The “make or take” decision in an electronic market: Evidence on the evolution of liquidity

This paper uses experimental asset markets to investigate the evolution of liquidity in an electronic limit order market. Our market setting includes salient features of electronic limit order markets, as well as informed traders and liquidity traders. We focus on the strategies of the traders and how these are affected by trader type, characteristics of the market, and characteristics of the asset. We find that informed traders use more limit orders than do liquidity traders. Our main result is that liquidity provision shifts as trading progresses, with informed traders increasingly providing liquidity in markets. The change in the behavior of the informed traders seems to be in response to the dynamic adjustment of prices to information; they take (provide) liquidity when the value of their information is high (low). Thus, a market-making role emerges endogenously in our electronic markets and is ultimately adopted by the traders who are least subject to adverse selection when placing limit orders.     

Technology and liquidity provision: The blurring of traditional definitions

Limit orders are usually viewed as patiently supplying liquidity. We investigate the trading of one hundred Nasdaq-listed stocks on INET, a limit order book. In contrast to the usual view, we find that over one-third of nonmarketable limit orders are cancelled within two seconds. We investigate the role these “fleeting orders” play in the market and test specific hypotheses about their uses. We find evidence consistent with dynamic trading strategies whereby traders chase market prices or search for latent liquidity. We show that fleeting orders are a relatively recent phenomenon, and suggest that they have arisen from a combination of factors that includes improved technology, an active trading culture, market fragmentation, and an increasing utilization of latent liquidity.     

Do Japanese candlesticks help solve the trader’s dilemma?

In this paper, we investigate whether Japanese candlesticks can help traders to find the best trade-off between market timing and market impact costs. Based on fixed-effect panel regressions on a sample of 81 European stocks, we show that implicit transaction costs are better characterized by using specific Japanese candlesticks patterns. Although market timing costs are not lower when Hammer-like and Doji configurations occur, market impact costs are significantly lower when and after a Doji structure occurs. We further check the potential gains through order submission simulations and find that submission strategies based on the occurrence of Doji result in significantly lower market impact cost than random submission strategies. These findings are of great interest for investors who look for occasional liquidity pools to execute their orders inexpensively such as institutional traders or hedgers.     

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.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.     

Trader Leverage and Liquidity

Does trader leverage drive equity market liquidity? We use the unique features of the margin trading system in India to identify a causal relationship between traders’ ability to borrow and a stock's market liquidity. To quantify the impact of trader leverage, we employ a regression discontinuity design that exploits threshold rules that determine a stock's margin trading eligibility. We find that liquidity is higher when stocks become eligible for margin trading and that this liquidity enhancement is driven by margin traders’ contrarian strategies. Consistent with downward liquidity spirals due to deleveraging, we also find that this effect reverses during crises.     

Dancing in the dark: post-trade anonymity,liquidity and informed trading

We analyze the impact of post-trade anonymity on liquidity and informed trading in an order driven stock market. The German stock market introduced the Central Counterparty (CCP) in March 2003 for German equities traded on its anonymous electronic trading platform Xetra leading to a major change in its existing transparency regime. Before the introduction trader IDs were revealed to the counterparties of a trade, with the introduction of the CCP even after the transaction the traders remain anonymous. Previous theoretical and empirical research documents that pre-trade anonymity results in increased liquidity, while results on post-trade anonymity are mixed. We find a significant increase in liquidity measured through a reduction of 25% in implicit transaction costs. We also document that the arrival rate of informed traders is reduced in the anonymous setting. Following recent findings of Bloomfield et al. (J Finan Econ 75:165–199, 2005) that informed traders take on the role of liquidity providers we interpret our findings as indication that informed traders change their behavior in providing liquidity more aggressively in an anonymous environment.     

Liquidity determination in an order-driven market

We exploit full order level information from an electronic FX broking system to provide a comprehensive account of the determination of its liquidity. We not only look at bid-ask spreads and trading volumes, but also study the determination of order entry rates and depth measures derived from the entire limit order book. We find strong predictability in the arrival of liquidity supply/demand events. Further, in times of low (high) liquidity, liquidity supply (demand) events are more common. In times of high trading activity and volatility, the ratio of limit to market order arrivals is high but order book spreads and depth deteriorate. These results are consistent with market order traders having better information than limit order traders.     

Glued to the TV: Distracted Noise Traders and Stock Market Liquidity

In this paper, we study the impact of noise traders’ limited attention on financial markets. Specifically, we exploit episodes of sensational news (exogenous to the market) that distract noise traders. We find that on “distraction days,” trading activity, liquidity, and volatility decrease, and prices reverse less among stocks owned predominantly by noise traders. These outcomes contrast sharply with those due to the inattention of informed speculators and market makers, and are consistent with noise traders mitigating adverse selection risk. We discuss the evolution of these outcomes over time and the role of technological changes.     

DOES SPEED MATTER? THE ROLE OF HIGH-FREQUENCY TRADING FOR ORDER BOOK RESILIENCY

We analyze limit order book resiliency following liquidity shocks initiated by large market orders. Based on a unique data set, we investigate whether high-frequency traders are involved in replenishing the order book. Therefore, we relate the net liquidity provision of high-frequency traders, algorithmic traders, and human traders around these market impact events to order book resiliency. Although all groups of traders react, our results show that only high-frequency traders reduce the spread within the first seconds after the market impact event. Order book depth replenishment, however, takes significantly longer and is mainly accomplished by human traders’ liquidity provision.     

Working Orders in Limit Order Markets and Floor Exchanges

We analyze limit order markets and floor exchanges, assuming an informed trader and discretionary liquidity traders use market orders and can either submit block orders or work their demands as a series of small orders. By working their demands, large market order traders pool with small traders. We show that every equilibrium on a floor exchange must involve at least partial pooling. Moreover, there is always a fully pooling (worked order) equilibrium on a floor exchange that is equivalent to a block order equilibrium in a limit order market.     

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.     

Short-selling,margin-trading,and stock liquidity: Evidence from the Chinese stock markets

This paper examines the impacts of two forms of leveraged trading—margin trading and short selling—on the trading liquidity of individual stocks in China. We find that trading liquidity for relevant stocks generally improves after restrictions on leveraged trading are removed. However, margin trading and short selling have opposite impacts on liquidity. During ordinary periods, margin trading benefits liquidity, whereas short selling damages liquidity; however, during market downturns, their roles are reversed. We also provide evidence suggesting that short sellers are informed traders in China and that short selling reduces stock liquidity because of the increased risk of adverse selection faced by uninformed traders.     

Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market

We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high‐frequency data that identify computer‐generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high‐frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.     

Information acquisition and market liquidity: Evidence from EDGAR search activity

We find that information acquisition is associated with an improvement in market liquidity. An empirical analysis of search activity using EDGAR as a proxy for information acquisition shows a theoretical gap in market microstructure models. We also find that EDGAR search is associated with nonsynchronicity, more efficient pricing, and negatively associated with the PIN measure. These results demonstrate that the main finding can be explained through improved incorporation of information into prices and use of sophisticated trading strategies by informed traders.     

Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.     

Liquidity in the cryptocurrency market and commonalities across anomalies

We examine how liquidity affects cryptocurrency market efficiency and study commonalities in anomaly performance in cryptocurrency markets. Based on the unique features of cryptocurrencies, we build a model with anonymous traders valuing cryptocurrencies as payments for goods and investment assets, and find that decreases in funding liquidity translate into lower asset liquidity in the cryptocurrency market. Empirically, we observe that many widely recognized stock market anomalies also exist in the cryptocurrency market, although some have opposite long and short legs. We also find evidence that a decrease in cryptocurrency liquidity enhances anomalous returns while preventing the cryptocurrency market from achieving efficiency.     

Market transparency: who wins and who loses?

This study uses laboratory experiments to determine the effectsof trade and quote disclosure on market efficiency, bid-askspreads, and trader welfare. We show that trade disclosure increasesthe informational efficiency of transaction prices, but alsoincreases opening bid-ask spreads, apparently by reducing market-makers'incentives to compete for order flow. As a result, trade disclosurebenefits market makers at the expense of liquidity traders andinformed traders. We find that quote disclosure has no discernibleeffects on market performance. Overall our results demonstratethat the degree of market transparency has important effectsof market equilibria and on trader and market-maker welfare.     

Transparency and Liquidity: A Comparison of Auction and Dealer Markets with Informed Trading

Trading systems differ in their degree of transparency, here defined as the extent to which market makers can observe the size and direction of the current order flow. We investigate whether greater transparency enhances market liquidity by reducing the opportunities for taking advantage of uninformed participants. We compare the price formation process in several stylized trading systems with different degrees of transparency: various types of auction markets and a stylized dealer market. We find that greater transparency generates lower trading costs for uninformed traders on average, although not necessarily for every size of trade.     

国外高频交易的发展现状及启示

证券市场上的高频交易模式大体上分为四类:订单拆分策略、做市交易策略、定量化交易策略和其他策略。研究发现:(1)高频交易降低了买卖价差,提高了市场流动性,而并没有增加市场波动率,甚至反而可能降低了市场波动率;(2)没有发现高频交易者存在系统性抢单行为(并不排除有特定高频交易者存在此类行为);(3)学术研究认为高频交易有导致市场风险的可能性,但是事件调查大多认为高频交易不是引发市场风险的罪魁祸首。本文认为,对高频交易的监管应该注重抓住重点区别对待,以维护公平、透明、高效的市场秩序。