# Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market

arXiv: https://arxiv.org/abs/1903.12258 · HTML: https://mlsanity.com/paper/1903.12258/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 8 of 8)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images](https://mlsanity.com/paper/2410.19291/) (2024)
- [Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs](https://mlsanity.com/paper/2106.03035/) (2021)
- [Stock Trend Prediction: A Semantic Segmentation Approach](https://mlsanity.com/paper/2303.09323/) (2023)
- [Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis](https://mlsanity.com/paper/2304.14870/) (2023)
- [Investigating Market Strength Prediction with CNNs on Candlestick Chart Images](https://mlsanity.com/paper/2501.12239/) (2025)
- [Do VLMs Truly "Read" Candlesticks? A Multi-Scale Benchmark for Visual Stock Price Forecasting](https://mlsanity.com/paper/2604.12659/) (2026)
- [Visual Chart Representations for Cryptocurrency Regime Prediction: A Systematic Deep Learning Study](https://mlsanity.com/paper/2605.00875/) (2026)

## Abstract (arXiv)

Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolutional Network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a Convolutional Neural Network model. This Convolutional Neural Network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of stock market. The effectiveness of our method is evaluated in stock market prediction with a promising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stock market dataset respectively. The constructed model have been implemented as a web-based system freely available at http://140.138.155.216/deepcandle/ for predicting stock market using candlestick chart and deep learning neural networks.