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

Sizhe Chen ()
Additional contact information
Sizhe Chen: Sydney Institute of Language and Commerce, Shanghai University, Department of Finance

A chapter in Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025), 2025, pp 202-208 from Springer

Abstract: 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.

Keywords: Chinese gold price prediction; LSTM; Random Forest (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-748-9_24

Ordering information: This item can be ordered from
http://www.springer.com/9789464637489

DOI: 10.2991/978-94-6463-748-9_24

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().