# The Influence of Sentimental Analysis on Corporate Event Study

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4 NR jt = α j + β j R mt + ε jt NR ji = normal rate of return for company j on day t; R mt = rate of return for market index m on day t; ε jt = error term for company j at time t. The coefficients α j and β j are the ordinary least squares parameters of the intercept and slope, respectively, for company j. The abnormal return AR jt for the company j will then be calculated as: t. intercept AR jt == R jt (α j + β j R mt ) AR jt = Abnormal return for company j on day R jt = Return for company j on day t α j = Estimate of OLS parameter of β j = Estimate of OLS parameter of slope R mt = Rate of return of market index m on day t. The Cumulative Abnormal Returns are calculated using: CAR (t, T) = t T ARt ARt = average abnormal return on day t; t, T = Accumulation period Examining the CAR of a set of sample securities will be used to look at whether or not the values of the average residuals, starting from the day of cumulation and up to a specific point, are systematically different from zero (Chatterji & Kuenzi (2001) as in Jucunda & Sophia (2014)). Multiple Regression Analysis: To finally examine the influence of twitter sentimental analysis on CAR, the multiple regression analysis is used. The regression functions of the form: Y = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3.+ β N X N + ε Where β 0, β 1, β 2.. β N = are constants called the model regression co-efficient or parameters, which means the regression scores identified statistically for the variables that have a significant level as.000. X 1 + X 2 + X 3 + X N = are the predictor variables in this study. Y = will be the response variable or dependent variable. It is a quantitative data. Ε = randomized disturbance or error. Regression computes the regression co-efficients β j while the independent or predictor variables are the actual values that are in the variables. Where j = 1, 2,.n. VARIABLE SELECTION: The variables used in the stud are the twitter parameters of emotion and polarity and CAR for each company. The variables for the twitter parameters were calculated from Zhang, Fuehres & Gloor (2011) and Bollen & Mao (2010) and CAR were computed following Chatterji & Kuenzi (2010) and Brown & Warner (1985). The table 1 below shows the list of variables used in the study. VARIABLES CAR (-7,+7) Bullishness Agreement Positive Negative EXPLANATION DEPENDENT VARIABLE Cumulative Abnormal Returns for 15 days around the announcement of the managerial decision. INDEPENDENT VARIABLES This represents the bullishness of the market at the time of the corporate event announcement. The agreement is the proportion of positive and negative feelings in the market at the time of announcement of the corporate event. This represents the percentage of positive feeling in the market due to managerial decision. This represents the percentage of negative feeling in the market due to managerial decision. Emotions (Joy, These variables represent the Sad, Surprise, various emotions of tweets around Fear, Anger, the managerial decision. Disgust) Table 1: Variables used in the study IV. ANALYSIS AND RESULTS Twitter sentimental analysis R-studio: As specified above, the tweets collected were subjected to analysis in R-Studio, data mining software. The R-Studio then classifies the tweets of each company as positive and negative and also brings out the various emotions in the tweets. The results from R-Studio are generated as shown in Figure 2 & Figure 3 for each company: 13

5 SAD ANGER FEAR SURPRISE Table 2 Figure 2 As seen from above, all the variables except surprise are significant at 0%, 1% and 5% level. The variables positive and joy are the most highly significant compared to all other variables. Bullishness, Agreement, Negative and fear are significant under 5% level. The variables sad and anger are significant under 10% level. Multiple Regression Analysis: The Cumulative Abnormal Returns for the fifteen days around the event date is calculated using event study methodology, market model. The cumulative abnormal returns for the companies were then subjected to regression analysis with the twitter parameters. The regression results are summarized in Table 3: MODEL BETA t- TEST SIGNIFICANCE CONSTANT BULLISHNESS Figure 3 Thus the results for each company were generated from R-Studio as shown above where the polarity and emotion of the tweets of companies have been brought out. After generating the results from R-Studio, the variables were computed using the formulae specified above. The t-tests of the variables computed is shown in Table 2: VARIABLES MEAN t- TEST BULLISHESS AGREEMENT POSITIVE NEGATIVE JOY SIGNIFCANCE AGREEMENT POSITIVE JOY SAD ANGER FEAR SURPRISE Dependent variable: CAR (-7, +7) Table 3 The table 3 above shows the results from multiple linear regression analysis. The variable negative has been removed in the regression analysis as it has been insignificant for the analysis. The variables Positive, joy, sad and surprise are highly significant at less than 14

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