Research on Influencing Factors and Prediction of Gold Prices in China: A Perspective Based on Macroeconomics and Machine Learning

Xiaoquan Dai

doi:10.47297/wspdecWSP2515-797325.20250906

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Research on Influencing Factors and Prediction of Gold Prices in China: A Perspective Based on Macroeconomics and Machine Learning

Volume 9, Issue 6

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Research on Influencing Factors and Prediction of Gold Prices in China: A Perspective Based on Macroeconomics and Machine Learning
Research on Influencing Factors and Prediction of Gold Prices in China: A Perspective Based on Macroeconomics and Machine Learning
Yunnan University of Finance and Economics, Financial Engineering, Kunming, Yunnan650221; China
Vol. 9, Issue 6, Pages: 130-134(2025)
Published：30 June 2025
DOI：10.47297/wspdecWSP2515-797325.20250906
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Dai Xiaoquan.Research on Influencing Factors and Prediction of Gold Prices in China: A Perspective Based on Macroeconomics and Machine Learning[J].Development Economics of China,2025,09(06):130-134.
DOI：

Dai Xiaoquan.Research on Influencing Factors and Prediction of Gold Prices in China: A Perspective Based on Macroeconomics and Machine Learning[J].Development Economics of China,2025,09(06):130-134. DOI： 10.47297/wspdecWSP2515-797325.20250906.

摘要

Abstract

The recent significant rise in gold prices in China has attracted widespread attention. Gold serves not only as an ornament but also plays a crucial role in monetary reserves

value storage

and international settlements. The surge in gold prices is closely linked to market supply and demand

global economic conditions

geopolitical factors

and financial market volatility

making it a complex and multidimensional economic phenomenon. Accurate gold price forecasting holds substantial reference value not only for investors and financial institutions but also for national economic policymaking and risk management. Historically

gold price prediction research has primarily relied on traditional time-series models such as ARIMA and GARCH

which perform well in capturing linear relationships and short-term fluctuations but exhibit limitations in handling nonlinear and high-noise financial data. This paper systematically reviews the current state of research on gold price movements in China

with a focus on influencing factors

volatility characteristics

forecasting methods

and market efficiency. The study finds that gold prices are affected by a combination of macroeconomic factors (e.g.

economic growth

inflation

interest rates

exchange rates)

financial market factors (e.g.

stock

bond

and foreign exchange market fluctuations)

and geopolitical factors. Gold price volatility exhibits long memory

asymmetry

and seasonal/cyclical patterns. In terms of forecasting methods

traditional time-series analysis and econometric models face limitations. This paper aims to explore the factors influencing gold price movements in the Chinese market using an LSTM model. Research on the efficiency of China's gold market reveals that price sequences demonstrate random walk characteristics

though futures and spot markets exhibit cointegration relationships. Market efficiency remains constrained

necessitating improvements in regulatory and trading mechanisms. Future research should further refine the study of gold price determinants

enhance volatility analysis

explore novel forecasting approaches

and strengthen interdisciplinary collaboration to advance the development of China's gold market.

关键词

Keywords

references

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