MIS775 Decision Modelling for Business Analytics Report Sample

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MIS775 Decision Modelling for Business Analytics Report Sample

Description

Purpose

This assignment task is aligned to the learning outcomes GLO1 & ULO1 and skills GLO4 & ULO3 and GLO5 & ULO2 required to build complex decision models and use advanced quantitative modelling techniques, such as optimization, to analyses and develop solutions to business problems. By completing this task, you will develop your skills in conceptualizing, formulating and representing a business problem as a decision model, developing business decision models using software tools, undertaking sensitivity analysis and evaluating the utility of alternative solutions.

Context/Scenario

This assignment is designed to let you explore and evaluate a number of approaches to investment portfolio optimisation, using live real-world data. The relevant URL for finding stock prices is: https://au.finance.yahoo.com/ via the “Quote lookup” search box.

In this assignment you will use asset return data for a period of 3 years to identify the optimum portfolio by applying a range of optimisation methods. In each case you must determine the percentage (or proportion) of the portfolio to invest in each of 10 assets, such that the percentages are non-negative and sum to 100% (or 1). You will then compare the performance of these portfolios on a further 1 year of data, so that you may observe just how well the optima generated from the first three years of data performed in that subsequent period, by comparison with the optimum portfolios for that period. This will allow you to make some assessment about the validity for future investment decisions of the optimisation methods which just use past data.

Specific Requirements

The assignment has three main sections: Preliminary Work, Optimisation Models; and Validation and Report.

The requirements of each section are detailed below. The breakdown of marks (total of 40) is given in this document and the Assignment 1 Rubric.

Section 1: Preliminary Work (4 marks)

Choose five investments listed on the Australian Stock Exchange, one from each of the given in the following table, to complete a set of 10 investments..

To access the Stocks, click Industries on the ribbon menu (via the home page), and select a category. Then click on the symbol for the asset you want to include in your portfolio. After you have selected a Stock, click Historical data on the ribbon menu, then set Time period to 1 February 2019 – 28 February 2023 and Frequency to Monthly, then press the Apply button, and download the data. Delete any rows showing dividends. We are only interested in the opening price, listed in the column headed Open. Discard the rest of the data.

The chosen stocks must satisfy the following general requirements:

• Each have 49 consecutive months of opening prices, up to and including 1 February 2023.

• They should be selected from the five industry categories (C1 to C5) listed in the table above, namely Real Estate, Industrial Goods, Consumer Products & Media, Financial, and Healthcare. You must choose only one asset from each of these five categories.

• For each of the 10 assets (i.e., the five given assets and the five you chose), calculate monthly returns, average return and then use their standard deviation to find their risk.

• The 10 assets should span a reasonable range of volatilities/risk. For this reason, you might try several assets in a category before settling on a final choice.

• Classify each of the 10 assets (i.e. the five given stocks and the five you chose) into one of four risk groups R1, R2, R3, and R4, where R1 < R2 < R3< R4. It is up to you to determine the basis for the risk classification, but you must have at least two assets in each risk group.

• Each asset must belong to one of the five industry categories and one of the four risk categories. See below template. Once you determined what risk group they belong to, you can write the asset/company name in the body of the table below.


The collected data needs to be divided into two sets:

• Modelling data: For your portfolio optimisations, you should use the first 37 months of data. Perform parts 1, 2, 3a, 3b and 3c on the modelling data (or Training set).

• Holdout sample: The last 12 month of collected data. This is to assess model performance as a means of model validation. Perform parts 1, 2, 3a, 3b and 3c again using Holdout sample. This is called validation task. In validation part, you should include the results of this comparison in a table.

Section 2: Optimisation Models (20 marks)

For your portfolio optimisations, you should use modelling data to undertake parts 1, 2, 3a, 3b, and 3c.

The assignment requires you to consider three different approaches to portfolio optimisation:

1. Choosing according to asset class restrictions, and individual asset risk appetite.

2. Choosing according to portfolio size restrictions and risk appetite.

3. Choosing according to portfolio risk and return requirements.

These three approaches allow exploration of three different optimisation techniques:

Solution

Section 1: Preliminary Work

The investigation focuses on 10 businesses that are listed in the stock exchange of Australia from 5 industry sectors. The sectors are real estate, consumer goods and media, financial sector, healthcare sector and industrial sector (Rabi, et al. 2020). University Assignment Help, Two companies are selected from each sector, one was shortlisted and one was chosen from each category. Lendlease group and Goodman group are real estate companies whereas Goodman Group is a major real estate group while Lendlease is a multinational construction and real estate company. Goodman group has offices across Europe, Asia Pacific and North America and Lendlease group has offices in Australia, Europe, Asia Pacific and America. The risk of project delay or cancellation are one of the major threat for Lendlease group. Due to the involvement of companies in large number of building projects and any cancellation might result its performance or reputation negatively. Economic situation of the Goodman group is at a major risk because of the interest rates and consumers sentiments as well as the growth rate in gross domestic product.

 

Table 1: Risk metrics
(Source: Created by author)

Meanwhile, the Commonwealth Bank of Australia (CBA.AX) is facing regulatory challenges. Banking is a heavily regulated industry, and regulatory changes can have a considerable impact on business operations and profitability. Ansell Limited (ANN.AX) bears risks connected to product recalls and clinical trial hazards in the healthcare sector. The company makes and sells a variety of healthcare items such as surgical gloves, contraceptives, and protective apparel (Boldog, et al. 2020). Defects in these items, as well as unfavourable patient outcomes, can lead to product recalls and significant financial losses for the company. Furthermore, the corporation is involved in a number of clinical trials, which expose it to risks related to the safety and efficacy of the products under consideration. Noncompliance with regulatory standards, negative consequences, and adverse patient effects are examples of these risks. The Australian Securities Exchange selected ten companies from a variety of industries, including real estate, manufacturing, consumer goods, media, finance, and healthcare. These companies confront numerous risks, including regulatory, economic, operational, credit, market, liquidity, concentration, environmental, and clinical trial issues. Investors should consider these risks while making investment decisions and diversify their portfolios to limit their impact.

Section 2: Optimization Models

The application of using data modeling is effective in dealing with the factor. Composing of relation with the data integrated within the section. The application of different modeling integrated, it has reflected the overall scenario for suggesting the approaches. Application of overall factor is proposed for considering the scenario.

1. LP model

The statistical output has mainly been preferred in evaluating the overall performance for dealing with the activities. Suggesting the implication can be integrated for responding in developing the scenario. Highlighting of value is further proposed for suggesting the overall evaluation system. Consideration of involvement is further proposed for approaching the scenario. Evaluation within the system is also considered for explaining the overall scenario. Application of this model has been used for making forecasting the data. Application of linear regression model that has applied in proposing the overall factor. Integration of the model is basically used for decision-making variable definition.

Figure 1: Presentation of graph

It has initiated the sensitivity analysis for developing the overall factor. The consideration of the scenario has been defined within the factor for controlling the factor. The selected stock scenario, it is mainly highlighted the overall situation with the data given (Paltrinieri et al. 2019). The objective of this linear programming (LP) model is to maximize overall return on investment within a set of defined risk and category limitations. It places reasonable constraints on risk categories (R1 to R4) and asset categories (C1 to C5) in order to maintain a broad and well-balanced portfolio. Once the LP model has been created, the best solution can be discovered using a suitable solver, such as the Simplex algorithm or an interior-point technique. Given the limitations, the solution will output the asset allocation that maximizes the overall return.

To comprehend how changes in risk and category limits affect the ideal portfolio, perform a sensitivity analysis. This research shows how the best solution could change depending on whether the limitations are tightened or loosen.

2. ILP model

Figure 2: Risk analysis

The application of ILP model is applied to explore the actual return value with the selected return. It has chosen the stock value and return factor for exploring the overall situation. Identification within the scenario has been defined as the actual scenario for the process (Chakraborty and Ghosh, 2020). Integration of process is also included for proposing the overall reflection


Figure 3: Risk analysis

The analysis of statistical review has explained the overall responses from the market which is evaluating the overall scenario. The consideration of statistical exposure has defined the overall situation for the data provided.

Sensitivity report

Conceptual model

3. NLP model

 

Table 1: Descriptive statistics

The aim of the factor for proposing the optimizing the risk scenario. Suggesting integrated approaches can be revealed for highlighting the actual scenario. It has considered several risk integration approaches that can deal with the factor (Bet et al. 2019). Proposing the scenario can be highlighted for focusing on the factor.

Reference

Bet, P., Castro, P.C. and Ponti, M.A., 2019. Fall detection and fall risk assessment in older person using wearable sensors: A systematic review. International journal of medical informatics, 130, p.103946.

Boldog, P., Tekeli, T., Vizi, Z., Dénes, A., Bartha, F.A. and Röst, G., 2020. Risk assessment of novel coronavirus COVID-19 outbreaks outside China. Journal of clinical medicine, 9(2), p.571.

Chakraborty, T. and Ghosh, I., 2020. Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals, 135, p.109850.

EFSA Scientific Committee, More, S.J., Bampidis, V., Benford, D., Bennekou, S.H., Bragard, C., Halldorsson, T.I., Hernández?Jerez, A.F., Koutsoumanis, K., Naegeli, H. and Schlatter, J.R., 2019. Guidance on harmonised methodologies for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals. Efsa journal, 17(3), p.e05634.

Paltrinieri, N., Comfort, L. and Reniers, G., 2019. Learning about risk: Machine learning for risk assessment. Safety science, 118, pp.475-486.

Rabi, D.M., McBrien, K.A., Sapir-Pichhadze, R., Nakhla, M., Ahmed, S.B., Dumanski, S.M., Butalia, S., Leung, A.A., Harris, K.C., Cloutier, L. and Zarnke, K.B., 2020.

Hypertension Canada’s 2020 comprehensive guidelines for the prevention, diagnosis, risk assessment, and treatment of hypertension in adults and children. Canadian Journal of Cardiology, 36(5), pp.596-624.

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