Mini Project 1 employs Python and optimization techniques to build two distinct investment portfolios – one designed for higher risk tolerance and another prioritizing lower risk, both based on historical stock data.

Mini Project 2 tests the common assumption in mathematical finance that stock and index log returns follow a normal distribution. Our analysis uses historical data, and the investigation explores several methods including identifying periods of normal behavior, evaluating the impact of removing extreme values, constructing portfolios with more normally distributed returns, analyzing the normality within constructed portfolios, and performing individual stock analysis.

For more information (including descriptions of Mini Projects 3 and 4, see the GitHub page.