Unlocking Insights: Analyzing Earnings Calls with NLP Techniques
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Chapter 1: Understanding Earnings Calls
Earnings calls represent a crucial interaction where the management of a public company engages with analysts, investors, and the media to review financial results for a specific reporting period, typically a quarter or a fiscal year. These calls usually follow an earnings report that summarizes the company's financial performance.
Under SEC regulations, corporate officials are prohibited from providing false or misleading information.
Companies and Index Overview
This analysis focuses on low to mid-market capitalization technology firms listed on NASDAQ, specifically those with market caps ranging from $300 million to $10 billion. Examples of such companies include:
['ACIW', 'CEVA', 'CMTL', 'COMM', 'CPSI', 'CRUS', 'CSGS', 'CSOD', 'CVLT', 'DCT', 'DGII', 'DIOD', 'DMRC', 'DSGX', 'EBIX', 'EPAY', 'ERII', 'EVBG', 'EXTR', 'FEYE', 'FORM', 'LSCC', 'LTRPA', 'MANH', 'MARA', 'MDRX', 'MGRC', 'MIDD', 'MITK', 'MTSI', 'NATI', 'NH', 'NTCT', 'NTNX', 'NVEC', 'NXGN', 'OMCL', 'OSIS', 'PCTY', 'PDFS', 'PEGA', 'SLAB', 'SLP', 'SMCI', 'SMTC', 'SPSC', 'SPWR', 'SSYS', 'SWIR', 'SYKE', 'SYNA', 'TCX', 'TRIP', 'TRUE', 'TTEC', 'TTMI', 'TWOU', 'UCTT', 'UPLD', 'VECO', 'VIAV']
The VGT (Vanguard Information Technology Index Fund ETF) is used as a benchmark to evaluate the performance of the technology sector.
Additionally, Exchange-Traded Funds (ETFs) are highlighted as attractive investment options due to their low fees compared to mutual funds and their broad diversification, which minimizes risk.
Modern Portfolio Theory: The Importance of Indexing
In summary, Harry Markowitz's groundbreaking work in diversification led to him winning a Nobel Prize, emphasizing that spreading investments across different assets can reduce risk while maximizing returns. This principle underpins the structure of mutual funds and ETFs.
When constructing models for stock performance, it is crucial to avoid direct comparisons of returns. A 10% return might be underwhelming if the overall market rises by 15%, suggesting that the investment did not capitalize on market conditions effectively.
Process Overview
- Data Collection
- Feature Engineering
- Implementing Random Forest Classifier
- Key Insights
Gathering Data
APIs such as the Financial Modeling Prep API are invaluable for obtaining earnings call transcripts. For instance, transcripts can be accessed via:
Feature Engineering Techniques
Language measurements play a significant role in analyzing transcripts. Some common measures include:
- Flesch-Kincaid: A score of 100 indicates very easy reading, while 0 signifies difficulty.
- Gunning Fog Index: A grade-level measurement for shorter texts.
- SMOG: A grade-level assessment for longer texts.
The TF-IDF Vectorizer extracts the top 1,000 features from transcripts, incorporating two n-grams.
The Loughran McDonald Master Dictionary, developed by a professor at Notre Dame, categorizes words based on their usage in earnings statements, aiding in sentiment analysis.
Random Forest Classifier
The Random Forest model was configured with parameters such as bootstrap settings, maximum depth, minimum samples per leaf, and the number of estimators. Extensive grid searches helped identify the best-performing feature combinations, with reading scores yielding the most accurate results.
The model predicts whether an investor should purchase a stock based on the forecasted performance compared to the VGT.
Metrics and Predictions
The model evaluates performance by assuming an investor holds the VGT. If the model suggests a stock will outperform, the investor would sell VGT and invest in the predicted stock.
The model's closed formula yields a result of 0.0097, indicating that the ticker outperformed the VGT by 1% over the specified quarter.
Takeaways
The gathered data was not flawless; transcripts included inputs from operators, questioners, and executives, which could skew results. Future models should consider filtering out non-key speakers and incorporating traditional investment metrics like market capitalization, EBITDA, and EPS.
While the analysis focused on small to mid-cap tech companies from 2017 to 2020, further research should expand to other sectors and periods to mitigate bias. This data can complement conventional financial analysis to identify potentially troubled companies.
This video demonstrates how to automatically analyze public company earnings calls using NLP techniques.
Learn how to perform sentiment analysis on earnings call transcripts using TextBlob and the FMP API.