Performance-weighted ensembles of random forests for predicting price impact

DOI10.1080/14697688.2014.983539zbMath1406.91402OpenAlexW2154033164WikidataQ58683091 ScholiaQ58683091MaRDI QIDQ4619486

Ash Booth, Enrico H. Gerding, Frank McGroarty

Publication date: 6 February 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2014.983539

zbMATH Keywords

transaction costsmachine learningtrading strategiesrandom forestsmarket microstructureprice impact

Mathematics Subject Classification ID

Portfolio theory (91G10)

Uses Software

• GeneSrF
• randomForest

Cites Work

• Modified support vector machines in financial time series forecasting
• Understanding the cubic and half-cubic laws of financial fluctuations
• The price impact of order book events: market orders, limit orders and cancellations
• Automated trading with boosting and expert weighting
• Continuous Auctions and Insider Trading
• 10.1162/153244303322753616
• Optimal execution strategies in limit order books with general shape functions
• Optimal trade execution under price-sensitive risk preferences
• Random forests

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