CNN with Limit Order Book Data for Stock Price Prediction
| Autor: | Andrés Arévalo, Javier Sandoval, Germán Hernández, Diego León, Jaime Niño |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Deep learning 020207 software engineering 02 engineering and technology computer.software_genre Convolutional neural network LTI system theory Task (computing) Order (exchange) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence Representation (mathematics) business computer Transaction data |
| Zdroj: | Proceedings of the Future Technologies Conference (FTC) 2018 ISBN: 9783030026851 |
| DOI: | 10.1007/978-3-030-02686-8_34 |
| Popis: | This work presents a remarkable and innovative short-term forecasting method for Financial Time Series (FTS). Most of the approaches for FTS modeling work directly with prices, given the fact that transaction data is more reachable and more widely available. For this particular work, we will be using the Limit Order Book (LOB) data, which registers all trade intentions from market participants. As a result, there is more enriched data to make better 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 FTS in short-term periods. We will present step by step methodology to encode financial time series into an image-like representation. Results present an impressive performance, ranging between 63% and 66% in Directional Accuracy (DA), having advantages in reducing model parameters as well as to make inputs time invariant. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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