This project explores the dynamic shifts in sentiment expressed by CEOs and CFOs during economic crises and assesses the accuracy of their communications in such times. We analyzed earnings call transcripts from four distinct companies, applying sentiment analysis to texts from both CEOs and CFOs. The sentiment scores obtained were then regressed against share price data to gauge the correlation with actual company performance.
This section focuses on implementing web scraping techniques to gather large datasets efficiently. It provides a scalable framework for data collection, essential for data-driven decision-making processes.
- Objective: Focused on scraping financial data related to cruises and airlines.
- Scraping Breakdown:
- Cruises Scraping: Data scraping from: Royal Caribbean and Norwegian Cruise Line.
- Airlines Scraping: Data scraping from: Delta Air Lines and Southwest Airlines.
- NumPy
- Pandas
- Selenium
- BeautifulSoup
- Implementation of a flexible and scalable data collection pipeline using Selenium.
- Scraped data stored in Pandas dataframes
- Verification steps to ensure all quaterly earnings transcripts have been completely scraped
- Company transcripts transformed to CSV files for Data Analysis
- Objective: Analyzing sentiment in company earnings call transcripts during the COVID-19 economic crises.
- Preprocessing: Prepare earning calls transcripts for analysis (lowercasing, stemming, removal of HTML tags, stopwords and extra spaces etc.)
- Sentiment Analysis: Create pipeline to do sentiment analysis, primary tool: Loughran McDonald Sentiment Analyzer.
- Sentiment Over Time: Analyzing CEO & CFO sentiment scores during COVID-19 crisis and plot against share price and trading volumes.
- Regression Analysis: OLS regression to explore relationships between CEO and CFO sentiment and market data.
- Yahoo Finance
- Seaborn
- BeautifulSoup
- Selenium
- Scikit-learn
- Creation of a preprocessing pipeline for earnings call transcripts.
- Aggregated word cloud generation using TF-IDF.
- Detailed sentiment analysis with a focus on economic downturns.
- Visualisation of CEO and CFO sentiment trends over time.
- Regression analysis to quantify the relationship between sentiment and stock performance.
Our study revealed that CEOs typically showed higher sentiment scores, but this difference narrowed during economic crises. Notably, CFOs consistently provided a more accurate reflection of company performance relative to share prices.
We recommend incorporating time lags into sentiment scores for a deeper analysis of their influence on future share prices and exploring Vector Autoregressive (VAR) models for a more comprehensive understanding of time-dependent variables interactions. Additionally, refining sentiment score computation by normalizing for speech length and style and implementing TF-IDF weighting will allow for a more nuanced and equitable sentiment analysis.