Interpretare lo Sharpe ratio

Nel video hai visto che lo Sharpe ratio del titolo Apple era sotto 1, precisamente 0,64. Ricorda: lo Sharpe ratio rappresenta il rendimento in proporzione al rischio. Come interpreti uno Sharpe ratio di 0,64? Quale delle seguenti affermazioni è corretta?

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Introduzione all'analisi di portafoglio in Python

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Introduzione all'analisi di portafoglio 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: Rendimenti annualizzati Exercise 2: Annualizzare i rendimenti del portafoglio Exercise 3: Confrontare i tassi di rendimento annualizzati Exercise 4: Rendimenti corretti per il rischio Exercise 5: Interpretare lo Sharpe ratio

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Exercise 6: Rapporto di Sharpe dell'S&P500 Exercise 7: Sharpe ratio del portafoglio Exercise 8: Distribuzione non normale dei rendimenti Exercise 9: Asimmetria dell'S&P500 Exercise 10: Calcolare asimmetria e curtosi Exercise 11: Confrontare le distribuzioni dei rendimenti azionari Exercise 12: Misure alternative del rischio Exercise 13: Rapporto di Sortino Exercise 14: Portafoglio con massimo drawdown

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