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This thesis explores the application of the ARIMA model and its enhancements for stock price prediction, utilizing techniques like Grid Search and Ensemble Learning to improve accuracy by approximately 15% and 19%, respectively. It introduces a new performance evaluation indicator, MSES, and discusses the historical context of quantitative analysis in stock trading. The research focuses on IT equipment stocks from the Shenzhen Stock Exchange between 2010-2019, providing insights into market behavior followi

Author

lsymqelzb336699

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EN