Co-movements of index options and futures quotes

We report evidence that the co-movements of index options and index futures quotes differ sharply from perfect correlation in periods with option trades. In half-hour intervals with (without) option trades 25% (12%) of call option quote changes have either the opposite sign or are larger in magnitude than the corresponding index futures quote changes. We calibrate a stochastic volatility model that allows for trade and no-trade periods using real data and simulate the joint co-movements of index quotes and option quotes in this model. We show that for trade intervals the observed co-movements differ from the benchmark case established by our simulations approximately three times too often. We provide empirical evidence that market microstructure effects – specifically, stale quotes and aggressive quotes – explain the majority of the deviations from the benchmark. Our findings are relevant for techniques that use estimates of local co-movements as inputs to price or hedge options.     

Inferring Trade Direction from Intraday Data

This paper evaluates alternative methods for classifying individual trades as market buy or market sell orders using intraday trade and quote data. We document two potential problems with quote-based methods of trade classification: quotes may be recorded ahead of trades that triggered them, and trades inside the spread are not readily classifiable. These problems are analyzed in the context of the interaction between exchange floor agents. We then propose and test relatively simple procedures for improving trade classifications.     

A functional approach to the price impact of stock trades and the implied true price

We propose a functional approach to estimate the instantaneous price impact of a trade and to infer an implied true price. Our model expresses price impact as an S-shaped function of signed volume. It has two parameters, one for price impact and one for liquidity depth. The latter measures the differential impact of small and large trades. The price impact is instantaneous, that is, it occurs at the instant of trade execution. Our specification also permits the price impact of buys and sells to be asymmetric. We compute an implied true price from our model, and we find that it is closer than the quote midpoint to the unobservable true price. Our empirical analysis also shows that the effective spread, which is computed using the midpoint, has an upward bias, and that the implicit transaction costs may be lower than previous estimates.     

Trade classification algorithms for electronic communications network trades

Ellis et al. [Ellis, K., Michaely, R., O’Hara, M., 2000. The accuracy of trade classification rules: Evidence from Nasdaq. Journal of Financial and Quantitative Analysis 35 (4), 529–551] find that trade classification rules have limited success in classifying trades which execute inside the quotes. We reconfirm this result and propose an alternative algorithm to improve the classification accuracy for trades inside the quotes. This alternative algorithm improves the overall success rate for classifying trades, especially for trades that occur inside the quotes. Additionally, we show that the Lee and Ready [Lee, C., Ready, M., 1991. Inferring trade direction from intraday data. Journal of Finance 46, 733–747] and Ellis et al. (2000) trade classification algorithms provide biased estimates of the actual effective spreads and price impacts, while our algorithm provides statistically unbiased estimates of actual effective spreads and price impacts.     

Information-based trading,price impact of trades,and trade autocorrelation

In this study we show that both the price impact of trades and serial correlation in trade direction are positively and significantly related to the probability of information-based trading (PIN). The positive relation remains significant even after controlling for the effects of stock attributes. Higher trading activity (i.e., shorter intervals between trades) induces both larger price impact and stronger positive serial correlation in trade direction. The effect of time interval between trades on quote revision is stronger for stocks with higher PIN values. These results provide direct empirical support for the information models of trade and quote revision.     

Trade signing in fast markets

This study assesses the accuracy of trade signing algorithms in fast trading environments using NASDAQ and NYSE trade and quote data. Using data that contain true trade signs, we show that the Lee and Ready algorithm outperforms the tick rule and classifies trades at least as well as in earlier studies from slower trading environments, even in subsamples where the market is particularly fast. We conclude that trade signing remains viable in fast markets, and that the use of quote data continues to increase trade classification accuracy.     

Tick test accuracy in foreign exchange ECN markets

Recent computer quoting activity has increased the allure of the tick test because the quote rule and its variants require matching asynchronous trade and quote records. We find tick test accuracy of 1.2 million forex trades is about 67% which falls to 63% for zerotick trades (half the sample). Accuracy declines as quoted spreads decrease and as time to the previous trade increases. We observe extreme asymmetry for midquote changes, where buyer accuracy is 96% (27%) for up (down) changes, respectively. The quote rule is about 77% accurate. The group tick test is superior to the bulk volume classification method.     

Nasdaq and The Chicago Stock Exchange: An Analysis of Multiple Market Trading

We analyze a set of 97 NASD-listed securities that trade on both the Nasdaq and Chicago Stock Exchange (CHX) to determine if trading costs and price improvement differ between the two markets. We find that order execution costs, which we define by the traded spread and the signed effective half-spread, are significantly lower on the CHX. This difference is consistent over trade types and for trades of at least 1,000 shares. Also, we find that trades occurring on the CHX receive more price improvement than do those occurring on Nasdaq.     

A test of the accuracy of the Lee/Ready trade classification algorithm

We analyze the accuracy of the Lee/Ready (1991) trade classification algorithm and the tick test. Our definition of true trade classification is based on whether the Makler (the equivalent of the specialist on the Frankfurt Stock Exchange) bought or sold shares. The Lee/Ready method classifies 72.8% of the transactions correctly. The simpler tick test performs almost equally well. We document that misclassification of trades may systematically bias the results of empirical microstructure research. Finally, we show that estimation of the bid–ask spread from transaction data results in a reasonably accurate estimate of the relative liquidity of our sample stocks. This is an important finding because quote data for the German stock market is not available on a regular basis.     

Trades and Quotes: A Bivariate Point Process

This article formulates a bivariate point process to jointlyanalyze trade and quote arrivals. In microstructure models,trades may reveal private information that is then incorporatedinto new price quotes. This article examines the speed of thisinformation flow and the circumstances that govern it. A jointlikelihood function for trade and quote arrivals is specifiedin a way that recognizes that an intervening trade sometimescensors the time between a trade and the subsequent quote. Modelsof trades and quotes are estimated for eight stocks using Tradeand Quote database (TAQ) data. The essential finding for thearrival of price quotes is that information flow variables,such as high trade arrival rates, large volume per trade, andwide bid–ask spreads, all predict more rapid price revisions.This means prices respond more quickly to trades when informationis flowing so that the price impacts of trades and ultimatelythe volatility of prices are high in such circumstances.     

Impacts of trades in an error-correction model of quote prices

In this paper we analyze and interpret the quote price dynamics of 100 NYSE stocks stratified by trade frequency. We specify an error-correction model for the log difference of the bid and the ask price with the spread acting as the error-correction term, and include as regressors the characteristics of the trades occurring between quote observations, if any. From this model we are also able to extract the implied model for the spread and the mid-quote. We find that short duration and medium volume trades have the largest impacts on quote prices for all one hundred stocks. Further, we find that buys have a greater impact on the ask price than on the bid price, while sells have a greater impact on the bid price than on the ask price. Both buys and sells increase spreads in the short run, but in the absence of further trades, the spreads mean revert. Trades have a greater impact on quotes for the infrequently traded stocks than for the more actively traded stocks.     

Quote-based competition and trade execution costs in NYSE-listed stocks

This study examines quotations, order routing, and trade execution costs for seven markets that compete for orders in large-capitalization NYSE-listed stocks. The competitiveness of quote updates from each market varies with measures of the profitability of attracting additional order and with volatility and inventory measures. The probability of a trade executing on each market increases when the market posts competitive quotes. Execution costs for non-NYSE trades when the local market posts competitive (non-competitive) quotes are virtually the same (substantially exceed) costs for matched NYSE trades. Collectively, these results imply a significant degree of quote-based competition for order flow and are consistent with off-NYSE liquidity providers using competitive quotations to signal when they are prepared to give better-than-normal trade executions.     

High frequency trading and the new market makers

This paper characterizes the trading strategy of a large high frequency trader (HFT). The HFT incurs a loss on its inventory but earns a profit on the bid–ask spread. Sharpe ratio calculations show that performance is very sensitive to cost of capital assumptions. The HFT employs a cross-market strategy as half of its trades materialize on the incumbent market and the other half on a small, high-growth entrant market. Its trade participation rate in these markets is 8.1% and 64.4%, respectively. In both markets, four out of five of its trades are passive i.e., its price quote was consumed by others.     

Market Integration and Price Execution for NYSE-Listed Securities

For New York Stock Exchange (NYSE) listed securities, the price execution of seemingly comparable orders differs systematically by location. In general, executions at the Cincinnati, Midwest, and New York stock exchanges are most favorable to trade initiators, while executions at the National Association of Security Dealers (NASD) are least favorable. These intermarket price differences depend on trade size, with the smallest trades exhibiting the biggest per share price difference. Collectively, these results raise questions about the adequacy of the existing intermarket quote system (ITS), the broker's fiduciary responsibility for “best execution,” and the propriety of order flow inducements.     

The Behavior of Stock Prices Around Institutional Trades

All trades executed by 37 large investment management firms from July 1986 to December 1988 are used to study the price impact and execution cost of the entire sequence (“package”) of trades that we interpret as an order. We find that market impact and trading cost are related to firm capitalization, relative package size, and, most importantly, to the identity of the management firm behind the trade. Money managers with high demands for immediacy tend to be associated with larger market impact.     

Intraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market

In this study, we use both quote and trade data for the FTSE-100 futures for 2001–2004 in order to examine asymmetric volatility in the context of extreme sells. We define extreme sells as ask quotes that involve large percentages of total depth, selling orders executed at prices much closer to bids than to asking prices, and consecutive sell-initiated trades. Sell trades tend to demand higher liquidity than buys, while extreme trading conditions demand more liquidity than non-extreme ones. In extreme sells, liquidity demand surpasses supply. We show that asymmetric liquidity (quote demand vs. supply) better explains the asymmetric volatility observed in high-frequency data than trade information does. Ask-depth share plays a dominant role in asymmetric volatility, while order flow (sell-initiated volume share) makes a far smaller contribution.     

Measuring the Information Content of Stock Trades

This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE issues suggest: a trade's full price impact arrives only with a protracted lag; the impact is a positive and concave function of the trade size; large trades cause the spread to widen; trades occurring in the face of wide spreads have larger price impacts; and, information asymmetries are more significant for smaller firms.     

When is inter-transaction time informative?

This paper estimates the role of inter-transaction time in price discovery for 100 NYSE-listed firms between 1993 and 2003. We find faster arriving trades move prices more than slower arriving trades across stocks and across time. We further document that the information content of inter-transaction time varies with trading activity, and is weakest for the most actively traded stocks. We then distinguish trades in the same direction as the previous trade from trades in the reverse direction. Our empirical findings document that inter-transaction time is informative for both types of trades, but in opposite directions. Faster arriving trades in the same direction are more informative, whereas faster arriving trades in opposite directions are less informative.     

NYSE execution costs: A case study of a leading bank

Using trade data obtained from a major bank and a measure of indirect execution costs based on the stock price when orders are placed, we investigate indirect costs and their relation to brokerage commissions. For all trades the mean brokerage commission is 6.5 cents per share, and the mean indirect execution cost is about 3.6 cents per share, or 0.1084% of the transactions amount. Contrary to the prediction of the price pressure hypothesis, indirect execution costs are lower for larger size trades. Further, higher indirect execution costs are not associated with lower brokerage commission.     

A note on execution costs for stock index futures: Information versus liquidity effects

This paper examines execution costs and the impact of trade size for stock index futures using price-volume transaction data from the London International Financial Futures and Options Exchange. Consistent with Subrahmanyam [Rev. Financ. Stud. 4 (1991) 17] we find that effective half spreads in the stock index futures market are small compared to stock markets, and that trades in stock index futures have only a small permanent price impact. This result is important as it helps to better understand the success of equity index products such as index futures and Exchange Traded Funds. We also find that there is no asymmetry in the post-trade price reaction between purchases and sales for stock index futures across various trade sizes. This result is consistent with the conjecture in Chan and Lakonishok [J. Financ. Econ. 33 (1993) 173] that the asymmetry surrounding block trades in stock markets is due to the high cost of short selling and the general reluctance of traders to short sell on stock markets.