This project contains python notebooks

Rolling returns in 5 years, ref. currency and distributions

Rolling_returns_in_5_years_with_distribution_summary.ipynb performs financial data analysis focusing on rolling returns over 5-year periods for various market indices and ETFs. Here is a summary of the key content and steps in the notebook:

1. Setup and Imports

• Installs the yfinance library for downloading financial data.
• Imports required libraries:
  • yfinance for fetching historical market data.
  • pandas for data manipulation and analysis.

2. Data Acquisition

• Downloads historical closing price data for various equity indices and ETFs.
• Tickers included: EWI, EWJ, EWW, ISF.L, MCHI, SMICHA.SW, SPY, VEA, VNM, VT, VWO.
• Data covers a long historical range, from early 1990s to 2025.
• Prices are stored in a DataFrame with dates as the index.
• Allow to use a reference_price for the analysis

3. Data Exploration

• Displays the head (first few rows) of the closing prices DataFrame.
• Data shows NaNs for dates before the instruments had prices and values for later dates.

4. Calculation of Rolling Returns

• Computes 5-year rolling returns for each ticker.
• This involves calculating the percentage change in prices over every rolling 5-year window in the dataset.

5. Distribution Summary of Returns

• Summarizes the 5-year rolling returns by computing statistical percentiles:
  • 2nd percentile
  • 5th percentile
  • Median return
  • 95th percentile
  • 98th percentile
• Also records the first and last available dates for each ticker's data.

6. Output Results

• Displays tables showing:
  • Summary statistics of rolling returns distributions for each ticker.
  • Date ranges covered by the data.
• These statistics help understand the risk and return characteristics of each instrument over 5-year investment horizons.

This notebook analyzes the historical performance of global indices and ETFs by examining their 5-year rolling returns and the distribution of those returns, useful for evaluating long-term investment risk and potential gains.