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• W2891296141 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)." @default.
• W2891296141 created "2018-09-27" @default.
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• W2891296141 date "2018-01-01" @default.
• W2891296141 modified "2023-09-24" @default.
• W2891296141 title "Price Prediction with CNN and Limit Order Book Data" @default.
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• W2891296141 doi "https://doi.org/10.1007/978-3-030-00350-0_11" @default.
• W2891296141 hasPublicationYear "2018" @default.
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