Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms

Hangwei Xu

doi:10.47297/wspceWSP2516-251904.20240802

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Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms

8卷第2期

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Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms
Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms
Zhejiang Wanli University, Ningbo, Zhejiang, 315100, P.R.China
2024年8卷第2期页码：36-42
纸质出版日期： 2024-08-29 ，
DOI：10.47297/wspceWSP2516-251904.20240802
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(2024). Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms. 创意经济（英文版）, 8(2), 36-42.

Xu Hangwei.Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms[J].Creative Economy,2024,08(02):36-42.
(2024). Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms. 创意经济（英文版）, 8(2), 36-42. DOI： 10.47297/wspceWSP2516-251904.20240802.

Xu Hangwei.Financial Market Trend Prediction Based on Big Data and Deep Learning Algorithms[J].Creative Economy,2024,08(02):36-42. DOI： 10.47297/wspceWSP2516-251904.20240802.

摘要

Abstract

Driven by deep learning technology

the financial market is becoming more and more advanced. Therefore

this study aims to introduce a financial market forecasting algorithm based on deep learning technology to solve the difficulty and unpredictability of dealing with financial data in the financial market. To this end

this paper first obtains a variety of data sources related to the financial market

such as stock prices

trading volume

market index and so on

and data cleaning and feature engineering are carried out. Then

the time series problem is solved by constructing a deep learning model based on GRU. In the model training stage

efficient model storage strategy and extensible distributed model training are adopted

and the model optimization stage begins with cross-validation strategy and early stop method. The analysis part of this paper further verifies the correctness of the model in the analysis of historical data

in which the minimum Mean Squared Error (MSE) is 0.06 in the best case and the maximum determination coefficient is 0.98 in the worst case

which shows that the model has good accuracy and reliability. The part of model sensitivity analysis (parameter sensitivity) also proves that the proposed model has strong stability and robustness at different learning rates. The conclusion part emphasizes once again that the prediction model based on big data and deep learning in this study is effective for experimental data

and provides traders

investors and analysts with a reliable source of further insights.

关键词

Keywords

Financial market trend forecastingBig dataGRU modelInvestment strategy

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