Regression analysis can be used to test whether the market efficiently uses information in valuing stocks. For concreten

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Regression Analysis Can Be Used To Test Whether The Market Efficiently Uses Information In Valuing Stocks For Concreten 1 (96.18 KiB) Viewed 128 times

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Regression analysis can be used to test whether the market efficiently uses information in valuing stocks. For concreteness, letretum be the total return for holding a firm's stock over the four-year period from the end of 1990 to the end of 1994. The efficient markets hypothesis says that these returns should not be systematically related to information known in 1990. If firm characteristics known at the beginning of the period help to predict stock returns, then we could use this information in choosing stocks. For 1990. let dkr be a firm's debt to capital ratio, let eps denote the earnings per share, let netinc denote net income, and let salary denote total compensation for the CEO. See file return.xls. The definition of the variables of interest is presented in the table below: Variable Definition / Unit return % change stock price, 90-94 dkr debt/capital, 1990 earnings per share. 1990 eps netinc net income, 1990 (millions $) salary CEO salary, 1990 (thousands $)

Consider the following model: Time left 2:56:35 retum= Bo +Bidkr +B2epsi +B3netinci + Basalaryi + U; 1) Estimate the parameters in the above equation with the given sample data. (3 marks) 2) Interpret the parameters estimated in your equation. (5 marks) 3) Are the coefficients statistically significant? *Hint: Conduct proper hypothesis testing for each coefficient using one approach. (5 marks) 4) How well does the estimated regression equation fit the data? Hint: Explain the R-squared (1 mark) 5) Write down the null and the alternative hypothesis to test the homoscedastic assumption of the classical linear regression model. (1 mark) 6) Conduct White test to test the stated hypothesis in part 5. (2 marks) 7) Write down one method to resolve heteroscedasticity if it exists. (1 mark) 8) Write down the null and the alternative hypothesis to test the absence of 1st order autocorrelation assumption of the classical linear regression model. (1 mark) 9) Conduct Breusch-Godfrey test to test the stated hypothesis in part 8. (3 marks)

hypothesis in part 8. (3 marks) Time left 2:56:23 10) Conduct Durbin Watson test to test the stated hypothesis in part 8. (3 marks) 11) Write down one way to deal with serial autocorrelation if it exists. (1 mark) 12) Write down the null and the alternative hypothesis to test the normality assumption. (1 mark) 13) Conduct Bera-Jarque test to test the stated hypothesis in part 12. (3 marks) 14) Write down one viable solution to adjust the non- normality if it exists. (1 mark) 15) Write down the null and the alternative hypothesis to test the stability of the parameters. (1 mark) 16) Conduct Chow test to test the stated hypothesis in part 15. (3 marks) Submit your Excel file and a soft copy of the answers to the questions.