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.
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.
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