Capire la frontiera efficiente

In che modo i portafogli che si trovano al di sotto e a destra della frontiera efficiente (EF) differiscono dai portafogli sulla frontiera efficiente? Vorresti investire in qualcuno di questi?

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Introduzione all'analisi di portafoglio in Python

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Questo esercizio fa parte del corso

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: 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: Teoria moderna del portafoglio Exercise 2: Capire la frontiera efficiente

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Exercise 3: Calcolare rischio e rendimento attesi Exercise 4: Funzioni di rischio di PyPortfolioOpt Exercise 5: Performance del portafoglio ottimale Exercise 6: Sharpe massimo vs. volatilità minima Exercise 7: Ottimizzazione di portafoglio: Sharpe massimo Exercise 8: Ottimizzazione a minima volatilità Exercise 9: Confronto tra max Sharpe e min vol Exercise 10: Ottimizzazione alternativa del portafoglio Exercise 11: Rendimenti e rischio a pesi esponenziali Exercise 12: Confrontare gli approcci Exercise 13: Modificare lo span Exercise 14: Riepilogo