The accuracy of the tick test: Evidence from the Australian stock exchange

In the absence of information regarding whether a trade is buyer or seller initiated, many researchers have employed the ‘tick’ rule as a proxy. These researchers have been supported in their endeavours by the work of Lee and Ready (1991) which suggests that the tick rule is 90% accurate. Unfortunately, the difficulty of securing data on this issue has made Lee and Ready's paper somewhat unique in that there have been few attempts to confirm their result in US markets and no attempts in other markets. The purpose of this work is to test the robustness of their result in the Australian securities market. Using cleaner intra-day data we mimic the Lee and Ready study to cast some doubt upon the robustness of their findings in different markets. Our results suggest an overall accuracy of approximately 74% as opposed to Lee and Ready's 90%. However, accuracy in excess of 90% is documented when zero ticks are excluded. Further analysis provides evidence that a volatile or trending market will decrease the accuracy of the tick rule. It is also demonstrated that the tick rule is less likely to accurately classify seller initiated trades and small buyer initiated trades.     

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.     

Signing trades and an evaluation of the Lee–Ready algorithm

We use techniques developed to analyze the Supply Curve in liquidity models in order to analyze the accuracy of the Lee and Ready algorithm, both for highly liquid and relatively liquid stocks. Through the use of order book data combined with tick data, we are actually (somewhat tediously) able to tell whether or not a given trade is buyer or seller initiated. For those trades where such knowledge is certain, the accuracy of the Lee and Ready algorithm is not as accurate as has been assumed previously. We can essentially prove that the Lee and Ready algorithm is always at least 55% accurate, and is around 61% accurate for highly liquid stocks (i.e., the top 50 of the S&P 100).     

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.     

Inferring investor behavior: Evidence from TORQ data

We use a unique dataset (TORQ) to calibrate several techniques commonly used to infer investor behavior from transactions data. Specifically, we evaluate the Lee–Ready (1991) Journal of Finance 46, 733–746) algorithm for distinguishing trade direction, and we examine the use of trade size as a proxy for trader identity. We find that, due to complexities in the NYSE auction process, up to 40% of reported trades cannot be unambiguously classified as either buyer- or seller-initiated. However, for those trades that can be classified, the Lee–Ready algorithm is 93% accurate. In addition, we construct a firm-specific trade size proxy that is highly effective in separating the trading activities of individual and institutional investors. These findings should be useful to researchers interested in inferring trader sophistication and buy/sell direction from transactions data.     

On the occurrence and consequences of inaccurate trade classification

The validity of many economic studies hinges on the ability to properly classify trades as buyer or seller-initiated. This study uses the TORQ data to investigate the performance of the Lee and Ready (1991, Journal of Finance 46, 733–746.) trade classification algorithm. I find that the algorithm correctly classifies 85% of the transactions in my sample, but systematically misclassifies transactions at the midpoint of the bid–ask spread, small transactions, and transactions in large or frequently traded stocks. I then provide evidence of the biases induced by inaccurate trade classification.     

Short sales and trade classification algorithms

This paper demonstrates that short sales are often misclassified as buyer-initiated by the Lee–Ready and other commonly used trade classification algorithms. This result is due in part to regulations which require that short sales be executed on an uptick or zero-uptick. In addition, while the literature considers “immediacy premiums” in determining trade direction, it ignores the often larger borrowing premiums that short sellers must pay. Since short sales constitute approximately 30% of all trade volume on U.S. exchanges, these results are important to the empirical market microstructure literature, as well as to measures that rely upon trade classification, such as the probability of informed trading (PIN) metric.     

Short sales,long sales,and the Lee–Ready trade classification algorithm revisited

Asquith et al. (2010) conclude that short sales are often misclassified by the Lee–Ready algorithm. The algorithm identifies most short sales as buyer-initiated, whereas the authors posit that short sales should be overwhelmingly seller-initiated. Using order data to identify true trade initiator, we document that short sales are, in fact, predominantly buyer-initiated and that the Lee–Ready algorithm correctly classifies most of them. Misclassification rates for short and long sales are near zero at the daily level. At the trade level, misclassification rates are 31% using contemporaneous quotes and trades and decline to 21% when quotes are lagged one second.     

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.     

Increasing the Tick: Examining the Impact of the Tick Size Change on Maker‐Taker and Taker‐Maker Market Models

We investigate the effects of an increase in tick size on order and trading flow across market fee models. Using the pilot firms in the U.S. Securities and Exchange Commission's Tick Size Pilot Program, we document that trade and order volume declines on maker‐taker fee models after the tick size implementation. We find that the inverted fee models (taker‐maker) experience an increase in both trade and order volume. Additionally, we find that a tick size adjustment has a substantial influence on market participation in maker‐taker fee models. We also find that measures of both hidden and algorithmic trading decline with an increasing tick size, which is strongly moderated by the differences in the maker‐taker and taker‐maker fee models.     

The impact of trading halts on liquidity and price volatility: Evidence from the Australian Stock Exchange

This study examines market behaviour around trading halts associated with information releases on the Australian Stock Exchange, which operates an open electronic limit order book. Using the Lee, Ready and Seguin (1994) pseudo-halt methodology, we find trading halts increase both volume and price volatility. Trading halts also increase bid-ask spreads and reduce market depth at the best-quotes in the immediate post-halt period. The results of this study imply that trading halts impair rather than improve market quality in markets that operate open electronic limit order books.     

NYSE order flow,spreads, and information

This paper uses unique NYSE audit trail data to evaluate spreads and information content for different order types. Actual spreads are positive for liquidity-demanding orders and negative for liquidity-supplying orders after controlling for order direction. However, because a large fraction of liquidity-demanding orders get price improvement, the actual spread for liquidity-demanding orders is up to 50 percent less than the Lee and Ready (1991) algorithm would suggest. Regression results show that the order composition of trades affects traditional measures of spreads and information. They also show that NYSE non-displayed liquidity reduces trading costs facing market orders, and that liquidity-demanding floor broker orders are the most informative order type.     

A comparison of some structural models of private information arrival

We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.     

The impact of tick size on intraday stock price behavior: evidence from the Taiwan Stock Exchange

This research examines the impact of tick size on intraday stock price behavior for stocks listed on the Taiwan Stock Exchange over the 2-year period of 1998–1999. The sample involves the same 80 firms that trade under the tick size of (New Taiwan Dollars) NT$0.1 and NT$0.5, respectively. The sample firms display a U-shaped intraday pattern of bid–ask spread, volatility, autocorrelation, and trading volume. The empirical results indicate that a larger tick size is associated with a wider bid–ask spread, larger volatility, and more negative autocorrelation. Moreover, a larger tick size is associated with a higher percentage increase of bid–ask spread and volatility in the middle of the trading period. Finally, the effect of tick size on trading volume is insignificant.     

Impact of MiFID II tick-size regime on equity markets—Evidence from the LSE

This study investigates the impact of MiFID II on the London Stock Exchange. We find that a tick-size reduction leads to lower bid–ask spreads, lower trade values, reduced cost of trading at and beyond the best bid-offer, an acceleration of quote updates, an increase in aggressive trades and a reduction in price impact. Increased tick size widens spreads and increases trading costs. Step functions reveal that liquidity adjusts opposite to the tick change. To determine if impacts are proportional, we identify potential functions that predict cost changes with tick updates, implying that traders adjust their trade sizes according to the new tick levels.     

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.     

The Behavior of Option Price Around Large Block Transactions in the Underlying Security

This paper investigates the behavior of stock and option prices around block trades in stocks. The results indicate that for both up tick and downtick block trades the stock prices adjust within a fifteen minute period after the block trade. Moreover, for uptick blocks there is no evidence of any stock price reaction before the block trade. However, the adjustment of stock price for downtick blocks begins about fifteen minutes before the block trade. We also find that option price behavior differs considerably from stock price behavior. Specifically, our results suggest that options exhibit abnormal price behavior starting thirty minutes before the block and ending one hour after the block. The pattern is more pronounced for downtick blocks and for put options. We interpret this abnormal price behavior of options before the block trade as consistent with intermarket frontrunning.     

The effect of stock splits on clientele: Is tick size relevant?

We explore whether the relation between stock splits and clientele is driven by binding tick sizes. We find little evidence that firms adjusted prices to maintain similarly binding tick sizes as the NYSE reduced tick sizes. Furthermore, though splits that increase the extent to which tick sizes are binding are associated with greater increases in spreads, these splits experience similar changes in measures related to clientele, including trade size, breadth of individual and institutional ownership, and analyst following. We find little evidence supporting theories, such as spread-induced sponsorship, that rely on binding tick sizes to link splits and clientele.     

Does the Probability of Informed Trading Model Fit Empirical Data?

The probability of informed trading (PIN) is used widely as a measure of information asymmetry. Relatively little work has appeared on how well PIN models fit empirical trade data. We reveal structural limitations in PIN models by examining their marginal distributions and dependence structures represented by copulas. We develop a distribution-free test of the goodness-of-fit of PIN models. Our results indicate that estimated PIN models have generally poor fit to actual trade data. These results suggest that researchers should be cautious when PIN estimates are plugged into empirical models as explanatory variables.     

One size fits all? High frequency trading,tick size changes and the implications for exchanges: market quality and market structure considerations

This paper offers a systematic review of the empirical literature on the implications of tick size changes for exchanges. Our focus is twofold: first, we are concerned with the market quality implications of a change in the minimum tick size. Second, we are interested in the implications of changes in the minimum tick size on market structure. We show that there is a large body of empirical literature that documents a decrease in transaction costs following a decrease in the minimum tick size. However, even though market liquidity increases, the incentive to provide market making activities decreases. We document a strong link between the minimum tick size regulations and the recent increase in high frequency trading activity. A smaller tick enhances the price discovery process. However, the question of how multiple tick size regimes affect market liquidity in a fragmented market remains to be answered. Finally, we identify topics for future research; we discuss the empirical literature on the minimum trade unit and the recent calls for a minimum resting time for quotes.