Two-Project Series

Linear Regression for Asset Pricing you own this product

intermediate Amazon Web Services​ ​Console • basics of Amazon Web Services Command Line Interface (AWS CLI) • basics of JavaScript
skills learned
interactions between AWS Lambda and microservice architectures • implementing your first AWS Lambda function • what is service orchestration
Abdullah Karasan
2 weeks · 8-10 hours per week average · BEGINNER

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Linear regression is one of the most straightforward approaches to making price predictions and financial forecasts. In this series of liveProjects, you’ll step into the role of a machine learning engineer at InvRes Bank, a bank that offers financial consulting services to its customers. To better serve your clients, you'll develop regression-based Python machine learning models for financial modeling and asset pricing. You’ll update the classic financial algorithms Capital Asset Pricing Theorem (CAPM) and Arbitrage Pricing Theorem (APT) to new automatic machine learning models and improve your understanding of financial data science. These skills are in high demand for portfolio optimization and financial services.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

liveProject mentor Jeremy Loscheider shares what he likes about the Manning liveProject platform.

here's what's included

Project 1 Capital Asset Pricing Model

In this liveProject, you’ll build a machine learning system based on the Capital Asset Pricing Model (CAPM) that can determine if certain stocks are over or undervalued, and the risk level of these stocks relative to the market index. CAPM is a powerful tool in finance due to its intuitive and easy-to-apply nature, and in this project you’ll use it to estimate coefficients, determine valuation accuracy, and finally find which stock promises the best risk-return relationship.

Project 2 Arbitrage Pricing Model

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.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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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.


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:

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

you will learn

In this liveProject, you’ll learn to use one of the most reliable machine learning tools for finance and improve your understanding of financial data science.

  • Risk management
  • Financial risk estimation
  • Financial modeling with multivariate case


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.