Using R for Regression and Machine Learning in Investment

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  • Understanding the big picture of the algorithm-driven investment decision-making process using machine learning and review of regression methodology
    • Understand the characteristics of predictive models and various data in investment
      The instructor will give you the big picture of the algorithm-driven investment decision-making process.
      After you understand that, we will review the regression concept and connect it with the core concepts of machine learning methodologies.
  • Regression and beyond
    • Use regression methodology for various investment analysis purpose and improve models by using ridge, lasso, and logistic regression. First of all, you will learn how you can gauge investment strategy using backtesting.
      You learned the first component of investment strategy, returns, in the first week. You will expand your study to assessing investment risks. To understand stocks' risks, you will calculate covariance and correlation matrix using historical time-series stock return data. You will extend this to market factor and three-factor models to understand the risk you are facing with your investment. Finally, you will calculate factor exposure using a 3-factor model from week 2 and separate common factor risk and idiosyncratic risk of the stock.