Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction
This work addresses stock prediction for finance experts and investors, but appears incremental as it builds on existing machine learning methods in this domain.
The paper tackles stock price prediction by proposing a deep learning approach that combines convolutional neural networks for feature extraction with Neural Arithmetic Logic Units, achieving unspecified results.
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.