Gold Price Forecasting: A Novel Approach Based on Text Mining and Big-Data-Driven Model

被引：0

作者：

Kian Poor, Saeed ^{[1
]}

Fattahi, Shahram ^{[2
]}

Hajian, Mohsen ^{[1
]}

机构：

[1] Payame Noor Univ, Fac Econ, Tehran, Iran

[2] Razi Univ, Fac Econ, Kermanshah, Iran

来源：

STUDIES IN BUSINESS AND ECONOMICS | 2024年 / 19卷 / 03期

关键词：

gold price Prediction; Trends; Online News; Data Mining; CNN; CONVOLUTIONAL NEURAL-NETWORKS; VOLATILITY; PREDICTION; INTEGRATION;

D O I：

10.2478/sbe-2024-0049

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This work uses Google Trends and online text mining to test a novel approach to gold price prediction. To explain the gold price prognosis, the convolutional neural network (CNN) also uses linguistic criteria for news items about gold. The findings of this study can be used to explain the theoretical underpinnings of information processing worldwide. According to experimental findings, big data forecasting and online data mining are more efficient than other methods. As a result, utilizing 19926 in sum news headlines acquired, the synergistic connection between news items and Google Trends is successful in accurately projecting the price of gold.

引用

页码：156 / 171

页数：16

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