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Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

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  • Shangeth Rajaa
  • Jajati Keshari Sahoo

Abstract

Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indicators, sentiment analysis etc to predict the future stock price. In recent years, many researches have extensively used machine learning for predicting the stock behaviour. In this paper we propose data driven deep learning approach to predict the future stock value with the previous price with the feature extraction property of convolutional neural network and to use Neural Arithmetic Logic Units with it.

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  • Shangeth Rajaa & Jajati Keshari Sahoo, 2019. "Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction," Papers 1905.07581, arXiv.org.
    • Handle: RePEc:arx:papers:1905.07581
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    File URL: http://arxiv.org/pdf/1905.07581
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    Cited by:

    1. Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.

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