Determine Beta And Alpha

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Economics

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Nov 24, 2024

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Determine Beta And Alpha Student's Name Institution Course Instructor Date

Determine Beta and Alpha The stock chosen is Apple Inc., represented by the ticker symbol (AAPL). Comparing the estimate (1.0124) and the current beta of the stock 1.31 shows a close margin. A Beta measures the stock's sensitivity to market movements, particularly to the movements of a benchmark index, in our case, the S&P 500 ( Hünermund & Louw, 2020 ). Then, the coefficient (1.0124) suggests that for each unit increase in beta, the stock's excess return is expected to increase by approximately 1.0124 units. Moreover, the stock's beta is a measure of the stock's sensitivity to market movements, particularly in relation to the benchmark index, often the S&P 500. A beta of 1.31 suggests that the stock is expected to be approximately 31%, and a beta of 1.0124 is expected to be approximately 1.24% more volatile than the market or, in our case, the S&P 500 index. Beta reflects the stock's systematic risk and how it moves compared to the broader market. Furthermore, a beta of 1 represents a stock expected to move in line with the market. In other words, its returns are expected to be as volatile as the overall market. In statistical analysis, the intercept's coefficient is interpreted as the company's alpha, representing a component of its performance unexplained by the model's independent variables. To gauge its statistical relevance, we rely on the t Stat. In our case, the t Stat for the alpha registers at -1.22, below the typical significance threshold of 1.64. The alpha is only statistically significant if t Stat is bigger than 1.64; this is a threshold for assessing the statistical significance of the alpha (intercept) in a regression model ( Hünermund & Louw, 2020 ). Consequently, our analysis concludes that the alpha is not statistically significant. This implies that the company's unexplained performance, as represented by the intercept, does not significantly deviate from a null value within the context of our model. When alpha is not statistically significant, it signifies that the portion of a company's performance unaccounted for by the model's independent

variables, represented by the intercept, is not meaningfully different from zero within the analysis context. In practical terms, it suggests that the company's returns or performance, after considering the variables in the model, do not exhibit a consistent and distinct pattern of outperforming or underperforming expectations.

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Reference Hünermund, P., & Louw, B. (2020). On the nuisance of control variables in regression analysis. arXiv preprint arXiv:2005.10314 .

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