-r
r
-= r
β
(4-3)Beta can then be calculated:
β = (7.5 – 2.5) / (5 – 2.5) = 2
Thus, the beta value is equal to 2 in this example. A beta value is considered ‘high’
if it is above 2 (Sharpe, 1990; Lee el at., 2010).
4.3.3 Implications of CAPM calculations
Linear regression is used to model CAPM, where the gradient is equivalent to the value of beta (β). If the gradient is a steep positive value, it means the possibility of being exposed to uncontrolled risks is high. If the gradient is a negative slope, the project has low risk value but is subject to further changes when circumstances have changed (eg uncontrolled rise of energy bills that reduces cost-saving).
Outputs of CAPM can include:
• Beta: a numeric value for uncontrolled risks
• Standard errors: statistical spread and variations from collected data.
• Durbin-Watson test (Durbin and Watson, 1950, 1951): a standard test for CAPM regression, which can include either positive or negative co-relation.
4.3.4 Limitations of the Capital Asset Pricing Model (CAPM)
Sharpe (1990) discussed how CAPM uses regression analysis to model return and risk. At the time of development in the 1960s, stock options were less volatile and the model was designed to perform linear regression. Until the development of the Black-Scholes-Merton model (otherwise known as Black-Scholes) in the 1970s, financial modelling had more variety of regression techniques. Another reason to adopt linear regression for return and risk is for a new product/service
development (Hull, 2009). When a new product/service is being supplied, return and risk usually have a linear phase of growth until further increasing demands and customers arise (Waters, 2008). CAPM is usable for risk and return analysis and
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there are ways to consolidate the validity of CAPM. For example, Gentzoglanis (2011) explains that the use of organisational data can ensure that linear
regression provides a good representation of the real risk and return analysis for projects. The most influential ‘modern’ CAPM was reported by Prechter and Parker (2007), who have demonstrated risk and return analysis.
There are two major limitations for CAPM. Firstly, the model does not provide detailed descriptions about how to handle a large number of datasets. This is an important area since the volume of data is growing for organisations which adopt new technologies to demonstrate added values for adoption (Fichman and
Kemerer, 1993; Hey, 2009). CAPM is a model developed in 1960s. At that time, the extremely large digital datasets which are common today were simply not possible.
CAPM (the original 1960s version) is not fully able to handle thousands of datasets at once. The capacity to handle thousands of datasets is important for
intensive research (Hey, 2009). The inability of basic CAPM models to handle data-intensive cases may result in a longer computational time or may process datasets a few times to optimise the speed. To tackle this problem, researchers developed their own models (e.g. Hamelink, 2000; Prechter and Parker, 2007). For example, Hamelink (2000) developed an improved algorithm for his “International CAPM”
model which can compute a large number of financial datasets at once. It is
apparent that the rapid growth of data and its complexity means that the ability to analyse thousands of datasets at once becomes important (Hey, 2009).
Secondly, currently CAPM is more commonly used for econometrics and to calculate risk and return analysis (Sharpe, 1990; Hull, 2009). Although it can be used as a generic solution, a different approach is required for computing, since the key input values should correspond to technical terms rather than financial terms such as return on the market and risk-free rate in the market. In addition, new technology adoption such as Cloud Computing requires more attention to calculate risk and return, as it is a fast-paced area (Klems, Nimis, and Tai, 2008;
Linet al., 2009; Kagermann, Österle and Jordan, 2011). Additional work is required to make CAPM usable for the adoption of a large computing system, which
includes the following steps:
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• The formula should be tailored to the system being considered for adoption rather than econometrics.
• Key variables in the formula should correspond to return and risk, including expected return, actual return and risk-free rate (or risk-occurring rate) as the input.
• The formula can work with technical data, costs and user data for risk analysis. Collected data is the result of the organisation’s activities during and after system adoption.
• The formula can calculate output such as risk measures (uncontrolled risks), standard error and so on to help stakeholders to quantify the measure of success for system adoption.
CAPM needs to be redesigned for use in IT system adoption scenarios; required attributes and key performance indicators are based on measuring expected and actual returns while keeping risk-control rate low. By doing so, the improved model can calculate risk analysis for organisations that adopt large systems such as Cloud Computing.
According to Prechter and Parker (2007), some researchers claim that CAPM does not reflect the real market. However, that is because they use CAPM for predicting the future return and risk analysis – i.e. extrapolating the model. The use of CAPM requires appropriate organisational data for modelling (Gentzoglanis, 2011). This limitation is not applicable to this thesis since appropriate technical, cost and user data are used.
4.4 Summary
Large computer system adoption (including adoption of Cloud-based systems) presents challenges for organisations which include technical, cost and
organisational factors. The choice of a risk analysis model is important for quantifying both the benefits and risks. The objective for this chapter was to compare four models and explain the rationale for the chosen risk analysis model.
Background information such as the identification of successful factors for adoption and how these can influence the choice of risk model have been
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presented. The most suitable model is considered to be CAPM since it is a generic model for analysing return and risk, whilst MCS is suitable for specialised sectors such as finance. However, CAPM has two limitations. Firstly, CAPM lacks the capacity to handle thousands of datasets at once. Secondly, CAPM needs to be redesigned to calculate risk and return for large computing system adoption including Cloud adoption. A new algorithm is required to improve on these two major aspects.
An improved method will allow stakeholders to analyse risk utilising thousands of datasets and will present the best quantitative understanding of return and risks for large computing system projects and investment. The improved method, Organisational Sustainability Modelling, will be presented in Chapter 5.
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