R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. This complete hands-on course will help you code some finance and ML projects.
In the first section of the course, you will learn some basic things to look at in money management, something very simple, something everybody knows and yet not many people start with. In the second section, you will look at stock market timing and focus on the coding aspect of how the algorithm is designed, then will land on the video on how to use the algorithm. We will also discuss soft thresholds and extend the market timing concept to a generalized framework.
In the last section of the course, you will understand Asset Pricing. We will try to answer questions such as What is market beta? What is Capital Asset Pricing Model? Why do we pay attention to the market coefficient? How to construct an efficient portfolio? We will also look at the growth strategy. We will be looking at our data and every period we are going to pick a few TOP performing stocks by looking at the returns of these stocks within the period. In the next period, we are simply going to hold these stocks.
By the end of the course, we are going to talk about how to build a web-based application so that we can come up with a software platform for clients to use.
All resources and code files are placed here: https://github.com/PacktPublishing/Introduction-to-FinTech-Using-R
Learn how to time the stock market using probability theory
Explore the qualitative nature of stock market timing
Know the quantitative nature of stock market timing
Learn the basics of asset pricing theory
Learn the intermediate and advanced asset pricing practices
Build trade-able factor-based algorithms
* Fundamentals of asset pricing and building a tradeable factor-based algorithm from scratch
* Get to know the most basic rules of thumb and intuition that every successful trader should know