- Open Access
About relationship between business text patterns and financial performance in corporate data
© The Author(s). 2018
- Received: 15 October 2017
- Accepted: 4 January 2018
- Published: 2 February 2018
This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.
- Corporate annual report
- Text mining
- Business keyword
- Financial performance
- Keyword trends
- Word cloud
- Sentiment analysis
- Correlation coefficient
- Hierarchical clustering
Korea’s small and medium-sized enterprises’ (SMEs) global competitiveness is relatively weak due to a national economic structure favoring larger corporations. To strengthen the competitiveness of SMEs, policy discourages large companies from entering industries that are appropriate for SMEs. In most industries, large companies and SMEs collaborate and compete at the same time; however there may be industries to which SMEs are more suited. Park et al. studied the selection of industries suitable for SMEs in Korea (Park et al., 2016). However, for SMEs to be able to compete internationally, they need to be competitive with global companies. To strengthen their competitiveness, SMEs that are not rich in human resources and financial status must be innovative. Open innovation can be a way of achieving this. Open innovation benefits from both internal and external knowledge. Research on innovation, or open innovation, has been actively pursued within academia. Witt has studied innovation that is necessary for our future (Witt, 2016). Yun et al. have studied dynamics that lead from open innovation to evolutionary change (Yun et al., 2016). Svirina, et al. conducted a study applying the concept of open innovation to social businesses (Svirina et al., 2016). By utilizing both internal knowledge and external knowledge, open innovation can strengthen SME competitiveness. This study analyzes the annual reports of US listed companies, with the aim of helping Korean SMEs absorb foreign knowledge and open innovation.
Bibliometric analysis studies using patent or paper data in the field of technology intelligence research are being studied for the purpose of discovering new opportunities from the technical planning point of view. In bibliometric research, the areas where many papers and patents are produced or cited are defined as promising technologies or areas. According to Jeong et al., patent analysis can identify new free technology and utilize it as a seed technology for SMEs and as a means of growth engine (Jeong et al., 2014). In another study, Jeong et al. extracted future promising research areas by using bibliographic coupling and clustering technique for the top 1% of cited papers in SCOPUS data (Jeong et al., 2008). These techniques can be used as a way to shorten the effort and time of R & D and technology development in countries with limited resources.
On the other hand, the analysis of the correlation between the business text and the financial analysis, which is the subject of this study, is interested in exploring the characteristics of the business documents of the companies with good financial performance and exploring the companies and the areas with the good financial performance. As a result, the bibliometric technique for papers and patents focuses on the area of people’s interest. On the other hand, this study is interested in finding companies or business area that is expected to perform well in the future in terms of financial or operating performance.
The purpose of this study is to find out whether there is a correlation between business sales patterns and business text patterns. This is based on the research that other researchers in the past have found that estimating future financial performance with a time-series pattern of financial performance is limited and business texts better represent future prospects (Shirata et al. 2011). In this paper, we use text mining techniques to create business text patterns and explore how they relate to corporate financial performance. In other words, if the financial performance of a company is good, we want to find out what patterns appear in the business text.
The study is a field of business intelligence that predicts the future financial performance of a company through business text analysis. In this field, robots that predict future stock prices through various data analysis have recently appeared, but there is not much research in academic aspect. This may be because new areas such as Big Data, as mentioned by Celia, SatikoIshikiriyama et al. (2015), have attracted much more research interest among researchers. In addition, traditional financial information researchers seem to be lacking knowledge about methodology that utilizes text mining techniques to forecast company financial performance. This field is a fusion field where text mining knowledge and financial knowledge are combined. However, as the interest of academia is concentrated elsewhere, it is considered that there is not much research in this field and it is sporadic. Although this study does not comprehensively deal with text mining and sales performance, it is expected to be a good alternative to predict future business performance by analyzing the correlation between business text pattern and business performance.
The annual reports of US listed companies is publicly available in 10-K format. These reports can contain information that illustrate the companies’ global economic conditions. Furthermore, these reports contain not only the financial information of a company but also the main business content, competitiveness and risk factors within each industry. This basic information is useful for grasping global industry trends. Therefore, in addition to traditional financial analysis, text analysis on annual reports has been attempted using text-mining techniques. Lee et al. attempted to analyze a company’s business model by applying text mining to an annual report (Lee et al., 2014). The study analyzed the report’s business descriptions and future strategies. Specifically, business keywords were extracted, and the average frequency and growth rate of each keyword were calculated. The position of the business keywords in the “business model evolution map” based on these two values was shown. Additionally, it is thought that annual reports have richer information in the text than in the numerical values. Consequently, there have been attempts to apply text-mining techniques to annual reports (Kloptchenko et al., 2002). Kloptchenko et al. study the implications for future financial performance that can be drawn from the textual portion of quarterly reports using self-organizing maps and text clustering techniques. Self-organizing maps and text clustering were used to analyze financial quantitative information and qualitative data analysis respectively. In the text-clustering process, word histograms were created from the text documents and made into distribution functions. Subsequently, Euclidean distances between histograms were obtained to measure the similarity between reports. A comparison of the quantitative result of financial information and the qualitative results of text showed inconsistencies. Thus, they argued that the style of writing changes before a dramatic change occurs in financial performance. For example, if the firm’s position is expected to deteriorate in the next quarter, the current quarter’s report will be more pessimistic, even though the financial performance is, as yet, unchanged. Another study aimed to predict the possibility of a firm defaulting, by applying text-mining techniques to annual reports (Shirata et al. 2011). Shirata et al. used textual information from Japanese companies’ annual reports to establish key phrases that could be used to predict corporate default. Prediction of default is one of the most important research topics among accounting researchers, with most of them using financial ratios. However, a change in the business performance of a company is actually articulated earlier in the nonfinancial information. Their study argued that “dividend” or “retained earnings” appearing in the same paragraph is effective for distinguishing between a defaulting company and a non-defaulting company. Lee et al. conducted a text- mining technique on 10-K reports to extract and visualize industrial service portfolios (Lee et al., 2016). In this study, they used a self-organizing map (SOM) technique to visualize the service portfolio and check word- usage patterns. Lee. et al.’s study predicted stock price by analyzing financial events of 8-K documents (Lee et al., 2014). In this study, the accuracy of the stock price prediction for the following day is improved by 10% when the text is considered. Of course, they emphasized that the analysis should not be considered a comprehensive trading strategy, but stressed that text analysis has predictive power in terms of stock price volatility. SatikoIshikiriyama et al., 2015attempted to ascertain topics of interest in business intelligence using sample analysis on the top 35 read papers in the field. Text-mining techniques determining the frequency of words, or word clouds, were created to identify the main topic of discussion in the paper. The research showed a decreasing interest in business intelligence in academia, suggesting that new areas, such as Big Data, may have received more attention. Pulliza’s study used sentiment analysis in the modeling of speculation in 10-K documents (Pulliza, 2015). This model was applied to the MPQA corpus, to extract features with high correlation with speculative sentences in the 10-K document set. As part of the results, they argued, that "regulation, fund, and supplier were ranked higher than in the documents with the highest amount of speculative sentences".
The studies reviewed indicate that applying text-mining techniques to annual reports is more effective for predicting the future value of business or business trends than analyzing financial information alone. The methods are diverse: ‘Term frequency-inverse document frequency’ (TF-IDF) is calculated to extract important words from text, followed by a clustering method; a SOM or word cloud can be produced as a visualization technique; and techniques such as sentiment analysis were also used. Sentiment analysis is often referred to as opinion mining, which is generally defined as being “aimed at determining the attitude of a speaker, writer, or other subject with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event." (Wikipedia 2018). In this study, we apply various text or data-mining techniques to text referring to risk factors and sales information in the annual reports of US listed companies. Sentence and word counts, sentiment analysis, keyword extraction and clustering are applied. In addition, we examine the correlation between these text patterns and sales.
Description of SIC codes used in our analysis
Number of Companies disclosed
Number of companies analyzed
Services-Computer Programming, Data Processing, etc.
Facebook, Twitter, Google(Alphabet), LinkedIn
Services-Computer Integrated Systems Design
Table of contents of annual report
Unresolved Staff Comments
Mine Safety Disclosures
Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities
Selected Financial Data
Management’s Discussion and Analysis of Financial Condition and Results of Operations
Quantitative and Qualitative Disclosures About Market Risk
Financial Statements and Supplementary Data
Changes in and Disagreements with Accountants on Accounting and Financial Disclosure
Controls and Procedures
Directors, Executive Officers and Corporate Governance
Security Ownership of Certain Beneficial Owners and Management and Related Stockholder Matters
Certain Relationships and Related Transactions, and Director Independence
Principal Accountant Fees and Services
Exhibits and Financial Statement Schedules
Hypotheses of our study
Category 7370 containing Google, Facebook, Twitter, etc. is better than category 7373 containing Yahoo in revenue performance.
(2–1) Companies with low revenue performance tend to write a shorter “Risk Factors” item section, to skip over risks.
(2–2) Companies with high revenue performance tend to write a shorter “Risk Factors” item section because they think there are fewer risks in their business.
(3–1) In item “Risk Factors”, a positive/negative tone correlates with sales performance
(3–2) Using the positive/negative text analysis results in the “Risk Factors” item section, we can group companies by sales performance.
The occurrence patterns of words in “Risk Factors” are correlated with sales performance.
Hypothesis 2 is to verify whether there is a correlation between sales performance and text length of ‘Risk Factors’ of 10-K report. The text length was defined as the number of sentences, the number of words, and the number of words per sentence. This is a direct way to see how each of the three variables representing text length correlates with sales performance. In addition, we examined whether the clustered firms are correlated with the sales performance by clustering firms with three variables such as the number of sentences, the number of words, and the number of words per sentence. That is, it is a method to check whether the clustering result considering the three variables representing the text length is correlated with the sales performance.
Hypothesis 3 is a method of examining whether the positive or negative tone in the business text correlates with sales performance. The technique of analyzing the tone of affirmation or negation of text is called ‘sentiment analysis’. According to Taboada et al., this is defined as follows: "Sentiment analysis refers to the general method to extract subjectivity and polarity from text (potentially also speech)" (Taboada et al., 2011). In sentiment analysis, for example, if the sentence contains a positive expression such as ‘good’, the tone of the sentence is evaluated as a positive sentence, and a sentence containing an expression such as ‘bad’ is determined as a negative sentence. In this study, we examine whether the number of positive statements or negative statements correlates with the sales performance of individual companies. For example, if companies with a large number of affirmative statements have good sales performance, companies with a lot of positive sentences will be expected to have good sales performance in the future.
To test hypothesis 3, we conduct a ‘sentiment analysis’ on each sentence in the “Risk Factors” item. Sentiment analysis, or opinion mining, is a method for determining whether a sentence’s tone is positive or negative. We use the ‘RSentiment’ package in R [Package ‘RSentiment manual]. Using the ‘calculate_total_presence_sentiment’ function in this package, the entire sentence is classified as positive, negative, very positive, very negative, neutral, or sarcasm. The number of sentences in each of these six categories is calculated from each company’s “Risk Factors” item. A scatter plot is used to determine the existence of a correlation between the CAGR values and the sentiment analysis. In addition, we apply a clustering technique to the sentiment analysis result to determine whether the companies are grouped by sales performance.
Hypothesis 4 assumes that the words in the business text will appear differently depending on sales performance. In other words, it is assumed that words that have a positive meaning in a group with good sales performance are shown, whereas in the opposite case, a lot of words with a negative meaning are expected to appear. However, this method differs from the sentiment analysis used in the hypothesis 3. The sentiment analysis analyzes the tone of the sentence, while the analysis focuses on what words are used directly. For example, a company with a good sales performance is expected to talk more about future prospects and plans, while a company with a poor sales performance is expected to talk more about immediate sales and risk reduction. Based on these assumptions, we check whether word patterns are correlated with sales performance. In the meantime, we extracted words using TF-IDF, which is widely used in the field of information retrieval, in order to remove unnecessary words and select good keywords that reflect the core of contents.
Comparison of CAGR values
CAGR statistics by SIC code
Relation between text statistics and CAGR
The text in “Item 1A. The Risk Factors” is a part of a company’s disclosure of current and future risk factors. Publishing such risk factors in an annual report can be embarrassing. Therefore, we assume companies with low sale performance tend to publish shorter Risk Factor items (hypothesis 2–1). However, if the company’s sales performance is good, it can be assumed this will also result in shorter Risk Factor items (hypothesis 2–2).
Relation between sentiment analysis result and CAGR
Correlation coefficients between CAGR values and nine categories of sentiment analysis
S + N
N + VN
P + VP
Relation between keywords and CAGR value
Non-overlapped and overlapped top 50 keywords from the two cases
Non-overlapping keywords from the bottom three companies include many words related to finance, contract/law, and risk. Specifically, words related to finance were as follows: revenues, assets, fiscal, cost, capital, budget, spending, and cash; for law: contracts, government, contract, claims, legal; and risk: failure, loss, risk. By comparison, the words derived from the top three companies in sales are very noticeable in terms of clients and advertisement. Words related to client were as follows: users, use, user, client, members; and for advertising: advertising, advertisers. As such, we find that companies with poor operating results tend to use terms relating to finance, contract and law and risk words more frequently, owing to discussions of financial pressure and risk of default. On the other hand, companies with good sales performance are thought to focus more on users and advertising. It becomes clear that, after analyzing the reports of these six companies, there is some correlation between the use of language and sales growth rate. Therefore, in this context, hypothesis 4 can be adopted.
In this study, we apply text mining to the annual reports of US companies. The aim was to investigate whether word patterns found in selected texts were related to the business performance of the company. We test four hypotheses: hypothesis 1 postulates that category 7370 companies, which include a large number of companies engaged in SNS activities, will have a better business performance than category 7373 companies, such as Yahoo. Hypothesis 1 is verified. Hypotheses 2 through 4 are analyzed by applying text and data-mining techniques to the risk factors of annual reports. Hypothesis 2 postulates that sales performance affects text statistics such as number of sentences. There is some evidence of correlation between sales performance and text statistics, however, further research is required. Hypothesis 3 postulates that the tone of the text correlates with sales performance. Applying sentiment analysis, no correlation was found and, thus, hypothesis 4 is rejected. Hypothesis 4 postulates that word usage in the text is correlated with the sales performance, and the hypothesis is temporarily adopted.
In summary, we identify a number of correlations between sales performance and the text pattern of company reports by applying text-mining technology. We expect to have more themes to be studied in the future. For hypothesis 2, better results can be expected by changing the data-processing method. Hypothesis 3 is rejected in this instance, but it is expected that better results can be obtained if the classification method of sentiment analysis is optimized for the text of annual reports. The analysis framework for hypothesis 4 needs to be designed to cover all the data, not only the highest and lowest three ranking companies in terms of sales performance. Also, if key phrases are extracted rather than the number of words, results may be more meaningful.
This study provides the following conclusions. Companies with good financial performance and bad companies often use different words. Therefore, it is very meaningful to analyze the words that appear predominantly in the business text in predicting a company’s future sales performance. However, the positive or negative tone of the business text is not relevant to forecasting the future financial performance of the company, as it appears to be lacking in correlation with sales performance. As a result, we hope that this study will serve as a stepping stone to develop the research contents by predicting future financial performance of companies and finding promising business areas.
This paper was received ‘best paper award’ at SOItmC 2017 conference.
The main ideas in this paper were previously reported in the conference (Lee et al., 2017).
This research was supported by Korea Institute of Science and Technology Information(KISTI) and Basic Science Research Program through the National Research Foundation of Korea (NRF-2015R1D1A1A09061299) funded by the Ministry of Education.
Availability of data and materials
All data can be obtained by manually querying the SEC EDGAR system (URL: https://www.sec.gov/edgar/searchedgar/companysearch.html). However, financial information for each company is attached as an Additional file 1 separately at the end of the manuscript.
BRL, JHP, LNK, and YHM carried out a systematic literature review. BRL, YHS, and GSK collected data and programmed algorithms to analyse the data. BRL and HJK wrote and revised the final manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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