This document outlines a project that uses natural language processing and machine learning techniques to analyze media headlines about IPOs and predict the trajectory of the stock price over the first three months. The project scraped over 5,000 headlines about 16 IPOs from various sources, performed sentiment analysis and feature selection, and tested several machine learning models to select a linear support vector machine model that best predicts positive or negative price movements. The document describes the methodology, data sources and wrangling processes, modeling approach, and concludes with plans for a product demonstration and areas for further improvement.

1. Outline
Introduction
• Background
• Purpose
• Audience
• Hypothesis
Statement
• Dataset
• Architecture
Product
Assessment
• What works
• What doesn’t
• Steps to improve it
• Conclusions
Methodology
• Data scraping & wrangling
• Feature selection
• NLP of headlines
• Modeling and prediction
• Testing and validation
• FP3 Product
Demonstration
1 2 3
Financial Performance Prediction Project (FP3):
Analyzing IPO Stories to Predict Stock Price Trajectories

2. Outline
Introduction
• Background
• Purpose
• Audience
• Hypothesis
Statement
• Dataset
• Architecture
Product Assessment
• What works
• Challenges
• Back-burner tasks
Methodology &
Data Product
• Data scraping &
wrangling
• Headline sentiment
analysis, NLP(s)
• Feature selection
• Modeling and
prediction
• DEMONSTRATION
1 2 3

3. Introduction

4. Introduction
• Intro to IPOs
• Hypothesis
Statement
• Dataset
• Architecture
Project Hypothesis: Sentiment
analysis of media headlines about
an IPO can be used to predict the
trajectory of the stock price over
the first three months.

5. Introduction
• Intro to IPOs
• Hypothesis
Statement
• Dataset
• Architecture
5395 headlines
16 IPOs
4 industries
Company Ticker Industry IPO Date Instances
Alibaba BABA Technology 2014-09-19 544
Blue Apron APRN Food/Drink 2017-06-29 125
Etsy ETSY Retail 2015-04-16 104
Facebook FB Technology 2012-05-18 1977
Ferrari RACE Automotive 2015-10-21 46
Fitbit FIT Technology 2015-06-18 114
General Motors GM Automotive 2010-11-18 368
GoPro GPRO Technology 2014-06-26 91
Groupon GRPN Retail 2011-11-04 426
LinkedIn LNKD Technology 2011-05-19 193
Shake Shack SHAK Food/Drink 2015-01-30 103
Snapchat SNAP Technology 2017-03-02 185
Stitch Fix SFIX Retail 2017-11-17 33
Tesla TSLA Automotive 2010-06-29 94
Twitter TWTR Technology 2013-11-07 902
Workday WDAY Technology 2012-10-12 90

6. Methodology and Data Product

7. Methodology &
Data Product
• Data scraping & wrangling
• Headline sentiment, NLP
• Feature selection
• Modeling and prediction
3 Data Sources:
• Dow Jones Factiva
• www.iposcoop.com
• Morningstar API
• Inner join merged into one dataframe
• Normalized prior to subsequent processing

8. Methodology &
Data Product
• Wrangling
• Headline sentiment, NLP
• Feature selection
• Modeling and prediction
Sentiment Analysis:
Empath vs. OpinionFinder
Built-in Lexicon vs. Built-in Lexicon
● Included both in model
Feature Selection:
● Early results were poor (32 – 52 intuitive features)
● CountVectorization to headline text -> 4K features
● Principal Component Analysis (k=0.95 ) → 2K
features

9. Methodology &
Data Product
• Data scraping & wrangling
• Headline sentiment, NLP
• Feature selection
• Modeling and prediction
Models Tested:
• LinearSVC (Support Vector Machine)
• NuSVC
• SVC
• Kneighbors
• SGDClassifier (Stochastic Gradient Descent)
Model Selected: LinearSVC
• Best at predicting price trajectory (both positive
and negative) over 90-day period
• LogisticRegression
• LogisticRegressionCV
• BaggingClassifier
• ExtraTreesClassifier
• RandomForestClassifier
• MultinomialNB (Naive Bayes)

10. Demonstration

11. Product Assessment

12. Product Assessment
● What works – scraping, wrangling, sentiment analysis
(eventually), predictor
● Challenges – knowing the sentiment analysis algorithm &
feature selection for best modeling results
● Back-burner improvements – end-to-end automation, GUI

13. Github Repo: https://github.com/georgetown-analytics/MFP3