3. REASERCH METHOD
3.3 CONTENT ANALYSIS METHOD
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3.3 CONTENT ANALYSIS METHOD
3.3.1 Approaches to Measure Notes to Financial Reports and Scoring
Notes to the annual financial reports are also scored by seven criteria pointed out by the Financial Supervisory Commission (FSC), as the competent authority responsible for development, supervision, regulation, and examination of financial markets and financial service enterprises in Taiwan. These points brought out by FSC are meant to provide guidance to help listed companies disclose more detailed notes to the annual financial reports after the adoption of IFRS since 2013. For test of Hypothesis 2, this study defines a variable NOTESCORE to represent the scoring of notes to financial statements, ranging from 1 to 7. This scoring is constructed manually, because the format and ways of presentation in the annual financial reports are not machine-readable without specific modifications. The manual scoring criteria of annual financial reports are summarized as follows:
a) Valuation of Investment Property.
b) Material Component of Property, Plant, and Equipment.
c) Benefits to the Management.
d) The Impact of IFRSs Implementation.
e) The Impact of the New IFRSs Standard.
f) Three level of fair value measurement of financial instruments.
g) Disclosure of Material Accounting Measurement.
The examples of the scoring criteria are presented in the appendix 1.
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3.3.2 Approaches to Measure News articles Language and Scoring
This study collects news articles published by Knowledge Management Winner (KMW) database between [0, +5] working days window. KMW is marketed and maintained by the largest financial news group in Taiwan and is the largest news database composed of China Times, China Times Express and Commercial Times. This research searches financial news articles using company names and codes from TSE as two key words2 and further screens out the news articles unrelated to the financial reporting. Only news articles related to financial information about target companies, industry analysis or stock performances and forecasts are chosen. Those relate to promotional and customer-related and other non-financial-related announcements and information are eliminated.
In the beginning of the news articles, the database name, the publish date and time, the title of the article and the author are shown. Then the text body follows and this study focus only on the text body. This study defines dummy variables BS_D and SCF_D to be 1 if these word counts are greater than one for balance sheet related words and greater than one for statement of cash flow related words, respectively. Then, this study constructs a dummy variable, DET_FS, equal to 1 if the sum of BS_D and SCF_D to be equal to or greater than 1. The word lists used are provided in appendix 2. This research employs computerized textual-analysis tools to measure language throughout each of the news articles in the sample. Two measurements of financial news article language are from prior research of the Ministry of Science and Technology project. In the project, the team of professors and three graduate students read the textual content of financial news articles and score each news report based on the amount of information disclosed in the financial news. They score each news report based on the
2 Use 4-digit numbers and 2-character abbreviated names on TSE to search news.
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amount of extra information disclosed in the financial news articles yet in financial reporting and construct word lists to count optimistic, negative and scoring criteria-related words in the full text of each news articles. The building processes of the word lists are manually select positive, negative and scoring-related words; include the word lists into computer software to act as features to automatically analyze financial news articles; and compare the results of both manual and automatic analysis. To increase validity of the word lists, they repeat the building processes many times to reduce subjectivity.
This study implements the word lists to analyze the sentiment and information content of financial news articles. The scoring of financial news articles is between 1 and 5. The word lists of positive, negative and scoring criteria are presented in the appendix 3 and 4, and scoring criteria are summarized as follows:
a) Financial news corresponds to the financial reports and extends the reports.
b) Financial news corresponds to the financial reports and provides the cross-year or cross-firm or cross-industry analysis.
c) Financial news corresponds to the financial reports and provides further information to explain the notes in the reports.
d) Financial news corresponds to the financial reports and provides opinions on the effect stock trends.
e) Financial news corresponds to the financial reports and provides further information about earnings, investments, merger and acquisition, management, headquarters changes, and accounting method changes.
For tests of Hypothesis 2, this study first defines a dummy variable TONE, using the word lists presented in the appendix 3 to count both optimistic (P) and negative (N) words in the full text of each financial news articles. TONE is calculated as the
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differences between frequency counts of optimistic words and negative words separately and divided by the sum of frequency count of positive and negative words.
That is, (P－N)/(P＋N). By dividing by the sum of frequency count of positive and negative words, TONE is the proxy for sentiment, which eliminates the common biases for financial news articles mostly containing positive words. Second, this research defines a variable TOTALCORE to represent the information abundancy by adding the scores of footnotes to financial statements and financial news articles. For all the content analysis variables, TONE and TOTALCORE, this study will take average of them if a company has news articles more than one.
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3.4 REGRESSION MODEL