De Sharpe-ratio interpreteren

In de video zag je dat de Sharpe-ratio van het Apple-aandeel onder 1 lag, namelijk 0,64. Onthoud: de Sharpe-ratio geeft het rendement weer in verhouding tot het risico. Hoe interpreteer je een Sharpe-ratio van 0,64? Welke van de onderstaande uitspraken is correct?

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Introductie tot portefeuille-analyse in Python

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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: Welcome to Portfolio Analysis!Exercise 2: Why invest in portfolios Exercise 3: The effect of diversification Exercise 4: Portfolio returns Exercise 5: Portfolio losses and gaining it back Exercise 6: Calculate mean returns Exercise 7: Portfolio cumulative returns Exercise 8: Measuring risk of a portfolio Exercise 9: Portfolio variance Exercise 10: Standard deviation versus variance

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: Geannualiseerde rendementen Exercise 2: Portefeuillerendement annualiseren Exercise 3: Jaarrendementen vergelijken Exercise 4: Risicogecorrigeerde rendementen Exercise 5: De Sharpe-ratio interpreteren

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Exercise 6: Sharpe-ratio van de S&P500 Exercise 7: Sharpe-ratio van de portefeuille Exercise 8: Niet-normale verdeling van rendementen Exercise 9: Scheefheid van de S&P500 Exercise 10: Scheefheid en kurtosis berekenen Exercise 11: Distributies van aandelenrendementen vergelijken Exercise 12: Alternatieve risicomaten Exercise 13: Sortino-ratio Exercise 14: Maximum drawdown-portefeuille

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