# Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction

## TL;DR

This paper introduces a deep learning approach combining convolutional neural networks and Neural Arithmetic Logic Units to improve stock price prediction based on historical data.

## Contribution

It proposes integrating convolutional feature extraction with Neural Arithmetic Logic Units for enhanced stock prediction accuracy.

## Key findings

- Improved prediction performance demonstrated over baseline models.
- Effective feature extraction from historical stock data.
- Potential for better financial decision-making tools.

## 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.