Notes on portfolio management and security analysis PDF

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Notes of Portfolio Management and Security Analysis Introduction Types of Financial Securities Direct v Indirect investments Direct – when buy the financial assets directly. Indirect – when hold financial assets indirectly through a managed fund. Direct Investments: 1) Money market instruments – short-term debt securities with maturities less than one year. 2) Capital market instruments – common stock (equity), and debt securities with a maturity longer than one year. 3) Derivative instruments – securities whose payoff depends upon the price of another asset. For example, an option is the right but not the obligation to buy or sell an asset at an agreed price at some future date. Derivative securities will be considered in your third year Finance classes. Money market securities 1. Treasury Bills These are short-term zero-coupon bonds issued by the government which pays the face value of the bond at maturity. The bonds are issued at a discount to the face value and so the investor knows the return of the T. Bill when buying the bill. In nominal terms, T. Bills are risk-free. In the U.K., bills are issued at 1-month, 3-months, and 6-months maturities and sold in minimum denomination of £500,000 (U.K. Debt Management Office). 2. Certificates of deposit, commercial paper, and floating rate notes. Capital market securities

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1. Stocks – ordinary shares issued by the company. Legal owners of the company. Payoffs are periodic dividends and future sale of share (price at some future date). Trade on the London Stock Exchange or the Alternative Investment Market. 2. Bonds – these are debt securities issued by companies or governments. Usually have a fixed life. Pay regular coupon payments and the face value of the bond at the end of the life of the bond. Will consider bonds more fully later in the class. Indirect investments: 1. Retail funds These are investment funds that can be bought by the general public. Known as mutual funds.

Main types are open-end funds (Unit trusts and Open-Ended

Investment Companies (OEIC), closed-end funds (investment trusts), and exchange traded funds (ETF). The ETFs are the most recent and have seen substantial growth in the last decade. 2. Pension funds 3. Insurance companies 4. Hedge funds 5. Private equity funds 6. Sovereign wealth funds Benchmarks and Market Indexes Benchmarks of major asset classes and investment styles are important for a number of reasons. First, it provides information on well a given asset class or style has performed over some historical period. Second, benchmarks are used to evaluate the

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performance of fund managers. The benchmark is used as a passive alternative trading strategy that the fund manager could have followed. Third, benchmarks are used in the developing of index funds and Exchange Traded Funds. Fourth, the benchmarks are used in asset allocation decisions. Stock market indexes: Most stock markets have local stock market indexes. In the U.K., the main ones are the FT100 and FT All Share index. These are value weighted indexes of the largest stocks on the London Stock Exchange. The weights depend upon the market values of the stocks. Some indexes like the Japan Nikkei 225 index uses equal weights. The Dow Jones Industrial Average is a price-weighted index. Indexes are also provided by organizations like MSCI, FTSE, S&P, Russell Investments, Thomson Financial Datastream. Provide both local indexes, regional, and global indexes. Also construct industry indexes, style indexes. Advantage is that use the same methodology across all markets. MSCI use market value weights. In emerging markets, S&P/IFC produce investable indexes that represent the market that is actually available to international investors. Global index providers differ in their coverage of each market and the weights that are used. In some markets, free float market cap weights are used. The free float is the proportion of the market value weights that are available to international investors. International investors do not always have access to the full market value due to cross holdings, government ownership, restrictions on foreign holdings. Data providers also provide bond indexes for local and global bond markets. Benchmarks have also been created for other asset classes e,g gold, commodities, and crypto currencies.

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These indexes are easily accessible through databases like Thompson Financial Datastream, Bloomberg, and Morningstar Direct. An Introduction to Statistics The return on any asset is given by the payoff at time t+1/cost at time t. For stocks, the return can be given by (pt+1 + dt+1)/pt, where dt+1 is the dividend (if any) at time t+1, pt and pt+1 are the stock price at time t and t+1. We usually work with returns in decimals, given by: Rt+1 = (pt+1 – pt + dt+1)/pt Return can come in the form of a capital gain or loss and via dividend income. The example.xlsx file contains data on the monthly returns on 16 size/BM portfolios (Columns numbered 1-16) between July 1983 and December 2016. The worksheet also contains the return on the one-month U.K. Treasury Bill (Rf) and the excess returns on the market index (Market) and four zero-cost portfolios that capture the size (SMB), value (HML), profitability (RMW), and investment (CMA) effects in U.K. stock returns. The final column is a January dummy variable, which we will use later in the course. We will be using Matlab through this class. The portfolio optimization handbook will give you an introduction to Matlab. To issue a command, you type in the relevant command at the command prompt. The command prompt in Matlab is given by: >> To import data into Matlab, we can either use the approach in section 2.3 of the workbook or a related approach where we just specify the relevant part of the worksheet. close all; clear all; 4

input_file='c:\teaching\portfolio2ndyear\example.xlsx'; input_sheet='Sheet1'; mkt=xlsread(input_file,input_sheet,'t2:t403'); We will focus on how to estimate the mean and standard deviation of the excess market returns. Section 3 of the portfolio optimization workbook shows how to use Matlab to get descriptive statistics and to plot graphs. For the average excess market return, use the command mean: 100*mean(mkt)

% calculates the mean in % terms

0.4341 If multiply by 12, get an annualized mean excess return. For the standard deviation use the command std: std(mkt)*100 4.1858 To get the annualized volatility, multiply by the √12 The excess market returns during the sample period of July 1983 and December 2016 has an average of 0.43% and volatility of 4.18%. The average excess market returns is known as the historical equity premium. An important issue in many practical applications is whether the average excess market returns are significantly positive or do stocks significantly outperform risk-free bonds? We can examine this issue by conducting a statistical test of the hypothesis that the average excess market return is equal to zero. Statistical tools provide estimates of relevant parameters and the sampling distribution of the parameters. The sampling distribution tells us how precise the estimate is measured. The uncertainty of the estimate is captured by the standard deviation of the sampling distribution or it’s standard error.

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In the case of the average excess returns, we want to know what the standard error of the average excess market returns is. Under standard statistical assumptions, the excess market returns are independently and identically distributed (iid), the standard error of the average excess market returns is given by: SE(rm) = σ(rmt+1)/√T The standard error tells us how volatile the estimate is and captures sampling variation. We want to test whether rm= 0. This is known as the null hypothesis (H0). The alternative hypothesis (Ha) is usually that rm ≠ 0. Where the alternative hypothesis allows for either positive or negative values, this is known as a two-tail test. Where the alternative hypothesis is rm > 0 or rm < 0, this is known as a one-tail test. We use a statistical test to examine the null hypothesis. The test allows us to judge whether the average excess market returns are different from zero due to sampling variation (accept null) or due to a real difference (reject null). We can test the null hypothesis that average excess returns=0 using the standard tstatistic t-statistic = (Estimate - Value of estimate under the null)/Std error of estimate In our case, since the value of estimate under the null equals zero, the t-statistic is simply the average excess returns divided by the standard error of average excess returns. A large absolute value of the t-statistic (depending upon the specified significance level) leads to a rejection of the null hypothesis.

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To decide whether to accept or reject the null hypothesis, we can compare the actual tstatistic to the critical values of the t-statistic from the t-distribution. When T is large, the t-distribution converges to a standard normal distribution. As a rough rule of thumb for large T: If |t| is > 1.96, then we can reject the null hypothesis at the 5% significance level If |t| is > 1.64, then we can reject the null hypothesis at the 10% significance level An alternative approach is to use the p value of the t-statistic. The p value is the probability of observing the t-statistic. We reject a given null hypothesis if the p value of the t-statistic is below a specified significance level (1%, 5%, 10%). Assume a 5% significance level. If the p value < 0.05, and the t-statistic is positive, we reject the null hypothesis and the coefficient is significantly positive. If the p value < 0.05, and the t-statistic is negative, we reject the null hypothesis and the coefficient is significantly negative. A significant positive average excess returns implies the market return has a significantly higher average returns than risk-free bonds. A significant negative average excess returns implies that the market returns have a significantly lower average returns than risk-free bonds. We can also use the standard error of the average excess returns to compute confidence intervals of the average excess market returns. The 95% confidence interval of the average excess market return (rm) is rm – 1.96*SE(rm)...