Order price clustering,size clustering,and stock price movements: Evidence from the Taiwan Stock Exchange

This paper examines order price clustering, size clustering, and stock price movements in an active emerging country’s equities market, the Taiwan Stock Exchange (TWSE). We first explore the relationships between investor types and order price/size clustering. Next, we investigate the joint determinants of the round-price and round-size orders based on daily and intraday data analyses. Finally, we look at the relationships among investor types, round prices/sizes, and stock price movements. The findings reveal that all investor types exhibit price and size clustering phenomena. After controlling for other factors, institutional investors have a relatively lower level of size clustering when compared with individuals. Our results confirm the price resolution hypothesis, whereby the levels of daily price and size clustering increase with firm risk, and the probability of a round-price or round-size order increases as transitory volatility rises. Partially consistent with the negotiation hypothesis, the probability of a round-price or round-size order increases when order competition turns fiercer. Mutual funds exhibit stronger quarter-end and session-end effects than do other investors. We also detect strategic trading behaviors, showing that the probability of an order with a tail price of one (nine) increases when buy (sell) order competition is fiercer. Lastly, stocks with mutual funds’ round-price or round-size buy (sell) orders experience rising (falling) future stock returns.     

Order Imbalance on Ex-Dividend Days

In this article we test an alternative to the tax‐based explanation for why prices decline on ex‐dividend days by an amount less than the dividend. We examine whether there is order imbalance on cum‐ and ex‐dividend days. We find that on ex‐dividend days, there are more buys than sells in the number of orders, but not in the number of shares ordered, and the imbalance in the number of orders is limited to small orders. We find that the difference between the dividend amount and the ex‐dividend‐day price drop is significantly related to the magnitude of the buy‐sell order imbalance. We find no order imbalance on cum‐dividend days in either the number of orders or the number of shares ordered.     

International Evidence on Institutional Trading Behavior and Price Impact

This study characterizes institutional trading in international stocks from 37 countries during 1997 to 1998 and 2001. We find that the underlying market condition is a major determinant of the price impact and, more importantly, of the asymmetry between price impacts of institutional buy and sell orders. In bullish markets, institutional purchases have a bigger price impact than sells; however, in the bearish markets, sells have a higher price impact. This differs from previous findings on price impact asymmetry. Our study further suggests that price impact varies depending on order characteristics, firm‐specific factors, and cross‐country differences.     

The power of patience: a behavioural regularity in limit-order placement

Abstract

In this paper we demonstrate a striking regularity in the way people place limit orders in financial markets, using a data set consisting of roughly two million orders from the London Stock Exchange. We define the relative limit price as the difference between the limit price and the best price available. Merging the data from 50 stocks, we demonstrate that for both buy and sell orders, the unconditional cumulative distribution of relative limit prices decays roughly as a power law with exponent approximately –1.5. This behaviour spans more than two decades, ranging from a few ticks to about 2000 ticks. Time series of relative limit prices show interesting temporal structure, characterized by an autocorrelation function that asymptotically decays as C(τ)～τ^?0.4. Furthermore, relative limit price levels are positively correlated with and are led by price volatility. This feedback may potentially contribute to clustered volatility.     

Price Clustering: Evidence Using Comprehensive Limit-Order Data

Employing comprehensive limit-order data which identify investor types, this paper examines the clustering pattern of limit-order prices. First, limit orders, particularly those submitted by individual investors (IIs), tend to cluster at integer and even prices. Second, nonmarketable limit-order prices cluster more than marketable limit-order prices, indicating that aggressive limit orders generally embed more information. Third, investors choosing even-priced limit orders are not penalized by lower execution ratios. Fourth, investors (particularly IIs) strategically exhibit front-running behavior. Fifth, price clustering indeed creates price barriers. Finally, the degree of price clustering using trade data is significantly underestimated, compared to that using limit-order data.     

Order dynamics: Recent evidence from the NYSE

We examine investor order choices using evidence from a recent period when the NYSE trades in decimals and allows automatic executions. We analyze the decision to submit or cancel an order or to take no action. For submitted orders, we distinguish order type (market vs. limit), order side (buy vs. sell), execution method (auction vs. automatic), and pricing aggressiveness. We find that the NYSE exhibits positive serial correlation in order type on an order-by-order basis, which suggests that follow-on order strategies dominate adverse selection or liquidity considerations at a moment in time. Aggregated levels of order flow also exhibit positive serial correlation in order type, but appear to be non-stationary processes. Overall, changes in aggregated order flow have an order-type serial correlation that is close to zero at short aggregation intervals, but becomes increasingly negative at longer intervals. This implies a liquidity exhaustion–replenishment cycle. We find that small orders routed to the NYSE's floor auction process are sensitive to the quoted spread, but that small orders routed to the automatic execution system are not. Thus, in addition to foregoing price improvement, traders selecting the speed of automatic executions on the NYSE do so with little regard for the quoted cost of immediacy. As quoted depth increases, traders respond by competing on price via limit orders that undercut existing bid and ask prices. Limit orders are more likely and market sells are less likely late in the trading day. These results are helpful in understanding the order arrival process at the NYSE and have potential applications in academics and industry for optimizing order submission strategies.     

Implied Lévy volatility

We study the cause of large fluctuations in prices on the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the volume of orders. Instead, large price fluctuations are driven by liquidity fluctuations, variations in the market's ability to absorb new orders. Even for the most liquid stocks there can be substantial gaps in the order book, corresponding to a block of adjacent price levels containing no quotes. When such a gap exists next to the best price, a new order can remove the best quote, triggering a large midpoint price change. Thus, the distribution of large price changes merely reflects the distribution of gaps in the limit order book. This is a finite size effect, caused by the granularity of order flow: in a market where participants place many small orders uniformly across prices, such large price fluctuations would not happen. We show that this also explains price fluctuations on longer timescales. In addition, we present results suggesting that the risk profile varies from stock to stock, and is not universal: lightly traded stocks tend to have more extreme risks.     

Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE

We study pre‐trade transparency by looking at the introduction of NYSE's OpenBook service that provides limit‐order book information to traders off the exchange floor. We find that traders attempt to manage limit‐order exposure: They submit smaller orders and cancel orders faster. Specialists' participation rate and the depth they add to the quote decline. Liquidity increases in that the price impact of orders declines, and we find some improvement in the informational efficiency of prices. These results suggest that an increase in pre‐trade transparency affects investors' trading strategies and can improve certain dimensions of market quality.     

Modelling the buy and sell intensity in a limit order book market

In this paper, we model the buy and sell arrival process in the limit order book market at the Australian Stock Exchange. Using a bivariate autoregressive intensity model we analyze the contemporaneous buy and sell intensity as a function of the state of the market. We find evidence that trading decisions are both information as well as liquidity driven. Confirming predictions from market microstructure theory traders submit market orders by inferring from the recent order flow and the book with respect to upper and lower tail expectations as well as trading directions. However, traders also tend to take liquidity when the liquidity supply is high. Moreover, we find evidence that traders pay more attention to recent order arrivals and the current state of the order book than to the past order flow.     

Limit Order Trading

We analyze the rationale for limit order trading. Use of limit orders involves two risks: 1) an adverse information event can trigger an undesirable execution, and 2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. Our empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.     

Post-Reform Market-Order Execution Quality: Multidimensional Comparisons Across Market Centers

We use market‐order data to determine execution quality on the NYSE, four regional stock exchanges, and the Nasdaq InterMarket. We examine a sample period after the reduction in the minimum price variation and after the SEC imposed new order‐handling rules, and analyze dimensions of execution quality in addition to trade prices. We find that in the postreform environment, the NYSE offers execution prices that are more favorable to the investor. However, the regional exchanges and the InterMarket offer executions that are faster and that more frequently allow investors to execute orders with sizes exceeding the quoted depth at the quoted price.     

Short-Sale Restrictions and Price Clustering: Evidence from SEC Rule 201

We provide a novel test of information-based theories of price clustering by examining trade, order, and the National Best Bid and Offer (NBBO) quote price clustering during periods when information is removed from the market. We use a natural experiment of short-sale restrictions resulting from Securities and Exchange Commission (SEC) Rule 201 to more effectively determine the impact of information on price clustering. We find evidence of increased price clustering for trades, orders, and NBBO prices during short-sale restrictions. Overall, our findings indicate that short-sale restrictions harm the price discovery process and lead to a reduction in market efficiency.     

The Development of Secondary Market Liquidity for NYSE-Listed IPOs

For NYSE‐listed IPOs, limit order submissions and depth relative to volume are unusually low on the first trading day. Initial buy‐side liquidity is higher for IPOs with high‐quality underwriters, large syndicates, low insider sales, and high premarket demand, while sell‐side liquidity is higher for IPOs that represent a large fraction of outstanding shares and have low premarket demand. Our results suggest that uncertainty and offer design affect initial liquidity, though order flow stabilizes quickly. We also find that submission strategies are influenced by expected underwriter stabilization and preopening order flow contains information about both initial prices and subsequent returns.     

The next tick on Nasdaq

The Nasdaq stock market provides information about buying and selling interest in its limit order book. Using a vector autoregressive model of trades and returns, I assess the effect of the entire order book on the next tick. I also determine the influence of individual market makers and electronic networks and find evidence that the identity of market participants can be useful information. Finally, I produce a set of dynamic market price responses to buy and sell orders, and I find that these estimates vary with standard measures of liquidity.     

Evidence of oil market price clustering during the COVID-19 pandemic

We use hourly data on opening price, closing price, opening ask price, opening bid price, closing ask price and closing bid price to show that while oil prices are characterized by price clustering behavior, prices tend to cluster on numbers closer to zero than to one. Comparing the pre-COVID-19 sample with the COVID-19 sample, we find that evidence of price clustering is 8% more in the COVID-19 sample. We test the determinants of price clustering and find that as much as 30% of the price clustering behavior can be attributed to the COVID-19 pandemic. Finally, using a simple technical trading strategy, we do not find any evidence that the oil market is profitable in the COVID-19 period.     

Price clustering on the limit-order book: Evidence from the Stock Exchange of Hong Kong

We examine the clustering pattern in trade and quote prices on the electronic limit order book of the Stock Exchange of Hong Kong (SEHK). Earlier research into clustering focuses on transaction prices only. We study clustering on quote prices over a maximum of five queues on the limit order book. We observe an abnormally high frequency of even and integer prices in trade and quote prices for all tick size groups on the SEHK. The deeper quotes display stronger clustering than the best quotes, indicating that the farther away the quotes are from the best queue, the less information they carry. Our analysis further reveals that an extremely fine tick size itself works as a binding constraint to hinder the price resolution process. We also find that short sale prohibition imposed on the majority of stocks listed on the SEHK causes a significant bias in clustering towards the ask side of the limit order book. This implies that a short sale prohibition impairs efficient price discovery in the market.     

Bid ask spread in a competitive market with institutions and order size

Large orders, particularly from institutions, are quite common these days and hence there is interest to know if institutional trading has any bearing on the price effect associated with large trades. Recent empirical studies contradict earlier evidence of negative price effect on selling large blocks and find no price effect associated with large trades. Existing theoretical framework suggests a monotonic and increasing adverse price effect for large trades, where the motivation for a large trade is private information. We model a trading system where pure information, information-liquidity, and pure liquidity traders trade small and large sizes. The pure information traders strategically choose an order size. Institutions trade only large sizes because of their low execution costs for large trades; they are information-liquidity traders whose ability to use an information signal to determine their trades is subject to a binding liquidity constraint. We show that in such a market a separating equilibrium where trade size is informative does not exist and hence there is no price effect for large trades. Trade size may be revealing only if there is a buy sell asymmetry (large buy size is not equal to large sell size) or the corresponding price effect is asymmetric (price effect due to a large buy is not equal to that of a large sell). Further for a pooling equilibrium to exist, where trade size is not informative, the width of the market denoted by the ratio of order size (large size/small size) needs to be small, while the shallowness (inverse depth) of the market denoted by the ratio between pure information and institutional trades and the information signal needs to be stronger (higher). Our results on bid and ask prices and spread confirm recent empirical evidence on price effect of large and institutional trades found in the literature.

Limit Orders, Depth, and Volatility: Evidence from the Stock Exchange of Hong Kong

We investigate the role of limit orders in the liquidity provision in a pure order-driven market. Results show that market depth rises subsequent to an increase in transitory volatility, and transitory volatility declines subsequent to an increase in market depth. We also examine how transitory volatility affects the mix between limit orders and market orders. When transitory volatility arises from the ask (bid) side, investors will submit more limit sell (buy) orders than market sell (buy) orders. This result is consistent with the existence of limit-order traders who enter the market and place orders when liquidity is needed.     

The Real Determinants of Asset Sales

I develop a dynamic structural model in which a firm makes rational decisions to buy or sell assets in the presence of productivity shocks. By identifying equilibrium asset prices, the model also examines the aggregate asset sales activity over the business cycle. It shows that changes in productivity, rather than productivity levels, affect decisions: Firms with rising productivity buy assets and firms with falling productivity downsize (“rising buys falling”). As such, industries in which firms have less persistent and more volatile productivity experience greater asset reallocation. Using plant‐level data from Longitudinal Research Database (LRD), I find strong support for the model's predictions.     

Block Trade Price Asymmetry and Changes in Depth: Evidence from the Australian Stock Exchange

This paper examines the price response to large block transactions on the Australian Stock Exchange during the 1999 sample period. We find asymmetry in the price reaction between buyer‐ and seller‐initiated trades with respect to size and resiliency following the trade. We extend previous research by examining order book changes surrounding block trades and relating price effects to changes in book depth. Purchases are associated with persistent order book imbalance, while the sales imbalance is insignificant. Cross‐sectional analysis demonstrates that price resiliency following a trade is related to the speed at which limit orders arrive to replenish book depth.