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

arXiv: https://arxiv.org/abs/2106.01920 · HTML: https://mlsanity.com/paper/2106.01920/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [Equilibrated adaptive learning rates for non-convex optimization](https://mlsanity.com/paper/1502.04390/) (2015)
- [Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent](https://mlsanity.com/paper/2002.10583/) (2020)
- [Data Dropout: Optimizing Training Data for Convolutional Neural Networks](https://mlsanity.com/paper/1809.00193/) (2018)
- [Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2010.13891/) (2020)
- [Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks](https://mlsanity.com/paper/1810.06773/) (2018)

## Abstract (arXiv)

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.