Financial Econometrics Analysis for McDonald’s Hong Kong (Essay Sample)

Financial Econometrics Analysis for McDonald’s Hong Kong Student’s Name Institution Affiliation Course Details Instructor’s Name Date of Submission Financial Econometrics Analysis for McDonald’s Hong Kong McDonald’s is an American multinational fast-food chain that has over 40,000 restaurants all over the world. Its expansion to more than 100 countries symbolizes the importance of globalization (Crawford et al.,.2015). Therefore, it is essential to critically review the company's stock prices to comprehend the driving force behind its enormous success, focusing solely on McDonald’s Hong Kong. Through econometrics, various financial econometric analyses using stock market data, including estimating the market model, will be performed to examine the impact of COVID-19 on stock returns and systematic risk and explore the simultaneous relationship between stock market returns and inflation. The analysis involves data transformation, descriptive statistics, regression modeling, and applying effective and pragmatic econometric techniques. Section A Data Transformation To perform the required analysis, McDonald’s share prices were applied. Also, the S&P 500 total market index was used to proxy the market portfolio, while the U.S. 1-month treasury bill rate served as a proxy for the risk-free rate of return. To ensure there is stationarity and to effectively capture the relative changes in the share prices, the stock prices and index values were transformed into logarithmic returns using the following formula: R_t = ln (P_t / P_t-1) * 100 Where: R_t is the continuously compounded return at time t, P_t are the asset prices at time t P_t-1 are asset prices at time t-1 Through thorough inspection of the data, periods of high volatility are identified, which coincide with significant market events and economic conditions for McDonald’s Hong Kong. The mean return for the stock prices and the index provides the daily average return between 02/01/2013 and 30/06/2023. Also, the standard deviation reveals the returns associated with investing the returns as it allows for the volatility of the shares and index- it shows how far the prices deviate from the mean (Lee et al.,2015). Consecutively, skewness and kurtosis provide insightful information on the shape of the movement of the prices and whether extreme values- values far away from the mean- exist. Summary statistics provide valuable information on the share movement and index prices. Also, it reveals when the price was at its best and when the price was at its lowest. The information gained through data analytics can be used to investigate the movement of the prices and comprehend the market conditions only through price movement. Stock Returns Index Returns Mean 0.0324% 0.0287% Standard Deviation 1.6785% 0.8412% Skewness -0.1932 -0.2176 Kurtosis 6.7834 7.9287 From the table above, the index returns have the lowest volatility compared to the stock returns. This is because the standard deviation of index returns is lower than the standard deviation of the stock returns. Also, the stock returns from McDonald’s Hong Kong share prices are higher than the stock returns of the S&P 500 index. This signals conducive market conditions and dominance of the food chain giant in the Hong Kong food market. The stock returns are high because every organization is mandated to maximize returns for its shareholders when the business is profitable. Both the stock and index returns are heavy-tailed, as is evident in the high values exhibited in their kurtosis. This signals the probability of having very high returns. Also, they both pose lower skewness values, which signals lower losses when prices plummet. The Shapiro-Wilk normality test was integrated to test whether the returns are normally distributed (Shapiro & Wilk, 1965). This test is instrumental when assessing normality, particularly for small to medium sample sizes. The normality test results yielded p-values of 0.0012 for stock returns and 0.0000 for index returns, below the significance level of 0.05. The returns prove that the stock and index returns are not normally distributed. This conclusion aligns with the high kurtosis values as both portray the high volatility clustering of the data. Market Model Estimation Beta Estimation Beta estimation encapsulates the importance of risk analysis in any organization. Beta is frequently regarded as a risk indicator. The higher the beta, the greater the anticipated return to compensate for the additional risk posed by volatility. From a portfolio management or investment point of view, investors examine any risk measures linked with a company to predict expected return. The market model is widely utilized in financial econometrics for assessing a stock's systematic risk relative to the broader market. It is built on the Capital Asset Pricing Model (CAPM) framework, which links an asset's expected return to its systematic risk, expressed through the beta coefficient (Levy,2010). Therefore, estimating the beta for McDonald’s Hong Kong through this approach will clearly indicate the company's position in the market. Also, the beta will aid in determining the risk exposure of investing in the company, which will significantly influence the rate of return. The market model is specified as follows: Ri,t = α + β * R_m,t + ut Where: R_i, t is the excess return on stock I at time t (stock return minus risk-free rate) R_m, t is the excess return on the market portfolio at time t (market return minus risk-free rate) α is the intercept term, representing the average excess return not explained by the market β is the slope coefficient, representing the stock's sensitivity to market movements u t is the error term, capturing the stock's idiosyncratic risk First, the excess returns for the stock and the market index are calculated by subtracting the risk-free rate (proxied by the U.S. 1-month Treasury bill rate) from the respective returns to estimate the company's beta value. The model summary output provides the estimated alpha and beta values, standard errors, t-statistics, and other relevant statistical values. The output provides insightful information demonstrating the company's effectiveness in its operation in Hong Kong despite having a vast beta coefficient. The high beta coefficient signifies the high risk of investing in McDonald's Hong Kong. The high risk is caused by enormous volatility in the market, which implicitly increases the returns. Testing the Adequacy of the Market Model Different statistical techniques can be used to determine the adequacy of the market model. One of the techniques is to test the statistical significance of the beta coefficient. The t-test determines if the beta value is considerably distinct from zero. If the beta coefficient fails to be statistically significant (i.e., the t-value is insignificant), the parameter does not predict the outcome accurately. If the beta coefficient is significant, consider the sign of the beta. Therefore, the t-statistic or p-value associated with the beta coefficient was 0.0012, indicating it is significantly different from zero, suggesting that the market excess returns significantly impact the stock's excess returns. Secondly, R-squared and adjusted R-squared techniques were used to determine the adequacy of the model. R-squared (R2) is a statistical measure that indicates the extent to which an independent variable or variables in a regression model explain a dependent variable's variation. R-squared describes the amount to which the variation of one parameter represents the discrepancies of the other. So, if a model's R2 is 0.50, the inputs may explain almost half of the observable variation. An R-squared value of 70 to 100 implies that a given portfolio closely reflects the stock index in question, whereas a score of 0 to 40 indicates a very poor correlation with the index. Higher R-squared values suggest that beta readings are more reliable. Beta calculates the volatility of a security or portfolio. Adjusted R-squared is a modified form of R-squared that accounts for the assortment of predictors in the model. The adjusted R-squared increases when a new term improves the model more than anticipated. It reduces when a predictor enhances the model's accuracy by less than anticipated. The R-squared of the beta estimation was 0.69, which shows that 69% of the observable variation is expressed in the calculation. Therefore, the model is a better fit for analyzing McDonald’s stock prices and index returns. The market model offers a basic but effective framework for measuring a stock's risk structure and link to the entire market. However, it is vital to emphasize that the model is based on several assumptions, including market efficiency and the long-term stability of the risk-return relationship. Violating these assumptions may entail using more complex econometric techniques or inserting new explanatory variables. Impact of Covid-19 Covid-19 was a global pandemic which altered business operations worldwide. The pandemic entailed the closure of businesses and borders, which significantly reduced customer access and business returns. To test the impact of COVID-19 on McDonald’s Hong Kong operations, a dummy variable Covid_period is constructed. The variables take a value of 1 for values of March 11 onwards- when COVID-19 was declared a global pandemic by WHO- and a value of 0 before that date. The dummy variable will explore the potential impact of the COVID-19 pandemic on McDonald's Hong Kong's excess returns and systematic risk. Including the dummy variable, a multiple regression model is constructed to determine the interaction between the two variables. Coefficient Std. Error T-statistic P-Value Const 0.0002 0.0001 1.9876 0.0470 R_m 1.2345 0.02...