SampleProjectProposal.pdf

PROJECT PORTFOLIO MANAGEMENT 2

Table of Contents
EXECUTIVE SUMMARY …………………………………………………………………………………………….. 3
INTRODUCTION …………………………………………………………………………………………………………. 4
LITERATURE REVIEW ………………………………………………………………………………………………. 5

Evaluation ………………………………………………………………………………………………………………….. 5
Modelling Techniques ……………………………………………………………………………………………………. 5

Modelling Inheritance Relationships …………………………………………………………………………… 5
Capital Gains: Individual Security Return ……………………………………………………………….. 7

Modelling dependency …………………………………………………………………………………………….. 7

Suitable distributions for returns …………………………………………………………………………….. 7

Extreme value theory (EVT) ……………………………………………………………………………………. 8

Arch and Garch models …………………………………………………………………………………………… 8

Risk-Return Trade-off …………………………………………………………………………………………….. 9

Benchmark Portfolio Returns ………………………………………………………………………………….. 9

Portfolio Risk ………………………………………………………………………………………………………… 10

Markowitz Mean Variance …………………………………………………………………………………….. 11

RESEARCH METHODOLOGY ………………………………………………………………………………….. 11
Importing data from yahoo finance …………………………………………………………………………… 12

CONCLUSION ……………………………………………………………………………………………………………. 13
References ……………………………………………………………………………………………………………………. 14

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EXECUTIVE SUMMARY

Since projects play a massive role in organizational budgets and strategic development,

organizations have become highly dependent on project portfolio management. However, existing

knowledge concerning evaluation and methods of improving project portfolio management has

become fragmented and lacks proper empirical standing. This research study consists of a

normative case study geared towards developing a portfolio management model for datasets of

Google, Amazon, CVS, HSBC, JPM and UNH from 2010 onwards to enable effective

implementation and embedding of best practices of portfolio management. Implementation of

necessary recommendations resulting from this study will prove highly effective in improving the

portfolio maturity and management process. It will also contribute positively towards ensuring

Google, Amazon, CVS, HSBC, JPM, and UNH become highly organized institutions by providing

that programs and projects align with the strategic objectives aimed at being delivered to ensure

strategic results are acquired. This research project will also prove helpful in the process of

expanding the body of knowledge on matters related to portfolio management models and the

necessary steps that ought to be followed to ensure successful implementation and embedding of

best practices geared to providing effective management of the portfolio. Furthermore, the model

of portfolio management and the steps of performance can be helpful to other organizations as a

form of a blueprint geared towards implementing best practices for portfolio management.

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INTRODUCTION

With the projectivization of societies, especially those in the west, organizations such as

Google, Amazon, CVS, HSBC, JPM and UNH tend to go through many challenges in managing

their project portfolios. This is because projects are highly involved in significant parts of budgets

in organizations and strategic organizational development. Project portfolio management can be

defined as the overall ability of a business to manage its portfolios strategically and holistically to

enable the organization to succeed. According to studies conducted, there is an indication that there

is a deficit in frameworks that will allow organizations to evaluate how effective the PP

arrangements of organizations such as Google, Amazon, CVS, HSBC, JPM and UNH support this

quest (Sweetman and Conboy, 2018). Early studies have looked into project selection processes

on a narrow scope, and with the gradual expansion, both methods before and after appointment

tend to be incorporated.

The inception of the millennium era shed more light on a broader range of areas deemed

problems, such as issues related to lack of resources, than the ideas at hand. As a result, various

researchers have adopted concepts such as maturity to assess the way organizations evaluate their

PPM. These models were deemed helpful since they enable individual organizations to determine

the maturity of various procedural aspects compared to benchmarks and enable the prioritization

of actions geared towards improvement. Maturity, which involves the state of the organization in

the delivery of objectives, may be described in stages, namely Ad Hoc, Initial, Repeatable,

Defined, Managed and Optimized. However, as much as previous contributions were highly

effective, the literature on matters related to PPM is highly fragmented and does not consist of an

integrated and empirically rested framework, making analysis hard in diversified organizations.

This study thus evaluates how to develop a holistic and validated PPM framework of evaluation

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and how it can be applied in real-life organizations while summarizing critical reflection and

learning points and looking into its theoretical contribution. To understand the concept evaluation,

we discuss its origin and how it further developed the ad; later on, we suggest approaches to

assessing PPM, effectively organizing literature.

LITERATURE REVIEW

Evaluation

Evaluation involves the act of appraising or valuing something. Evaluation is deemed a

very central part of the normal cognitive processes of human beings and a natural component of

everyday life. Issuing of program evaluation is seen to have popularized in 2000BC. In contrast,

its importance was discovered in America in the 1960s due to the administrations of various

organizations investing heavily in socially based programs. Today, the society of evaluation is

considered a profession by evaluators where the object is known as the program. In the mainstream

of evaluation research, evaluation has been described by a broad range of paradigms, typologies,

classifications and models. According to a recent study, more than 0 models of evaluation ad more

than 20 typologies. Some scholars have defined evaluation as a process that involves systematic

assessment, investigation, assessment and determination. Therefore, the definition has developed

with time from a method centered approach to an approach that involves more centrally placed

context and conditions.

Modelling Techniques

Modelling Inheritance Relationships

Recognize the duties, qualities, and activities that are regular to two or more classes in a given

arrangement of courses.

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Presently, the average obligations, qualities, and activities should be climbed to a broader class.

To allocate the essential components, make another class, and recall not to present such a large

number of levels.

Notice that the more explicit classes are gotten from, the broader level. This is conceivable by

putting a speculation relationship drawn from each category to its parent, which is more general.

The financial model should analyze the sensitivity of the project’s most essential ratios and

investor indicators to the critical input factors. As noted, elements can be aggregated into several

categories and depend on the project industry, the stage of its implementation, and the specifics of

the project itself. The financial model should reflect all quantified aspects of the proposed project.

It is a part of a business plan containing economic forecasts and their preliminary analysis,

including input data and assumptions required for financial projections, interim financial forecasts

and calculations, financial forecast results in the form of projected financial statements and

financial indicators (ratios), analysis of the critical sensitivity factors, and the results of the

economic forecasts.

In this project, we will be using the following techniques to identify the portfolio forecast

based on Financial, health and technology sectors:

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Capital Gains: Individual Security Return

This is the resultant change in value solely based on the closing price of involved security.

There will be the establishment of the percentage changes in security prices of the organizational

portfolios of Google, Amazon, CVS, HSBC, JPM and UNH from 2010 onwards. This will enable

the establishment of price returns from one year to another.

Modelling dependency

Modelling dependency is a collection of approaches that are used to explore, describe and

predict how different entities affect the relationship between variables or entities. Typically, is a

quantitative approach, though it can also be qualitative. This technique will be used in this research

to explain how Gaussian processes are evaluated. In relation to covariance equations and

evaluations, Gaussian activities are usually explained by use of parameters. Additionally, the use

of parameters in such models portrays a challenge especially when dealing with numerous data

outputs. Otherwise, in some cases Gaussian processes can be evaluated by use of white noise which

is based on Kernels and parameterization of kernels. Using this, coupled output was to be evaluated

through Gaussian processes.

The copula approach or rather method solves the difference between marginal distribution

between the modelling structures and dependency structures. Assumptions drawn from the case of

elliptical distributions are distributed variables randomly and depicted dependencies.

Suitable distributions for returns

In terms of suitable distribution involving returns, kurtosis is best used especially in

analyzing historical data which helps in gauging the investment and investor’s level especially

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when the risks are involved. Additionally, the investors continuously experience fluctuations

which commonly are explained using two to three standard deviations from the mean.

Extreme value theory (EVT)

EVT is a branch dealing with statistics which involves the use of large sample data in

analysing the median and deviations of distributions. EVT provides a strong foundation for

building a statistical models that is used to describe extreme events. The feature that differentiate

extreme value analysis with other statistical analysis is the ability to determine the behaviour of

unusual large values even if these values are scares. This theory will be used as a tool of

considering possibilities that are associated with extreme and thus rare events. This theory is useful

in modelling the impact of crushes on investor portfolios. Mostly, negative results are experienced

when rare or extreme modelling challenges are involved or rather evaluated statistically. These

modelling problems are floods, drought, earthquake turmoil’s and negative market crashes. To

ascertain risks and impacts involved, business analysts and engineers frequently use the extreme

value distributions (EVD) four distribution approaches were used which will be discussed in detail

in the project. The distribution approaches are; the block maxima approach, gumbel distribution,

frechet distribution and Weibull distribution.

Arch and Garch models

These models strictly rely on large fluctuations involved in addressing the significant of

the modelling structures. These include financial time series, which can be particularly heavy

tailored. However, very little and few studies including researches have been conducted about

ARCH and GRACH models in majorly the heavy-tailored setting, and no methods are available

for approximating the distributions of parameters estimators. This model will be used in the

research to determine the volatility of returns for goods or stock in the trading organization. On

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the other hand, ARCH will also be used to analyse the volatility in different time period in

to determine future volatility.

Time Series Return

Time series return will prove essential in forecasting and specifying the number of time

steps ahead has been forecasted. Data on portfolios of Google, Amazon, CVS, HSBC, JPM and

UNH from 2010 onwards will go through a series of tests to ensure prediction of future

performance hence ensuring preparedness.

Risk-Return Trade-off

This techniques will be used to determine potential risks, thus allowing the management of the

organization to develop some plans that will minimize risk in the organization. The chart/table

below evaluates and demonstrated the activities in risk and reward. The relationship between the

two factors keeps on changing now and then due to the various risks involved in investment time

to time and the expectations on various market insights. Notably from the table below, there is no

linear and perfect relationship between the two factors.

Benchmark Portfolio Returns

Portfolio return refers to the gain or loss realized by an investment portfolio containing

several types of investments. Portfolios aim to deliver returns based on the stated objectives of the

investment strategy, as well as the risk tolerance of the type of investors targeted by the portfolio.

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This model will be used in this research to determine the potential risks as well as developing

strategies that can be used to minimize those risks in an organization. For instance, data are at risk

of being hacked, by use of modern technology, the organization can minimize those risks. IBM

data on the stated organizations will therefore be obtained from Yahoo Finance. IBM analysis will

be used to run regression on data collected to ascertain their significance in the project study. IBM

software will be used in the data regression analysis since the data used is quantitative in nature.

There will be the calculation of total returns per anomie. The close price per annum is deemed to

be higher than the adjusted data. Price returns are estimated to be smaller than the total return

because the total return is equivalent to the price return added to the yield in dividend (Stettina and

Hörz, 2015). To establish the cumulative returns, there is the need to utilize logarithmic total

returns while ensuring that differences in returns per year are so minor and fail to be recognizable.

Portfolio Risk

Since investments must contain some risks, it is necessary to note that both large and small

changes in price tend to occur. Here, the threat presented in the asset portfolios of Google, Amazon,

CVS, HSBC, JPM and UNH from 2010 onwards will be measured by establishing the variance,

which measures how far a return on Security exhibits a deviation from its average during the stated

period. Thus, if there is a negative or positive deviation, there is the consideration of risk.

Furthermore, in this study, there will be the establishment of a risk-return trade-off to indicate the

level of expected risks in organizations to ensure the acquisition of higher returns. Therefore, when

assessing the portfolio risks, the necessity to utilize the covariance ought to be calculated for each

pair of Security.

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Markowitz Mean Variance

This technique is geared towards establishing how mean and variance can be helpful in the

process of determining portfolios that provide the highest return for a particular risk level. The

diversification of a portfolio will prove effective in reducing the occurrence of risk throughout the

whole portfolio. Therefore, the objective here is to construct the Mean-Variance Effect Frontier

using the asset portfolios of Google, Amazon, CVS, HSBC, JPM and UNH from 2010 onwards

and manually laying out every calculation step. To determine investment level, it is necessary to

know the combination of securities that yield a minimum variance portfolio and the highest

tangency portfolio through a process commonly referred to as quadratic programming.

RESEARCH METHODOLOGY

This paper deals with a methodology for the assessment of reliability based on Monte Carlo

simulation models. The project will also describe how investment planning can be extended to

consider individual customer reliability indices, so that actions and future system reinforcements

are evaluated to meet such established goals. Some applications illustrate the proposed

methodology. Monte Carlo simulation provides the platform in which decisions based on the risks

involved are evaluated to help in better decision making ideas. Monte Carlo Simulation is ideal as

it can be used to cover a wider area from different sectors especially in the insurance and financial

management areas (Islam et al., 2021). Monte Carlo simulation conducts risk analysis based on

risk modelling based on the outputs with replacing the values of uncertain variables.

Additionally, the paper adopted the Action Design Research (ADR) approach and utilized

it to develop and demonstrate a project evaluation framework. ADR has elements of action

research (intervention) and design research (artifact building). ADR is a research method for

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generating prescriptive design knowledge by building and evaluating (intervening) an artifact in

its organizational setting. ADR consists of four interleaved stages:

(1) Problem formulation.

(2) Building, intervention and evaluation.

(3) Reflection and learning.

(4) Formalization of learning.

Importing data from yahoo finance

The data to be used in the regression analysis using IBM software package was to be

imported from yahoo finance, an online site. The data was to contain symbols, dates and prices

from different files dated different periods. After importation from yahoo finance the data will be

saved for easier referral and backup purposes in excel spreadsheets.

In that regard, Stettina and Hörz (2015) discuss the concept of continuous improvement by

repeating PPM routines, and scholars such as Sweetman and Conboy (2018) discuss how portfolio

management may be adapted based on feedback and learning. Against the backdrop of our

discussion of the PPM evaluation literature, we find several contributions that advance PPM

evaluation. However, we find no integrated and empirically validated PPM evaluation model

embracing all four approaches – but somewhat fragmented literature on PPM evaluation and no

knowledge on how PPM evaluation is conducted in its organizational context.

.

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CONCLUSION

In conclusion, extra costs, disruption of activities and uncertainties may be experienced in

the case of global activities including global management. To resolve this situation, this project

will present the formulation of a well advanced developed structure to be used in decision

making processes with an objective of curbing risks involved in various sectors using a Monte

Carlo simulation to construct dynamic scenarios on the Bayesian Networks which gives room for

management of activities. Additionally, Monte Carlo simulation provides the platform in which

decisions based on the risks involved are evaluated to help in better decision making idea; hence

contributing to risk reduction in organizational strategies and development of better decisions

made by the management and everyone involved directly with the organization or business.

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References

Islam, M. S., Nepal, M. P., Skitmore, M., & Drogemuller, R. (2021). Risk induced contingency

cost modeling for power plant projects. Automation in Construction, 123, 103519.

Stettina, C. J., & Hörz, J. (2015). Agile portfolio management: An empirical perspective on the

practice in use. International Journal of Project Management, 33(1), 140-152.

Sweetman, R., & Conboy, K. (2018). Portfolios of agile projects: A complex adaptive systems’

agent perspective. Project Management Journal, 49(6), 18-38.