使用XAI和SAFE人工智能镜头预测BRICS NIFTY50的回报
Indranil Ghosh1, Tamal Datta Chaudhuri2, Golnoosh Babaei3
1IT & Analytics Area, Institute of Management Technology Hyderabad, Hyderabad, Telangana, India.
Frontiers in artificial intelligence
|October 6, 2025
概括
预测全球基金回报是具有挑战性的,因为国家特定的风险. 本研究开发了一个使用机器学习的框架,用于预测金国家NIFTY 50指数的回报,并确定关键国家因素.
科学领域:
- 量化金融 量化金融
- 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- 全球基金经理在国际上多元化投资组合,以提高回报率和管理风险.
- 国际投资使投资者面临特定国家的波动,宏观经济冲击和汇率波动,使回报预测复杂化.
- 高盛的金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金金
研究的目的:
- 开发和介绍一个新的预测框架,用于预测金国家NIFTY 50指数的回报.
- 确定影响全球基金回报的重要国家特定解释变量.
- 帮助全球基金经理和投资者做出明智的投资决策.
主要方法:
- 使用渐变增强回归 (GBR) 和基于SHAP的可解释AI (XAI) 来识别关键的国家特定预测因素.
- 六种机器学习模型 (GBR,CatBoost,光梯度增强机 (LGBM),极端梯度增强 (XGBoost),随机森林 (RF) 和额外树回归器 (ETR)) 用于回报预测.
- 用SAFE人工智能框架和多标准决策 (MCDM) 框架来评估模型性能,预测准确性,可持续性和预测贡献.
主要成果:
- 特定国家的市场波动,工业业绩,金融部门发展和汇率波动被确定为全球回报的重要驱动因素.
- 发现来自印度,中国和巴西的解释性因素对金国家NIFTY 50的回报特别有影响.
- 该研究成功开发了一种具有强大的评估指标的两步预测框架.
结论:
- 开发的预测框架为全球基金经理和投资者提供了实际实用性.
- 调查结果为决策者提供了有关影响外国直接和投资组合投资的因素的见解.
- 该方法强调了国家特定因素在全球投资组合回报预测中的重要性.
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