Classifying a Company’s True Earnings Quality using Text Analytics and Machine Learning on S&P Proxy Statements’ Compensation Discussion and Analysis [R, Python]

This was submitted as a project for my Text Analytics class in my MS Business Analytics program. The original title is “Text Analytics on the Compensation Discussion and Analysis of S&P 1500 Proxy Statements. My other teammates are Minglu Sun, Jiawen Zhou, and Yi Luo. This project was done for educational purposes only. Click the photos to enlarge. Check out the GitHub page for the files and data set. 

Problem Statement

The purpose of this study is to explore whether the sentiment, structure, and contents of a company’s Proxy Statement Compensation Discussion and Analysis (CD&A) reflects the company’s real financial performance in terms of the relationship of Earnings per Share and Operating Cash Flow per Share

Rationale

  • Public companies submit C-level management compensation reports to SEC every year. The Compensation Discussion and Analysis (CD&A) section discloses all material elements of the company’s executive compensation programs and provides the appropriate reasoning as to why the C-suite are being paid their respective salaries.
  • The compensation report is highly sensitive and is required to be explained with utmost transparency. In an attempt to standardize transparency in the document, in early 2017, SEC proposed rules and regulations that will require companies to disclose the relationship between executive pay and a company’s financial performance.
  • That being said, whether the Compensation Discussion and Analysis reflects the company’s real financial performance or not needs to be tested.

Hypothesis

In this project, we assumed that the more positive a company’s proxy statement’s CD&A was written, the better the earnings quality of a company is in a given fiscal year.

According to Investopedia, Two financial indicators are being used to present whether companies’ earnings with high quality or low quality. A company has high quality earnings if it is generating more cash than is reported in the income statement. Earning quality is low if the company’s statements are not showing the negative operating results of the company. True cash operating results are also overstated.

  • High quality earnings: Earnings Per Share (EPS) > Operating Cash Flow Per Share (CFS)
  • Low quality earnings: Earnings Per Share (EPS) < Operating Cash Flow Per Share (CFS)

Data Description

For this project, three data sets were collected:

  1. Randomly selected 1,500 companies’ S&P Proxy Statements’ Compensation Discussion and Analysis (CD&A) from the U.S.  SEC EDGAR System.
  2. Company performance (Earnings Per Share and Cash Flow Per Share) using the Intrinio Financial Marketplace API.
  3. Ticker and registered company industries from Google Finance.

In addition, two popular sentiment lexicons were selected for the sentiment analysis portion: Bing Liu’s sentiment dictionary and LoughranMcDonald Master Dictionary, which was specifically developed for Tim Loughran and Bill McDonald’s paper in the Journal of Finance entitled “When is Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks” (2011).

Document Structural Dimension

  • Although the SEC did not rule out the structure of the Proxy Statement, most of the companies share similar structure of the statement, as well as the Compensation Discussion and Analysis part.
  • In general, the first paragraph of the CD&A is the introduction, which briefly introduces what the content is included in this part.
  • The second paragraph is the Executive Summary. A large number of the companies disclose the current year financial performance in the Executive Summary, making it the important section for sentiment information. Most of the positive or negative words and phrases are extracted from the Executive Summary.
  • The rest of the Compensation Discussion and Analysis are detailed descriptions of the compensation policy, subcategories of the compensation, and the approval from the compensation committee. A few companies explain the compensation decisions in these detailed compensation components, which reveal the sentiment information.

Document Content Dimension

  • The documents share the characteristics of public financial statements. Most of the sentences analyze and compare numeric values, which represent financial performance.

Methodology

Data Preprocessing

  • Text data was extracted from the CD&As. It underwent cleaning, which involved removal of punctuations and special characters.
  • Domain-specific lexicon creation. In the process, positive and negative words, phrase, and templates were extracted from 200 of the 500 documents. In the process, positive and negative words, phrase, and templates were extracted.
    • Templates:
      • e.g. an <increase>/<decrease> of <amount> from <number>/<year>
      • <metric> <increased>/<decreased> <amount> over/compared to <year>
Domain-specific lexicon sample

Domain-specific lexicon sample

  • The team simulated “expertise” and classified the 200 documents into positive or negative performance/sentiment.

Sentiment Analysis

Feature-level Analysis

Feature-level Sentiment Analysis model

  • Polarity-based sentiment analysis was conducted using the two publicly available lexicons mentioned above.
  • Due to inadequate results, the team decided to create a new domain-specific lexicon that will hopefully produce a better result.
  • To complement the sentiment analysis, IBM Tone Analyzer was used to acquire 13 tonal dimension results for each company’s CD&A.

 Document-level Analysis

Document-level Sentiment Analysis Model

  • Using the “expert” classifications of the 200 labeled data and the domain-specific lexicon as the feature set, a term-document matrix data set, containing the quantity/existence of each feature in all the documents (500 in total, was created.

Term-Document Matrix

  • Using a Neural Network, the remaining 300 documents were classified into positive or negative sentiment classes.

Classification of Earnings Quality

  • Considering the sentiment classification from the polarity-based sentiment analysis model, using the domain-specific dictionary and the tonal information as predictors and the earning quality as the target variable, four scenarios were used and subjected to multiple classification models (random forest, neural network, and logistic regression).
  • The following scenarios were tested:
    • Scenario 1 : Financial Performances ~ CD&A Tones
    • Scenario 2: Financial Performances ~ CD&A Sentiment
    • Scenario 3: Financial Performances ~ CD&A Tones + Sentiment
    • Scenario 4: Financial Performances ~ Top 5 Predictor Importance (Tone + Sentiment)

Results and Discussion

Sentiment Analysis

Sentiment analytics, in this project, was approached in two ways: feature-level analysis by using polarity-based classification models and document-level analysis using document classification.

Feature-level Analysis

  • Feature-level sentiment analysis is initially conducted with two dictionaries: Bing Liu’s Lexicon and the Loughran McDonald Master Lexicon, which focuses on financial concepts and finance-driven directional phrases.
  • Running these dictionaries into a polarity-based sentiment analyzer (netting of counts of positive and negative words based on existence produced very bi-polar results.

Bing Liu and LonghranMcDonald Sentiment Results

  • Due to the unsatisfactory results of these dictionaries, it became clear that there was a need to use a more domain-specific lexicon.
  • Since such as dictionary is nonexistent, the we decided to create one by reading 200 documents and extracting positive and negative words, phrases, and templates.
    • For instance, positive dictionary include “strong performance”, “outperformed”, “exceeding our target”, “revenue increased”, etc.
    • The negative dictionary included “decrease”, “slow down in ”, “reduction”, “did not achieve”, etc.
    • In the process, each document is categorized as positive or negative. This serves as input in the document-level approach.
  • Surprisingly, the new dictionary classifies 487 documents as positive, 1 as negative, and the remaining 12 as neutral.

Total Polarity-based Sentiment Results

  • The accuracy of the model using the domain-specific dictionary is  67%.

Document-level Analysis

  • Using the classifications generating from the domain-specific dictionary creation phase, classification models were used to determine the sentiment class of the remaining 300 unlabeled documents.
  • Using the words and phrases in the created dictionary as predictors to sentiment, a neural network model with an accuracy of 57.89% was created. That being said, the it was decided that the classifications from the polarity-based model that used the domain-specific dictionary will be used as input for the succeeding steps.

Evaluation of Document-level Sentiment Analysis model

Tonal Analysis

Input data also included tonal results computed by the IBM Tones Analyzer. The 13 dimensions extracted are anger, disgust, fear, joy, sadness, analytical, confident, tentative, openness, conscientiousness, extraversion, agreeableness, and emotional range.

Sample Tonal Results

To give a better idea of how tonal results performed throughout the company list, we decided to aggregate results up to the industry level.

Slice of the Industry-level Tonal Results

  • Telecommunication services industry’s compensation discussion and analysis has the highest joy value, which is 0.41.
  • Basic Materials, Energy, and Industrials are the three industries share the same highest sadness value, which is 0.41.
  • Compare to sadness and joy, tentative tone value is less obvious. The radar chart above is a slice of the tonal analysis that contains only three tones.

Classification of Earnings Quality

Model Evaluation

  •  Among all the created models, the random forest model in scenario 3 produces the highest accuracy (83%), precision (0.8), recall (0.84), and F-score (0.8).

Conclusion

According to the classification results, CD&A documents with positive sentiment score will be more likely to have high earning ability, which is characterized by a higher Earnings per Share compared to the company’s Cash Flow per Share.

In addition, there are no significant difference in tone score and sentiment score among different industries.

Implications

  • The domain-specific lexicon of Compensation Discussion and Analytics will assists the users and stakeholders of the Proxy Statement to recognize positive and negative features, and enables them to make effective and efficient decisions.
  • Since the Compensation Discussion and Analytics shares the characters of financial statements, the dictionary can also be applied to analyze other financial statements.

Limitations and Future Direction

  • The syntactic template has not been matched to the text content and loss some of the features.
  • Secondly, the CD&A prefers to use positive words and phrases and avoid using negative expressions. Even though some of the companies in the negative situation in this year, the description in the discussions seems to be positive. Therefore, the positive frequency of featured words and phrases are higher than the actual number.
  • Thirdly, the data records from the original training set are imbalanced, there are far more positive documents than the negative class. A model that uses a balanced dataset can be created in the future.
  • Also, other financial performance parameters could be used as the target variables instead of cash flow per share or earning per share.

References

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