Trade signing in fast markets

Abstract

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.

The Accuracy of the Tick Rule in the Bitcoin Market

“…According to previous research, the classification success rate of the tick rule ranges from 72.2% (Theissen, 2001) to 92.15% (Aktas & Kryzanowski, 2014) on the U.S. and non-U.S. stock markets. Of the research cited in this work, the study by Carrion and Kolay (2020) presents the similar fast trading environment by using high-frequency NASDAQ data stamped to seconds. And the accuracy of the tick rule assessed in this study is close to the corresponding values of individual stocks in Carrion and Kolay (2020), namely from 69.75% to 83.34%.…”

“…Of the research cited in this work, the study by Carrion and Kolay (2020) presents the similar fast trading environment by using high-frequency NASDAQ data stamped to seconds. And the accuracy of the tick rule assessed in this study is close to the corresponding values of individual stocks in Carrion and Kolay (2020), namely from 69.75% to 83.34%.…”

“…Tests by Chakrabarty et al (2007) on NASDAQ stocks traded on INET and ArcaEx revealed that the overall success rate of the tick rule was 75.4% during the sample period. A recent examination by Carrion and Kolay (2020) showed that the classification success rate of the tick rule was 78.62% in a sample of data stamped to seconds from NASDAQ HFT database over a subset of dates during the period of 2008 to 2010 when trades were more frequent than before. And the classification success rate ranged from 69.75% to 83.34% across individual stocks.…”

“…This study also contributes to the existing literature on trade classification algorithms. Except for Aktas and Kryzanowski (2014) and Carrion and Kolay (2020), the classification accuracy of the tick rule in the above-mentioned articles is examined in slower trading environments, namely when high-frequency trading is not widely applied. Using tick data stamped to seconds from the Bitcoin market, this article also confirms the findings of Carrion and Kolay (2020), namely that the classification accuracy of the tick rule is similar to those in slower trading environments.…”