Introduction to Portfolio Analysis in Python
Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
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Course Description
Have you ever had wondered whether an investment fund is actually a good investment? Or compared two investment options and asked what the difference between the two is? What does the risk indicator of these funds even mean? Or do you frequently work with financial data in your daily job and you want to get an edge? In this course, you’re going to get familiar with the exciting world of investing, by learning about portfolios, risk and return, and how to critically analyze them. By working on actual historical stock data, you’ll learn how to calculate meaningful measures of risk, how to break-down performance, and how to calculate an optimal portfolio for the desired risk and return trade-off. After this course, you’ll be able to make data-driven decisions when it comes to investing and have a better understanding of investment portfolios.
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Introduction to Portfolio Analysis
FreeIn the first chapter, you’ll learn how a portfolio is build up out of individual assets and corresponding weights. The chapter also covers how to calculate the main characteristics of a portfolio: returns and risk.
Welcome to Portfolio Analysis!50 xpWhy invest in portfolios50 xpThe effect of diversification100 xpPortfolio returns50 xpPortfolio losses and gaining it back50 xpCalculate mean returns100 xpPortfolio cumulative returns100 xpMeasuring risk of a portfolio50 xpPortfolio variance100 xpStandard deviation versus variance100 xp - 2
Risk and Return
Chapter 2 goes deeper into how to measure returns and risk accurately. The two most important measures of return, annualized returns, and risk-adjusted returns, are covered in the first part of the chapter. In the second part, you’ll learn how to look at risk from different perspectives. This part focuses on skewness and kurtosis of a distribution, as well as downside risk.
Annualized returns50 xpAnnualizing portfolio returns100 xpComparing annualized rates of return100 xpRisk adjusted returns50 xpInterpreting the Sharpe ratio50 xpS&P500 Sharpe ratio100 xpPortfolio Sharpe ratio100 xpNon-normal distribution of returns50 xpSkewness of the S&P500100 xpCalculating skewness and kurtosis100 xpComparing distributions of stock returns100 xpAlternative measures of risk50 xpSortino ratio100 xpMaximum draw-down portfolio100 xp - 3
Performance Attribution
In chapter 3, you’ll learn about investment factors and how they play a role in driving risk and return. You’ll learn about the Fama French factor model, and use that to break down portfolio returns into explainable, common factors. This chapter also covers how to use Pyfolio, a public portfolio analysis tool.
Comparing against a benchmark50 xpActive return100 xpIndustry attribution100 xpRisk factors50 xpSize factor50 xpMomentum factor100 xpValue factor100 xpFactor models50 xpFama French factor correlations100 xpLinear regression model100 xpFama French Factor model100 xpPortfolio analysis tools50 xpPerformance tear sheet100 xpIndustry exposures with Pyfolio100 xp - 4
Portfolio Optimization
In this last chapter, you learn how to create optimal portfolio weights, using Markowitz’ portfolio optimization framework. You’ll learn how to find the optimal weights for the desired level of risk or return. Lastly, you’ll learn alternative ways to calculate expected risk and return, using the most recent data only.
Modern portfolio theory50 xpUnderstanding the efficient frontier50 xpCalculating expected risk and returns100 xpPyPortfolioOpt risk functions100 xpOptimal portfolio performance100 xpMaximum Sharpe vs. minimum volatility50 xpPortfolio optimization: Max Sharpe100 xpMinimum volatility optimization100 xpComparing max Sharpe to min vol100 xpAlternative portfolio optimization50 xpExponentially weighted returns and risk100 xpComparing approaches100 xpChanging the span100 xpRecap50 xp
In the following tracks
Finance Fundamentals in PythonCollaborators
Charlotte Werger
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Director of Advanced Analytics at Nike
Dr. Charlotte Werger currently works at Nike as a Director of Advanced Analytics. Charlotte is a data scientist with a background in econometrics and finance. She loves applying Machine Learning to a broad variety of problems, ranging from image recognition to fraud detection, to customer recommender systems. Charlotte has previously worked in finance as Head of Data Science at Van Lanschot Kempen, and as a quantitative researcher and portfolio manager for BlackRock and Man AHL. In those roles, she specialized in using data science to predict movements in stock markets. As the former Head of Education at Faculty, she loves teaching data science on- and off-line. Charlotte is also active as a Data Science mentor for the Springboard program. Charlotte holds a P.h.D from the European University Institute.
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