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. 

Welcome to Portfolio Analysis!

Why invest in portfolios

The effect of diversification

Portfolio returns

Portfolio losses and gaining it back

Calculate mean returns

Portfolio cumulative returns

Measuring risk of a portfolio

Portfolio variance

Standard deviation versus variance

Introduction to Portfolio Analysis

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. 

Annualized returns

Annualizing portfolio returns

Comparing annualized rates of return

Risk adjusted returns

Interpreting the Sharpe ratio

S&P500 Sharpe ratio

Portfolio Sharpe ratio

Non-normal distribution of returns

Skewness of the S&P500

Calculating skewness and kurtosis

Comparing distributions of stock returns

Alternative measures of risk

Sortino ratio

Maximum draw-down portfolio

Risk and Return

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. 

Comparing against a benchmark

Active return

Industry attribution

Risk factors

Size factor

Momentum factor

Value factor

Factor models

Fama French factor correlations

Linear regression model

Fama French Factor model

Portfolio analysis tools

Performance tear sheet

Industry exposures with Pyfolio

Performance Attribution

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. 

Modern portfolio theory

Understanding the efficient frontier

Calculating expected risk and returns

PyPortfolioOpt risk functions

Optimal portfolio performance

Maximum Sharpe vs. minimum volatility

Portfolio optimization: Max Sharpe

Minimum volatility optimization

Comparing max Sharpe to min vol

Alternative portfolio optimization

Exponentially weighted returns and risk

Comparing approaches

Changing the span

Recap

Portfolio Optimization

Small portfolio

S&P500

Large portfolio

Factors for portfolio returns

Portfolio factors

Have you ever had wondered whether an investment fund is actually a good investment? Or compared two investment options and asked what the difference between the two is? What does the risk indicator of these funds even mean? Or do you frequently work with financial data in your daily job and you want to get an edge? In this course, you’re going to get familiar with the exciting world of investing, by learning about portfolios, risk and return, and how to critically analyze them. By working on actual historical stock data, you’ll learn how to calculate meaningful measures of risk, how to break-down performance, and how to calculate an optimal portfolio for the desired risk and return trade-off. After this course, you’ll be able to make data-driven decisions when it comes to investing and have a better understanding of investment portfolios.

Manipulating Time Series Data in Python

Intermediate Python for Finance

Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off. 

Introduction to Portfolio Analysis in Python

Finanzgrundlagen in Python

Why invest in portfolios

Introduction to Portfolio Analysis in Python

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