Fattore dimensione

Le azioni con valore di mercato più basso (mid e small cap) in genere realizzano rendimenti più alti rispetto a quelle con valore di mercato o capitalizzazione più elevati (large cap). Il fattore dimensione misura questa differenza di rendimento tra le società small cap rispetto alle large cap. Supponi di calcolare la correlazione tra i rendimenti dell'S&P500 e i rendimenti del fattore dimensione. Cosa ti aspetti di trovare?

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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: 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: Confronto con un benchmark Exercise 2: Rendimento attivo Exercise 3: Attribuzione per settore Exercise 4: Fattori di rischio Exercise 5: Fattore dimensione

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Exercise 6: Fattore momentum Exercise 7: Fattore value Exercise 8: Modelli fattoriali Exercise 9: Correlazioni dei fattori di Fama-French Exercise 10: Modello di regressione lineare Exercise 11: Modello a fattori di Fama-French Exercise 12: Strumenti per l’analisi del portafoglio Exercise 13: Scheda di performance Exercise 14: Esposizioni settoriali con 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