3. RESEARCH METHOD
3.4 REGRESSION MODLE
STDEV = 𝛃0+ 𝜷𝟏𝐐𝐔𝐀𝐍𝐓𝐈𝐓𝐘 + 𝜷𝟐𝐒𝐂𝐎𝐑𝐄 + 𝛽3𝐓𝐎𝐍𝐄 + 𝛽4𝑹𝑶𝑬 + 𝛽5𝑳𝑬𝑽 + 𝛽6𝑶𝑺𝑯𝑨𝑹𝑬𝑺 + 𝛽7𝑩𝒕𝒐𝑴 + 𝜀 .
Where:
STDEV = the stock price volatility is calculated as standard deviation (STDEV) of stock price.
QUANTITY = the measure of the quantity (QUANTITY) of news is based on the quantity of news that are reported company’s conference call by the mass media in our time window.
SCORE = the Scores (SCORE) are given by reading the news that refer to company’s conference calls, and its rules appears on Appendix 2.
TONE = the affect or feeling of news (TONE) that refer to conference calls of a company. TONE is calculated as (Positive word-Negative word) divide by (Positive word+ Negative word) and use this research’s dictionary to calculate.
ROE = Return on Shareholders' Equity (ROE) is a company's net income divided by its average stockholder's equity.
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LEV = The Leverage (LEV) is computed as net debt/total asset.
OSHARE= Outstanding shares (OSHARE) are the tradable shares available on the market at the end of every our research year.
BtoM= The Book to Market (BtoM) is the ratio between book value and market capitalization.
3.4.1 DEPENDENT VARIABLE
The most commonly used measure of stock return volatility is standard deviation Schwert (1990). This statistic measures the dispersion of returns. Financial economists find the standard deviation to be useful because it summarizes the probability of seeing extreme values of return. If the price of a stock moves up and down rapidly over short time periods, it has high volatility. If the price almost never changes, it has low volatility. When the standard deviation is large, the chance of a large positive or negative return is large.
The methodology used to test our hypothesis follows the work of Johnson and Westberg (2004). Their study examined the role of corporate news reported by mass media on the stock price volatility after IPOs. Specifically, Johnson and Westberg (2004)2 employed a cross sectional multivariate regression using the average monthly idiosyncratic stock price volatility as dependent variable, a number of dummies indicating the level of news on the stock as independent variables and various control variables. In this study because the aim is to analyze the stock volatility on daily basis, this use a daily average stock volatility measure (STDEV) expressed as follows:
2 The average monthly idiosyncratic volatility in Johnson and Westberg (2004) is measured as the sum of the daily squared differences between firm returns and those on a value weighted-index. Thus, monthly firm idiosyncratic volatility is: 𝜎𝑚𝑜𝑛𝑡ℎ𝑙𝑦=√∑ (𝑅𝑇 𝑗−𝑅𝑉𝑊𝑖𝑛𝑑𝑒𝑥
1 )2 Where T is the number of trading days in the month, R j is the one-day firm return and R VWindex is the one-day value weighted index return.
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𝜎=√∑(𝑅𝑗−R̅𝑗
𝑇 1
)
2
Where T is the number of trading days in our time window, Rj is the one-day closing price and R̅𝑗 is the one-day average stock price.
3.4.2 INDEPENDENT VARIABLE
Liu (2012) Sentiment classification is usually formulated as a two-class classification problem, positive and negative. Training and testing data used are normally product reviews. Since online reviews have rating scores assigned by their reviewers, e.g., 1-5 stars, the positive and negative classes are determined using the ratings. For example, a review with 4 or 5 stars is considered a positive review, and a review with 1 to 2 stars is considered a negative review. Most research papers do not use the neutral class, which makes the classification problem considerably easier, but it is possible to use the neutral class, e.g., assigning all 3-star reviews the neutral class.
Follow Liu’s concept, I use numeric score expressing the strength/intensity scores. Because news provide abundant information, I expand score to positive 10 scores and negative 10 scores to reflect information content.
If the news make mention of company’s stock price limit up, the company will get positive 8~10 scores, and accurate scores will depends on how the news describe company’s condition; if the news refer to the company’s net income is significantly increasing, earning per share is higher than company in the same industry or customers love their products very much so their future performance is worthy expectation, the company will get positive 4~7 scores and accurate scores will depends on how the news describe company’s condition; if the news refer to the company is during the off season, so its operating achievements is not good as expectation or any other condition of the company’s performance is at the same level
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but still have room for improvement , the company will get positive 1~3 scores and accurate scores will depends on how the news describe company’s condition.
If the news refer to a company’s operating performance is under expectation, inferior to the industry performance, their operating income is negative, earning per share is negative or any other condition of poor performance, the company will get negative 1~3 scores and accurate scores will depends on how the news describe company’s condition; if the news mention that a company’s revenue is significantly decreasing or face the condition of operating difficulty or any other severe problem or severely poor performance, the company will get negative 4~7 scores and accurate scores will depends on how the news describe company’s condition; if the news make mention of company’s stock price limit down, the company will get negative 8~10 scores, and accurate scores will depends on how the news describe company’s condition. The variable of SCORE will follow the above rules and the rules of scoring are presented in Appendix 2.
The measure of the quantity (QUANTITY) of news is based on the quantity of news that are reported company’s conference call by the mass media in our time window. When quantity of news is increasing, it means market pay more attention to the company, and market are in the midst of a lively discussion of the company. This paper expected that the more quantity of news reported, the more stock price volatility will produce, so this research expect the positive sign.
The measure of Tone (TONE) is based on a frequency count of the number of positive and negative words, using the dictionary of this research. The words counted as positive and negative within conference calls are shown in Appendix 5 and 6 and the computed results are shown in Appendix 2. TONE is calculated as the count of positive words minus the count of negative words, divided by the sum of positive and negative word counts so the maximum and minimum values of TONE are 1 and –1,
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respectively. Formula is as following：
𝑇𝑂𝑁𝐸𝑗 =𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑗 − 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑗 𝑃𝑜𝑠𝑡𝑖𝑣𝑒𝑗 + 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑗 Where：
𝑃𝑜𝑠𝑡𝑖𝑣𝑒𝑗= Positive is the total positive words in the news.
𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑗 = Negative is the total negative words in the news.
3.4.3 CONTROL VARIABLE
Wei and Zhang (2003) provide evidence of an inverse link between ROE and volatility, when the ROE of a company is high, investors are more willing to invest in the stock market, so this paper insert the variable of ROE, this paper expected a positive sign.
This paper also use control variables as indicative of the size, value and level of indebtedness. The variables for size and value are included because they are factors that significantly affect the expected returns of stock prices in financial markets (see Fama and French, 1992). The size is measured by the number of outstanding shares (OSHARE) as another variable. Because a larger quantity of tradable outstanding shares should be correlated to greater stability in stock prices. They are less likely to be influenced by volatility resulting from a scarce availability of shares on the market.
The coefficient of the variable is expected to be negative (Dell'Acqua et al., 2010).
The value is measured by the Book-to-market ratio (BtoM), calculated as the ratio of the book value on the market capitalization of the firm, both taken at the end of every our research year (2010-2014). As for the previous variable even the coefficient of the Book-to-market ratio is expected to be negative: a higher Book-to-market ratio has to be related to a lower volatility
Firm’s indebtedness is determined by the Leverage (LEV) and is computed through the ratio of net financial debt on total assets, using for both balance sheet data
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at the end of every our research year (2010-2014). This research expect a positive coefficient of this variable since higher leverage results in higher financial risk and ultimately higher stock price volatility.