Predicting Chinese Gold Prices: A Comparative Study of LSTM and Random Forest Models

DOI

10.2991/978-94-6463-748-9_24How to use a DOI?

Keywords

Chinese gold price prediction; LSTM; Random Forest

Abstract

The rapid recovery of the domestic economy in China after the pandemic is now making the gold market more popular. Therefore, the technical methods to predict the gold price are required for the current market situation. This paper presents a comparative analysis of Long Short-Term Memory (LSTM) and Random Forest (RF) models for forecasting gold prices in China from 2013 to 2023. The study aims to evaluate the predictive accuracy of these two machine learning models and determine which one is more suitable for forecasting gold prices. The LSTM model, with its ability to capture temporal dependencies, and the RF model, known for its robustness in handling non-linear relationships, are both trained and tested on the same dataset. The results of this study can provide valuable insights for investors and policymakers in the financial sector. The conclusion indicates that the LSTM model shows better performance on the prediction, which means this model is more likely to be widely used in practical operations. The findings are expected to shed light on the most effective machine learning approaches for predicting gold prices, thereby enhancing the predictive power and reliability of financial models in the context of Chinese gold markets.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sizhe Chen
PY  - 2025
DA  - 2025/07/03
TI  - Predicting Chinese Gold Prices: A Comparative Study of LSTM and Random Forest Models
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 202
EP  - 208
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_24
DO  - 10.2991/978-94-6463-748-9_24
ID  - Chen2025
ER  -