Financial returns

Introduzione alla gestione del rischio di portafoglio in Python

Dakota Wixom

Quantitative Analyst | QuantCourse.com

Course overview

Learn how to analyze investment return distributions, build portfolios and reduce risk, and identify key factors which are driving portfolio returns.

Univariate Investment Risk
Portfolio Investing
Factor Investing
Forecasting and Reducing Risk

Investment risk

What is Risk?

Risk in financial markets is a measure of uncertainty
Dispersion or variance of financial returns

How do you typically measure risk?

Standard deviation or variance of daily returns
Kurtosis of the daily returns distribution
Skewness of the daily returns distribution
Historical drawdown

Financial risk

Returns

Probability

A tale of two returns

Returns are derived from stock prices
Discrete returns (simple returns) are the most commonly used, and represent periodic (e.g. daily, weekly, monthly, etc.) price movements
Log returns are often used in academic research and financial modeling. They assume continuous compounding.

Calculating stock returns

Discrete returns are calculated as the change in price as a percentage of the previous period's price

Calculating log returns

Log returns are calculated as the difference between the log of two prices
Log returns aggregate across time, while discrete returns aggregate across assets

$$ Rl = \ln(\frac{P_{t_2}}{P_{t_1}}) $$

or equivalently

$$ Rl = \ln(P_{t_2}) - \ln(P_{t_1}) $$

Calculating stock returns in Python

Step 1:

Load in stock prices data and store it as a pandas DataFrame organized by date:

import pandas as pd 
StockPrices = pd.read_csv('StockData.csv', parse_dates=['Date'])
StockPrices = StockPrices.sort_values(by='Date')
StockPrices.set_index('Date', inplace=True)

Calculating stock Returns in Python

Step 2:

Calculate daily returns of the adjusted close prices and append the returns as a new column in the DataFrame.

StockPrices["Returns"] = StockPrices["Adj Close"].pct_change()
StockPrices["Returns"].head()

Visualizing return distributions

import matplotlib.pyplot as plt
plt.hist(StockPrices["Returns"].dropna(), bins=75, density=False)
plt.show()

Let's practice!

Introduzione alla gestione del rischio di portafoglio in Python