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

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 $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.     

Limit order books and trade informativeness

In the microstructure literature, information asymmetry is an important determinant of market liquidity. The classic setting is that uninformed dedicated liquidity suppliers charge price concessions when incoming market orders are likely to be informationally motivated. In limit order book (LOB) markets, however, this relationship is less clear, as market participants can switch roles, and freely choose to immediately demand or patiently supply liquidity by submitting either market or limit orders. We study the importance of information asymmetry in LOBs based on a recent sample of 30 German Deutscher Aktienindex (DAX) stocks. We find that Hasbrouck's (1991) measure of trade informativeness Granger causes book liquidity, in particular that required to fill large market orders. Picking-off risk due to public news-induced volatility is more important for top-of-the book liquidity supply. In our multivariate analysis, we control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity.     

Tick Size,Share Prices,and Stock Splits

Minimum price variation rules help explain why stock prices vary substantially across countries, and other curiosities of share prices. Companies tend to split their stock so that the institutionally mandated minimum tick size is optimal relative to the stock price. A large relative tick size provides an incentive for dealers to make markets and for investors to provide liquidity by placing limit orders, despite its placing a high floor on the quoted bid-ask spread. A simple model suggests that idiosyncratic risk, firm size, and visibility of the firm affect the optimal relative tick size and thus the share price.     

On the origin of power-law tails in price fluctuations

We analyse large stock price changes of more than five standard deviations for (i) TAQ data for the year 1997 and (ii) order book data from the Island ECN for the year 2002. We argue that a large trading volume alone is not a sufficient explanation for large price changes. Instead, we find that a low density of limit orders in the order book, i.e. a small liquidity, is a necessary prerequisite for the occurrence of extreme price fluctuations. Taking into account both order flow and liquidity, large stock price fluctuations can be explained quantitatively.     

Presidential Address: Liquidity and Price Discovery

This paper examines the implications of market microstructure for asset pricing. I argue that asset pricing ignores the central fact that asset prices evolve in markets. Markets provide liquidity and price discovery, and I argue that asset pricing models need to be recast in broader terms to incorporate the transactions costs of liquidity and the risks of price discovery. I argue that symmetric information‐based asset pricing models do not work because they assume that the underlying problems of liquidity and price discovery have been solved. I develop an asymmetric information asset pricing model that incorporates these effects.     

Trading and Liquidity on the Tokyo Stock Exchange: A Bird's Eye View

The trading mechanism for equities on the Tokyo Stock Exchange (TSE) stands in sharp contrast to the primary mechanisms used to trade stocks in the United States. In the United States, exchange-designated specialists have affirmative obligations to provide continuous liquidity to the market. Specialists offer simultaneous and tight quotes to both buy and sell and supply sufficient liquidity to limit the magnitude of price changes between consecutive transactions. In contradistinction, the TSE has no exchange-designated liquidity suppliers. Instead, liquidity is provided through a public limit order book, and liquidity is organized through restrictions on maximum price changes between trades that serve to slow down trading. In this article, we examine the efficacy of the TSE's trading mechanisms at providing liquidity. Our analysis is based on a complete record of transactions and best-bid and best-offer quotes for most stocks in the First Section of the TSE over a period of 26 months. We study the size of the bid-ask spread and its cross-sectional and intertemporal stability; intertemporal patterns in returns, volatility, volume, trade size, and the frequency of trades; and market depth based on the response of quotes to trades and the frequency of trading halts and warning quotes.     

Undisclosed orders and optimal submission strategies in a limit order market

Reserve orders enable traders to hide a portion of their orders and now appear in most electronic limit order markets. This paper outlines a theory to determine an optimal submission strategy in a limit order book, in which traders choose among limit, market, and reserve orders and simultaneously set price, quantity, and exposure. We show that reserve orders help traders compete for the provision of liquidity and reduce the friction generated by exposure costs. Therefore, total gains from trade increase. Large traders always benefit from reserve orders, whereas small traders benefit only when the tick size is large.     

Correlated Trading and Returns

A German broker's clients place similar speculative trades and therefore tend to be on the same side of the market in a given stock during a given day, week, month, and quarter. Aggregate liquidity effects, short sale constraints, the systematic execution of limit orders (coordinated through price movements) or the correlated trading of other investors who pick off retail limit orders do not fully explain why retail investors trade similarly. Correlated market orders lead returns, presumably due to persistent speculative price pressure. Correlated limit orders also predict subsequent returns, consistent with executed limit orders being compensated for accommodating liquidity demands.     

Upstairs Market for Principal and Agency Trades: Analysis of Adverse Information and Price Effects

This paper directly tests the hypothesis that upstairs intermediation lowers adverse selection cost. We find upstairs market makers effectively screen out information-motivated orders and execute large liquidity-motivated orders at a lower cost than the downstairs market. Upstairs markets do not cannibalize or free ride off the downstairs market. In one-quarter of the trades, the upstairs market offers price improvement over the limit orders available in the consolidated limit order book. Trades are more likely to be executed upstairs at times when liquidity is lower in the downstairs market.     

Do aggressive orders affect liquidity? An evidence from an emerging market

This paper investigates how aggressive orders affect spreads and trading activity measures on the stock market. Based on a sample of stocks listed on the Warsaw Stock Exchange this study finds that spreads and trading activity measures increase significantly when aggressive orders are executed, but quickly revert to initial levels. The reaction to these orders on the bid and ask side of the market is similar. The effect of aggressive orders differ depending on the size of the firms. Trading activity measures such as volumes or number of transactions increase stronger for bigger than for smaller stocks, while spreads increase more for smaller firms than for bigger ones. These findings enrich the understanding of liquidity dynamics especially on the emerging markets where liquidity is an important price formation factor.     

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

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 order's 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.     

Trader Anonymity, Price Formation and Liquidity

Using data from the Frankfurt Stock Exchange we analyze priceformation and liquidity in a non-anonymous environment withsimilarities to the floor of the NYSE. Our main hypothesis isthat the non-anonymity allows the specialist to assess the probabilitythat a trader trades on the basis of private information. Heuses this knowledge to price discriminate. This can be achievedby quoting a large spread and granting price improvement totraders deemed uninformed. Consistent with our hypothesis wefind that price improvement reflects lower adverse selectioncosts but does not lead to a reduction in the specialist's profit.Further, the quote adjustment following transactions at thequoted bid or ask price is more pronounced than the quote adjustmentafter transactions at prices inside the spread. Our resultsindicate that anonymity comes at the cost of higher adverseselection risk. JEL Classification: G10.     

Anonymity,liquidity and fragmentation

We examine the effects of the removal of broker identifiers from the central limit order book of the Australian Stock Exchange. We find that spreads and order aggressiveness decline, and order book depth increases, with the introduction of anonymous trading. This is consistent with the hypothesis that limit order traders are more willing to expose their orders when they can do so anonymously. Anonymous markets attract order flow from non-anonymous substitute markets, but this effect is only seen in large stocks. Our results suggest that exchanges operating in fragmented markets should consider anonymous trading to improve price competition and liquidity, although some of these benefits may be significant only if the stocks are sufficiently large and liquid.     

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.     

Does option trading convey stock price information?

After executing option orders, options market makers turn to the stock market to hedge away the underlying stock exposure. As a result, the stock exposure imbalance in option transactions translates into an imbalance in stock transactions. This paper decomposes the total stock order imbalance into an imbalance induced by option transactions and an imbalance independent of options. The analysis shows that the option-induced imbalance significantly predicts future stock returns in the cross section controlling for the past stock and options returns, but the imbalance independent of options has only a transitory price impact. Further investigation suggests that options order flow contains important information about the underlying stock value.     

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.     

Order Flow and Liquidity around NYSE Trading Halts

We study order flow and liquidity around NYSE trading halts. We find that market and limit order submissions and cancellations increase significantly during trading halts, that a large proportion of the limit order book at the reopen is composed of orders submitted during the halt, and that the market-clearing price at the reopen is a good predictor of future prices. Depth near the quotes is unusually low around trading halts, though specialists and/or floor traders appear to provide additional liquidity at these times. Finally, specialists appear to 'spread the quote' prior to imbalance halts to convey information to market participants.     

Two months in the life of several gilt-edged market makers on the London Stock Exchange

We investigate the micro structure of the UK gilt market studying the behaviour of several gilt-edged market makers on the London Stock Exchange. Through a structural model of the price process we can test different microstructural hypotheses, concerning information asymmetries, transaction and inventory carrying costs, and market liquidity. Our results suggest that inventories do not alter the price process in the gilt market. Moreover, in contrast to customer orders, inter-dealer transactions possess an information content. Transaction costs in the inter-dealer market are also substantially smaller than those for external customers.     

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.     

Asset fire sales (and purchases) in equity markets

This paper examines institutional price pressure in equity markets by studying mutual fund transactions caused by capital flows from 1980 to 2004. Funds experiencing large outflows tend to decrease existing positions, which creates price pressure in the securities held in common by distressed funds. Similarly, the tendency among funds experiencing large inflows to expand existing positions creates positive price pressure in overlapping holdings. Investors who trade against constrained mutual funds earn significant returns for providing liquidity. In addition, future flow-driven transactions are predictable, creating an incentive to front-run the anticipated forced trades by funds experiencing extreme capital flows.