BUSANA-7003 Business Analytics Project Semester 2, 2023
Project Title: Predicting Stock Market Bubbles
Background
This project is designed as an industry project for the Securities and Exchange Commission (SEC), the
regulator of the U.S. stock markets. The SEC's mission is to protect investors; maintain fair, orderly,
and efficient markets; and facilitate capital formation. As part of its mandate, the SEC is interested in
predicting stock market bubbles in individual stocks to protect retail investors and ensure market
stability.
Stock market bubbles can lead to significant market volatility and can result in substantial financial loss
for investors when they burst. Therefore, the ability to predict and potentially prevent these bubbles can
contribute to the overall stability of the market and protect individual investors.
Project Description:
In this project, you will work with a real-world dataset from the stock market, specifically the CRSP
Daily Stock data. The dataset contains daily stock prices, stock characteristics, and market returns for
the period 2000-2022. The goal of the project is to train a machine learning model that can predict stock
market bubbles.
Project Steps:
1. Business issue understanding. Understand the challenge that SEC is facing. Formulate the main
project question. Narrow down the scope of the project.
2. Data Understanding: Understand the dataset, the variables and their relationships. Define what
a 'bubble' is in the context of this project.
3. Data Cleaning: Clean the dataset by handling missing values, outliers, and incorrect data
entries.
4. Feature Engineering: Create new features that might be relevant for predicting stock market
bubbles. This could be technical indicators, sentiment analysis from news data, or other
macroeconomic indicators.
5. Data Visualization: Create meaningful visualizations to present your findings from the
exploratory data analysis.
6. Model Training: Train a machine learning model to predict stock market bubbles. This could
be a classification model that predicts whether a bubble will occur within a certain time frame,
or it could be a regression model that predicts the size of the bubble.
7. Model Evaluation: Evaluate the performance of the model using appropriate metrics. Fine-tune
the model for better performance.
8. Report Writing: Write a comprehensive report detailing your methodology, findings, and
insights. Include visualizations and code in the report.
9. Presentation: Prepare a presentation to share your findings with the class. The presentation
should be understandable to a non-technical audience and highlight the insights from your
findings.
BUSANA-7003 Business Analytics Project Semester 2, 2023
Project Deliverables:
1. Jupyter notebooks with all the code, comments, and outputs.
2. An Executive summary of findings in PDF format.
3. A PowerPoint presentation.
Evaluation criteria
1. CLO1 - Application of Analytics Knowledge and Skills: You should demonstrate the analytics
knowledge and skills obtained throughout the programme to recalibrate solutions to the
business problem of predicting stock market bubbles. This includes your ability to apply
appropriate data cleaning techniques, perform exploratory data analysis, engineer relevant
features, and select and train a suitable machine learning model.
2. CLO2 - Understanding of Practical Challenges: You should demonstrate an understanding of
the academic learning and practical challenges in implementing data analytics in an
organisation. This includes your ability to handle real-world data, deal with any issues or
limitations in the data, and implement a machine learning model in a way that could be feasibly
deployed in a real-world setting.
3. CLO3 - Communication of Results: You should effectively communicate the results of the
business analytics project. This includes your ability to write a clear and comprehensive
executive summary, create meaningful visualizations, and present your findings in a way that
is understandable to a non-technical audience.
Dataset Variables:
The dataset is available in the Box folder. Here are the variables in the CRSP dataset and their
definitions:
Variable Definition
PERMNO Unique identifier assigned by CRSP
date Date of the observation
SHRCD Share code
EXCHCD Exchange code
TICKER Ticker symbol
COMNAM Company name
SHRCLS Share class
PERMCO Permanent company ID
BUSANA-7003 Business Analytics Project Semester 2, 2023
Variable Definition
CUSIP CUSIP identifier
SHRFLG Share flag
DIVAMT Dividend amount
BIDLO Low bid price
ASKHI High ask price
PRC Price
VOL Volume
RET Return
BID Bid price
ASK Ask price
SHROUT Shares outstanding
OPENPRC Opening price
NUMTRD Number of trades
RETX Return excluding dividends
vwretd Value-weighted return including dividends
vwretx Value-weighted return excluding dividends
ewretd Equal-weighted return including dividends
ewretx Equal-weighted return excluding dividends
sprtrn S&P 500 Total Return
2. Stock Market Bubbles
A stock market bubble is a type of economic bubble taking place in stock markets when market
participants drive stock prices above their value in relation to some system of stock valuation. The
identification of a bubble in the stock market is challenging. Therefore, you will need to define and
quantify what a 'bubble' is in the context of this project. This could be a rapid increase in price followed
by a rapid decrease, or it could be a price that is significantly higher than a certain benchmark.
Here are some relevant research papers on stock market bubbles that can provide further insights (these
papers are also available in the Box folder):
• Asset Price Bubbles and Systemic Risk (2020)
BUSANA-7003 Business Analytics Project Semester 2, 2023
• Bubbles (a book chapter)
• Testing for Speculative Bubbles in Stock Markets: A Comparison of Alternative Methods
(2012)
3. Useful Links:
Here are some online resources that can help you understand stock market bubbles better:
• What is an economic bubble?
• What is a stock market bubble?
• What is a market bubble?
Related literature
Markus Brunnermeier and others, Asset Price Bubbles and Systemic Risk, The Review of Financial
Studies, Volume 33, Issue 9, September 2020, Pages 4272–4317, https://doi.org/10.1093/rfs/hhaa011
Brunnermeier, M. K. (2016). Bubbles. In Banking Crises: Perspectives from The New Palgrave
Dictionary (pp. 28-36). London: Palgrave Macmillan UK.
Ulrich Homm , Jörg Breitung, Testing for Speculative Bubbles in Stock Markets: A Comparison of
Alternative Methods, Journal of Financial Econometrics, Volume 10, Issue 1, Winter 2012, Pages 198–
231, https://doi.org/10.1093/jjfinec/nbr009