Scholarlink

Foreign exchange reserves play a pivotal role in stabilizing a nation’s economy, influencing exchange rates, trade balance, and macroeconomic policy. This study develops a data-driven predictive model using a Random Forest Regressor to forecast India’s foreign exchange reserves based on ten years of key economic indicators—namely Foreign Direct Investment (FDI), Foreign Portfolio Investment (FPI), Imports, Exports, Trade Deficit, and Net Remittances. The model achieves a high predictive performance with an R2 value of approximately 0.85, indicating robust explanatory power. The findings highlight the relative significance of trade deficit, FDI inflows, and remittances in determining reserve levels, offering insights for monetary and fiscal policymakers.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Forecasting India’s Foreign Exchange Reserves Using Machine Learning: A Random Forest Approach

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Nilesh Dashore,
  • Vandit Hedau,
  • Rachana Dashore

摘要

Foreign exchange reserves play a pivotal role in stabilizing a nation’s economy, influencing exchange rates, trade balance, and macroeconomic policy. This study develops a data-driven predictive model using a Random Forest Regressor to forecast India’s foreign exchange reserves based on ten years of key economic indicators—namely Foreign Direct Investment (FDI), Foreign Portfolio Investment (FPI), Imports, Exports, Trade Deficit, and Net Remittances. The model achieves a high predictive performance with an R2 value of approximately 0.85, indicating robust explanatory power. The findings highlight the relative significance of trade deficit, FDI inflows, and remittances in determining reserve levels, offering insights for monetary and fiscal policymakers.