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A model based LSTM and graph convolutional network for stock trend prediction.

Xiangdong Ran1, Zhiguang Shan2, Yukang Fan3

  • 1Beijing Information Technology College, Beijing, China.

Peerj. Computer Science
|December 9, 2024
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Summary
This summary is machine-generated.

This study introduces a novel model using Long short-term memory (LSTM) and graph convolutional networks to predict stock market trends by analyzing interdependencies. The model demonstrates improved prediction accuracy and profitable trading strategies for investors.

Keywords:
Graph convolutional networkLong short-term memoryStock trading decisionsStock trend prediction

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Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Financial Econometrics

Background:

  • Stock market dynamics are complex, influenced by interdependencies between individual stocks.
  • Accurate stock trend prediction is crucial for stable investment profits.
  • Identifying and modeling these hidden dependencies from data is a significant challenge.

Purpose of the Study:

  • To develop an advanced model for stock trend prediction by effectively capturing interdependencies.
  • To enhance the accuracy of stock price trend forecasting.
  • To provide investors with tools for optimal timing and pricing in stock trading.

Main Methods:

  • Utilized Long short-term memory (LSTM) networks to extract features from stock data.
  • Constructed graph nodes from LSTM hidden state outputs.
  • Employed Pearson correlation coefficient to establish graph structures.
  • Applied graph convolutional networks (GCN) for feature extraction and prediction.

Main Results:

  • The proposed LSTM-GCN model significantly outperformed baseline methods in stock trend prediction accuracy.
  • Trading backtests using the model yielded favorable returns in both rising and falling markets.
  • Identified effective trading strategies derived from the model's insights.

Conclusions:

  • The integrated LSTM-GCN model effectively captures complex stock interdependencies for improved trend prediction.
  • The model offers a valuable tool for investors seeking to optimize trading decisions and enhance profitability.
  • Demonstrated practical applicability and profitability in real-world stock market scenarios (China A50).