The Hong Kong University of Science and Technology
Python and Statistics for Financial Analysis
The Hong Kong University of Science and Technology

Python and Statistics for Financial Analysis

Taught in English

Some content may not be translated

219,438 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Xuhu Wan

Instructor: Xuhu Wan

4.4

(4,038 reviews)

Intermediate level

Recommended experience

12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

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Assessments

5 quizzes

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There are 4 modules in this course

Why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? What makes Python one of the most popular tools for financial analysis? You are going to learn basic python to import, manipulate and visualize stock data in this module. As Python is highly readable and simple enough, you can build one of the most popular trading models - Trend following strategy by the end of this module!

What's included

7 videos3 readings1 quiz1 discussion prompt4 ungraded labs

In the previous module, we built a simple trading strategy base on Moving Average 10 and 50, which are "random variables" in statistics. In this module, we are going to explore basic concepts of random variables. By understanding the frequency and distribution of random variables, we extend further to the discussion of probability. In the later part of the module, we apply the probability concept in measuring the risk of investing a stock by looking at the distribution of log daily return using python. Learners are expected to have basic knowledge of probability before taking this module.

What's included

4 videos1 quiz3 ungraded labs

In financial analysis, we always infer the real mean return of stocks, or equity funds, based on the historical data of a couple years. This situation is in line with a core part of statistics - Statistical Inference - which we also base on sample data to infer the population of a target variable.In this module, you are going to understand the basic concept of statistical inference such as population, samples and random sampling. In the second part of the module, we shall estimate the range of mean return of a stock using a concept called confidence interval, after we understand the distribution of sample mean.We will also testify the claim of investment return using another statistical concept - hypothesis testing.

What's included

5 videos1 reading1 quiz4 ungraded labs

In this module, we will explore the most often used prediction method - linear regression. From learning the association of random variables to simple and multiple linear regression model, we finally come to the most interesting part of this course: we will build a model using multiple indices from the global markets and predict the price change of an ETF of S&P500. In addition to building a stock trading model, it is also great fun to test the performance of your own models, which I will also show you how to evaluate them!

What's included

6 videos1 reading2 quizzes5 ungraded labs

Instructor

Instructor ratings
4.3 (1,057 ratings)
Xuhu Wan
The Hong Kong University of Science and Technology
1 Course219,438 learners

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