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Python for Finance Cookbook - Second Edition

You're reading from  Python for Finance Cookbook - Second Edition

Product type Book
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Pages 740 pages
Edition 2nd Edition
Languages
Author (1):
Eryk Lewinson Eryk Lewinson
Profile icon Eryk Lewinson

Table of Contents (18) Chapters

Preface Acquiring Financial Data Data Preprocessing Visualizing Financial Time Series Exploring Financial Time Series Data Technical Analysis and Building Interactive Dashboards Time Series Analysis and Forecasting Machine Learning-Based Approaches to Time Series Forecasting Multi-Factor Models Modeling Volatility with GARCH Class Models Monte Carlo Simulations in Finance Asset Allocation Backtesting Trading Strategies Applied Machine Learning: Identifying Credit Default Advanced Concepts for Machine Learning Projects Deep Learning in Finance Other Books You May Enjoy
Index

Finding the Efficient Frontier using Monte Carlo simulations

According to the MPT, the Efficient Frontier is a set of optimal portfolios in the risk-return spectrum. This means that the portfolios on the frontier:

  • Offer the highest expected return for a given level of risk,
  • Offer the lowest level of risk for a given level of expected returns.

All portfolios located under the Efficient Frontier curve are considered sub-optimal, so it is always better to choose the ones on the frontier instead.

In this recipe, we show how to find the Efficient Frontier using Monte Carlo simulations. Before showing more elegant approaches based on optimization, we employ a brute force approach in which we build thousands of portfolios using randomly assigned weights. Then, we can calculate the portfolios' performance (expected returns/volatility) and use those values to determine the Efficient Frontier.For this exercise, we use the returns of four US tech companies from 2021.

How to do it...

Execute...

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