<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>There are numerous stock market theories as a result of the gradual usage of mathematical models by researchers to forecast equities during the past few decades. By quantifying the rise and fall range, the prediction problem can be changed into a multi-classification problem based on the related data. This paper describes an Adversarial Learning-based stock forecast model by building a three-tier LSTM training network using the Adversarial Learning concept, selecting 300 stocks to represent the overall performance of the Chinese stock market, increasing the proportion of large to small training datasets, and strengthening the model’s ability to obtain detailed information from a small amount of data.</p>
</abstract>

There are numerous stock market theories as a result of the gradual usage of mathematical models by researchers to forecast equities during the past few decades. By quantifying the rise and fall range, the prediction problem can be changed into a multi-classification problem based on the related data. This paper describes an Adversarial Learning-based stock forecast model by building a three-tier LSTM training network using the Adversarial Learning concept, selecting 300 stocks to represent the overall performance of the Chinese stock market, increasing the proportion of large to small training datasets, and strengthening the model’s ability to obtain detailed information from a small amount of data.

China’s Stock Market Trend Prediction Model based on Adversarial Learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"China\u2019s Stock Market Trend Prediction Model based on Adversarial Learning"}

There are numerous stock market theories as a result of the gradual usage of mathematical models by researchers to forecast equities during the past few...

China’s Stock Market Trend Prediction Model based on Adversarial Learning

Online veröffentlicht: 25. Sept. 2023

Seitenbereich: 3289 - 3304

Eingereicht: 28. Juli 2023

Akzeptiert: 24. Aug. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01130

Schlüsselwörter
Stock Market, Adversarial Learning, LSTM, Prediction

© 2023 Dan Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

China’s Stock Market Trend Prediction Model based on Adversarial Learning

Online veröffentlicht: 25. Sept. 2023

Seitenbereich: 3289 - 3304

Eingereicht: 28. Juli 2023

Akzeptiert: 24. Aug. 2023

DOI: https://doi.org/10.2478/amns.2023.2.01130

SchlüsselwörterStock Market, Adversarial Learning, LSTM, Prediction

© 2023 Dan Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Stock Market, Adversarial Learning, LSTM, Prediction