How Geopolitical Conflicts Impact India's Economy: A Quantitative Analysis of Middle East Situation, Oil Prices, Exchange Rates, and Stock Market

This project uses data science methods (ARIMA forecasting, machine learning, Streamlit visualization) to quantitatively analyze the cascading impacts of Middle East geopolitical conflicts (primarily the Israel-Iran-U.S. game) on India's oil prices, Rupee exchange rate, and NIFTY50 index. It builds an end-to-end analysis pipeline to reveal the "butterfly effect" of geopolitical events on distant economies in the context of globalization, providing decision-making references for investors and policymakers.

India is the world's third-largest oil consumer, but domestic production meets only about 15% of its demand, making it highly dependent on crude oil imports from the Middle East. The transmission mechanisms include: 1. Oil prices: Conflict → supply concerns → rising international oil prices → increased import costs for India → domestic inflation → consumption contraction; 2. Exchange rate: Rising oil prices → expanding trade deficit → reduced foreign exchange reserves → Rupee depreciation → further increase in import costs (vicious cycle); 3. Stock market: Oil price/exchange rate fluctuations → changes in corporate profit expectations → investor sentiment → NIFTY50 volatility.

Data Collection: Integrate geopolitical events (timeline, severity), oil prices (Brent/WTI), exchange rates (INR/USD), NIFTY50, and India's macroeconomic indicators (inflation, interest rates, trade data). Feature Engineering: Conflict intensity index, price change rate (daily return/volatility), lag features (time-lag effect), interaction features (nonlinear relationships). Analysis Methods: Correlation analysis, causal inference (controlling confounding variables), ARIMA time series forecasting (oil price/exchange rate trends), machine learning (Random Forest/XGBoost, etc., to predict stock market reactions).

1. Major conflict events (military strikes, escalation of sanctions) trigger a short-term jump in oil prices, which quickly transmit to India; 2. Dual role of Rupee exchange rate: Absorbs part of the impact (depreciation offsets rising oil prices), but excessive depreciation leads to capital outflows and inflation; 3. NIFTY50 constituent stocks show differentiation: Oil upstream enterprises benefit while downstream ones suffer; export enterprises benefit from depreciation, import-dependent ones face double hits; consumer goods enterprises are affected by inflation; 4. ARIMA limitations: Difficult to predict "black swan" geopolitical events, more suitable for describing the adjustment process after shocks.

The project uses Streamlit to build an interactive dashboard, including: Time series visualization (oil price/exchange rate/stock market linkage with annotations of major conflict events), correlation heatmap (variable correlation intensity), ARIMA forecast comparison (predicted trajectory vs. actual trend), scenario simulation (adjust conflict intensity to observe indicator impacts). This dashboard allows non-technical users to understand complex analysis results.

Limitations: Subjective conflict intensity rating, observational data makes it hard to establish strict causality, difficulty in predicting black swan events. Improvements: Introduce intraday high-frequency data, use NLP to analyze news/social media sentiment to build real-time conflict indicators, oil trade network analysis (supply disruption cascade effect), policy simulation (buffer effects of strategic reserve release, exchange rate intervention, etc.).

This project is an excellent end-to-end example: 1. Problem-oriented (real economic issues); 2. Multi-source data integration (collect/clean/fuse data from different sources); 3. Method combination (select statistical analysis, time series, machine learning as needed); 4. Deliverables (interactive dashboard improves accessibility).