 Enhancing trading strategies with order book signalsÁlvaro Cartea, Ryan Donnelly and Sebastian Jaimungal Applied Mathematical Finance, 2018, vol. 25, issue 1, 1-35 Abstract: We use high-frequency data from the Nasdaq exchange to build a measure of volume imbalance in the limit order (LO) book. We show that our measure is a good predictor of the sign of the next market order (MO), i.e., buy or sell, and also helps to predict price changes immediately after the arrival of an MO. Based on these empirical findings, we introduce and calibrate a Markov chain-modulated pure jump model of price, spread, LO and MO arrivals and volume imbalance. As an application of the model, we pose and solve a stochastic control problem for an agent who maximizes terminal wealth, subject to inventory penalties, by executing trades using LOs. We use in-sample-data (January to June 2014) to calibrate the model to 11 equities traded in the Nasdaq exchange and use out-of-sample data (July to December 2014) to test the performance of the strategy. We show that introducing our volume imbalance measure into the optimization problem considerably boosts the profits of the strategy. Profits increase because employing our imbalance measure reduces adverse selection costs and positions LOs in the book to take advantage of favourable price movements. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:apmtfi:v:25:y:2018:i:1:p:1-35 Ordering information: This journal article can be ordered from DOI: 10.1080/1350486X.2018.1434009 Access Statistics for this article Applied Mathematical Finance is currently edited by Professor Ben Hambly and Christoph Reisinger More articles in Applied Mathematical Finance from Taylor & Francis Journals |
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