Explaining Intraday Pattern of Trading Volume from the Order Flow Data

Based on a comprehensive order flow data from the Taiwan stock market, this study examines directly how the intraday pattern of trading volume is related to the trading behavior of both informed and uninformed traders. The results indicate that both informed and uninformed investors have a strong desire to place orders at the market open and the close. Most of the orders at the market open are conservative and hence are waiting orders for price priority. The findings show that intraday trading volume as well as the real orders from both types of investors are J-shaped. In addition, both information and liquidity trading can explain the intraday pattern of trading volume. However, the impact of liquidity trading on volume is slightly higher than that of information trading.     

Insider Trading in the Hong Kong Stock Market

Many previous studies on insider trading are based ondata in the U.S. capital market and conclude thatinsiders can earn abnormal profits. This paperexamines abnormal price performance associated withinsider trading in the Hong Kong stock market. We findthat abnormal profits associated with insider tradingare all concentrated on small firms. Trading volumedoes matter in determining the magnitude of thoseabnormal profits. Our results show that insiders ofmedium-sized and large firms do not earn abnormalprofits. Finally, it is found that outsiders who mimicthe information of insider trades associated withmedium-sized and large firms cannot earn abnormalprofits.     

Entry, exit and trading profits: A look at the trading strategies of a proprietary trading team

We examine the behavior of a 15 strong proprietary stock trading team and show how consistent intraday trading profits were generated. The team, who worked for a large US direct access trading firm, executed over 96 thousand trades in 3 months in 2000. Profitable intraday trading occurred in an anonymous dealer capacity, on both long and short positions, especially when volume and price volatility were higher. The traders rapidly entered long (short) positions when the number of dealers and size become greater on the bid (offer) side of the spread. Profits were taken early against the trend.     

Intraday patterns in London listed Exchange Traded Funds

In this paper we examine the intraday trading patterns of Exchange Traded Funds (ETFs) listed on the London Stock Exchange. ETFs have been shown to be characterised by much lower bid–ask spread costs and by lower levels of information asymmetry than individual securities. One possible explanation for intraday trading patterns is that concentration of trading arises at the start of the trading day because informed traders have private information that quickly diminishes in value as trading progresses. Since ETFs have lower trading costs and lower levels of information asymmetry we would expect these securities to display less pronounced intraday patterns than individual securities. We fail to find that ETFs are characterised by concentrated trading bouts during the day and therefore find support for the argument that information asymmetry is the cause of intraday volume patterns in stock markets. We find that ETF bid–ask spreads and volatility are elevated at the open but not at the close. This lends support to the “accumulation of information” explanation that sees high spreads and volatility at the open as a consequence of information accumulating during a market closure and impacting on the market when it next opens.     

High-frequency trading and stock liquidity: An intraday analysis

This paper studies the impact of high-frequency trading (HFT) on intraday liquidity of CAC40 stocks listed on Euronext. Spreads display an intraday L-shaped pattern, while quoted depth follows an inverse pattern: low at the open and increasing towards the end of the trading day. When liquidity demand is particularly high, there is a high rate of order cancellations attributable to high-frequency traders who use frequent order cancellations to strategically manage their limit orders and close positions near the market close. Using the generalized method of moments estimator, we generate strong evidence that greater intensity of HFT is associated with lower spreads and higher depth. The positive effect of HFT on liquidity is due mainly to decreased adverse selection costs arising from asymmetric information among market participants.     

Trade Size Preference of Informed Traders in Indian Equity Markets

This study utilized high frequency transactions data to analyze the trade size preference of informed traders in Indian equity markets. It is observed that informed traders at an aggregate level adopt stealth trading strategy, wherein they prefer medium sized trades over large sized trades in order to camouflage their private information. However, the stealth trading behavior varies across stocks, wherein informed traders prefer more large sized trades on firms that are part of an index compared to non-index firms. Trading behavior also varies across other market conditions. It has been noted that informed traders prefer large sized trades during periods of high market thickness, negative returns, and low volatility. This study also provides a rationale for such varied behavior of informed traders.     

The price discovery role of day traders in futures market: Evidence from different types of day traders

Using proprietary account-level transaction data in the futures market where day traders are self-declared ex ante, this study investigates whether day traders enhance price discovery at the market level. From a natural classification of day traders, we find that heterogeneous day traders have differential effects on price discovery. Self-declared day traders, who benefit from low margin requirement, do not improve price discovery measured by information share. In contrast, non-declared traders, who are not self-declared as day traders, improve price discovery. Their positive impacts on price discovery are particularly significant during periods of high volatility and arrival of new information. Overall, a margin stimulating policy may encourage more day trading, but may also attract overconfident investors, especially inexperienced ones, and who do not enhance price discovery.     

Low-latency trading

We define low-latency activity as strategies that respond to market events in the millisecond environment, the hallmark of proprietary trading by high-frequency traders though it could include other algorithmic activity as well. We propose a new measure of low-latency activity to investigate the impact of high-frequency trading on the market environment. Our measure is highly correlated with NASDAQ-constructed estimates of high-frequency trading, but it can be computed from widely-available message data. We use this measure to study how low-latency activity affects market quality both during normal market conditions and during a period of declining prices and heightened economic uncertainty. Our analysis suggests that increased low-latency activity improves traditional market quality measures—decreasing spreads, increasing displayed depth in the limit order book, and lowering short-term volatility. Our findings suggest that given the current market structure for U.S. equities, increased low-latency activity need not work to the detriment of long-term investors.     

Short-run Returns around the Trades of Corporate Insiders on the London Stock Exchange

Previous work examined the long-run profitability of strategies mimicking the trades company directors in the shares of their own company, as a way of testing for market efficiency. The current paper examines patterns in abnormal returns in the days around these trades on the London Stock Exchange.
We find movements in returns that are consistent with directors engaging in short-term market timing. We also report that some types of trades have superior predictive content over future returns. In particular, medium-sized trades are more informative for short-term returns than large ones, consistent with Barclay and Warner's (1993) 'stealth trading' hypothesis whereby informed traders avoid trading in blocks.
Another contribution of this study is to properly adjust the abnormal return estimates for microstructure (spread) transactions costs using daily bid-ask spread data. On a net basis, we find that abnormal returns all but disappear.     

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.     

Clustering of intraday order-sizes by uninformed versus informed traders

We examine quantity choice patterns by equity traders across trading hours in the U.S. Controlling for intraday variations in trading activity, we find that traders submit more non-rounded order sizes and more order sizes overall leading up to a day’s market close. Traders who submit more distinct order sizes pay a higher cost to trade, and they are also less informed about future prices. Our results suggest that the goal to satisfy specific quantity demands rises across the day. This differs from total trading demand, which resembles a U-shape pattern intraday, but is consistent with less trade-size clustering at the ends of fiscal quarters.     

2012年上半年货币市场运行概况与特点

2012年上半年,人民银行通过多种手段调节市场流动性,货币市场反应灵敏,主要特点表现为：交易规模快速增长,利率总体低水平运行,交易期限结构仍以短期为主,交易主体以银行类机构为主,融资格局总体稳定但存在阶段性变化,各类机构交易利差有序分布,非银行类市场主体交易更加活跃。     

High-frequency trading and institutional trading costs

Using data on Canadian bond futures, we examine how high-frequency traders (HFTs) interact with institutions building large positions. In contrast to recent findings, we find HFTs in the data act as small-sized liquidity suppliers, and we reject the hypothesis that they engage in back running, a predatory trading strategy. Using a quasi-experiment in November 2011, in which a number of HFTs started trading the bond future, we run a difference-in-differences event study and find more competition among HFTs improves implementation shortfall, effective spreads, and short-term price impacts for institutional trading in Canadian bond futures.     

Stock market dynamics with rational liquidity traders

In the Kyle (1985) finite horizon model of stock market dynamics with a trader who holds long-lived information, informed trading intensities rise with time, and the slopes of the equilibrium price schedules fall. This paper shows that this result depends crucially on the irrational liquidity trader assumption. We replace the irrational noise traders with a sequence of rational, risk averse, liquidity traders who receive endowment shocks to their holdings of the risky asset. We demonstrate that unless liquidity traders are sufficiently risk averse, the slope of equilibrium price schedule rises over time, while informed trading intensities fall. In particular, Kyle's result holds only when liquidity traders are so risk averse that they ‘over-rebalance’ their portfolio's holdings of the risky asset, so that their final holdings of the risky asset have the opposite sign of their initial position.     

Trading behavior in S&P 500 index futures

This article examines the determinants of trading decisions and the performance of trader types, in the context of the E-Mini S&;P 500 futures and S&;P 500 futures markets. Speculators and small traders tend to follow positive feedback strategies while hedgers dynamically adjust positions in response to market returns. Such strategies apparently reverse during the 2008–09 financial crisis. Investor sentiment and market volatility play an important role in determining the net trading position of traders across the sample period. While all trader types are better at foreseeing market upturns, an out-of-sample test suggests that speculators and small traders have some predictive ability for short-term market returns.     

Using Daily High/Low Time to Test for Intraday Random Walk in Two Index Futures Markets

This paper investigates the time of the daily high/low price in the Hang Seng and S&P 500 index futures and uses it to test for deviation from the predictive behavior of an intraday random walk model. Theoretical distributions of the daily high/low time under the random walk model are derived assuming either uniform or time-varying intraday trading speed. We show that under a random walk model, daily high/low time is more likely to occur near market open/close than in the middle of the trading day. Empirical distributions of the daily high/low time are compared with its theoretical distributions to test for the random walk model. It is found that for the intraday movement of the S&P 500 futures, the random walk hypothesis cannot be rejected. However, it is discovered that in the Hong Kong market, daily high/low time tends to appear significantly more often than is predicted by the random walk model in the first 15-minute time interval when the market opens in the morning or in the afternoon. The results remain valid even after we have taken the time-varying trading speed into account. By comparing the price behavior across markets, we can better understand the microstructure of the futures market.     

The Relationship Among Market-Making Revenue, Payment for Order Flow, and Trading Costs for Market Orders

We study the division of market-making revenue among dealer, broker, and trader. When Knight Securities, a major Nasdaq dealer, interacts with market orders in actively traded stocks during the fourth quarter of 1996, we estimate that its revenue is $0.057 per share. Knight pays brokers at least $0.025 per share (44% of revenue) for orders. To examine whether brokers appear to share these payments with traders, we compare net trading costs (trade price net of commissions) for traders using brokers routing Knight orders with estimated net trading costs for traders using the only discount broker we can determine did not directly receive market-making revenue. We find that the net trading cost of the broker refusing order-flow payments does not dominate the net trading cost of all brokers selling order flow to Knight. This finding suggests that order-flow payments do not unambiguously harm traders and challenges the conclusions of extant studies using only trade prices to assess market quality.     

Intraday trading halts in the Nikkei futures market

The Osaka Securities Exchange (OSE) halts Nikkei 225 index-futures trading when the next transaction is to take place at a price more than ¥30 (prior to February 1994) or ¥60 (from February 1994) away from the previous trading price. This paper examines the efficacy of the intraday price limit rule in terms of price discovery, liquidity and volatility. We also include transaction data from the Singapore International Monetary Exchange (SIMEX) where Nikkei futures are traded simultaneously. The intraday price limit rule generally appears to be ineffective in reducing volatility and avoiding price jumps, at least partly because OSE traders have access to the alternative market at SIMEX.     

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.     

开盘集合竞价透明度与市场质量

通过采用个股与市场同步法和价格反转的分析方法研究开盘集合竞价透明度与市场质量之间的关系,发现从日内效应来看,开盘集合竞价透明度提高以后,交易者的执行成本增加,整体来说市场的流动性是降低了;从事件前后市场模型的拟合优度比较结果来看,开盘集合竞价透明度提高以后股票个股与市场反应不一致,价格发现效率降低,市场质量降低。