Convolutional Neural Network(CNN/ConvNet) in Stock Price Movement Prediction

TL;DR

This paper explores the application of convolutional neural networks to predict stock price movements by training on historical stock data, aiming to classify future price directions.

Contribution

It introduces a CNN-based approach specifically designed for stock movement prediction, which is a novel application of CNNs in financial forecasting.

Findings

CNN can effectively model stock price data

The model achieves promising prediction accuracy

Potential for real-time stock trading applications

Abstract

With technological advancements and the exponential growth of data, we have been unfolding different capabilities of neural networks in different sectors. In this paper, I have tried to use a specific type of Neural Network known as Convolutional Neural Network(CNN/ConvNet) in the stock market. In other words, I have tried to construct and train a convolutional neural network on past stock prices data and then tried to predict the movement of stock price i.e. whether the stock price would rise or fall, in the coming time.