Basic Principles in Finance and Investments - Research Paper Example

Simulation Investing: London Stock Exchange Introduction. Success investing depends on both skill and luck. Different individuals have different tolerance for risk and their investment approaches are marked by their risk-return preferences. In order to gain practical knowledge, we have taken the challenge of investing in stocks. Assuming that we have a £10,000 capital, we would like to buy shares in stocks that are listed in the London Stock Exchange, But first, a consideration of some basic principles in finance and investments would be in order. Modern portfolio theory The portfolio model developed by Markovitz is based on the common concept that the average investor is risk-averse and would not expose his capital to risk unless there is a proportionate reward or premium for it. Risk is related to uncertainty about future outcomes. All other things being equal, an investor would prefer less rather than more risk for a given outcome; for a given risk, he would, if possible, try to gain maximum returns. (See Hagin 1978; Reilly and Brown 1997; Block and Hirt 2002). He can only be induced to risk more by, for example, switching his bank savings to shares of stock if the stock promises higher returns commensurate with the incremental risk. A basic implication of the so-called efficient market hypothesis (EMH) is that stock picking for a portfolio does not make for superior investment results; rather, more properly the achievement of high returns is related to the additional risk than to superior stock-selection ability. Although many fundamental and technical analysts would take issue with this claim, many theoreticians believe that this is confirmed by empirical research. In fact, it has been argued that based on the inferior results achieved by the majority of fund managers who pick their stocks carefully compared to the indexes such as the S&P500, throwing darts on the stock list pages of financial dailies as a means of picking stocks would do just as well if not better. (See Malkiel 1992). Standard deviations Risk for any stock or security is normally measured by the standard deviation around a given expected value. Where there is wide dispersion of possible outcomes, the standard deviation would be larger, implying more risks. It is common knowledge that the greater the variability of returns, the less certain the actual outcomes would be. One would therefore prefer less dispersion and variability in the returns for a particular stock. The single figure called the standard deviation provides a clue as to the risk of a particular stock, and stocks could therefore be compared on the basis of its size. For a two-asset portfolio of a stock , one tries to obtain, firstly, the weighted average of the expected returns, and, secondly, the portfolio standard deviation. The two stocks that are combined will interact in such a way that the risk of the two-asset portfolio will be reduced, depending on the coefficient of correlation. Where the coefficient of correlation is less than 1, there will be some reduction in the portfolio standard deviation compared to their weighted average. Where the coefficient is negative, it is certain that the portfolio standard deviation will be significantly reduced - and this indicates less risk. From the foregoing analysis, one can design an efficient portfolio because the standard deviation has been reduced. A good fund manager can consider many alternative portfolios, each with different expected value and standard deviation. He can build an efficient frontier that represents the optimal risk-return trade-offs at different risk levels. An efficient frontier is defined as a set of portfolios of investments in which the investor receives maximum return for a given level of risk or a minimum risk for a given level of return (Hirt and Block 2002). The optimal portfolios plotted below along the curve shows the best possible returns. Fig. 1 The efficient frontier (Source: Investopedia) One can then match ones risk-return indifference curves (a concept we learned in microeconomics) with the efficient frontier to find out where they would like to be along this efficient frontier scale. The capital asset pricing model (CAPM) The capital asset pricing model (CAPM) was introduced in the 1960s and 1970s by Sharpe and Lintner as a step forward along the same approach, adding as input the risk-free asset into the analysis. Under this model, the individual may combine an asset yielding a risk-free return with one earning the market rate, and still guaranteeing superior returns on the efficient frontier at all points except one at which they are equal. Any increase in higher portfolio returns requires a matching level of higher portfolio risks, as indicated by the standard deviation. The Beta In order to apply the Capital Asset Pricing Model, it is necessary to find a method to estimate the future beta, a component of a security’s risk which cannot be eliminated through diversification, and which can applied in the estimation of future returns. The least squares regression can be used to develop a linear equation to explain the relationship between return on the stock and return on the market. The basic equation is Ki = ai + iKm + e (The random error e is usually disregarded) Although intuitively sound, the capital asset pricing model has not shown complete consistency when tested in the real world. For example, an exhaustive study was conducted using the stocks listed in the New York Stock Exchange over a 35- year period. Contrary to the hypothesis that high beta stocks returned higher earnings than low-beta stocks and that low beta stocks should yield lower returns, the results were in the reverse. Even zero-beta stocks displayed rates of return that were superior to the risk-free rate, and higher beta stocks gave lower returns than anticipated, and further, the low-beta stocks yielded higher returns than could be predicated from the model. (Black, Jensen & Scholes, cited in Hirt and Block 2002). Another dimension of the theory that has come under criticism has been its apparent unreliability for predicting the future. The method of extrapolating past beta into the future takes the form of a cross-sectional regression between two variables, the broad index on one hand (as independent variable), and the security (as dependent variable) on the other. A regression line is drawn on the basis of the returns of both securities over time, and a certain figure summarizing that relationship, the beta, is obtained. We know, however, that that the price of the security can be subject to extra-systematic influences. Major changes in the industry, shifts in popular demand for the companys product, a new product that gains immediate favor with the consumers, and a host of other unforeseen events can affect the stocks – events that had played no part in the previous calculations. In this regard, a stocks beta may have very limited uses for predictive purposes. A different but reassuring picture is presented with regard to the betas of diversified portfolios. According to a study by Blume (1975, cited in Hirt and Block 2002) stability can be achieved with reasonably sized portfolios of even 10 to 20 stocks. Blume measured the correlation between portfolio betas during two different periods and confirmed his hypothesis. At least in defense of the value of the CAPM model, it can be said that large portfolios can have stable betas over time. Richard Roll (1977) raised another issue with the CAPM theory. He said that it is impossible to observe the markets return because of the limited coverage of the existing proxies of the general market. In principle, he said, the market should include all stocks, all financial instruments, and all other types of investment. Indexes are imperfect proxies for the true market. Different indexes would yield different relationships with stocks and therefore different betas; by changing indices, say from the S&P 500 to the Wilshire 5000, a very different index of stock sensitivity could be obtained. Since the true market has not been known, he argued, the CAPM cannot be said to have been properly and fully tested. We sometimes encounter more than beta estimate from Internet data providers. By using different proxies, different period lengths and periodicities, they inevitably come up with different beta estimates, although not by a wide margin. How we invested £10,000 We saw in the above discussion that careful stock picking has not had a good track record in terms of profitable results. We may say that fund managers give the impression of skill and expertise in this area in order to impress their clients and justify their fees. We do not expect to do better. Aside from the time constraints, we avoided using detailed decision rules founded on such measures as P/E ratios, dividend payout, ratio of price to book value, and the like, and instead went for a simplified method. Inasmuch as we were not to hold our investments for a long period of time as value investors (the likes of Warren Buffett) would, and because we were to appraise our results at the end of 45 days, the use of fundamental criteria would be unnecessary. For such short time-horizon trading, technical analysis applied on sound companies comprising a portfolio would have been sufficient. We therefore selected 10 companies we have often heard about, companies that are listed in the London Stock Exchange as constituents of of the FTSE100. We took our positions on 15 February 2010. We appraised the results of our portfolio on 31 March 2010 closing prices. The stocks we picked. The following were the companies we picked along with their industry categories and their betas sd er;; sd a short descriptive profile. Technical charts showing their price movements and the movements of market index FTSE100 are also drawn on the charts. At the base of the chart is a technical indicator called Moving Average Convergence Divergence (MACD) that gives buy or sell signals, included here for illustrative purposes. We did not use the MACD because we had no timing was not a decision element in our entering he market. 1. Astrazeneca (AZN) - Industry: Pharmaceuticals. Beta 0.64 One of the world leaders in the pharmaceutical industry, this British group produces and sells various medicines to pharmacies and hospitals. It operates in seven major therapy fields: anesthesia, cardiology, gastroenterology, pneumology, oncology, neurology and infection. All charts are excerpted from the Internet technical charting screens of uk.yahoo.com. 2. BAE Systems. Industry: Aerospace and Equipment. Beta: 0.46 An offshoot of the 1999 merger between British Aerospace and Marconi Electronic Systems, the company is one of the global leaders in the field of military equipment. BAE Systems designs, and develops systems maintenance for the army, the navy, and aerospace. 3. BHP Billiton. Industry: Mines, Metals Beta 1.78 Mining producer BHP Billiton emerged from the merger of Billiton plc and Australian Group BHP Limited, an Australian Group. and is one of the world leaders in the production of diversified resources 4. Experian (EXPN) Industry: Services to Businesses Beta 0.49 The company offers information services enabling companies to improve their levels of efficiency. Experian works with consumer and company databases from which it develops four main activities, namely, credit analysis, decision making tools, marketing solutions, and interactivity. 5. Hsbc Hldgs.uk (HSBA) Industry: Banking Beta 1.32 Of Scottish origin, HSBC Holdings is a well-established company in the banking and finance sectors in UK. It has almost 10 000 branches in 77 countries in Europe, Asia, Africa the Middle East, and the Americas. Divided into several branches, HSBC Holding offers private banking and personal or retail banking. 6. LLOYDS BANKING GRP (LLOY.L) Industry: Banking Beta 1.93 The company is operates in the field of retail and commercial banking, and in the field of insurance, pensions, investment funds and general insurance (house, car, travel). In addition to over 2,200 branches in Great Britain, Lloyds TSB also has presence throughout Europe and Asia. 7.SABMiller Industry: Drinks Beta 1.31 The company was founded in South Africa in 1895 and only acquired its present name through the purchase of the Milwaukee U.S.A. brewer in 2002. This was in the wake of a series of acquisitions of local breweries in Europe, Asia, and the United States. The company carries six of the worlds top 50 beer brands; it is also a major bottler of Coca Cola products. 8. Sainsbury(j) (SBRY Industry: Mass market distribution Beta 0.39 Since 1869, the British Group Sainsbury has been a leading distributor of consumer goods. The group runs 350 Sainsbury supermarkets and 300 shops branded Sainsburys Local and Bells Stores. It also serves customers buying online via the Internet 9. Tesco. Industry: Mass market distribution Beta 0.42 Tesco is the leading supermarket in Britain. Apart from being the national leader in the food sector, it also sells books, CD/DVD/mini-discs, hi-fi and household appliances, household equipment, etc. Its online sales site complements its brick and mortar presence. 10. Vodafone Grp. (VOD) Industry: Telecommunications Operator Beta 0.78 A world specialist in mobile phones, the Vodafone Group extends its operations to many countries through its subsidiaries in North and South America, Africa, Asia, and Oceania. Analysis After spending about £10,000 on 15 February, 2010, we tracked the results for the next 45 days. The world economy had began to recover slightly. All companies that we picked showed price improvements, especially Lloyd TSB. The results of our investment activities are shown in the following table. Table 1: Value of stocks when purchased and when appraised after 1-1/2 months Deducting 1 percent brokerage commission reduced the value of the stock purchase from £10,414 to 10, 310. The growth of 11.8 percent implies that the market value on 31 March 2010 would be £11,516 to reflect the transaction costs. The portfolio beta is derived by obtaining the percentage weight of the purchase value of each stock, multiplied by its beta. The weighted portfolio beta was 1.04, which confirms the hypothesis that a well-balanced portfolio tends to approximate the market proxy beta of 1.0. (See table in the Appendix.) Reflective Report Because this was a short-term investment (or speculation), it was not deemed appropriate to use fundamental analysis for the purpose of exploiting gaps in market information. The prices taken on 15 February were actual prices that did not take into account valuation criteria or technical signals. In other words, it was a pure bet. A good technical analyst would have waited for the technical indicators, such as the MACD, before asking his broker to buy 100 or shares of stock of a listed company. If he were also good at fundamental analysis, he would have considered the financial ratios, cash flows, and news from the economy or the industry before picking the stocks that also showed confirming signals from technical indicators. Incidentally, the bets panned out well, as all the stocks appreciated in terms of market price. The portfolio registered a growth rate of 11.8 per cent over the 45-day period, which, if annualised, would have been translated to an annual growth of 94.4 percent. Bibliography Hagin, P 1979, Dow-Jones Irwin Guide to modern portfolio theory, Dow Jones-Irwin, Homewood, IL Hearth, D & Zaima, JK 1998, Contemporary investments: Security and portfolio analysis, 2nd edn, The Dryden Press, Orlando, FL Hirt, GA & Block, SB 2002, Fundamentals of investment management, 7th edn., McGraw Hill, New York Jones, CP 1991, Investments: Analysis and management, 3rd edn., John Wiley & Sons, New York Malkiel, BG 1990, A random walk down Wall Street, WW Norton & Co., New York. http://uk.finance.yahoo.com/q/pr?s=SAB.L Appendix The calculated table showing weighted average beta Read More