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Order anticipation around predictable trades
Author(s) -
Sağlam Mehmet
Publication year - 2018
Publication title -
financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 68
eISSN - 1755-053X
pISSN - 0046-3892
DOI - 10.1111/fima.12255
Subject(s) - anticipation (artificial intelligence) , predictability , order (exchange) , construct (python library) , commission , econometrics , empirical evidence , trading strategy , computer science , economics , financial economics , finance , mathematics , statistics , artificial intelligence , philosophy , epistemology , programming language
Abstract I study the presence of order anticipation strategies by examining predictable patterns in large order trades. I construct three simple signals based on child‐order execution patterns and find empirical evidence that stronger signals are correlated with higher execution costs. I use the SEC's (Securities and Exchange Commission's) ban on unfiltered access and increase in noise trading as shocks to order anticipatory activities of algorithmic traders and find that the price impact of predictability is smaller when order anticipation becomes difficult. The empirical findings are mostly consistent with the back‐running theory that predicts delayed price impact as strategic traders learn about large orders gradually.