Short-Term Stock Fund Forecasting & Risk Analysis

This project analyzes over 50 years of global stock index data to identify the most promising short-term investment opportunities.
It combines historical performance analysis, time series forecasting, and risk assessment to support data-driven fund selection.

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📂 Dataset

• Source: Historical stock index data since 1965
• Indexes: 13 major global indexes
• Columns:
  • Index – Ticker symbol for indexes
  • Date – Date of observation
  • Open – Opening price
  • High – Highest price during the day
  • Low – Lowest price during the day
  • Close – Closing price
  • Adj Close – Adjusted close price
  • Volume – Shares traded
  • CloseUSD – Close price in USD

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🎯 Project Objectives

1. Identify the index with the highest average annual return.
2. Visualize 30-day moving averages for trend analysis.
3. Compare volatility across indexes.
4. Forecast short-term prices using Facebook Prophet.
5. Evaluate risk with volatility measures & Sharpe ratios.
6. Test forecast stability using ±10% price shock stress tests.

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🛠 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• Facebook Prophet
• Statsmodels
• Scikit-learn

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📊 Key Insights

• Some high-return indexes show high volatility and are unstable under small market shocks.
• A decision matrix was built to integrate:
  • Return
  • Risk
  • Forecast stability
• High stability indexes may provide more reliable short-term returns than the highest-return options.

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📈 Sample Visuals

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🚀 How to Run

1. Clone this repository:

   git clone https://github.com/yourusername/index-fund-forecasting.git
   

Future Work

• Expand dataset with more indexes.

• Integrate macroeconomic indicators into forecasting.

• Automate dashboard updates using Streamlit.

Author: Uday Meka Contact: Email – satyaudaymeka@gmail.com