基于文本情感分析和LightGBM-LSTM模型的黄金期货价格预测研究
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作者单位:

兰州财经大学统计与数据科学学院

中图分类号:

TP391

基金项目:

国家自然科学基金(72061020);2022年甘肃省陇原青年创新创业人才项目;兰州财经大学金融统计科研融合团队(XKKYRHTD202304)


Research on Gold Futures Price Forecasting Based on Text Sentiment Analysis and LightGBM-LSTM Model
Author:
Affiliation:

Lanzhou University of Finance and Economics

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    摘要:

    在金融市场中,黄金期货价格受到多种因素的影响,对其进行准确的预测具有重要的意义。本文融合多源数据提出一种结合LightGBM(Light Gradient Boosting Machine)特征选择方法和LSTM模型的黄金期货价格预测模型。首先,将获取的宏观经济指标和技术指标进行预处理,对非结构化新闻标题数据采用不同方法进行情感倾向标注,进而构建加权情感指数,并将多个关键词的百度搜索指数合并为百度综合搜索指数。其次,分别利用LightGBM方法对宏观经济指标和技术指标进行特征重要性排序,提取关键特征。最后,将筛选后的特征与加权情感指数以及百度综合搜索指数共同作为LSTM预测模型的输入变量。实证结果表明,融合多源数据的LightGBM-LSTM模型预测表现优异,模型预测误差最小,与基准模型相比,能够对黄金期货收盘价作出更准确的预测。

    Abstract:

    In the financial market, gold futures prices are influenced by a variety of factors, and accurate prediction of these prices holds significant importance. To address this issue, a new model for predicting gold futures prices has been proposed that integrates multiple data sources, combining a LightGBM(Light Gradient Boosting Machine)feature selection method with an LSTM model. Firstly, the paper preprocesses the acquired macroeconomic indicators and technical indicators. It then annotates the sentiment tendencies of unstructured news headlines using various methods, leading to the construction of a weighted sentiment index. Additionally, it merges the search indices of multiple keywords from Baidu into a comprehensive Baidu search index. Secondly, the LightGBM method is used to rank the importance of features from macroeconomic and technical indicators, extracting key features. Finally, the selected features, along with the weighted sentiment index and the comprehensive Baidu search index, serve as input variables for the LSTM forecasting model. Empirical results show that the LightGBM-LSTM model with multi-source data has excellent prediction performance and the model prediction error is the smallest. Compared with the benchmark model, it can make a more accurate prediction of the closing price of gold futures.

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引用本文

孙景云,魏琛.基于文本情感分析和LightGBM-LSTM模型的黄金期货价格预测研究[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-11-02
  • 最后修改日期:2025-01-08
  • 录用日期:2025-01-10

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