Author(s)

Mingming Qu ¹, Yurong Shi ²

Affiliation(s)

¹ Yantai Aizhi Intelligent Technology Co., Yantai, Shandong, China
² Dalian Maritime University, Dalian, Liaoning, China

Corresponding Author

Mingming Qu

ABSTRACT

To enhance the prediction accuracy of gold futures price trends and address the challenge of low prediction accuracy amidst numerous features and noise, a novel research method combining random forest with feature engineering is introduced. Manual feature engineering methods, including Pearson coefficients, Mean Decrease in Impurity, and Mean Decrease Accuracy, are employed for feature selection. Subsequently, automatic feature engineering techniques are utilized to generate new features, which are then integrated with the Pearson coefficients. Finally, the selected features are used for modeling and regression prediction through Random Forest, from which the final conclusions are drawn. Experimental results indicate that the Random Forest based on automatic feature engineering surpasses the original Random Forest and other Random Forest models in predictive evaluation metrics.

KEYWORDS

Feature Engineering, Random Forest, Futures, Gold, Forecasting

CITE THIS PAPER

Mingming Qu, Yurong Shi, Feature Engineering-Based Random Forest Model for Predicting Chinese Gold Futures Prices. Financial Engineering and Risk Management (2024) Vol. 7: 178-187. DOI: http://dx.doi.org/10.23977/ferm.2024.070224.

REFERENCES

[1] Zhengxu Yan, Chao Qin, Gang Song. Random forest model stock price prediction based on Pearson feature selection [J]. Computer Engineering and Applications, 2021, 57(15):286-296.
[2] Yu Ai. Research on the prediction of CSI 300 index trend based on random forest optimization [D]. Shandong University, 2020.
[3] Leo Breiman. Randomforests [J].Machine Learning, 2001, 45(1).
[4] Nana Lin, Jiangtao QIN. Research on predicting the rise and fall of A-share stocks based on random forests[J]. Journal of Shanghai University of Technology, 2018, 40(3):267-273.

Sponsors, Associates, and Links

---

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics