Your application

Please complete the following fields to be considered for this project.

Please fill in this required field.
Please fill in this required field.
Please fill in this required field.
Please fill in this required field.
Please fill in this required field.
How much commitment will you have to this project?
Please select an option.
Are you available to dedicate 1-2 hours per week to the Build Project?
Please select an option.
Your application has been 
successfully submitted!
Explore more projects
Close
You already submitted an application for this project.
Explore more projects
Close
There was an error submitting your form. Please try again later or contact us.
Oops! Something went wrong while submitting the form.

This project is no longer accepting applications. Subscribe to our newsletter to be notified of new projects!

Get updates
Predicting Stock Price in a Quantitative Way
Yue (Mike) Yao
Yue (Mike) Yao
Get updates
Register today
Apply now

Predicting Stock Price in a Quantitative Way

Use Python and Linear Regression to predict stock prices in a quantitative way

Register today
Apply now
Fridays
 at
6:00
P.M.
 ET /
3:00
P.M.
PT
8 weeks, 2-3 hours per week
Expert
No experience required
No experience required
Some experience required
Degree and experience required

Description

What about let’s start to solve some real-world finance problems using computer science and math? Quantitative finance might sound unfamiliar to you, but in this Build Project, you’ll have an end-to-end experience of the quantitative investment process – using Python to do data mining and data cleaning, applying keyword detection statistical algorithms on earnings call data, implementing a linear regression model to generate stock price prediction, and building your own portfolio with risk control. By the end of the 8-weeks project, you will develop a published online notebook with code and text that includes a full pipeline of building up a portfolio in a quantitative way.

Session timeline

  • Applications open
    December 1, 2024
  • Application deadline
    January 15, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    February 3, 2025
  • Project end date
    Week of

What you will learn

  • Understand what quantitative investment is, why it is different from traditional investment, and how modern technologies like Python coding can help qualitative analysis. 
  • Learn coding skills in Python for data mining, including how to process large excel/csv programmatically
  • Apply statistical algorithms such as term-frequency-inverse-document-frequency to extract keywords from long paragraphs.
  • Implement an end-to-end stock price prediction model using linear regression in Python, with understanding of hyper parameter tuning, overfitting detection and accuracy measurement.
  • Build a portfolio based on stock prediction and measure the risk and sharpe ratio of your quantitative strategy.

Project workshops

1
Introduction
2
Market Data mining and cleaning
3
Earnings Call Data processing
4
Build Qualitative Term
5
Linear Regression model using Terms (Part I, Training)
6
Linear Regression model using Terms (Part II, Inference, accuracy testing)
7
Portfolio construction and visualization
8
Presentation and feedback

Prerequisites

Proficiency in python programming including using Numpy for data processing, using data structures like dictionary/dataframe to store processed regression features, using Scipy to build up a linear regression framework, and generating risk plots / reports via MatPlotLib.

Deep understanding of financial concepts such as volume and earnings call. This needs to be detail oriented such as understanding the difference between minutely volume and daily volume, and how the distribution of volume of stocks with different capital look like intraday, and how volume is correlated with price. For earnings calls, you need to know the key words for an earnings call (e.g beat expectation, below expectation) and understand the financial meaning of those key words and why they will affect stock price.

Full understanding of the logic and implementation of feature engineering and linear regression.  

Ability to use the Python MatPlotLib package to build up visualizations for your portfolio. This will involve how to do multiple line plots in python, how to implement labeling and legends and how to structure the graphs so your research results could be easily digested by senior management from financial firms who might not have context of the research process.

Sign up today

Get access to all of our Build projects, including this one, by creating your Build account!

Register today
Log in

Apply to

Yue

's project today!

Get started by submitting your application.

Apply now

Stay updated!

Subscribe to our newsletter to be notified when projects reopen!

Please fill in this required field.
By clicking “Subscribe” you agree to our Terms of Services and Privacy Policy.

Thanks for subscribing!

We'll notify you when projects reopen. In the meantime, you can explore our resources and learn more about our Fellows.

Discover our articles
There was an error submitting your form. Please try again later or contact us.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
About the expert

Yue (Mike) Yao

Quantitative Finance Fellow
Open Avenues Foundation

Mike is a Quantitative Finance Build Fellow at Open Avenues, where he works with students leading projects in quantitative finance.

Mike is a quantitative research analyst at Citadel, where he focuses on portfolio construction, portfolio optimization and data visualization.

Mike has over 4 years of experience in the quantitative finance field. He interned at Bank of America in 2018 as a software engineer and interned at Citadel in 2019 as a quantitative research engineer.

He holds a bachelor degree of science in computer science.

A fun fact about Mike is that he knows to play 4 different kinds of musical instruments.

Visit
Yue
's Linkedin
More like this Project