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BUSANA-7003 Predicting Stock Market Bubbles
创建日期：2024-05-31 16:08:22
标签： BUSANA-7003
Predicting Stock Market Bubbles

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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.

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