Portfolio Theory in Practice (with Python)

79 Pages Posted: 20 Dec 2023 Last revised: 17 May 2024

See all articles by Alexandre Landi

Alexandre Landi

SKEMA Business School; IBM; Balanced Research

Date Written: December 17, 2023


These lecture notes present an in-depth exploration of portfolio management with a focus on practical applications using Python. They begin with the fundamental concepts of measuring investment returns, contrasting arithmetic and logarithmic methods, and highlighting their respective advantages and limitations. Through detailed examples, they demonstrate the accurate assessment of multi-period returns using logarithmic calculations, underscoring the compounded nature of investment growth.

The notes then transition to hands-on exercises in Python, guiding students to calculate and visualize returns using historical data. They emphasize the importance of understanding and visualizing both arithmetic and logarithmic returns to accurately assess portfolio performance.

Next, the discussion shifts to risk measurement, introducing volatility as a key metric. They explain the calculation of standard deviation from historical returns and the process of annualizing volatility for consistent risk comparison across different time frames. Practical Python exercises reinforce these concepts, enabling students to compute and interpret the annualized volatility of financial instruments like the S&P 500 futures.

Additionally, the notes delve into advanced topics such as the limitations of volatility as a sole risk metric. Python-based exercises further solidify these advanced analytical skills, preparing students to handle real-world financial data.

By integrating theoretical foundations with practical coding exercises, these lecture notes equip students with the analytical tools necessary for effective portfolio management and performance evaluation in a professional setting.

Keywords: Risk and Reward Measures, Capital Asset Pricing Model (CAPM), Modern Portfolio Theory (MPT), Portfolio Construction Techniques, Back-Testing Methodologies, Academic and Professional Finance Education

JEL Classification: C58, C61, G11, G17

Suggested Citation

Landi, Alexandre, Portfolio Theory in Practice (with Python) (December 17, 2023). Available at SSRN: https://ssrn.com/abstract=4667204 or http://dx.doi.org/10.2139/ssrn.4667204

Alexandre Landi (Contact Author)

SKEMA Business School ( email )


IBM ( email )

Balanced Research ( email )

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Antibes, Alpes Maritimes 06600

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