This course combines statistical concepts and applies into analysing financial data using Python. It aims to provide students with the skills to conduct statistical analysis on financial data to make inferences about risks and returns. Students will learn how to use Python to import, prepare and analyse financial data that will then be used to build prediction models. The students will be guided through a series of well designed projects such that they will get to wrangle financial data and implement a few models and algorithms
The first step to modeling is to clearly define the problem we seek to solve. In the first module of the course, we introduce the mechanics of capital markets and theunderlying ideas of risk and return. Students will learn to read and analyse stock data using trend analysis as well as studying the distributions of stock returns and applying statistical inference. We will then introduce one of the most commonly used prediction models, multiple linear regression, and apply it to predict price changes of real-world stocks and indices.
We start with covering the basics of Python programming, such as Python’s functions and data types, ”for loop” and ”if-else” commands. Further we explore how financial data can be organized as vectors, and the operations that one could potentially perform on them, e.g. sorting. Moreover we will cover the topics of data wrangling with pandas, and data visualization with matplotlib. We will apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts. We understand how to import financial data build a model using (multiple) linear regression model and use it to predict price changes stocks and indices. We learn how to evaluate the model using different indicators.