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Updated: Sep 21, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Stock Selection Model of Image Classification Method Based on Convolutional Neural Network.

Pengfei Li¹, Jungang Xu¹, Keyao Li¹

¹University of Chinese Academy of Sciences, Beijing, China.

Computational Intelligence and Neuroscience

|May 31, 2022

Summary

This summary is machine-generated.

Convolutional neural networks (CNNs) improve stock selection by analyzing multifactor data, outperforming traditional machine learning models. This AI approach enhances trading efficiency and accuracy in dynamic markets.

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Area of Science:

Quantitative Finance
Artificial Intelligence
Machine Learning

Background:

Quantitative trading relies heavily on stock selection strategies.
Existing methods struggle with the increasing scale and complexity of stock data.
Deep learning offers potential for more robust and efficient trading models.

Purpose of the Study:

To apply convolutional neural networks (CNNs) for stock selection using a multifactor data set.
To evaluate the performance of CNNs against traditional machine learning models in stock selection.
To address the limitations of current stock selection techniques in handling large-scale, high-dimensional data.

Main Methods:

Construction of a multifactor stock selection dataset from China's stock market.
Development of a "factor picture" representation for stock data.
Application of convolutional neural networks (CNNs) for stock classification and selection.
Comparative analysis with decision trees, support vector machines, and feedforward neural networks.

Main Results:

The CNN-based stock selection method demonstrated superior performance.
Outperformed other models in key financial metrics: annual return, Sharpe ratio, and maximum drawdown.
Validated the effectiveness of the "factor picture" approach for stock analysis.

Conclusions:

Convolutional neural networks offer a significant advancement in quantitative stock selection.
The proposed method effectively handles complex multifactor stock data.
AI-driven approaches, particularly CNNs, are crucial for future quantitative trading strategies.