Waarom beleggen in portefeuilles

Als belegger kun je kiezen om in één aandeel te beleggen of in een portefeuille van aandelen. Wat zijn geldige redenen om voor een portefeuille van aandelen te kiezen?

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Introductie tot portefeuille-analyse in Python

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Deze oefening maakt deel uit van de cursus

Introductie tot portefeuille-analyse in Python

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In 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.

Exercise 1: Welkom bij portefeuilleanalyse!Exercise 2: Waarom beleggen in portefeuilles

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Exercise 3: Het effect van diversificatie Exercise 4: Portefeuillerendement Exercise 5: Portefeuilleverliezen en het weer goedmaken Exercise 6: Gemiddelde rendementen berekenen Exercise 7: Cumulatieve portefeuillerendementen Exercise 8: Risico van een portefeuille meten Exercise 9: Portefeuillevariantie Exercise 10: Standaarddeviatie versus variantie

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.

Exercise 1: Annualized returns Exercise 2: Annualizing portfolio returns Exercise 3: Comparing annualized rates of return Exercise 4: Risk adjusted returns Exercise 5: Interpreting the Sharpe ratio Exercise 6: S&P500 Sharpe ratio Exercise 7: Portfolio Sharpe ratio Exercise 8: Non-normal distribution of returns Exercise 9: Skewness of the S&P500 Exercise 10: Calculating skewness and kurtosis Exercise 11: Comparing distributions of stock returns Exercise 12: Alternative measures of risk Exercise 13: Sortino ratio Exercise 14: Maximum draw-down portfolio

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.

Exercise 1: Comparing against a benchmark Exercise 2: Active return Exercise 3: Industry attribution Exercise 4: Risk factors Exercise 5: Size factor Exercise 6: Momentum factor Exercise 7: Value factor Exercise 8: Factor models Exercise 9: Fama French factor correlations Exercise 10: Linear regression model Exercise 11: Fama French Factor model Exercise 12: Portfolio analysis tools Exercise 13: Performance tear sheet Exercise 14: Industry exposures with Pyfolio

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.

Exercise 1: Modern portfolio theory Exercise 2: Understanding the efficient frontier Exercise 3: Calculating expected risk and returns Exercise 4: PyPortfolioOpt risk functions Exercise 5: Optimal portfolio performance Exercise 6: Maximum Sharpe vs. minimum volatility Exercise 7: Portfolio optimization: Max Sharpe Exercise 8: Minimum volatility optimization Exercise 9: Comparing max Sharpe to min vol Exercise 10: Alternative portfolio optimization Exercise 11: Exponentially weighted returns and risk Exercise 12: Comparing approaches Exercise 13: Changing the span Exercise 14: Recap