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2018 | OriginalPaper | Buchkapitel

Price Prediction with CNN and Limit Order Book Data

verfasst von : Jaime Niño, Andrés Arévalo, Diego Leon, German Hernandez, Javier Sandoval

Erschienen in: Applied Computer Sciences in Engineering

Verlag: Springer International Publishing

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Abstract

This work introduces how to use Limit Order Book Data (LOB) and transaction data for short-term forecasting of stock prices. LOB registers all trade intentions from market participants, as a result, it contains more market information that could enhance predictions. We will be using Deep Convolutional Neural Networks (CNN), which are good at pattern recognition on images. In order to accomplish the proposed task we will make an image-like representation of LOB and transaction data, which will feed up into the CNN, therefore it can recognize hidden patterns to classify Financial Time Series (FTS) in short-term periods. Data enclose information from 11 NYSE instruments, including stocks, ETF and ADR. We will present step by step methodology for encoding financial time series into an image-like representation. Results present an impressive performance, 74.15% in Directional Accuracy (DA).

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Vorheriges Kapitel Methods for Generating Contexts Based on Similarity Relations to Multigranulation

Nächstes Kapitel Feature Group Selection Using MKL Penalized with -norm and SVM as Base Learner

Some considerations should be done, particularly related to the dimensionality of the FTS.

Bivariate if volumes are included.

For full details please refer to [18].

We used other DL topologies.

Data provided by DataDrivenMarket Corporation.

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Titel: Price Prediction with CNN and Limit Order Book Data
verfasst von: Jaime Niño
Andrés Arévalo
Diego Leon
German Hernandez
Javier Sandoval
Verlag: Springer International Publishing
Buch: Applied Computer Sciences in Engineering
Print ISBN: 978-3-030-00349-4

Electronic ISBN: 978-3-030-00350-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00350-0_11

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