Arbitrage Pricing Model you own this product

prerequisites
basics of Python, finance, Jupyter Notebook, and machine learning
skills learned
risk management • multi-factor modeling • multivariate linear regression
Abdullah Karasan
1 week · 8-10 hours per week · BEGINNER
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In this liveProject, you’ll build a machine learning system based on the Arbitrage Pricing Theorem (APT) that can create a diversified investment portfolio designed to avoid risks. APT allows you to model the effects of different scenarios on an investment portfolio, and you’ll test this theorem on a portfolio of social media companies. You’ll run analysis to estimate coefficients, and conduct sensitivity analysis to determine important macroeconomic factors. Finally, you’ll interpret your results to find out what issues your portfolio is the most sensitive to.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Abdullah Karasan
Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master's degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.

prerequisites

This liveProject is for data analysts with an interest in financial services. Some knowledge of the finance industry will be useful. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Basics of Python
  • Basics of Jupyter Notebook
TECHNIQUES
  • Basics of finance
  • Usage of APIs for data extraction
  • Python for data cleaning and exploration
  • Running linear regression via statsmodel in Python

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