FIN9010M
Portfolio Analysis
Table of Contents
- Alpha and Beta. 8
- Mean-Variance Optimization and the Efficient Frontier. 9
- Construct a complete portfolio. 13
- Recommendations. 14
Introduction
In this report, we are expected to mainly gather data on prices of stock and carry out mainly mean-variance optimization utilizing MS Excel. We will express our understanding of Portfolio Theory and Risk Return. In this report, we will mention the processing of data, analysis of outcomes, description of the findings, the significant assumptions essential for MVO, and in the end the recommendations for all investors who mainly want to hold that particular portfolio. The key objective of course work is to differentiate returns mainly between six organizations from various industries mentioned in the list of FTSE 100 from 17th August 2021 to 17th August 2022. We will plot the histories of return of the stocks and present them in a graph and will give expressive statistics of a sample of data and then summarize the main findings from all those statistics (Sheta, et al., 2015).
The different organizations were selected from various distinct industries for diversifying the portfolio far from non-systematic risk.
The selected organizations are given below with their particular profile and industries.
| S/No | Name of Organization | Industry | Profile |
| 1 | Wal-Mart Inc. | Retail and wholesale business | Wal-Mart Inc. operates in the wholesale and retail business. The organization provides a variety of services and merchandise at each day low prices. It mainly operates through the given segments of business: Sam’s Club, Wal-Mart International and Wal-Mart U.S. The US segment of Walmart operates as the merchandiser of products of consumers mainly functioning under the brands of e-commerce. The international segments of Walmart control cash, warehouse club, hypermarkets, supermarkets, and supercenters and also carry mainly outside of the US (Sheta, et al., 2015). |
| 2 | Pinterest Inc. | Internet | Pinterest is the social media and image sharing service and also the discovery of data or information on the internet utilizing pictures, and on a smaller scale, dynamic videos and GIFs in the system of pinboards. |
| 3 | Coca-Cola | Beverage | The Coca-Cola Company it’s the American Corporation mainly founded in 1892 and in today’s time, it engaged initially in the sale and manufacturing of syrups and focused on Coca-Cola, the sweetened carbonated drink. The organization also sells and produces other kinds of citrus beverages and soft drinks. With further than 2,800 products mainly available in further than 200 countries, it is the largest and most popular distributor and manufacturer of beverages in the whole world and also one of the greatest companies in the United States. |
| 4 | ICICI Bank Ltd | Banking | ICICI Bank is the chief private sector bank in India. The combined total assets of the bank stood mainly at Rs 14.76 trillion on September 30, 2020. The bank provides a varied range of products of banking and services of finance to retail and corporate consumers through the assortment of channels of delivery and its particular group organizations (Emamgholipour, et al., 2013). |
| 5 | Walt Disney Company | Media and entertainment | The Walt Disney Company, mainly together with its affiliates and subsidiaries, is the chief expanded international family media and entertainment organization with five segments of business that include networks of media, resorts and parks, studio entertainment, interactive media and products of the consumer. |
| 6 | Delta Airlines | Air transportation | Delta Airlines Inc. involves in the facility of scheduled transportation of air for cargo and passengers. It mainly operates through the segments of Refinery and Airline. The segment of Airline gives scheduled transportation in the air for cargo and passengers. The segments of the Refinery comprise products of jet and non-jet fuel (Emamgholipour, et al., 2013). |
2. Alpha and Beta
Alpha and Beta are mainly two of the main measurements utilized for assessing the stock performance as funds or the portfolio of investment. Alpha measures the particular amount that the investment has reverted in differentiation to the index of the market or some other comprehensive benchmark that is mainly differentiated against. Beta particularly measures the relative instability of the investment mainly against the benchmark as well. It is a sign of its relative risk.
Beta here is accurately measured initially by scheming the benchmark and stock returns that are FTSE 100, then the second process is utilizing the formula of slope in excel sheet by mainly ingoing the return on the single stock as X and return of the market as X. Beta could also be calculated by utilizing the covariance between every index of market and stock and then it should be divided by the variance of the particular market (Prathera, et al., 2018).
Alpha is calculated by utilizing the intercept feature on excel and then entering the returns of stock as x and the return of the market as y.
Mean-variance optimization is done through the analysis of the risk which involves the process of researching the market with a particular company. The mean-variance is optimised through the risks of the organisation or a company which can be worn by the company to make its expected return on any assets of the company (Kourtis 2015). There are the key elements that are used in the process of MVO which are analysed and helps to take decisions about the risk in the particular company, MVO also reflects how much there is spread of the return can be made from the company and specify the security that will be on the daily or weekly basis. The MVO determine the differentiation in the investment that reduces the chances of loss in the changing market condition which means the market has a dynamic nature that changes day by day according to the needs and supply of the market and the demands of the customers (Kourtis 2015).
Table 1 (MVO)
| Walmart Inc. | Pinterest Inc. | Coca-Cola Co. | ICICI Bank ltd. | Walt Disney co. | Delta Airlines | ||||||
| Mean | 139.8540873 | Mean | 32.7183 | Mean | 59.7161 | Mean | 19.541 | Mean | 139.994 | Mean | 38.3674 |
| Standard Error | 0.629289506 | Standard Error | 0.8296 | Standard Error | 0.23238 | Standard Error | 0.07522 | Standard Error | 1.77532 | Standard Error | 0.25979 |
| Median | 141.49 | Median | 26.27 | Median | 60.535 | Median | 19.435 | Median | 143.205 | Median | 39.25 |
| Mode | 143.5 | Mode | 19.67 | Mode | 60.94 | Mode | 19.25 | Mode | 94.01 | Mode | 31.33 |
| Standard Deviation | 9.989661207 | Standard Deviation | 13.1695 | Standard Deviation | 3.68892 | Standard Deviation | 1.194 | Standard Deviation | 28.1823 | Standard Deviation | 4.12411 |
| Sample Variance | 99.79333104 | Sample Variance | 173.436 | Sample Variance | 13.6081 | Sample Variance | 1.42564 | Sample Variance | 794.243 | Sample Variance | 17.0083 |
| Kurtosis | -0.568273885 | Kurtosis | -1.1109 | Kurtosis | -1.1593 | Kurtosis | -0.33 | Kurtosis | -1.2084 | Kurtosis | -0.3193 |
| Skewness | -0.426104347 | Skewness | 0.62841 | Skewness | -0.2287 | Skewness | 0.34485 | Skewness | -0.1546 | Skewness | -0.698 |
| Range | 41.95 | Range | 44.8 | Range | 14.4 | Range | 5.63 | Range | 94.88 | Range | 17.17 |
| Minimum | 118.3 | Minimum | 17.11 | Minimum | 52.6 | Minimum | 16.77 | Minimum | 91.48 | Minimum | 28.62 |
| Maximum | 160.25 | Maximum | 61.91 | Maximum | 67 | Maximum | 22.4 | Maximum | 186.36 | Maximum | 45.79 |
| Sum | 35243.23 | Sum | 8245.02 | Sum | 15048.4 | Sum | 4924.33 | Sum | 35278.4 | Sum | 9668.58 |
| Count | 252 | Count | 252 | Count | 252 | Count | 252 | Count | 252 | Count | 252 |
Table1 suggests that there is a mean-variance of the following companies that are chosen in the above table with the MVO there will be a minimum variance with 0.075 of Walt Disney with the minimum risk of the portfolio with the maximum return but in the case of Delta Airlines where the variance is maximum of 1.75 have the high risk with the less return.
Table 2 (Covariance Matrix)
| Covariance matrix | Walmart Inc. | Pinterest Inc. | Coca-Cola co. | ICICI Bank Ltd. | Walt Disney Co. | Delta Airlines | |
| Walmart Inc. | 99.3973 | ||||||
| Pinterest Inc. | 5.58906 | 172.748 | |||||
| Coca-Cola co. | 4.82721 | 2.56434 | 13.5541 | ||||
| ICICI Bank Ltd. | 9.60896 | 13.8287 | 7.44857 | 1.41999 | |||
| Walt Disney Co. | 46.3529 | 10.6749 | 48.2583 | 79.8918 | 791.091 | ||
| Delta Airlines | 4.72833 | 5.2152 | 3.00173 | 9.59343 | 32.0161 | 16.9408 |
Table 2 shows the covariance matrix of the companies this matrix is used to define the relationship between the different companies and help in the making of the comparison concerning the other companies and this helps in easy analysing the proper and accurate comparison between the companies which determine the risk factors and returns of the companies concerning other (Fontaine et al., 2020). The diversification of the stocks is important because this helps in making your loss recover from any company and also there is the potential of any loss then it will be recovered by another stock of the company and this way the portfolio can be balanced (Fontaine et al., 2020).
Graph 1
The above graph shows that the efficient frontier that tells that there is the ratio between the return of the portfolio and investment that is for the risk this graph tells us that there is less in the given companies with the proper and good return that will be considered as a good investment for the companies. This tells that there is a risk at every level of the investment and shows that there are also areas where the return is less but there is return is not so good but there is also a high return with high risk (Calvo et al., 2016).
The long and short constraints are the investment terms that are used by the investors to know that there are several returns with the risk. With the less deviation of 0.378 tells that there is less risk in Coca-Cola for the long term and also for the short term.
The long constraints are the policies of the investors which are analysed and also the forecast through the mean-variance analysis which was determined through the performance of the companies and the chances of the return for the long-time investment and this will be allowed by the time of period (Calvo et al., 2016).
The short-term constraints are the short-term investment in the company which are analysed properly but these constraints are for the short term in which investors interact with the analyse of the company with the proper return in the short time with less risk.
| S&P 500 Index | |
| Mean | 4352.97 |
| Standard Error | 17.7043 |
| Median | 4411.59 |
| Mode | #N/A |
| Standard Deviation | 281.048 |
| Sample Variance | 78987.7 |
| Kurtosis | -0.603 |
| Skewness | -0.6017 |
| Range | 1138.61 |
| Minimum | 3665.9 |
| Maximum | 4804.51 |
| Sum | 1096948 |
| Count | 252 |
| 0 | |
Above index of the S&P 500 index have the standards values of the deviation and also this index helps in determining the market capitalization of the company and also there are standards which need to follow when constructing the investment portfolio of the organisation.
Table 3
| Walmart Inc. | Pinterest Inc. | Coca-Cola co. | ICICI Bank Ltd. | Walt Disney Co. | Delta Airlines | S&P 500 Index | 0.58747 | |
| ER | 0.04406 | 0.46074 | -0.0396 | -0.0517 | 0.16125 | 0.08469 | 0.02269 | 0.23229 |
| ST.DEV | 1.59029 | 4.75935 | 1.17609 | 1.72068 | 2.07271 | 2.84423 | 1.19368 | 0.4123 |
| VAR | 2.52902 | 22.6515 | 1.38319 | 2.96073 | 4.29611 | 8.08965 | 1.42487 |
The above table helps in determining the portfolio of Optimal risky portfolio this portfolio has the Capital Allocation Line which should be tangent at the point from the efficient frontier which means that when the slope of the Capital Allocation Line will be at the highest point in the graph that means there are chances of the high returns per unit risk (Zakamulin 2016). This means ORP should be chosen at the point of the time that states and analyse the relationship between risk and ER with keeping in mind the standard deviation of the company from the standard capital of the market (Zakamulin 2016). Whenever there is less deviation with high ER then there are fewer chances of loss because the deviation from normal is low and if there is a high deviation from the standard of the market capitalization then there will be a high risk with the investment so a portfolio should be constructed in formal ways.
5. Recommendations
In the given report above, we have been capable of generating the regular returns for the significantly selected organizations, also determined their non-systematic and estimated risks, and differentiated this mainly with the return of market for determining the Beta and alpha. Then we also computed the covariance of the organizations with another selected organization, constructed effective ORP and portfolio and the overall portfolio by mainly adding the ORP with the risky portfolio by mainly putting the risk antipathy of the investors into consideration.
I recommend the investor to hold the portfolio to mainly invest in any one of the portfolios on the allocation line of capital given in the figure that was mainly got through calculating the risky and risk-free portfolio relying on the risk antipathy of the investor. Any of the assets given above the point of tangency will be mainly leveraged (Prathera, et al., 2018).
References
Calvo, C., Ivorra, C. and Liern, V., 2016. Fuzzy portfolio selection with non-financial goals: exploring the efficient frontier. Annals of Operations Research, 245(1), pp.31-46.
https://sci-hub.hkvisa.net/10.1007/s10479-014-1561-2
Emamgholipour, M., Pouraghajan, A., Tabari, N.A.Y., Haghparast, M. and Shirsavar, A.A.A., 2013. The effects of performance evaluation market ratios on the stock return: Evidence from the Tehran stock exchange. International Research Journal of Applied and Basic Sciences, 4(3), pp.696-703.
https://irjabs.com/files_site/paperlist/r_734_130328111041.pdf
Fontaine, M.C., Togelius, J., Nikolaidis, S. and Hoover, A.K., 2020, June. Covariance matrix adaptation for the rapid illumination of behavior space. In Proceedings of the 2020 genetic and evolutionary computation conference (pp. 94-102).
https://sci-hub.hkvisa.net/10.1145/3377930.3390232
Kourtis, A., 2015. A Stability Approach to Mean‐Variance Optimization. Financial Review, 50(3), pp.301-330.
https://sci-hub.hkvisa.net/10.1111/fire.12068
Prathera, L.J., Chena, H.S. and Lina, Y.C., 2018. Building optimal risky and utility maximizing TIAA/CREF portfolios. Global Journal of Accounting and Finance, 2(1), p.37.
https://www.igbr.org/wp-content/uploads/2018/12/GJAF_Vol_2_No_1_2018.pdf#page=43
Sheta, A.F., Ahmed, S.E.M. and Faris, H., 2015. A comparison between regression, artificial neural networks and support vector machines for predicting stock market index. Soft Computing, 7(8),p.2.
https://pdfs.semanticscholar.org/5923/9d5c6c3ef17c0fb860d3809c1d2d3988bd5f.pdf
Zakamulin, V., 2016. Optimal dynamic portfolio risk management. The Journal of Portfolio Management, 43(1), pp.85-99.
https://sci-hub.hkvisa.net/10.3905/jpm.2016.43.1.085