Stock Analysis of Apple

                       Stock Analysis of Apple

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Stock Analysis

Part I. Company’s Profile.

Firstly, some information regarding the company under consideration should be provided. Apple is one of the most well-known companies in the world. Company’s production includes computers’ software, online services, personal computers, cell-phones, media players, smart phones, etc. The company’s headquarters is in Cupertino, California.

The company’s stocks are traded at the stock markets and, that is why, there are many company’s owners. However, the most important company’s owners include Vanguard Group Inc. (9.07%), State Street Corp. (6.72%), FMR LCC (4.82%), Bank of New York Mellon Corp. (2.47%) and others.

According to Apple’s 2014 annual report, the company’s net operating revenues increased from $156.5 billion in 2012 and to $170.9 billion in 2013 and $182.7 billion in 2014. (Apple 2014 annual report, 2015). Additionally, the company’s net income decreased from $41.7 billion in 2012 to $37 billion in 2013 and $39.5 billion in 2014 (Apple 2014 annual report, 2015). However, the company’s net income decreased by 5.33% in 2014 as compared to the 2012 year. In general, the organization’s total revenue increased by 16.8% in 2014 as compared to the 2012 year.

Part II. Results of Regression

It is worth mentioning that regression models are often applied to analyze the connection between independent and dependant variables. As it is known, regression models are often applied to disclose a character of the connection between variables, the solidity of their connection, etc. That is why it would be reasonable to build regression models to reveal the connection between return of the chosen company and the overall market return. Applying regression models it can be concluded about the relation between the variables: a directly proportional or inverse connection. Also, it should be stated that the correlation coefficient is often used to determine the strength of relations between variables. Thus, the regression models should be used to research the connection between the variables.

Taking into account that the market return presented by S&P 500 index is a factor variable, while the Apple’s shares return is a resulting variable, a simple linear regression can be created. Thus, the equation of connection between the mentioned two variables based on 10-year data can be expressed as:

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The connection between the factor and resulting variables during the past 10 years are also represented in the figure below.

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The model’s R-squared is 0.1622. It means that the connection between the return of the Apple’s shares and market return is quite low.

Additionally, the analyzed 10-year period has been splitted into two  5-year windows and regressions under these two windows have been performed. Thus, the connection between the variables during the timeframe from October, 2010 to October 2015 is expressed by the following equation:

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Additionally, the connection between the variables during the timeframe from October, 2005 to October, 2010 is expressed by the following equation:

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Part III. Discussion of Results

The results of performed regressions should be discussed. Primarily, the Apple’s beta was 1.16 during the analyzed 10-year period from October 2005 to October 2015. As it is known, Beta-coefficient is a number that describes the relationship between the change in asset prices and an overall change in the market. High beta means that the asset price rises sharply, when the market moves up and drops sharply, when the market moves down. Low beta means that the asset changes are relatively insensitive to fluctuations in the overall market.

Since the beta is considered relative to the total market, it is needed a benchmark, representing the overall market. In most cases, when it comes to the U.S. market, the S & P 500 is used, although the beta can be considered against the sector specific indices. Exact methods for calculating Beta may vary. It should be also noted that Beta greater than 1.0 means that the volatility of the asset is higher, than the market volatility and correlation of changes is positive. For example, a beta of 2 means that the asset changes are at least twice as much volatility than changes in the market, probably, even greater. A beta of less than 1.0 may indicate that the volatility of the asset is less than the volatility of the market, or simply the correlation of changes the asset and the market is low. A beta of 0.0 means that the company’s dynamics of asset and the market is not correlated, i.e. they move independently. Therefore, the volatility of Apple’ assets is more than the volatility of overall market. However, the company’s beta is higher than its competitors and, that is why, the company’s assets’ price rises sharply than the overall market.

That is why the company’s beta can be calculated by using the following formula:

Beta = Unleveled Beta / [1 + (1-Tc) + (D/E)],

where:

Тc is the company’s tax rate.

D/E  is the company’s debt to equity ratio.

Also, alpha indicates the difference between the real fund’s return and its expected return evaluated by its beta. The positive alpha means that the fund’s return is higher, than it was expected, considering its beta. Conversely, the negative alpha means that the funds were underperformed as compared to the expected value. In our case, the annualized alpha is 3.34 and, thus, the funds will be performed better as compared to the predictions made using its beta. Therefore, the chosen portfolio has a better profitability, than the benchmark.

Additionally, the r-squared of regression is 0.16 and it means that 16% of the market return of the Apple’s shares may be explained by general market return. As it is known, for the securities with fixed-income rate, the benchmark is three-month treasury bonds, while for the equities SP 500 is used as a benchmark. The portfolio’s standard deviation is 12.1 and it indicated that the funds’ return is less variable in comparison with the benchmark’s volatility. It should be also noted that the standard deviation is often used by investors in order to estimate the expected fund’s volatility.