Portfolio

Statement of problem

Automotive Industry has significantly increased its contribution to overall industrial growth in the country. By 2030 India will be the third largest car market in the world after China and Japan. This coupled by the purchasing power of the ultra rich makes India a top destination for manufacturers of luxury cars Investment by foreign companies in automobiles implies a bright future for the auto industry India. This will lead to the creation of jobs, and a wider range for consumers to choose from. It will also give Indian companies a chance to compete globally for clients.

This will greatly benefit the auto component and ancillary industry that will get access to the latest technology and manufacturing practices. According to Commerce Minister Kamal Nath, India is an attractive destination for global auto giants like BMW, General Motors, Ford and Hyundai who were setting base in India, despite the absence of specific trade agreements.

Current Scenario

On the cost front of Indian automobile industry, OEMs are eyeing India in a big way, investing to source products and components at significant discounts to home market.

Overview

By 2010, India is expected to witness over Rs 30,000 crore of investment. Maruti Udyog has set up the second car with an investment of Rs 6,500 crore. Hyundai will bring in more than Rs 3,800 crore to India. Tata Motors will be investing Rs 2,000 crore in its small car project. General Motors will be investing Rs 100 crore and Ford about Rs 350 crore. Ashok Leyland and Tata Motors have each announced over Rs 1,000 crore of investment.

Introduction

In India there are 100 people per vehicle, while this figure is 82 in China. It is expected that Indian automobile industry will achieve mass motorization status by 2014. Industry Overview: Since the first car rolled out on the streets of Mumbai (then Bombay) in 1898, the Automobile Industry of India has come a long way. During its early stages the auto industry was overlooked by the then Government and the policies were also not favourable. The liberalization policy and various tax reliefs by the Govt. of India in recent years have made remarkable impacts on Indian Automobile Industry.

Indian auto industry, which is currently growing at the pace of around 18 % per annum, has become a hot destination for global auto players like Volvo, General Motors and Ford. A well developed transportation system plays a key role in the development of an economy, and India is no exception to it. With the growth of transportation system the Automotive Industry of India is also growing at rapid speed, occupying an important place on the ‘canvas’ of Indian economy. Today Indian automotive industry is fully capable of producing various kinds of vehicles and can be divided into 03 broad categories: Cars, two-wheelers and heavy vehicles.

Snippets

The first automobile in India rolled in 1897 in Bombay. India is being recognized as potential emerging auto market. Foreign players are adding to their investments in Indian auto industry. Within two-wheelers, motorcycles contribute 80% of the segment size. Unlike the USA, the Indian passenger vehicle market is dominated by cars (79%). Tata Motors dominates over 60% of the Indian commercial vehicle market. 2/3rd of auto component production is consumed directly by OEMs. India is the largest three-wheeler market in the world. India is the largest two-wheeler manufacturer in the world.

India is the second largest tractor manufacturer in the world. India is the fifth largest commercial vehicle manufacturer in the world. The number one global motorcycle manufacturer is in India. India is the fourth largest car market in Asia – recently crossed the 1 million mark. 1 Segment Knowhow: Among the two-wheeler segment, motorcycles have major share in the market. Hero Honda contributes 50% motorcycles to the market. In it Honda holds 46% share in scooter and TVS makes 82% of the mopeds in the country. 40% of the three-wheelers are used as goods transport purpose.

Piaggio holds 40% of the market share. Among the passenger transport, Bajaj is the leader by making 68% of the three-wheelers. Cars dominate the passenger vehicle market by 79%. Maruti Suzuki has 52% share in passenger cars and is a complete monopoly in multipurpose vehicles. In utility vehicles Mahindra holds 42% share. In commercial vehicle, Tata Motors dominates the market with more than 60% share. Tata Motors is also the world’s fifth largest medium & heavy commercial vehicle manufacturer.

Company profiles Ashok Leyland

In 1948, The Company was incorporated on 7th September, at Chennai.

The Company Manufacture Comet chassis and Leyland `Tiger’ and `Titan’ Chassis and Leyland diesel engines. In 1955, the name of the Company was changed from Ashok Motors Ltd. , to AshokLeyland Ltd. in July. Ashok Leyland Motors Ltd. , are the associates Of the company In 2006, Ashok Leyland gets ISO/TS 16949 corporate certification In 2010, Ashok Leyland, the flagship company of Hinduja group, unveiled the Country’s first electric plug-in CNG hybrid bus, HYBUS, at the Delhi Auto show.

Eicher Motors Ltd has informed that the Board of Directors of the Company in its meeting held on October 22, 2007 approved appointment of Mr. Rajesh Arora as Company Secretary as well as Compliance Officer of the Company.

The name of the Company was changed from Escorts (Agents) Pvt. Ltd. , to Escorts Ltd. upon its conversion into a Public company. In 2005, Escorts win . 5-m tractor order from Ghana Escorts Ltd has acquired its Polish joint venture partner, Farmtrac Tractors Europe Escorts’ US subsidiary teams up with SAME Deutz-Fahr Italia In 2006, Escort India is set to manufacture tractors in Bangladesh through a Joint venture with the Nitol-Niloy group.

The Company Manufacture motor cycles up to 100 cc capacity. The Company Was promoted by Hero Cycles (P) Ltd. (HCPL). In 2005, New product launches widen HHML’s product portfolio Two-wheeler major Hero Honda on October 5 announced launch of its First scooter ‘Pleasure’ Hero Honda rolls out 150-cc motorcycle Achiever.

Tata Motors Limited is India’s largest automobile company, with consolidated revenues of Rs. 70,938. 85 crores (USD 14 billion) in 2008-09. It is the leader in commercial vehicles in each segment, and among the top three in passenger vehicles with winning products in the compact, midsize car and utility vehicle segments. The company is the world’s fourth largest truck manufacturer, and the world’s second largest bus manufacturer. The company’s 24,000 employees are guided by the vision to be “best in the manner in which we operate, best in the products we deliver, and best in our value system and ethics. Established in 1945, Tata Motors’ presence indeed cuts across the length and breadth of India. Over 4 million Tata vehicles ply on Indian roads, since the first rolled out in 1954.

Beginning with launching a simple, easy-to-use moped for the middle class in India in the 1980s to launching 7 new bikes in a single day (first time in the history of the automotive industry in the world), TVS has often taken the unbeaten path to innovation. The Group’s principal activity is to manufacture and sell motor cycles and components. The Group operates in two segments: Automotive Vehicles and Automotive Components. Automotive Vehicles include motorcycles, mopeds, ungeared scooters and three wheelers. The products of the Group include TVS Apache, TVS Scooty, TVS Fiero, TVS Super XL, TVS Victor, TVS Centra, TVS Star etc. It’s plants are located at Hosur, Tamil Nadu , Mysore, Karnataka and Solan, Himachal Pradesh.

It employs over 1,00,000 people across the globe and enjoys a leadership position in utility vehicles, tractors and information technology, with a significant and growing presence in financial services, tourism, infrastructure development, trade and logistics. The Mahindra Group today is an embodiment of global excellence and enjoys a strong corporate brand image. Mahindra is the only Indian company among the top tractor brands in the world and has made an entry in the two-wheeler segment, which will see the company emerge as a full-range player with a presence in almost every segment of the automobile industry.

The Mahindra Group expanded its IT portfolio when Tech Mahindra acquired the leading global business and information technology services company, Satyam Computer Services. The company is now known as Mahindra Satyam. Mahindra’s Farm Equipment Sector is the proud recipient of the Japan Quality Medal, the only tractor company worldwide to be bestowed this honour. It also holds the distinction of being the only tractor company worldwide to win the Deming Prize. The US based Reputation Institute recently ranked Mahindra among the top 10 Indian companies in its Global 200: The World’s Best Corporate Reputations list.

Objectives

Primary Objective

• Construction of optimal portfolio using Sharpe Index Model
• To analyze the risk and return of Indian automobile companies.

Secondary Objectives

• To understand the Sharpe’s Portfolio Selection Model over the Standardized Index Portfolio called Market portfolio in respect of stock market perations in India. It also involves the estimation of Beta for each potential asset; these estimations are obtained based on past data and using statistical methods in order to obtain future Beta.
• To understand the current scenario of Indian automobile industry.

Scope & Limitations

Scope

• To get overview outline about the selected Indian automobile company, their performance comparison, market share, potential and their volatility. Serves as a source of information for investors in identifying the risk averse and risk seeking shares (more return and less risk)of selected automobile industry.
• To get insight about the application of Sharpe index model in risk and return analysis of portfolio management.

Limitations

1. Only selected industries in Indian automobile sector.
2. The data obtained and collected are only approximate and not more accurate.
3. Market fluctuations in share price of the selected industries.
4. Application of Sharpie index model alone.

Literature review

“The Accounting Review”: Elgers, Pieter T. Murray, Dennis ( Apr 1982) published that a measure of investment risk-the systematic risk of the Sharpe-Linter capital asset pricing model (CAPM)-is now widely employed. The relationship between beta estimates and various accounting risk measures (ARMs) have been extensively studied by accounting researchers, but results have led to different inferences about the usefulness of ARMs. The impact of the choice of market index on inferences concerning the usefulness of ARMs in explaining and predicting beta is investigated. The association of ARMs and beta tests are always joint tests.

Beta reflects the expected co variation between the returns of a given security and those of the market portfolio of all risky capital assets. The market portfolio, however, is not observable. Empirical evidence showed:

1. that the stability of beta estimates over time are quite sensitive to the market index employed,
2. that the ability of ARMs to explain differences among betas for a cross-section of firms is highest when the betas are estimated using the CRSP equal-weighted index,
3. that the ability of ARMs to improve upon market-based forecasts of beta depends upon the choice of market index and the error metric employed.

The Journal of Finance”: Kwan, Clarence C. Y (Dec 1984) published that a simple common algorithm that is applicable to 7 models is suggested for optimal portfolio selection disallowing short sales of risky securities. The 7 models considered are:

1. Sharpe’s (1963) single index model,
2. Cohen and Pogue’s (1967) multi-index models in diagonal and covariance forms,
3. Two multi-index models with orthogonal indexes,
4. Two constant correlation models.

The proposed algorithm successfully bypasses the requirement of explicitly ranking securities that is essential in previous research on the topic.

Because of this feature, the algorithm is especially useful for the 2 multi-index models with orthogonal indexes where there are problems in establishing a ranking criterion. An illustrative example is provided showing the results of all the iterative steps. It is demonstrated in a simulation study performed on the 5 models with multiple groups that the procedure involved in the search for optimality requires only small numbers of simple iterative steps. Thus, the method can enhance the usefulness of these index models and constant correlation models in portfolio analysis.

The Journal of Portfolio Management”: Gressis, N., Vlahos, G. , Phillipatos, G. C. (Spring 1984) published that the recent establishment of stock index futures markets has opened up a variety of new investment opportunities that should improve the performance of both secondary markets and individual investor portfolios. Trading in stock index futures has been proposed as an effective hedge against investment risk. A technique based on the capital asset pricing model (CAPM) framework is here developed to identify the profit opportunities of stock index futures trading.

With this technique, the systematic risk of a stock index futures contract can be identified for the investor buying on margin, along with the abnormal returns that can be expected from the contract and its equilibrium price. The technique is demonstrated in application to the Standard & Poor’s 500 Index futures. It is shown that the risk of a stock index futures contract declines with the length of the investment horizon. However, the degree of abnormal performance and the deviation of the equilibrium price of the contract from the market price increases with time to maturity.

The Journal of Portfolio Management”: French, Dan W. , Henderson, Glenn V (Winter 1985) published that the investment portfolio performance measures based on the capital asset pricing model are examined under ideal conditions that work around the problems that their critics have discovered. These problems include Miss specified independent variables, omitted variables, errors in variables, and unstable parameters, all of which are basically beta problems. A database is constructed by simulating 60 portfolios or security return series, each containing 3 random variants having their own distribution.

Regression analysis results show that winners cannot be distinguished from random performers, and that winners cannot even be labelled as such unless they are remarkably successful. If random noise is the only contaminating factor in performance evaluation, then the 4 currently popular performance measures rank in an internally consistent fashion and rank portfolio performance correctly “The Journal of Portfolio Management”: Peters (Summer 1985) published that Evidence is presented suggesting that early mispricing of stock index futures was due to market inefficiencies, but that the markets have become more efficient over time.

This growing efficiency is the result of more experienced traders and the increasing availability of accurate valuation models. This evidence is derived from a test of market efficiency done using a cost-of-carry valuation model. The test is limited to the Standard & Poor’s 500 and the New York Stock Exchange Composite indexes. The theoretical value for each future contract over the period June 1982-December 1983 is computed using data from CE/ICD’s database. Results indicate that both index futures markets have become more efficient with time.

If it is assumed that investors are rational and that expectations of the index value are not considered in valuation, it can further be assumed that dividend stream estimation is the major source of market inefficiency. Portfolio managers can now use index futures for hedging with greater confidence. “The Journal of multinational financial management”: Javier Estrada and Ana Paula Serra (July 2005) published that the proper identification of the risk variables that explain the cross-section of returns in emerging markets has many and far-reaching implications for both companies and investors.

We examine this risk–return relationship by focusing on three families of models, over 25 years of data, and over 1600 companies in 30 countries. We perform a statistical analysis that seeks to identify the variables that should be incorporated into the calculation of required returns on equity, and an economic analysis that seeks to determine the variables that produce the most profitable portfolio strategies. We find rather weak statistical results that prevent us from strongly recommending a given family to estimate required returns on equity.

And we find somewhat stronger economic results that show that a variable belonging to our downside risk family, the global downside beta, is the one that has the largest impact on returns when portfolios are rebalanced every 5 years. “University of Mannheim – Department of Business Administration and Finance”: Alen Nosic (March 6, 2007), published that the determinants of investors’ risk taking behavior. We find that investors’ risk taking behaviour is affected by their subjective risk attitude and by the risk and return of an investment alternative. Our results also suggest hat consistent with previous findings in the literature objective or historical return and volatility of a stock are not as good predictors of risk taking behavior as subjective risk and return measures. Moreover, we illustrate that overconfidence or more precisely miscalibration has an impact on risk behavior as predicted by theoretical models. However, our results regarding the effect of various determinants on risk taking behavior heavily depends on the domain the respective determinant is elicited. We interpret this as an indication for extended domain specificity.

In particular with the Markets of Financial Instruments Directive (MiFID) coming into effect we believe practitioners could improve on their investment advising process by incorporating some of the determinants we argue to influence investment behavior. ” European Journal of Operational Research”: Xiang Li, Zhongfeng Qin, Samarjit Kar (April 1, 2010) published Numerous empirical studies show that portfolio returns are asymmetric, and investors would prefer a portfolio return with larger degree of asymmetry when the mean value and variance are same.

In order to measure the asymmetry of fuzzy portfolio return, a concept of skewness is defined as the third central moment in this paper, and its mathematical properties are studied. As an extension of the fuzzy mean-variance model, a mean-variance-skewness model is presented and the corresponding variations are also considered. In order to solve the proposed models, a genetic algorithm integrating fuzzy simulation is designed. Finally, several numerical examples are given to illustrate the modeling idea and the effectiveness of the proposed algorithm. Banking and Finance”: Cheol S Eun, Jinso Lee (April 2010) published that the risk-return characteristics of our sample of 17 developed stock markets of the world have converged significantly toward each other during our study period 1974-2007, and (ii) that this international convergence in risk-return characteristics is driven mainly by the declining ‘country effect’, rather than the rising ‘industry effect’, suggesting that the convergence is associated with international market integration. Specifically, we first ompute the risk-return distance among international stock markets based on the Euclidean distance and find that the distance thus computed has been decreasing significantly over time, implying a mean-variance convergence. In particular, the average risk-return distance has decreased by about 50% over our sample period. We also document that the risk-return characteristics of our sample of 14 emerging markets have been converging rapidly toward those of developed markets in recent years. This development notwithstanding, emerging markets still remain as a distinct asset class.

Lastly, we show that the convergence in risk-return characteristics has exerted a negative impact on the efficiency of international investment during our sample period. “Journal of investment management”, Lisa R Goldberg, Michael Y Hayes (first quarter 2010) published that a practical and effective extension of portfolio risk management and construction best practices to account for extreme events. The central element of the extension is (expected) shortfall, which is the expected loss given that a value-at-risk limit is breached.

Shortfall is the most basic measure of extreme risk, and unlike volatility and value at risk, it probes the tails of portfolio return and profit/loss distributions. Consequently, shortfall is (in principle) a guide to allocating reserve capital. Since it is a convex measure, shortfall can (again, in principle) be used as an optimization constraint either alone or in combination with volatility. In principle becomes in practice only if shortfall can be forecast accurately.

A recent body of research uses factor models to generate robust, empirically accurate shortfall forecasts that can be analyzed with standard risk management tools such as betas, risk budgets and factor correlations. An important insight is that a long history of returns to risk factors can inform short-horizon shortfall forecasts in a meaningful way.

Research methodology

Sources of data

We selected the companies based on the market capitalisation and for this we referred money control. om from where we sorted out the top ten automobile companies in India based on the market capitalisation value given as of March 1, 2010. Then the opening and closing stock price of the top ten automobile companies for the previous five financial years (2005-2006, 2006-2007, 2007-2008, 2008-2009, 2009-2010) was downloaded from NSE website(nseindia. com).

Findings

Ashok Leyland: The value gives us a stock’s risk profile. Here we can take the average beta value and interpret and comment on the overall risk for the five years taken by the concern.

Average beta value = 1 which means it is neither stable nor unstable. It is a neutral share and is expected to follow the market. From the table when we look at the value its average value is 01233 which means that the minimum riskless return is 1. 23%. The company’s earnings from stock investment has reduced in the year 2010. We get a positive correlation value which implies that a 0. 5% in the market return will affect a company’s stock return by 0. 5% in the same direction. Eicher Motors: The company’s earnings from stock investment has reduced in the year 2010 from 2009. Here he company expects less volatility and less risk and therefore less returns. These are called defensive shares and will generally experience smaller than average gains in a rising market, will generally experience smaller than average falls in a declining market. From the table the average value 0. i6691. The minimum risk free return is 16. 69%. Mahindra is having high risk free rate so it is safe to hold this stock. Correlation value = 0. 44% 0. 44% of change in market return affects the stock return by 0. 44% in the same direction. Bajaj Auto: The return on stock investments is good during 2009 & 2010 when compared to the year 2008.

Since beta value < 1. The company expects a stability, less risk and less returns. These are called defensive shares and will generally experience smaller than average gains in a rising market, will generally experience smaller than average falls in a declining market. Alpha From the table the average value 0. 24715. The minimum risk free return is 24. 715%. Bajaj is having the highest risk free retun in all the ten companies so it is very safe to invest. Correlation value = 0. 46% 0. 46% change in Rm = 0. 46% change in Ri in the same direction.

Summary of calculation

HMT is having high stock return because they are using stock investments efficiently in the business The low cut-off point is good which implies less payback. Ashok Leyland has minimum payback whereas Bajaj has maximum payback. Escorts involves in high risky projects expecting more returns rather Bajaj is not involving in risky projects.

Suggestions

Hero Honda is having low risk and high return. So it is good for the investors to invest in this company. (for investors) HMT is taking high risk and provides decent returns. So next to Herohonda, HMT is a good company to invest.

Bajaj is having a low return at a medium risk so the company have to indulge in risky projects to get good returns in the future. (for company) HMT and Escorts have high unsystematic risk, so they can go for product diversification to reduce the unsystematic risk.

Cut off point is the point at which the required rate of return is worth the expense. If it is high then that company is going to take a long time to repay its initial investment.

In our case Ashok Leyland will be able to recover the money invested in the project as soon as possible than others. Ashok Leyland might serve as the best company to invest to get their investment back whatever the return may be. Based on the stock return, risk and the cut off point, Herohonda is a good company to invest because they have an optimum return at an optimum risk level. TVS motors has a high cut off point, less stock return at a high risk. They can reduce their risk level, because it might involve large sum of investment.

Conclusion

According to our findings we suggest that Hero Honda is the best Automobile company in India to invest and the investment can range up to 42% as per our analysis. Although India has been much discussed in recent years, and has been the recipient of major foreign investment in its automotive industry, it has in many ways not received the attention of the world’s other major developing country, China – but this is about to change. With the world’s second largest and fastest-growing population, there is no denying India’s potential in both economic and population terms and the effect it will have on the auto industry in the years to come.

The country is already off to a good start, with a well-developed components industry and a production level of one million four-wheeled vehicles a year, plus a further five million two- and three-wheelers. India also has substantial strength in mass production techniques and is particularly well served in the fields of research and development and software design. Therefore, as always, the question is when will expansion occur and to what level?

The implications, market drivers and scope of a future massive Indian vehicle market are covered in the India Strategic Market Profile, a brand-new forecast of Indian automotive and related activity to 2020.

References

1. Robert A. Strong, year, Portfolio Management, 82-85,123-131. Jeff Madura, 2009, Finance Markets and Institutions, 243-283.
2. Dr. G. Ramesh Babu, 2007, Portfolio Management Including Security Analysis, 577-647.
3. www. nseindia. com
4. www. moneycontrol. com
5. www. springerlink. com
6. www. proquest. com
7. www. sciencedirect. com
8. www. jstor. org
9. www. informaworld. com

Richer Sounds sell all sorts of equipment such as hi-if separates, home cinema systems and plasma screen TV’s, so they obviously have competitors who also sell this kind of equipment such as Dixon, Curry’s and Comet, which is why Richer Sounds always try to keep up with all the new developments and also specialist In specific types of equipment. They are also very good at keeping competitive. They are constantly trying to please their customers and also Investigate their competitors In order to Improve their own sales by keeping lower prices.

Ownership

Richer Sounds is an unlisted public limited company, this means they are not listed on the stock exchange and the owner has a limited liability for any debts. Julian Richer decided not to be listed on the stock exchange because if he did then he would lose some of the control of the business to the new shareholders and therefore would have added pressure from them to Improve their profits, so If he keeps the company unlisted then he will have the benefit of having full control over the business.

Richer Sounds think that it is very important for everyone in the organization to understand their aims so they can achieve them efficiently. They have made a mission statement, which is explained to all the staff to ensure they understand. Richer Sounds main aims are: To provide an excellent service to their customers. To provide generous salaries in relation to the Job being done.To make a profit, which is very important.

Objectives

The objectives of Richer Sounds are as follows:

• To open four to six stores in the current year.
• To control their costs.
• To develop the audiovisual home cinema range.
• To keep the business fun.
• To keep a good level of customer service.

It is very important for Richer Sounds to have aims and objectives because they want to make sure the business works well. Also they need aims and objectives for their colleagues to work to, to make sure that everyone does a good Job and provides a great service, which keeps a good reputation for the business. Therefore people will keep purchasing goods from their stores and Richer Sounds will continue to make a profit.

Richer Sounds is obviously very successful in achieving their aims and objectives because the business has continued to grow and make profits. They also never hang their mission statement so the staff always know what they aiming to do. They also have a suggestion system for the staff so they can suggest any improvements to the business or any ideas on how they can achieve their aims more sufficiently. Julian Richer personally reads out these suggestions to the staff and contributes towards the ideas, which I think is great because it shows he has good communication with his staff.

Functional Areas

Finance department Colleague support department, purchasing department. store operations department. marketing department , administration and IT support, customer service function, finance Department.  The main responsibilities of the finance department at Richer Sounds are as follows:

• The preparations of financial documents – invoices and statements of accounts, etc. credit checks – to check customers are reliable enough to purchase goods on credit.
• Checking and recording payments – they need to record all the payments for goods sold and check that customers don’t owe any money or the business doesn’t owe any money to their suppliers. checking financial documents relating to purchasing of goods – making sure that odds have been delivered from people who have sent invoices.
• Monitoring value of items held in stock – to make sure payments that are due will not affect the cash flow of the business. paying stock suppliers promptly – to make sure the business does not fall into debt with the supplier and lose their contract and to take advantage of any discount terms. paying all other suppliers – other suppliers include heating and electricity, etc.
• Checking bank statements – to ensure there are no overdrafts and to make sure that the correct amount of money has been put in and taken out. paying the payroll – paying staff wages and tax at the end of each year.
• Preparing monthly management accounts – making sure all the monthly documents are in order such as the cash flow forecasts and profit and loss accounts.
• Cost for new projects – obtaining capital for new developments.

IT Used for Financial Documents

IT is used for the following financial documents:

• To record all financial transactions.
• To pay suppliers. Produce all financial documents.
• Check deliveries have been made.
• Record and check supplier terms.
• Check sales in stores.
• Produce financial reports and accounts.
• Prepare the payroll.
• Communicate with other departments.

IT is very useful to Richer Sounds for all these financial purposes. IT is used to communicate by email, to prepare invoices and has many other uses such as spreadsheets.

The main responsibilities for the warehousing and distribution department are as follows:

• To receive the goods – supplier and company arrange a delivery date by completing a delivery booking form, the goods are then delivered on this date and checked by staff to the delivery note and any discrepancies are noted. Ottawa (distribution and storage) then complete a goods receipt note and send it with the delivery note to the stock control department, store the goods – goods are stored in a secure warehouse and stacked appropriately with forklift trucks and stored on large pallets.

Any waste is put into a skip and cardboard is recycled. Richer Sounds also use electric forklifts to create less pollution. Goods are distributed to stores – goods are listed and checked with Store Operations to ensure the correct amount of stock is sent to certain stores. Ottawa drivers make the deliveries and the store has a delivery date to ensure staff are ready for stock being delivered. IT used in the Purchasing, Warehousing and Distribution department.  IT is used within this department for checking stock levels, sending orders and communicating with suppliers and other departments within the company. IT is very useful for this department, mainly for communication because email is used to contact suppliers and other stores.

Store Operations department

Responsibilities of store operations department:

• Setting each store’s budget – ensuring that each store’s running costs are not too expensive, so the store does not cost too much to run.
• Checking that all the stores achieve their targets – monitoring stores and making Taking action if targets are not met – those stores would be visited by the director and the problems would be investigated.
• Checking stock orders – ensuring correct stock is being ordered, communicating with stores – feedback and advice, deciding the minimum staff level – ensuring the correct number of staff is working in the stores, organizing store visits – directors are sent to the stores for procedure visits and people visits to ensure that the stores are carrying out procedures correctly and checking that the staff arena having any problems.
• Customizing individual stores – store managers decide what improvements are deed within the stores.
• Managing colleague problems – colleagues are able to speak openly or privately to the store managers. Ensuring store managers undertake health and safety risk assessments – colleagues receive health and safety training. supporting and advising store managers and colleagues – store managers and colleagues can contact store operations if they have any problems. eliminating any problems or discrepancies – identified as a result of quarterly stock-takes.

IT used in Store Operations

 IT is used mainly for sales information. Richer Sounds have an EPOSES system which means the EPOSES tills are linked to the main computer system, which enables the sales and stock records to be updated. Also read ?

This system also keeps an up-to-date customer database, which enables customers to return goods to their nearest store without any complications even if they have lost their receipt. It is also used to communicate with other stores by email.

Marketing department

The main activities of the marketing department are split up into their own sections and those sections carry out their own activities which are as follows: customers in order to make decisions on advertising and promoting products. Marketing is also responsible for the company’s website.

• Design – design is responsible for producing the company’s catalogues and other advertisements relating to the company’s products.
• Point-Of-Sale – POS is responsible for keeping the company’s stores as modern and interesting as possible, which includes everything from fixtures and fittings to posters and notices.

The use of CIT in the Marketing Department

CIT is used in this department for analyzing customer data, designing catalogues and advertisements, producing documents, communicating via email and to send text and graphic attachments.

Administration and IT Support Department

The main activities of this department are the maintenance of the current IT systems and support for users and planning future developments. Richer Sounds’ computer system is very useful. It has security, which involves all staff having to have an ID and password to access information and it is also protected by a firewall to ensure that no viruses can affect the computer system. Each store uses an EPOSES till that is linked to the main computer system to update stock and sales information. IT support assists computer users when there is any problems.

A combination of several financial assets constitutes a portfolio. Finding an optimal portfolio position for an investor is the central theme of the ‘Portfolio Theory’. This theory advocates that the return expected by any investor on his return is subject to the interaction of certain factors. In order to determine the correlation between the risks and returns the statistical values of market returns like the ‘mean value’ and ‘variance’ can be used. In the place of variance its square root ‘standard deviation’ can also be used.

Hence these two statistical values can be considered as the two basic determinants the value of the expected market return from the various investments made by the investor. These values can be ascertained by collecting data on the returns of a particular security over a fixed historical period and a statistical analysis of these historical returns will provide the expected return from the investment. ‘Mean-variance portfolio theory’ or ‘two-parameter portfolio theory’ are the other names attributed to this theory because of the usage of these statistical values in the analysis.

Under normal circumstance the investor desires to achieve a higher mean return instead of a lower mean return. On the other hand deriving a lower variance of return instead of a higher one would be the preference of the investor. (Citring Group). The expected return on a portfolio is represented by the weighted arithmetic average of the expected returns of the assets comprised in the portfolio. The standard deviation calculated on the portfolio’s rate of return gives the extent of riskiness of the portfolio concerned. Efficient Portfolios

There are a number of possible combinations in which the investments can be planned and each one of them will represent a portfolio. It is possible to combine the investment opportunities of a particular investor into a number of portfolios, depending on his choice of risk taking levels. Normally the expected return from any portfolio forms the basis of arriving at the possible portfolios for investment. In any decision on the portfolio the standard deviation of the return also is considered to be a major determinant factor. The expressed indices are then plotted on a two-dimensional graph.

It is for the investor to select that portfolio in which he is expected to derive maximum utility of the investment. The investor can make this choice by following two steps:  demarcating the set of efficiency portfolios and  selection of the optimal portfolios from the selected efficient portfolios It is to be noted that the efficient frontier would most likely to be the same for all the investors as the all the investors have expectation which is homogenous in character. The task of choosing an efficient portfolio from a set of available alternative efficient portfolios is complex.

Generally the following methods are followed for the purpose of determining the efficient portfolio:

• Graphical analysis method;,
• Calculus analysis method;
• Quadratic programming analysis method

The graphical analysis is easier to use. However the graphical analysis cannot be used because it carries a disadvantage. The disadvantage is that this method of analysis can make only a maximum of three securities for analysis. On the other hand the mathematical analysis method is considered superior. This is because it can hold control for an n-dimensional space where more number of securities than the graphical analysis method.

Calculus method also can analyze portfolios with more combinations of securities than the other methods. Of all the analysis models the quadratic programming is considered as the better method to analyze the portfolios. The reason being the quadratic programming can also handle more number of securities without difficulty. At the same time this method is capable of handling the inequalities in the portfolios as well. Hence the quadratic system is considered by the analysts as the most useful approach for all analysis of the efficiency of the portfolios.

This paper will Identify and explain the major factors driving the market during this week. It will also identify and explain causes of changes in the market and the index. As well as developed investment objectives and defined risk tolerance levels. I will also include a snap shot of my investments and an explanation of why I choose those particular companies.

The trends that I noticed that were going on with my investments this week were:

Apple – Apple has asked Foxconn to tighten quality control measures while manufacturing the iPhone 5 after complaints of scratches on the device’s body, and that has worsened its supply shortfall. Stricter benchmarks are affecting production of the anodized aluminum housing used in the device, delaying orders for the phone, Bloomberg said. Apple consumers started complaining of nicks and scrapes to the body of the new phone soon after its launch last month, with some saying there were scratches even before the device was unpacked.

While the company said in response to the complaints that it was “normal” for an aluminum product to “scratch or chip with use, exposing its natural silver color,” internally, it expressed its displeasure to Foxconn. (Rathee, 2012)

Microsoft – Microsoft is reportedly bringing its flagship product, Microsoft Office to both Apple and Google’s Android-based devices. This comes from Microsoft’s Czech Republic Team, and Petr Bobek, a Microsoft Office productivity manager.

Microsoft has been hesitant in the past to bring its software outside of the Windows ecosystem, with the noted exception of haphazard versions of Microsoft Office being available for Mac. Bringing Office to iOS and Android, the two dominant mobile operating systems, would be a change in philosophy for Microsoft. (Ciaccia, 2012)

Johnson & Johnson – The delay for Eliquis is perhaps not unexpected given the size of the trial and the mass of data collected. No matter, it is widely believed to be more efficacious and safer than its Johnson & Johnson’s Xarelto and Boehringer Ingelheim’s Pradaxa.

If approved, it will likely see strong sales growth. The delay will no doubt please JNJ shareholders whose rival drug Xarelto is only just establishing sales. Its partner Bayer has been forecasting peak sales of over Euro 2 billion for Xarelto even after the FDA refused to expand its indications. If these sorts of numbers are baked into JNJ’ forecasts and Eliquis (if approved) starts to grab market share then this will be a blow. (Samaha, 2012)

Pfizer – Pfizer had four major events expected. It had one success (Inlyta), one failure (bapineuzumab) and two delays.

As ever with pharma the patience of a Saint is needed. The two biggest drugs (Eliquis and Tofacitinib) saw potential approvals delayed until November and March next year respectively. Both are expected to be blockbusters. (Samaha, 2012)

Nike – Corporate Responsibility Magazine has named Nike one of its top corporate citizens. The magazine ranked the Top 10 Best Corporate Citizens in categories including consumer items, consumer stables, energy and health care. Nike topped the consumer items list, sharing the honor with Mattel and Gap.

The rankings were determined using public data related to companies’ responses to climate change, employee relations, environment, governance and human rights, among other variables. [ (Journal, 2012) ]

Sprint – Sprint Nextel Corp. ’s Chief Executive Officer Dan Hesse, who took over in December 2007, has worked to fix the mess he was handed after Sprint’s $36 billion acquisition of Nextel in 2005 failed, causing 3. 1 million subscribers to leave the carrier. Now he says that Sprint is on track to return to the black in 2014. Wall Street has shown increasing faith.

Sprint shares are up more than 122% this year, but Hesse is quick to qualify his optimism. I tell the team here, “You’re not going to see any mission accomplished signs anywhere on this campus. ”? Bloomberg Business Week reported, that this is a long process. (Marin, 2012)

Verizon – Earlier in the year, wireless carrier Verizon said it was planning on expanding its 4G LTE coverage to p over 400 markets by year’s end. That was an aggressive goal considering it started the year with only about 190, meaning it was looking to more than double that figure in just one year.

Turns out that Big Red wasn’t just being overly ambitious, and is actually reaching that goal ahead of time. Speaking at MobileCon, a conference all about mobile IT, Verizon CTO Nicola Palmer said the carrier is launching LTE in a handful of markets on Oct. 18, bringing its total tally up to a whopping 417, further extending its LTE lead against rivals AT;T and Sprint Nextel. (Evan Niu, 2012)

Target – Radio Shack’s partnership with Target Corp. to place its employees in Target electronics departments is a money-losing deal that Radio Shack should consider ending.

The deal, signed in 2009 and expanded upon in 2011, hasn’t replaced the revenue generated by an earlier agreement Radio Shack had to run kiosks in Wal-Mart Stores Inc. Radio Shack’s Target business lost $17 million more than it did in 2010, Chai said. But the two companies are apparently working to improve things. Radio Shack’s kiosks now get placement in Target circulars, and Radio Shack is boosting training of its staff. [ (Journal S. P. , 2012) ]

Wal-Mart – Wal-Mart Stores Inc. , the world’s largest retailer, rose to the highest ever after the company’s U.

S. merchandising head said the back-to-school season was “very strong. ” The shares climbed 3. 3 percent to $76. 59 at 12:46 p. m. in New York after earlier reaching $76. 73, the highest intraday price since its initial public offering in 1970. The stock also gained after Costco Wholesale Corp. posted fiscal fourth-quarter profit that topped analysts’ estimates. Costco rose 3. 5 percent to $103. 08. (Townsend, 2012)

Toyota – Toyota is issuing a safety recall for 7. 43 million vehicles across the globe. The Japanese car manufacturer said 2. million of the vehicles with potential window problems are in the U. S. The issue involves the power window master switches of some of the company’s most well-known brands, including Camry and Corolla sedans and RAV4 sport utility vehicles, built between 2005 and 2010. Toyota said in a press release that commercial lubricating agents applied to a “sticky” window switch could potentially melt the switch or even cause a fire. The above chart is a snap-shot of my portfolio for the week of October 17th as you can see I made a 0. 80% increase or $7,944. 81 cash profit since my initial investment, which is a $ 7069. 02 positive difference and a huge increase from last week’s profits.

Works Cited

1. Carroll, D. (2012, October 10). Toyota Recalls 7. 43 Million Vehicles. Retrieved October 10, 2012, from The Mootley Fool: http://www. fool. com/investing/general/2012/10/10/toyota-recalls-743-million-vehicles-globally. spx Ciaccia, C. (2012, October 10). Here’s Microsoft’s Most Bullish Move in Years.
2. Retrieved October 10, 2012, from The Street: http://www. thestreet. com/story/11733394/1/heres-microsofts-most-bullish-move-in-years. html? puc=yahoo;cm_ven=YAHOO Evan Niu, C. (2012, October 10).
3. Verizon’s Big Red Footprint Gets Bigger. Retrieved October 10, 2012, from The Mootley Fool: http://www. fool. com/investing/general/2012/10/10/verizons-big-red-footprint-gets-bigger. aspx Journal, P. B. (2012, October 9). Nike on ‘Best Corporate

International investing seems to pull many investors resulted from the many benefits of the published international investing portfolio by houses all over the universe. Fund investors can play a portion in the economic growing of the other states, able to reexamine their rate of hazard, recognizing variegation effects and taking advantage of different market sections on a planetary graduated table. Globalization reflects the world-wide growing of marketing single states. These advantages may look alluring but the hazards involved for international investing must non be overlooked. In an international investing position, fiscal investings are non merely subjected to currency and political hazard. However, there are many organisations drawbacks and troubles, one of which is related to revenue enhancement issues. These failings of an organisation normally benefited those investors who are able to pull off to get the better of those troubles in a well-organised attack.

Introduction

The international economic activities presently have been increased dramatically due to the investing of concern internationally. International economic systems have become incorporated through a huge web of communicating and trade with the aid of globalisation. Due to globalisation, international flow of fiscal assets have been improved by many progresss in better lower cost of communicating and conveyance, which means that geographical distances are unimportant and therefore national economic systems are closely linked.

Investing portfolio normally involves the purchase of bonds, portions, stocks and assets by foreign international investors, all of them with the cardinal aim of accomplishing a fiscal net income. It works in a assortment of different ways toward the end of conserving and bring forthing net incomes. Money could be made from perchance any investing environment even though international fiscal markets are really much composite. International portfolio investing is someway hazardous. The greatest challenge faced about by all investors in doing an investing portfolio work is by cognizing precisely what to make at the right clip. The factors that usually affects are foreign exchange rates, involvement rates and revenue enhancement rates on involvements. Therefore, a well-diversified portfolio is recommended to extenuate hazard. When the investors want to broaden their investing, they will detect the international market and puting in foreign companies. The important ground why international portfolio investing might heighten stable returns and cut down hazard is the broader variegation. One of the benefits of broader variegation is risk-return trade-off, which is more profitable while puting internationally. Another possible benefit is the variegation of currency, which means it non merely exposed a foreign company ‘s operation, but besides to this foreign currency. As an investing fund director, the direction includes make up one’s minding what assets to buy, how many to buy, and when is the best clip to buy. These determinations must hold some analysis of measurings, which typically involves expected return on the portfolio and the hazard on the return.

Evaluation of the listed houses

Brief debut of the 3 list houses

• DASHANG GROUP ‘A ‘
• Code: CN: DDS ( P ) explain
• SPECIALTY FASH.GP. ( BER )
• Code: Calciferol: MVJ ( P ) explain
• Pacific Internet
• Code: Joule: PNET ( P ) explain

Asses the VaR exposure of the investings

Analyzing the economic exposure of each company

• Figure2.3.1:
  
• Figure 2.3.2:
  
• Figure 2.3.3:

Arrested development Analysis

Arrested development analysis is a widely used statistical tool agencies on concentrating on analysing the relationship between a dependant variable, Y, and independent variable, X, utilizing the simple additive theoretical account Y = a + bX. Regression analysis gives an apprehension on how the dependant variable Y alterations with changing independent variable Ten. The values of X and Y are inputted into Microsoft Excel and by utilizing the arrested development attack, values of a and B are calculated. Excel will so end product a drumhead consisted of a arrested development statistics table and ANOVA.

The R2 is a grade of fluctuation, measured in per centum, in the dependant variable that can be accounted for by the independent variables. Multiple R is fundamentally the square root of R2. The standard mistake is an estimated value that is determined by Excel in concurrence with the estimated coefficient. Adjusted R2 is calculated utilizing All calculated values are as shown in table 2.3.1.1. explain observations ( N ) .

Table 2.3.1.1:Arrested development Statisticss

ANOVA ( Analysis of Variance )

There are two tabular arraies in ANOVA.

ANOVA OUTPUT I

The constituents of the ANOVA were tabulated utilizing the undermentioned equations:

P represents the figure of coefficients and K represents the entire figure of coefficients which in this instance k=p+1= 2.

• Regression df = k – 1.
• Residual df = n – K.
• Entire df = n – 1.
• Entire SS = Regression SS + Residual SS.
• Regression MS = Regression SS/ ( k – 1 ) .
• Residual MS = Residual SS/ ( n – K ) .
• F =Regression MS/Residual MS.
• Significance F = FDIST ( F, k – 1, n – K ) .

The consequences are as shown in tabular arraies 2.3.2.1, 2.3.2.2 and 2.3.2.3.

Table 2.3.2.1:ANOVA end product I – DASHANG GROUP ‘A ‘

Table 2.3.2.2:ANOVA end product I – Forte FASH.GP.

Table 2.3.2.3:ANOVA end product I – PACIFIC Internet

ANOVA OUTPUT II

The following phase is the coefficients. ( Note that the Numberss have been converter to 3 denary topographic points to salvage infinite ) . It gives the coefficient for each parametric quantity, including the intercept. T-stat value is the ratio of the estimated coefficient value divided by the standard mistake value. T-stat value can be compared across all variables in comparing with the criterion error.. The p-value is associated with the variable, and the assurance intervals of the parametric quantity estimates as evaluated by Excel.

Table 2.3.3.1:ANOVA end product II – DASHANG GROUP ‘A ‘

Table 2.3.3.2:ANOVA end product II – Forte FASH.GP.

Table 2.3.3.3:ANOVA end product II – Pacific Internet

Assurance Time intervals for Slope Coefficients

95 % assurance interval for incline coefficient & A ; szlig ; 2 is from Excel end product ( -1.4823, 2.1552 ) .

Excel computes this as

b2 ± t_.025 ( 3 ) – Se ( b2 )
= 0.33647 ± TINV ( 0.05, 2 ) – 0.42270
= 0.33647 ± 4.303 – 0.42270
= 0.33647 ± 1.8189
= ( -1.4823, 2.1552 ) .

Other assurance intervals can be obtained.
For illustration, to happen 99 % assurance intervals: in the Regression duologue box ( in the Data Analysis Add-in ) , look into the Confidence Level box and set the degree to 99 % .

Test of Statistical Significance

The coefficient of HH SIZE has estimated standard mistake of 0.4227, t-statistic of 0.7960 and p-value of 0.5095.
It is hence statistically undistinguished at significance degree a = .05 as P & gt ; 0.05.

The coefficient of CUBED HH SIZE has estimated standard mistake of 0.0131, t-statistic of 0.1594 and p-value of 0.8880.
It is hence statistically undistinguished at significance degree a = .05 as P & gt ; 0.05.

There are 5 observations and 3 regressors ( intercept and ten ) so we use t ( 5-3 ) =t ( 2 ) .
For illustration, for HH SIZE P = =TDIST ( 0.796,2,2 ) = 0.5095.

Test Hypothesis on a Regression Parameter

Here we test whether HH SIZE has coefficient & A ; szlig ; 2 = 1.0.

Example: H0: & A ; szlig ; 2 = 1.0 against Ha: & A ; szlig ; 2? 1.0 at significance degree a = .05.

Then

• T = ( b2 – H0 value of & A ; szlig ; 2 ) / ( standard mistake of b2 )
• = ( 0.33647 – 1.0 ) / 0.42270

= -1.569.

Using the p-value attack

• • p-value = TDIST ( 1.569, 2, 2 ) = 0.257. [ Here n=5 and k=3 so n-k=2 ] .

• • Do non reject the void hypothesis at degree.05 since the p-value is & gt ; 0.05.

Using the critical value attack

• • We computed t = -1.569

• • The critical value is t_.025 ( 2 ) = TINV ( 0.05,2 ) = 4.303. [ Here n=5 and k=3 so n-k=2 ] .

• • So make non reject void hypothesis at degree.05 since T = |-1.569| & lt ; 4.303.

Overall Test of Significance of the Regression Parameters

We test H0: & A ; szlig ; 2 = 0 and & A ; szlig ; 3 = 0 versus Hour angle: at least one of & A ; szlig ; 2 and & A ; szlig ; 3 does non equal nothing.

From the ANOVA tabular array the F-test statistic is 4.0635 with p-value of 0.1975.

Since the p-value is non less than 0.05 we do non reject the void hypothesis that the arrested development parametric quantities are zero at significance degree 0.05.

Conclude that the parametric quantities are jointly statistically undistinguished at significance degree 0.05.

Note:Significance F in general = FINV ( F, k-1, n-k ) where K is the figure of regressors including the intercept.

Here FINV ( 4.0635,2,2 ) = 0.1975.

Predicted Value of Y Given Regressors

See instance where x = 4 in which instance CUBED HH SIZE = x^3 = 4^3 = 64.

yhat = b1 + b2 x2 + b3 x3 = 0.88966 + 0.3365-4 + 0.0021-64 = 2.37006

Excel Restrictions

Arrested development in Excel has a figure of restrictions:

• • No standardized coefficients. It was really hard to construe unstandardised coefficients. The standardized coefficients could be calculated utilizing the unstandardised coefficient if it is needed.

• • Lack of diagnostic graphs. The standard diagnostic graphs were non available in Excel, such as the secret plan of the remainders, the scatter-plot or remainders against predicted values.

• • Lack of Diagnostic statistics. There were no co-linearity nosologies, which would supply a more apprehension of the informations that was analyzed.

• • Excel standard mistakes and t-statistics and p-values are based on the premise that the mistake is independent with changeless variable. Excel does non supply alternaties, such autocorrelation criterion mistakes and t-statistics and p-values.

Decision

Mention

• • hypertext transfer protocol: //www.qimacros.com/qiwizard/regression.html

• • hypertext transfer protocol: //mallit.fr.umn.edu/fr4218/assigns/excel_reg.html

• • hypertext transfer protocol: //www.jeremymiles.co.uk/regressionbook/extras/appendix2/excel/

James Augustine Joyce was born on the 2nd of February 1882 to a couple in Dublin. Two of his ten siblings died young from typhoid. As a boy, James studied at a boarding school in County Kildare.

When his father could no longer pay the fees, the young Joyce transferred to a Christian Brothers school. However, Joyce rejected Catholicism in 1898 although philosophies of St. Thomas Aquinas became a strong influence in him even after he had left the brothers (Bradley 23).

He studied modern languages at the University College Dublin in 1898. His first published work was a review of Ibsen’s New Drama in 1900 which resulted in a letter of thanks from the Norwegian dramatist himself (Ellmann 13).

Joyce left for Paris immediately after graduating. Unfortunately, he ended up squandering what little fortune his family still had. He returned to Ireland shortly after only to witness the final days of his mother who died of cancer in August 13, 1903. James resorted to heavy drinking after his mothers death, at the same time trying to make a meager living out of reviewing books, teaching and singing (Ellmann 15).

In 1904, he met a young woman from Connemara by the name of Nora Barnacle who worked as a chambermaid who later on became his wife. The couple moved from Dublin and James experienced a great deal of trouble in finding work (Ellmann 16).

James’ two major contributions to poetry are the books Chamber Music which is his first full length collection composed of 36 short lyrics published in 1907 and Pomes Penyeach which was published in twenty years later. (Ellmann 25)

James’ works have been highly scrutinized by several well known personalities in their own right such as Máirtín Ó Cadhain, Jorge Luis Borges, Flann O’Brien, and Samuel Beckett.

He died on the 11th of January 1941 following complications after surgery for a perforated ulcer. James Joyce’s life is annually celebrated as Bloomsday every June 16 in Dublin and in other cities around the world. (Ellmann 20)

Abstract

This paper provides information on specific ideas embedded in single index model/construction of optimal portfolios compared to the classic Markowitz model. Important arguments are presented regarding the validity of these two models. The researcher utilises SPSS analysis to demonstrate important research findings. This type of analysis is conducted to explore the presence of any significant statistical difference between the variance of the single index model and the Markowitz model. The paper also includes implications for investors.

Introduction

In the contemporary environment involving business investments, selecting appropriate investments is a relevant task of most organisations. Rational investors try to minimise risks as well as maximise returns on their investments (Better, 2006). The ultimate goal is to reach a level identified as optimal portfolios. The focus in this process is on initiating the portfolio selection models, which are essential for optimising the work of investors. Research shows that the Markowitz model is the most suitable model for conducting stock selection, as this is facilitated through the use of a full covariance matrix (Bergh and Rensburg, 2008).

The importance of this study reflects in the application of different models so as to develop adequate portfolios in organisations. It is essential to compare certain models because investors may be provided with sufficient knowledge about how they can best construct their portfolios. In this context, the precise variance of the portfolio selection model is important, as it reflects portfolio risk (Bergh and Rensburg, 2008). Information on the parameters of different models is significant to make the most appropriate decisions regarding portfolio creation. Markowitz is a pioneer in the research on portfolio analysis, as his works have contributed to enhancing investors’ perspectives on the available options regarding specific models of constructing optimal portfolios (Fernandez and Gomez, 2007).

Research Methodology

The research question presented in this study referred to the exploration of ideas embedded in single index model/construction of optimal portfolios and comparing them with the classic Markowitz model. The focus was on the construction of optimal portfolios, as the researcher was concerned with the evaluation of constructed portfolios with specific market parameters (Better, 2006). Moreover, the researcher paid attention to the stock market price index, including stocks of organisations distributed in three major sectors: services, financial, and industrial (Fernandez and Gomez, 2007). The behaviour of this index was explored through the implementation of SPSS analysis. The data covered a period of seven years, starting on January 1, 2000 and ending on December 31, 2006. It was essential to evaluate the effectiveness parameters of the single index model/construction of optimal portfolios and the Markowitz model. The criteria for the selection of companies included that all organisations shared the same fiscal year (ending each year on December 31) as well as they have not demonstrated any change in position.

Results and Data Analysis

The research methodology utilised in the study is based on the model of single index/optimal portfolios and the Markowitz model. The exploration of the relationship between these two models required the selection of 35 equally weighted optimal portfolios, as two sizes of portfolio were outlined. An approximate number of 10 optimal portfolios represented the first size, which further generated 12 portfolios. In addition, the researcher considered the option of simulating of optimal portfolios represented at second sizes (Bergh and Rensburg, 2008). The criterion of queuing randomise portfolio selection has been used to generate approximate 23 portfolios from the second size category. The researcher selected five and 10 stocks to analyse the data. The portfolio size split allowed the researcher to explore how the portfolio size could be used to affect the relationship between the single index model/optimal portfolios and the Markowitz model (Fernandez and Gomez, 2007). Results of testing the data are provided in the table below:

Optimal portfolio numberVariance of Single Index ModelVariance of the Markowitz ModelOptimal portfolio numberVariance of the Single Index ModelVariance of the Markowitz Model
100.00370.003950.00210.0023
100.00140.001750.00280.0038
100.00210.002850.00420.0051
100.00200.002150.00250.0030
100.00310.003550.00260.0024
100.00190.001950.00330.0038
100.00880.008650.00670.0071
100.00280.003750.00370.0053
100.00250.002450.00380.0043
100.00220.002350.00210.0020
100.00190.002050.00630.0061
100.00230.002650.02120.0202

Table 1: Variance of Five and 10 Optimal Portfolios

Based on the results provided in the table, it can be concluded that the variance between the single index model/construction of optimal portfolios and the Markowitz model is similar. For instance, values of 0.0020 and 0.0019 for the variance of the two models are similar. This means that the results do not show substantial statistical differences between the two models. The tables below contain a descriptive summary of the results presented in the previous table:

MeasureSingle Index ModelMarkowitz Model
Mean0.00440.0047
Minimal0.00210.0020
Maximum0.02120.0202
Standard Deviation0.00370.0035

Table 2: Descriptive Summary of 10 Optimal Portfolios

The results in Table 2 were derived from testing the performance of 10 optimal portfolios. It has been indicated that the mean for the single index model of 10 portfolios is 0.0044, while the mean for the Markowitz model is 0.0047, implying an insignificant statistical difference. The minimal value of the single index model is reported at 0.0021, while the minimal value of the Markowitz model is 0.0020. The difference is insignificant. The maximum value of the single index model is 0.0212, while the same value of the Markowitz model is 0.0202. Based on these values, it can be argued that there is a slight difference existing between the two models. The standard deviation of the single index model is 0.0037, while the standard deviation of the Markowitz model is 0.0035, which also reflects an insignificant statistical difference.

MeasureSingle Index ModelMarkowitz Model
Mean0.00280.0031
Minimal0.00140.0017
Maximum0.00880.0086
Standard Deviation0.00200.0019

Table 3: Descriptive Summary of 5 Optimal Portfolios

Table 3 provides the results for five optimal portfolios. These results are similar to the ones reported previously (10 optimal portfolios). The mean for the single index model of 5 optimal portfolios is 0.0028, while the mean for the Markowitz model is 0.0031, implying an insignificant statistical difference. There are insignificant differences between the two models regarding other values, such as minimal and maximum value as well as standard deviation.

Furthermore, the researcher performed an ANOVA analysis of 10 optimal portfolios, which are presented in the table below. It has been indicated that the effective score for the single index model and the Markowitz model is almost the same. Yet, an insignificant difference was reported between the two means and standard deviations for both models.

ANOVA AnalysisSum of squaresDfConditionMeanStandard DeviationStandard Error MeanFSig.
Between Groups.00011.000.003125.0018704.0005399.089.768
Within Groups.000222.000.002892.0019589.0005655
Total.00023

Table 4: ANOVA Analysis for the Variance between the Single Index Model and the Markowitz Model of 10 Portfolios

From the conducted analysis, it can be also concluded that the F-test presents an insignificant statistical value, implying that the researcher rejected the hypothesis of a significant difference existing between portfolio selections with regards to risk in both models used in the study (Fernandez and Gomez, 2007). Hence, the hypothesis of a significant difference between the variance of the single index model and the Markowitz model was rejected (Lediot and Wolf, 2003). In the table below, the researcher provided the results of an ANOVA analysis conducted on five optimal portfolios:

ANOVA AnalysisSum of SquaresDfConditionMeanStandard DeviationStandard Error MeanFSig.
Between Groups.00011.000.004852.0036535.0007618.096.758
Within Groups.001442.000.004509.0038595.0008048
Total.00145

Table 5: ANOVA Analysis for the Variance between the Single Index Model and the Markowitz Model of 5 Portfolios

The results from Table 5 show that the variance between the single index model and the Markowitz model of five optimal portfolios is almost the same. Regardless of the stock number in the selected optimal portfolios, there is no significant statistical difference between the single index model and the Markowitz model.

The main finding based on the reported data is that the single index model/construction of optimal portfolios is similar to the Markowitz model with regards to the formation of specific portfolios (Bergh and Rensburg, 2008). As indicated in this study, the precise number of stocks in the constructed optimal portfolios does not impact the final result of comparing the two analysed models. The fact that these models are not significantly different from each other can prompt investors to use the most practical approach in constructing optimal portfolios (Haugen, 2001). Placing an emphasis on efficient frontiers is an important part of investors’ work, as they are focused on generating the most efficient portfolios at the lowest risk. As a result, optimally selected portfolios would be able to generate positive returns for organisations. This applies to both the single index model and the Markowitz model (Fernandez and Gomez, 2007).

Conclusion and Implications of Research Findings

The results obtained in the present study are important for various parties. Such results may be of concern to policy makers, investors as well as financial market participants. In addition, the findings generated in the study are similar to findings reported by other researchers in the field (Bergh and Rensburg, 2008). It cannot be claimed that either of the approaches has certain advantages over the other one. Even if the number of stocks is altered, this does not reflect in any changes of the results provided by the researcher in this study. Yet, the major limitation of the study is associated with the use of monthly data. It can be argued that the use of daily data would be a more viable option to ensure accuracy, objectivity as well as adherence to strict professional standards in terms of investment (Better, 2006).

In conclusion, the similarity of the single index model and the Markowitz model encourage researchers to use both models equally because of their potential to generate optimal portfolios. Moreover, the lack of significant statistical differences between the variance of the single index model and the Markowitz model can serve as an adequate basis for investors to demonstrate greater flexibility in the process of making portfolio selection decisions (Haugen, 2001). The results obtained in the study were used to reject the hypotheses that were initially presented. As previously mentioned, the conducted F-test additionally indicates that the single index model and the Markowitz model are almost similar in scope and impact (Fernandez and Gomez, 2007).

Investors should consider that portfolio selection models play an important role in determining the exact amount of risk taking while constructing optimal portfolios. Hence, investors are expected to thoroughly explore those models while they select their portfolios (Garlappi et al., 2007). Both individual and institutional investors can find the results generated in this study useful to facilitate their professional practice. A possible application of the research findings should be considered in the process of embracing new investment policies in the flexible organisational context (Bergh and Rensburg, 2008). Future research may extensively focus on the development of new portfolio selection models that may further expand the capacity of organisations to improve their performance on investment risk taking indicators.

References

Bergh, G. and Rensburg, V. (2008). ‘Hedge Funds and Higher Moment Portfolio Performance Appraisals: A General Approach’. Omega, vol. 37, pp. 50-62.

Better, M. (2006). ‘Selecting Project Portfolios by Optimizing Simulations’. The Engineering Economist, vol. 51, pp. 81-97.

Fernandez, A. and Gomez, S. (2007). ‘Portfolio Selection Using Neutral Networks’. Computers & Operations Research, vol. 34, pp. 1177-1191.

Garlappi, L., Uppal, R., and Wang, T. (2007). ‘Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach’. The Review of Financial Studies, vol. 20, pp. 41-81.

Haugen, R. (2001). Modern Investment Theory. New Jersey: Prentice Hall.

Lediot, O. and Wolf, M. (2003). ‘Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection’. Journal of Finance, vol. 10, pp. 603-621.