How to lose $100,000 in 100 days
In order to lose the most money possible one must identify high risk stocks in unstable markets. To choose the markets we must qualitatively analyse companies and anticipate market trends to discover which business sectors will perform badly.
The analysis will take place on the risk values associated with the stock. When talking about risk we can define it into categories:
- Systematic Risk – This type of risk will influence a large number of assets, it is very difficult to protect against. Systematic risk could occur due to a widespread event eg oil shortages (Investopedia, n.d.).
- Unsystematic Risk – This type of risk will only influence a small number of, or single stock. Unsystematic risk is characterised by a small scale event such as employee strikes in a company. It can be mitigated by diversification of a portfolio (Investopedia, n.d.).
- Market Risk – This type of risk will affect all companies in the market. An example of market risk is a recession, where all companies will have their performance affected.
To select the companies I will be researching different industrial areas to see if there are any situations which would cause a reduction in profit, this is characterised as systematic risk. I will also analyse particular companies in areas which are performing badly to see if they have a higher unsystematic risk than others.
Markets that provide a necessary service are unlikely to be good choices as they will be profitable regardless of the amount of customer spending, as are companies that sell non-luxury items eg supermarkets. Due to the downturn in consumer spending (BBC, 2008), companies which are performing the worst will be the ones that provide a luxury item or service, eg car manufacturers or electronics products. Although some smaller companies will be a good choice as they have a smaller cash ‘buffer’ many of the larger companies have particularly small margins, because of this they are very susceptible to large drops in income. The choice of companies is also limited due to the requirement of stock market presence; many smaller companies are not part of the markets and therefore cannot be chosen.
The quantitative analysis will be performed on companies selected from the following markets:
• Car manufacturers
• Electronics manufacturers (televisions, stereo equipment etc)
• Clothes retailers
• Travel companies and airlines
• Home improvement and building
Although banks would be an obvious choice it can be argued that their performance is unlikely to become any worse, furthermore government bailouts have allowed many banks to recover from poor financial performance (Telegraph.co.uk, 2008), and loses will begin to lessen throughout the next 100 days. This Government bailout normally occurs because large companies are perceived as, ‘to big to fail’ and if they were to fail it could have an irreparable effect on industry. However, even with this in mind their share prices and incomes are likely to decline.
Although it might benefit to invest the entire $100,000 into one particular stock, this may help to increase the possible investment; good performance by one stock is not buffered by poor performance of another stock. So, in an attempt to mitigate any positive affects of the stock one must try and spread the choices of company over the different market areas. This allows maximisation of the amount of loss that can be made; the buffer can be created to soften small increases in company performance. However, if we introduce too many companies into the portfolio we are in danger of spreading the risk of the portfolio. Appendix VI shows how diversifiable risk can be reduced by introducing more companies into the portfolio. The graph shows that as the number of companies is increased the risk is reduced exponentially.
The best method to reducing this buffering effect is to choose companies which have a high diversity; i.e. they all show a positive correlation in their returns to each other. By choosing one company that is sure to lose money and then selecting the rest of the companies by matching a positive correlation the performances should follow each other; hence it is more likely that all the stocks in the portfolio will lose money.
The main values that will be used to evaluate the risk and performance of each company are:
• Daily Standard Deviation – This is a good indicator of the risk of investing in a particular company as it quickly and clearly shows the risk associated with an investment in the company. The standard deviation shows the uncertainty that is associated with a particular return or how volatile it is.
• Average Daily Return – This shows the return that could be expected from a company, also a good indicator of company performance as low daily returns show a company has a low return on an investment.
• Value at Risk for one day at 5% (Parametric) - This shows the value that an investor could stand to lose for a given investment. The parametric value at risk has been used instead of the historical value at risk as it provides a more smoothed view of the date, this is because it uses to normal distribution to model the data; it does however assume that the data can be fitted by a normal distribution.
These three factors can allow a quick analysis of a company stock, for example, if a company has a high daily standard deviation with a low average daily return they it can be said that they have risky, low return stock.
Analysing the companies
By performing a historical summary the performance of the companies can be analysed over a specified time period; this will, in general terms, illustrate the stability of the company. The analysis of risk will include both weekly and daily rate of return. By using the standard deviation and mean of a company’s historical data I can evaluate the expected return for the company and the risk associated with that return.
Risk analysis will allow me to speculate about the performance of the company over the 100 day period. I will also use Yahoo finance to find data for the companies. I will look at the stock performance over the last year up to the 1st November 2008 (daily prices). This will give me a representation of how the company has performed throughout the ‘credit crunch’, allowing me to estimate the future performance of the stock. The main spreadsheet will be used to make a choice of 2 companies (tracker companies) that all other companies will be matched against. Once these companies are chosen, others can be selected by their positive correlation.
The companies that have been chosen as possible tracker companies are the following:
Car manufacturers - General Motors, Ford, VW
Car manufacturers are currently struggling do to the cutback on spending from consumers due to the smaller amount of spare money (BBC, 2008). One of the main factors that is problematic for the car manufacture industry is the cost of fuel (reference). Due to these factors this particular market sector has been performing badly recently (
WhatCar?, 2008).
Electronics / Technology - Nokia, HP, Carphone Warehouse, Sony, Western Digital, Samsung, Panasonic, Dell
As the amount of disposable income has reduced throughout the recession (BBC, 2008) the amount available to spend on luxury items is smaller. With this in mind these manufacturers / retailers will be struggling to sell goods, possibly being forced to reduce prices in order to make sales.
Home improvement - Home Depot (US company)
One of the areas that has suffered the most during the past few months in the construction industry (BBC 2008), this is due to the collapse of housing prices and the dramatic reduction in new building (BBC, 2008). Consequently people have begun to cutback on major home improvements as house sales prospects fall; therefore shops such as home depot are likely to suffer.
Department stores - Debenhams
Although these types of store sell many necessary goods they also sell high value luxury items such as televisions and home furnishings. As previously mentioned these types of goods are receiving a smaller amount of spending due to market downturns which could result in lower profits for these companies.
Travel - British Airways
With the lack of disposable income many people are cutting spending on holidays (BBC, 2008) and that, in conjunction with the high operating costs due to factors such as fuel price increases (Doran J., 2008) of British Airways could cause a loss in profits.
Clothing - GAP, Nike
The use of disposable income on clothing has been cut down (BBC, 2008). Due to the reduction of disposable income many people will choose to reduce spending on clothing especially from more expensive producers such as the above.
Other – Starbucks, Woolworths,
Although the price of coffee is low it is an easy luxury that can be removed from everyday life, for example, people not buying coffee on the way to work or while shopping (possible reference?). Woolworths will also suffer as they are not a unique provider of any product and because of this people can choose to shop elsewhere where goods may be cheaper.
Risk analysis of the chosen companies
These companies were chosen because they represent different areas of the market which are currently performing badly. These 18 companies can be ranked by their daily standard deviation. This shows the daily risk that is associated with each company.
| Company | Daily Standard Deviation | Average Daily Return | Value at Risk for 1 Day (5%) (Parametric) |
| Home Depot | 2.90% | -0.04% | 47.7 |
| GAP | 2.91% | -0.10% | 47.85 |
| HP | 2.53% | -0.10% | 41.67 |
| Nike | 2.66% | 0.00% | 43.72 |
| Samsung | 2.79% | 0.03% | 45.85 |
| Panasonic | 3.06% | -0.05% | 50.33 |
| Dell | 3.15% | -0.31% | 51.87 |
| Sony | 3.15% | -0.25% | 51.88 |
| Starbucks | 3.18% | -0.21% | 52.31 |
| Nokia | 3.30% | -0.33% | 54.25 |
| Carphone Warehouse | 3.33% | -0.33% | 54.73 |
| British Airways | 4.21% | -0.32% | 86.43 |
| Ford | 4.88% | -0.43% | 80.19 |
| Debenhams | 5.40% | -0.28% | 88.85 |
| General Motors | 5.61% | -0.56% | 92.36 |
| Woolworths | 5.62% | -0.46% | 92.48 |
| VW | 11.56% | 0.87% | 237.52 |
| Western Digital | 46.72% | 7.80% | 768.49 |
Fig I, Taken from appendix I, values assume an investment of $1,000.
This table shows that Western Digital has the highest standard deviation of the group of 9 companies. This means that, on a daily basis, Western Digital has the highest risk associated with its investment. If the company were to have a large potential return then this may not be a poor investment as the high return would balance the high risk. However, Western Digital also has the highest daily return, meaning that this is a high risk investment with a high potential return. The value at risk at the 5% level is by far the greatest of all companies in the table. Taking into account both the daily standard deviation and the value at risk the company looks as if it will be a very poor performer. However, the high average daily return indicates that this company, although risky, provides high returns.
If we combine this notion of a risky company that has high returns with the qualitative analysis performed earlier we can attempt to predict the performance of the company. As Western Digital is a producer of computer parts they are likely to be suffering from the current state of the financial markets, many people have reduced their spending on expensive luxury items such as computers (Cellular-News, 2008) which would reduce the revenue of Western Digital. This combined with their high daily standard deviation and high value at risk is a sign that they may perform badly over the nest 100 days.
The other company which exhibits the potential for loss high daily standard deviation and low average daily return is Volkswagen. Their value at risk for one day is also amongst the highest in the table. This suggests that they are, overall, one of the more risky companies in the table. (Crawley J., & Ferraro T., 2008) shows that the car manufacture industry is struggling. This is related to the current economic climate and high operating costs in comparison with companies such as Starbucks and GAP.
Although some of the companies higher than Volkswagen and Western Digital also seem to be poor performers they would be more profitable investments. Woolworths is currently going into administration in the UK (Leroux M., & Power H., 2008) and therefore they will be making decisions that are purely based on making money as opposed to continuing the business. This means that Woolworths may not be trading for the 100 day period and therefore cannot be invested in.
General Motors may not be a good choice as a ‘tracker’ company, but may in fact be a good choice for the portfolio as they show a high daily standard deviation with a poor average daily return as well as a high value at risk for one day. General motors also suffers from the same problems as other car manufacturers and this may be reflected in poor sales.
To confirm that the figures displayed in the above table are in fact poor performance figures the same analysis has been performed on a group of companies that are listed at the top of 3 different indexes, FTSE100, Dow Jones and NASDAQ. The results are displayed below:
| Company | Daily Standard Deviation | Average Daily Return | Value at Risk for 1 Day (Parametric 5%) |
| Legal & General Group | -0.16% | 3.75% | 61.66 |
| Actuate Corporation | -0.36% | 4.22% | 69.47 |
| JP Morgan Chase & Co | 0.09% | 4.51% | 74.26 |
Fig II, Taken from appendix II, values assume an investment of $1,000.
The data in the above table is comparable to that of the other 9 companies and the figures are is not vastly different to some companies. However, Western Digital and Volkswagen show larger values for all of the sections. In particular, these companies have a much lower value at risk. A company with a lower value at risk is beneficial when the market is performing poorly as although there is a greater chance of loss, there is less value that will be lost. This confirms that as these three companies are currently (as of 06/12/08) top of the previously mentioned indexes the likelihood of Western Digital and Volkswagen returning on an investment is low.
Western Digital and Volkswagen will be used as the tracker companies as described earlier. To choose the rest of the companies for the portfolio other companies will be selected for risk analysis using the qualitative statements defined earlier. From this the companies that have a correlation with Western Digital or Volkswagen which is close to unity will be chosen; risk analysis will also be taken into account.
The rest of the companies that are being analysed to make up the rest of the portfolio are detailed below. Some of the companies have been chosen because they exist in the same market sectors as the previously chosen companies.
Car manufacturers - Honda
Department stores - John Lewis
Electronics / Technology – Seagate, Creative technology, Apple, Acer, Vodafone
Travel -
EasyJet
However some do not fit into these categories and therefore an explanation to why they have been chosen is provided:
Siemens – This company has recently had problems with corruption claims (BBC, 2008), due to this their share price and consumer confidence is likely to have dropped, effecting profits.
Calculating the correlation
The following table shows the correlation to both the tracker companies (VW & Western Digital)
| Company | Correlation to VW | Correlation to Western Digital |
| Dell | 0.188876343 | -0.126505024 |
| Samsung | 0.175858397 | -0.048185015 |
| Home Depot | 0.16417254 | -0.049189476 |
| GAP | 0.125033445 | -0.00638901 |
| Nike | 0.11945513 | -0.017868254 |
| Starbucks | 0.07889588 | -0.074770947 |
| Panasonic | 0.064679018 | -0.044047791 |
| Ford | 0.059356756 | 0.039205669 |
| HP | 0.059069453 | -0.023675522 |
| Sony | 0.021832037 | -0.007429665 |
| Debenhams | 0.017937639 | -0.143246647 |
| General Motors | -0.003520139 | 0.023286906 |
| Apple | -0.005314632 | -0.030646063 |
| Creative Technology | -0.019841205 | 0.053267398 |
| Nokia | -0.024867752 | 0.003607624 |
| John Lewis | -0.027656002 | -0.027294654 |
| Vodafone | -0.04570601 | 0.025377693 |
| Carphone Warehouse | -0.050045836 | -0.049612829 |
| Woolworths | -0.050533324 | -0.00185325 |
| Honda | -0.056199329 | 0.039237346 |
| Easy Jet | -0.062014776 | 0.008471988 |
| Acer | -0.098633673 | -0.080698611 |
| Seagate | -0.103405604 | -0.01056415 |
| British Airways | -0.216156776 | 0.026930215 |
| Siemens | -0.222068327 | -0.015163767 |
Fig. III - Taken from appendices III & IV
The table shows that although there are no very strong correlations there are some positive ones with both the tracker companies. From this data one can begin to build a portfolio by linking the positive correlations as mentioned previously. Although the main consideration in choosing the companies that will form the rest of the portfolio there will also be consideration of the standard deviation and mean values of the historical data. By doing this one can ensure that the companies chosen also have the highest risk associated with them and lowest possible return.
The following table displays the 5 companies for which have the greatest correlation to each tracker company:
| Company | Standard Deviation | Mean | Variance | Correlation to VW |
| Dell | 3.15% | -0.31% | 0.002883926 | 0.188876343 |
| Samsung | 7.27% | 0.03% | 0.005282995 | 0.175858397 |
| Home Depot | 2.90% | -0.04% | 0.002436565 | 0.16417254 |
| GAP | 2.91% | -0.10% | 0.002537669 | 0.125033445 |
| Nike | 6.55% | 0.00% | 0.004290931 | 0.11945513 |
Fig. IV - Taken from appendix III – Correlation to VW
Although Dell has the strongest correlation to Volkswagen, it also has a poor mean or daily return. Dell does not have the largest standard deviation however suggesting that it is not the riskiest investment in the group. If a comparison is made with the companies in figure II then Dell shows that it has carries less risk than companies that were the top performers of their respective markets. If Samsung is compared with Dell and the companies in figure II then it can be seen that it has far more risk than any of these companies. Although Samsung shows a positive mean (average daily return) it is still minimal and because of these reasons Samsung will be chosen as one of the companies in the portfolio.
In this table Samsung is the only likely candidate for a portfolio choice as the others have far lower standard deviations and much more acceptable means (average daily return) hence the rest of the companies that make up the portfolio will be selected from the table below, which shows their correlation to Western Digital.
| Company | Standard Deviation | Mean | Variance | Correlation to Western Digital |
Creative Technology | 7.76% | -0.24% | 0.006014736 | 0.053267398 |
Honda | 8.16% | -0.10% | 0.006666581 | 0.039237346 |
Ford | 4.57% | -0.43% | 0.002093004 | 0.039205669 |
British Airways | 5.71% | -0.32% | 0.003260692 | 0.026930215 |
Vodafone | 5.20% | -0.16% | 0.002702075 | 0.025377693 |
Fig. V - Taken from appendix IV – Correlation to Western Digital
The table above shows a weaker correlation between Western Digital and the potential portfolio companies than figure IV, however the companies that could be chosen have far greater standard deviations and lower means (average daily returns). The companies which have the best correlation in comparison with there risk or standard deviation are: Creative Technology, Honda and British Airways. Although Ford has a slightly positive correlation with Western Digital than British Airways they have a much lower standard deviation, comparable with the performing companies in figure II. Under normal circumstances this would show British Airways as the obvious candidate for the portfolio choice, however due to reports from Ford that they will be ‘idling’ their production plants (Ramsey M., Bensinger G., 2008), I will opt to choose Ford over British Airways.
Building the portfolio
As these companies all have a positive correlation their performance should follow the tracker companies, e.g. if Western Digital’s performance drops and they start losing money, the same will happen to Creative Technology, Honda and British Airways. In order to increase the amount of risk in the portfolio to its largest value I must analyse the percentage of the $100,000 dollars that will be invested into each stock. This will be achieved by the use of the following formula, which gives the risk of a portfolio given different percentages of investment in different stocks:
Figure 1. Nguyen C., 2008, M591 - Analytical Management Techniques, page 10
Where xi and yi are the weights of each investment in the portfolio, the p’s are the correlations between the investments and the σ’s are the standard deviations of each investment .
The formula below shoes how to calculate the return for the portfolio. Although it will be using the historical return it will allow me to estimate the return for the 100 day period:
Figure 2. Nguyen C., 2008, M591 - Analytical Management Techniques, page 8
Where Wi is the weight of the stock in the portfolio and r^i is the expected return on individual assets (16).
By performing this analysis on my portfolio of 6 companies I can find a weighting for each company that will provide the highest risk and lowest possible return. Another technique, known as the efficient frontier, can be then used to select the weighting that provides the optimum solution for the situation. The efficient frontier shows how much risk is a return is associated for each combination of company weightings in the portfolio. Appendix V shows the efficient frontier graph for my selected companies. The following table shows the combination of stocks that provides the highest variance with the lowest expected return, as shown on the efficient frontier graph (Appendix V):
| Company | Weight In Portfolio | Investment Amount ($) | Portfolio Risk (Mean) | Expected Return | Standard Deviation |
Volkswagen | 0.4 | 40,000 | ^ | ^ | ^ |
Western Digital | 0.1 | 10,000 | | | | | | |
Creative Technology | 0.1 | 10,000 | 0.00487915 | 0.010568627 | 0.069850911 |
Honda | 0.1 | 10,000 | | | | | | |
Ford | 0.1 | 10,000 | | | | | | |
Samsung | 0.2 | 20,000 | v | v | V |
Figure VI - A table showing the company weighting and investments in the portfolio
This combination of weightings in the portfolio gives a low return as wells as an above average standard deviation for all the possible weightings, the average standard deviation being 0.013222. Although the variance or portfolio risk is low, the overall choice provides the best risk to return combination of all the possible portfolio weightings.
Calculating the value of the portfolio on 10th March 2009
The final value of the portfolio will be estimated by expanding the parametric method from the average daily return to find the 100 day return for my portfolio. Although it is specified that I must find the value of the portfolio after 100 days there are only 75 trading days in this period. I have calculated that my estimated return over the period will be $1.20. Although this is a gain it may not be the actual outcome after the 100 day period due to fluctuations in the market such as overvalued shares.
References
Crawley J., Ferraro T., Republicans Criticize auto bailout, Retrieved 10th December 2008 from
http://uk.reuters.com/article/topNews/idUKTRE4B96IT20081210
Leroux M., Power H.,, Closing-down sales herald end of Woolworths stores group by Christmas, Retrieved 11th December 2008 from
http://business.timesonline.co.uk/tol/business/industry_sectors/retailing/article5321669.ece
Cellular-News, Huge Decline in US consumer Electronics Spending, Retrieved 4th December 2008 from
http://www.cellular-news.com/story/29986.php
Nguyen C., 1st December 2008, M591 Notes – Technology Exploration Project, cnfolio.com
Page 10, 2008
Ramsey M., Bensinger G.,, Chrysler, Ford Idle Plants; GM Halts Engine Factory, Retrieved 21st December 2008 from
http://www.bloomberg.com/apps/news?pid=20601103&sid=aki6wd3J2FQQ&refer=news
Telegraph.co.uk, Government must oil the wheels of business to lift the economy out of recession, Retrieved 28th December 2008 from
http://www.telegraph.co.uk/comment/telegraph-view/3835592/Government-must-oil-the-wheels-of-business-to-lift-the-economy-out-of-recession.html
BBC, Households have less spare cash, Retrieved 15th December 2008 from
http://news.bbc.co.uk/1/hi/business/7783084.stm
BBC, US house building slump continues, Retrieved 26th December 2008 from
http://news.bbc.co.uk/1/hi/business/7786065.stm
BBC, Mortgage lending slump continues, Retrieved 27th December 2008 from
http://news.bbc.co.uk/1/hi/business/7679363.stm
BBC, Clothes and holiday spending down, Retrieved 30th December 2008 from
http://news.bbc.co.uk/1/hi/business/7749929.stm
Doran J., Fuel costs kill off a US airline every week, Retrieved 5th January 2009 from
http://www.guardian.co.uk/business/2008/may/25/theairlineindustry.usa
WhatCar?, Car manufacturers face sales slump, Retrieved 5th January 2009 from
http://www.whatcar.com/news-article.aspx?NA=235584
BBC, Siemens in corruption settlement, Retrieved 9th January 2009 from
http://news.bbc.co.uk/1/hi/business/7784512.stm
Investopedia, Risk and Diversification: Different Types of Risk, Retrieved 5th January 2009 from
http://www.investopedia.com/university/risk/risk2.asp
Appendices
Appendix I (i & ii) – A complete list of all figures for the 18 base companies - failing (Sheet: Sort)
Appendix II – A complete list of all figures for the 3 top companies – performing (Sheet: Sort)
Appendix III – A complete list of all data for other portfolio companies (Sheet: Correlation to VW)
Appendix III – A complete list of all data for other portfolio companies (Sheet: Correlation to WD)
Appendix III - Efficient Frontier Graph (Sheet: Efficient Frontier)
Appendix IV – Diversifiable risk graph
Figure 3. Nguyen C., 2008, M591 - Analytical Management Techniques, page 27
