday liquidity, but most of them (e.g., Lee et al., 2004; Ranaldo, 2004, Menkhoff et al., 2010) focus on order submission behavior. Until recently, research has been increasingly aware of the role of cancellations and revisions of limit orders in liquidity provision. 未能成交風險 Motivations and Purposes For example, Ellul et al. (2007) and Yeo (2005) observe that in the NYSE more than one-third limit orders were canceled. Hasbrouck and Saar (2009) state that 93% of limit orders submitted through Nasdaq INET are canceled, and 36.69% of those are fleeting orders canceled in two seconds after submission. Employing comprehensive order-level data for years 2007 and 2008, we examine the patterns of order cancellations in the Taiwan Stock Exchange (TSE), a pure order-driven market. Analyze the motives to cancel limit orders, facing trade-offs between the costs of monitoring and the risks of limit orders, including the non-execution (NE) risk and the free trade option (FTO) risk. Unlike the literature, this paper pays extra attention to the behavior differences among investor groups, including foreign investors, domestic institutions, and individual investors.

  4. Literature Review

  5. Risks of limit orders (1/4) • The placement of a limit buy (sell) offers other market participants the opportunity of selling (buying) shares to the limit order liquidity supplier at that price. Hence, a limit buy (sell) is analogous to writing a free put (call) option to the market (Copeland and Galai, 1983). • The exercise price of the call (put) is equivalent to the ask (bid) price of the limit order. Adverse price changes may further make the order stale and the associated FTO in-the-money. The uncertainty that the stale order may be picked off by other informed traders is called the FTO risk. If not exercised, possibly either the option is out-of-the-money or the order is canceled/revised (Liu and Sawyer, 2003). • When the prevailing bid-ask midpoint moves away from the submitted price, executions become less possible, the uncertainty that is recognized as the NE risk.

  6. Risks of limit orders (2/4) • Responding to variations in the FTO and the NE risks, traders can either price-protect themselves by submitting limit orders at prices far from the best quotes, or closely monitor the market and cancel/revise their stale limit orders, if necessary (Liu, 2009, Goldstein and Kavajecz, 2000; Foucault, 1999). • Since information randomly arrives, although it is appealing to actively monitor the information flow and prevent limit orders from being stale and picked off, the behavior inevitably incurs nontrivial search costs  the monitoring cost (Foucault et al., 2003). Investors have to make efforts to find news sources, to interpret information, and to justify its accurateness. • Only if the monitoring cost is not large, limit orders are actively canceled or revised in response to changes in the NE and the FTO risks (Fong and Liu, 2010).

  7. Risks of limit orders (3/4) • Thanks to technological advancements (e.g., on-line trading system and internet) that effectively enhance information transmission and reduce the monitoring cost (Jain, 2005; Stoll, 2006), order cancellations/revisions have substantially increased (Liu, 2009; Hasbrouck and Saar, 2009). • Even given the improvement, the monitoring cost still varies different across investors. For example, unlike institutional investors, individual investors cannot continuously monitor the market (Aitken et al., 2007). Especially for individual investors with dedicated daytime jobs in non-securities-related industries, closely monitoring the market during trading hours is costly and even jeopardizes their career.

  8. Risks of limit orders (4/4) • Institutional investors are perceived to be better informed than individual investors (Chakravarty, 2001; Anand et al., 2005; Kaniel and Liu, 2006). Institutional investors can effectively gather and interpret necessary information from closely monitoring the market. As information arrives, their order behavior is expected to be more sensitive and quickly respond to market changes than individual investors’ (Lee et al., 1991; Menkhoff et al., 2010).

  9. Contributions (1/2) To investigate the strategic roles in order cancellations among investor groups, we take advantage of the richness of our data, named “the TSE Order and Quote Files” that unambiguously classify each limit order into one of five groups: foreign investors, mutual funds, securities dealers, individual investors, and corporate institutions. To our knowledge, this is the first study applying such voluminous and comprehensive data to examine this issue. We improve the measures of the FTO and the NE risks originally proposed by Liu and Sawyer (2003). Our results empirically support the improvement.

  10. Contributions (2/2) Unlike the FTO and the NE risks, the monitoring cost is not only unobservable, but also conceptual. We explicitly apply the proportion of limit orders submitted before the open as a proxy for the availability of investors to monitor the market or, simply, the monitoring cost. We pay attention to the levels and the sensitivities of order cancellations by institutional investors over the 2008, known as the year of financial tsunami.

  11. The determinants of order cancellations Research Design Distributions Characteristics Order aggessiveness Order price prioroty Order size Regression analysis Tobit

  12. Sample period Data and Sample The major concern is that part of small-cap stocks may not be traded, at least frequently, by institutional investors, which creates difficulty to analyze their order cancellation behavior. Other securities, such as debt securities, rights, warrants, and preferred shares, are excluded. The Order and Quote Files obtained from the TSE Source From 1/2007 to 12/2008, totaling 496 trading days, 18 snapshots of the limit order book are taken for each stock at 15-minute intervals from 09:00 am to 1:30 pm. Sample firms The largest 50% of common stocks, a total of 375 firms, based on the market capitalization at the beginning of the sample period

  13. Order data • The data contain the intraday information on all original limit orders. For each order, the data include the time stamp (to the nearest one hundredth of a second), stock code, investor type, a buy-sell indicator, order size, and limit price. • The investor type classifies each limit order into one of five groups: foreign investors, mutual funds, securities dealers, individual investors, and corporate institutions. • For brevity and without loss of generality, we aggregate mutual funds, securities dealers, and corporate institutions into a group of domestic institutions.

  14. Quote data • The data provide snapshots of the limit order book for each listed stock. It shows the time as well as bid (ask) information at 20-30 second intervals. The bid (ask) queue information includes the number of shares waiting to be executed at the best bid (ask) and at each of the four consecutive lower (higher) ticks. • On each trading day, we take 18 snapshots of the limit order book for each stock at 15-minute intervals from 09:00 am to 1:30 pm.

  15. Empirical Results

  16. Table 1. Summary statistics of selected stocks 本表計算樣本的交易特性。樣本期間為2007年1月2日至2008年12月31日，共計496個交易日，375支個股。在樣本挑選方面，只選取研究期間市值前50％的上市公司，總共包含375支普通股。市值是指個股於研究期間內的平均市場價值，價差則由每日收盤的最佳買賣價計算而得，絕對價差是最佳賣價與最佳買價之差距，而相對價差則是絕對價差占報價中點的比例。 The market value is calculated, using the closing prices and the number of outstanding shares at the beginning of the sample period. The absolute spread is the difference between the ask and the bid prices at the close. The relative spread is defined as the ratio of the quoted spread to the midpoint of the best quotes. All reported statistics are computed, using the time-series average of each selected firm. The reported statistics are cross-sectional means over the time-series averages of individual stocks.

  17. Figure 1. The daily proportions of canceled orders to all submitted orders Figure 1. The daily proportions of cancelled orders to all submitted orders In terms of dollar volume, the averages are 15.84% and 19.43% respectively over years 2007 and 2008, whereas in terms of number of cancelled orders, they are 19.52% and 21.75%. The yearly proportions

  18. Figure 2. The proportions of canceled orders by investor groups (1/2) Panel A. For large-cap stocks

  19. Figure 2. The proportions of canceled orders by investor groups (2/2) Panel B. For small-cap stocks

  20. Figure 3. The proportions of canceled orders by each investor group over 15-minute intervals All U-shaped. • To defend our argument, let us a first look at Tables 3 and 4, describing order submissions based on price aggressiveness before and the open, as defined in Equation (1) and (2), respectively. It is obvious that individual investors obvious play a more dominant role before the open than after. They submit more than 90% of orders before the open, possibly reflecting different monitoring costs borne by individual investors and institutional investors. • Unlike institutional investors, most of individual investors neither work fulltime in security-related industries nor are available to monitor the market all the time, but can still submit orders before the open while not at work yet. • After 12 pm, about the lunchtime, individual investors are temporarily free from work. They can monitor the market and cancel stale limit orders. The market is close at 1:30 pm when part of individual investors do not back to work yet. • By contrast, foreign investors seems to be conservative about submitting limit orders before the open, since submitting limit orders before the open may expose themselves to unnecessary overnight uncertainty. With a low monitoring cost, foreign investors can effectively monitor the market and fast react to news after the open. Thus, the observed pattern is consistent with the daily working schedule of regular workers. • Order cancellations by individual investors rise right after 12 pm. • This is possibly because individual investors have a higher monitoring cost particularly during the morning hours.

  21. Figure 4. The proportions of canceled orders for large-cap stocks in years 2007 and 2008

  22. Table 2. Submitted orders and canceled orders by three investor groups Quite interestingly, albeit the limit orders by foreign investors are more than four times larger than those by domestic institutions, the foreign investors’ order size is only about a half of the domestic institutions’. The observation is consistent with Chiao and Wang (2008) who observe that foreign investors in Taiwan are more inclined to split their orders into smaller ones to camouflage their intention and reduce unnecessary impact costs.

  23. Table 3. Aggressiveness of limit orders submitted before the open • Price aggressiveness of limit orders or order aggressiveness by investors reflects their eagerness for order executions at the time of submission. Investor can submit very different buy or sell orders, trading off probabilities of execution with transaction costs (Hasbrouck and Saar, 2001). • For all intents and purposes, a marketable limit order in a pure limit order book is equivalent to a market order in floor or dealer markets. A trader who seeks immediate execution will price the limit order to be marketable, e.g., a buy order priced at or above the current ask price. Pi,C is the closing price for stock i for the preceding trading day and Pi,j is the submitted price of order j for stock i. For a given stock, the applied order aggressiveness measures the deviation of the submitted price from the preceding day’s closing price. All orders are sorted into the following five groups: 0 ≤ the aggressiveness (the most aggressive), -0.5% ≤ the aggressiveness < 0%, -1% ≤ the aggressiveness < -0.5%, -2% ≤ the aggressiveness < -1%, and the aggressiveness < -2% (the least aggressive).

  24. Table 4. Aggressiveness of limit orders submitted after the open where Pi,j,ask (Pi,j,bid) is the best (ask) ask price of stock i for buy (sell) order j and Pi,j is the order price of order j for stock i. Price aggressiveness measures the relative distance from the submitted prices to the best quotes when the orders are submitted during regular trading hours. All orders are sorted into the following five groups: 0 ≤ the aggressiveness (the most aggressive or marketable), -0.5% ≤ the aggressiveness < 0%, -1% ≤ the aggressiveness < -0.5%, -2% ≤ the aggressiveness < -1%, and the aggressiveness < -2% (the least aggressive).

  25. Table 5. Canceled orders with different price priorities Limit orders for large-cap stocks submitted near the best quotes are exposed more to the FTO risk. Aitken et al.(2007) state that aggressive institutional investors (e.g., hedge funds) actively monitor the market and particularly large-cap stocks’ trading activities, which equivalently raises the possibility for limit orders of being picked off around the best quotes. By contrast, the NE risk for small-cap stocks is more pronounced. Market capitalizations make differences in order cancellation behavior. Taking the sell orders as an example, the difference of the order cancellation ratios in group 1 (the group with the highest price priority) by all investors between the large-cap and the small-cap stocks is 0.052, while that in group 6 (the group with the lowest price priority) is -0.070 . Obviously, the order cancellations for large-cap stocks are likely to occurat the prices closer to the best quotes than those for small-cap stocks. U-shaped There is a monotonically decreasing pattern from group 1 (with the highest price priority) to group 5, but a sudden jump for group 6 (with the lowest price priority), reflecting the trade-off facing investors between the FTO and the NE risks mentioned above. Differently, for those group-6 limit orders with the lowest price priority, the NE risk emerges and induces traders who have limit orders unfilled to cancel the orders and resubmit orders at more aggressiveness prices, when they expect stock prices to trend away from the orders’ prices where Pi,j,ask,c (Pi,j,bid,c) is the concurrent best (ask) ask price of stock i for canceled buy (sell) order j and Pi,j is the order price of canceled order j for stock i. All canceled buy and sell order are separately grouped into six groups, based on price priority. 0  price priority > -0.2%, -0.2%  price priority > -0.4%, -0.4%  price priority > -0.6%, -0.6%  price priority > -0.8%, -0.8%  price priority > -1%, -1%  price priority, Across investor groups, foreign investors proportionally cancel the most limit orders in the best price priority group, while domestic institutions and individual investors do in the least price priority group.

  26. Here we see a different pattern or no pattern for foreign investors. As we can earlier, foreign investors trade much more than other institutional investors, but their average order size is smallest among institutional investors, so foreign investors are more likely spilt orders. If truly so, their order size may not as important as other institutional groups’ The larger the orders, the higher possibility to be cancelled, consistent with Fong and Liu (2010). If investors decide not to monitor the market, the larger the orders, the higher the opportunity costs, implying that orders are more likely to be cancelled. Table 6. Canceled orders in different size groups

  27. Table 7. Tobit regressions • Consider the following scenario: • If the midpoint during interval t+1 falls below the end-of-period ask price in interval t, the option associated with a limit buy becomes in-the-money and opposite-side traders have a chance to exercise the option. However, suppose that the midpoint at the end rebounds above the previous end-of-period ask, and the put value is set to zero. It follows that the FTO would not be exercised over the entire interval, even thought it had been in-the-money for a while. Is the argument right? The answer is clearly no. • In this scenario, the end-of-period midpoint fails to capture the true variation of the put option value over the interval. On the contrary, the revised put value remains positive and well captures the turning point of the FTO risk than that under equation (5) does. The revised measures can better reflect the trade-off traders truly face over the 15-minute interval. The one proposed by Liu and Sawyer (2003): Tobit regressions are employed, because the dependent variable (CANCELi,t, the cancellation ratio for stock i over 15-minute interval t) is continuous between 0 and 1. The coefficients across investor groups on (the original) PUTi,t and CALLi,t are very different from those reported in Table 7. For instance, only half of these coefficients on the original PUTi,t/100 are significant at the 10% level, while those reported in Panel A are all significant at the 1% level. Furthermore, the coefficient on the original PUTi,t/100 for limit sells by individual investors (0.173) is counter-intuitively greater than that by foreign investors (0.118). We propose:

  28. Table 8. Tobit regressions with an additional dummy of year 2008 Most results for institutional investors (foreign investors and domestic institutions) are significantly positive except the changes in the bid and the ask depths , suggesting that order cancellation by all institutional investors becomes more sensitive over 2008. The coefficients are less obvious for individual investors. • Year 2008 is known as the year of financial tsunami. It is plausibly an information-rich period and provides us a perfect opportunity to examine whether investors would become extra prudent and substantially change their behavior on order cancellation. • Figures 1 and 2, as preliminary evidence, show the average cancellation ratios separately by all investors and each investor group in 2008 are all higher than those in 2007. • Without loss of generality, we further conjecture that order cancellations may have different sensitivities to the included variables in Table 7 between 2007 and 2008. To verify our conjecture, we highlight the increment of each by adding an interaction term for each independent variable.

  29. Conclusions

  30. Conclusions (1/6) • Albeit a trader can submit a limit order to buy/sell a pre-specified quantity at a pre-specified price considered optimal, but neither the transaction is guaranteed nor time to execution is certain. When adverse information arrives, the limit order possibly becomes stale, making the trader face the limit-order risks, such as the FTO and the NE risks. • To mitigate these risks, the trader can either submit orders at prices far from the best quotes or closely monitor the market and cancel his orders, if necessary, which unavoidably incurs another costs – the monitoring cost.

  31. Conclusions (2/6) • Employing intraday data, this paper studies order cancellation behavior among different investor groups. • Noteworthy is that this paper proposes a revision of the FTO and the NE risks initially developed by Liu and Sawyer (2003). • To further differentiate this paper from prior studies, the derived conclusions are separated into two major categories: the ones consistent with the literature and the others previously unexplored.

  32. Conclusions (3/6) In the first category: The limit order risks, including the FTO and the NE risks, are crucial determinants for investors to cancel their limit orders. The relation between order cancellation and concurrent price priority is U-shaped. The larger the limit orders, the more frequently they are canceled. Fourthly, firm size matters for order cancellations. Traders who submit limit order for large-cap (small-cap) stocks face higher FTO (NE) risk, so they cancel their limit orders when the prevailing quotes move closer to (farther from) the submitted prices.

  33. Conclusions (4/6) In the second category: Institutional investors, especially foreign investors, closely monitor the market and actively cancel their orders. Individual investors with a higher monitoring cost do not frequently cancel their orders until noon, reflecting their unavailability (high monitoring cost) during regular working hours. Foreign investors and domestic institutions, having lower monitoring costs and submitting limit orders around the best quotes, are highly aware of the FTO risk and tend to cancel limit orders with high price priorities.

  34. Conclusions (5/6) Order cancellations are tightly related to trading strategies, e.g., order splits. Since foreign investors often split their orders into smaller ones to camouflage their intention, the relation between their order cancellation and order size is not obvious, while the relations for other investor groups are clearly positive. Applying a regression analysis, the sensitivities of order cancellations by foreign investors to the FTO and the NE risks are the highest, while those by individual investors are the lowest.

  35. Conclusions (6/6) In year 2008, known as the year of financial tsunami, order cancellations proportionally rise, relatively to those in year 2007, possibly due to the growing uncertainty. Especially, the sensitivities of order cancellation by foreign investors and domestic institutions to the FTO and the NE risks double from 2007 to 2008.

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