A binary ensemble classifier for high-frequency trading | IEEE Conference Publication | IEEE Xplore

A binary ensemble classifier for high-frequency trading


Abstract:

The aim of this study was to model and use machine learning techniques to maximize the chance of a market maker be executed successfully in a stock market, that is, when ...Show More

Abstract:

The aim of this study was to model and use machine learning techniques to maximize the chance of a market maker be executed successfully in a stock market, that is, when their bid and ask orders are filled at the desired prices. In this context, a binary ensemble classifier was created to decide whether, at a specific time, is or not propitious to start a new market making process. Conducting the study over a large volume of data for high-frequency traders, we showed that the new proposed ensemble classifier was able to improve the efficiency of the isolated models and the precision of the models are better than random decision makers.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

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