Financial management is one of the most important approaches that is used for making varied finance related decisions. Risk is the factor to which people give much importance while making an investment in any security. The risk and return are closely associated with each other. Theory of trade off between risk and return reflect that if risk is high then return must also be high on investment. This reflects that both risk and return are closely associated with each other. However, there are some securities where in case there is high risk, good amount of return is also earned on it. Thus, one must be very cautious while making investment related decisions. In the current report appropriate tools to measure risk and return are identified. Apart from this, Exchange Traded Funds are compared for 5 years and best tool that can be used for analysis of risk and return trade off is identified. Investment is made in securities by individuals and business firms of small and large amount. There is common intention of people that if they are taking heavy risk on investment than return must also be high. This is because risk is high thus one will want to earn more amount of money so that taking a heavy amount of risk can be justified. This concept of investors is covered by topic of trade off between risk and return. In the current report, appropriate method of historical risk and return are described in detail. In depth discussion is carried out on these approaches such as standard deviation and beta values. This report also considers the best way to analyze risk and return and in this regard standard deviation and returns are compared with each other and results are discussed in detail. In order to support this investigation three objectives are prepared. First objective is to identify best approach to analyze risk and return trade off in respect to security STW which is ETF. Second objective is to analyze risk return trade off in case of security SLF and third objective is to identify which of these schemes have best risk and return trade-off.
Investment risk has been a warmly debated topic by both academics and investors for a lengthy period of time and never failed the limelight across the broad spectrum of financial market. When it comes to risk measure there’s no individual theorem to support this application. Indeed, risk is the psychological phenomenon that you individually deal thus it could never be constant and could depend upon an array of various elements. The following Literature Review discusses wide range of theses, examinations, studies and findings on risk measures and relationship between risk and return carried out by various professors and students alike.
Jeffrey, Levesque and Maxwell (2016, pp.189-209) stated in their study risk and return is the main elements that closely associated with an investment. High value addition in the money is one of the main objectives of investors when their decision in relation to investing money in the securities or portfolio. In this regard, investors undertake several measures with the motive to quantify the risk level and estimate the return associated with the securities.
Sherwood and Pollard (2017, pp. 1-19) defined several methods in their study that can be undertaken to measure the risk level such as standard deviation, magnitude of loss etc. By doing investigation author found that standard deviation is one of the most effectual measures that helps in assessing the risk associated with an investment or portfolio. Moreover, standard deviation clearly reflects the extent to which investment will be influenced from the average return. For example: If standard deviation of ABC portfolio is 4% then it shows that in the near future return of the same will either incline or decline with such limit from average return. In addition to this, by assessing the magnitude of loss, investor can also determine the risk level. For instance: It magnitude of loss is 30% then it shows that value of fund will be declined with such rate. In this way, through making assessment of standard deviation and loss magnitude investors can measure risk.
Wagner and Lau (1977) depicted the ex post standard deviation is frequently used as a proxy for ex ante standard deviation. Admittedly the residual risk is a measure of the dispersion about the regression line. Variance of the returns on a security can be reflected upon to major points. Modigliani and Pogue’s (1974) research showcases that the market prices of securities vary depending on the risk associated. Total risk measured by the Standard Deviation of return, Unsystematic risk measured by residual variance and the Systematic risk measured by Beta coefficient are three risk measures of the security used by them. They analysed individual security portfolios with respect to the connection between rate of return and three risk measures. Finally, the study showed that higher the standard Deviation wider the distribution. So, the investment risk becomes sizable.
However, on the critical note, Mascarenas and Yan (2017, pp. 145-151) presented that beta is the best measure that provides deeper insight about the volatility of investment or portfolio. By undertaking such measure investors can identify the extent to which investment risk is high or low in against to the market benchmark. Thus, by calculating the beta ratios investors can assess the level to which specific investment is risky. Moreover, it is assumed that securities with higher beta such as more than 1 are considered as risky over others. Liu and Zeng (2017, pp.782-788) said that by calculating value at risk investors can make idea about the same and thereby would become able to measure the likelihood of loss. Thus, by calculating VAR investors can identify the level of portfolio loss in the best possible way.
Montford and Goldsmith (2016, pp.101-106) depicted that by doing assessment of EPS investment return can be identified by the investors. Earning higher return from the investments is one of the main objectives of investors behind investing money. In this, by calculating such measure investors can assess the trend of return. However, it is to be critically evaluated by author, who said that EPS is not highly effectual measure because it avoids cash flow and inflation rate (Limitations of earnings per share, 2016). In addition to this, accounting policies that are employed by the companies highly differ from one to another. In this regard, it is not possible for the investors to appropriately evaluate the historical returns of individual companies on the basis of such measure. It has been assessed through investigation that PE ratio measure is highly significant which in turn provides assistance in estimating the investment return in the right direction (P/E ratio, 2017). It provides assistance to the investors in comparing the price of stock with historical earnings per share.
By calculating such ratio analyst can make idea about the sentiments of investors. In this, higher P/E ratio shows that investors are expecting higher growth and return in the near future. Thus, by calculating P/E ratio investors can get information about the stock return and would become able to appropriate evaluation of the same. On the contrary to this, Jeffrey, Levesque and Maxwell (2016, pp.189-209) examined that during inflationary trend P/E ratio is lower which shows that such measure does not provide assistance to the investors in doing valuation of stock during bear phase. Further, EPS is considered to measure or calculate P/E ratio, so there is the risk that company will manipulate its earnings and thereby distort the outcome. Hence, by undertaking all the above-mentioned measures investors can measure and evaluate both risk as well as return.
Aslanidis, Christiansen and Savva (2016, pp.84-103) identified that trade off which is facing by investors between risk and return at the time of taking investment decision termed as risk RRT. In the case of investment, higher risk level entails that probability of attaining return is greater. On the other side, lower risk level accounts for smaller return. This aspect clearly entails that both the variables like risk and return are highly correlated. Black, Jenson and Scholes (1972) attempted to investigate the New York stock exchange over the course of three and half decades by disintegrating in to 10 separate portfolios. As a result, it was revealed that higher risk portfolios pay bigger returns and lower risk components were underrated while higher risk category was overrated.
However, Montford and Goldsmith (2016, pp.101-106) considered the equation of higher risk equals to the greater return as misconception. Moreover, risk-return trade off only entails that higher risk may result into more returns but there is no guarantee for the same. The rationale behind this, greater risk level implies for both potential returns and losses. Thus, on the basis of greater risk level it cannot be assumed by the investors that specific securities will offer high margin or return. The main concern in a portfolio management system is to search a notable trade-off between the risk and expected return together with healthy balances in accepted risk and actual return.
A-Y model is what Fard, Ansar and Yekezare (2014) have putting to play in measuring the performance of portfolio and analysing it during the bull and bear market. The suggested model by them show lost profit and unrealised lost to analyse the portfolio performance. In contrary to the popular belief of higher the risk greater the return, they have come up with results otherwise. In hindsight A-Y model usage to measure portfolio management performance indicates reduce risks and higher returns. Wang, Yan and Yu (2017, pp.395-414) investigated that risk-return tradeoff is the principle which in turn helps in assessing the extent to which returns will increase in line with the risk level. Hence, as per the risk return trade off investors can generate high profit only when they are willing or ready to bear high losses. Further, appropriateness of RRT is highly influenced from several factors like risk tolerance, years to retirement etc. Besides this, time is another major factor that plays a vital role in identifying the suitable risk and reward.
Portfolio theory connects with two prominent factors. Measurement of risk and Relationship between risk and return Investors can make sound decisions about their portfolios based on the repercussions above elements toward security prices. Two noteworthy professors named Lintner and Sharpe developed the solution called CAPM to the question that is not answered till 1964. The question of the connection between required return and the risk in portfolios.
Frazier and Liu (2016, pp.43-55) shared their views that CAPM model is highly significant which in turn presents or describes relationship that takes place between risk and expected return. By using such model investors can determine the price of risky securities. Along with this, CAPM also helps in assessing the risk that is tolerable to generate the required rate of return. There are several types of risk such as inflation, interest rate, business, financial, tax, market, volatility, event and FOREX that have high level of impact on the returns will be generated through investment. In this, by applying CAPM model investors can make evaluation of both risk as well as return. He also argued that CAPM model considers systematic risk and thereby helps investors in identifying the level to which they need to undertake risk for getting expected returns. Hence, it can be presented that risky securities offer higher return to the investors and vice versa.
Kumar and Rao (2017, pp.21-27) tested that risk-return trade-off is when capital market attains the position of equilibrium. As per the modern theory of portfolio, investors can get higher returns by taking additional risk. Further, study reveals that the main motives of people behind making investment is to earn high returns that made value addition in the money to a great extent. However, usually investors receive less or negative returns in comparison to the initial investment or expected return. However, by applying the CAPM model investors can measure the accepted risk level for the Exchange-Traded funds such as SPDR® S&P®/ASX 200. Above all, by using the historical data of stock and rates of treasury bills investors can analyze the risk-free rate of return.
According to the CAPM’s forecast intercept is meant to be zero whilst the slope shows market portfolio’s excess return. However, Pamane and Vikpossi’s (2014) findings suggest above projections may vary in contrast to the data against the CAPM’s slope during the whole period. The connection between the return and the betas is investigated throughout the test and the non-linearity of the above falls in line with findings. Residual variance of the stocks is also being closely monitored together with all other considerable determinants of returns in their examination to find out CAPM’s capability in comprehending. The last period of 2003 -2008 gets eliminated as the residual risk has no impact for the returns of the stocks of so called period according to the findings. Even though the residual risk includes all other periods and the entire sub period’s stock returns.
In addition to this, Aslanidis, Christiansen and Savva (2016, pp.84-103) assessed that Beta is one of the main elements of CAPM model because it assists in measuring the risk that is associated with the stock. Moreover, beta reflects volatility and thereby helps in identifying the level to which price of particular stock will fluctuate in comparison to the stock market movements. Hence, author summarized that CAPM is a highly effectual model that helps in developing the linear relationship between investment risk and return. Such model undertakes systematic risk and thereby helps in presenting the fair view of risk as well as return. However, Hedegaard and Hodrick (2016, pp.135-145) criticized that under CAPM, it is highly difficult for the investors to calculate ERP. Along with this, projects proxy beta also limits the significance of CAPM model. New York stock exchange was the market Hung (2007) used in taking to count 288 companies for his study in non-viability of CAPM. He also used S&P 500 index being a proxy for market portfolio in order to figure out the market model of beta had nothing to do in calculating the market risk. Hence, the expected return by inclusion of market model beta showed a no success in the New York stock exchange.
Bundoo S.K. (2006)’s study was about systematic risk investigation in Mauritian companies and he wanted to predict the time variation of beta by considering two components which are CAPM and Market model. After his test, it was made certain that traditional beta predictions varied from the beta value of thin trading. Moreover, his findings made a key development in understanding systematic risk and the thin trading involvement and the time variation in beta was vital in expansion of systematic risk in smaller firms. Thus, greater systematic in smaller firms and lower systematic risk in larger firms was characterised to fluctuation in the market to a bigger scale.
Blume (1971), Levy (1971) and Levitz (1974)’s proposal stated the strength and stability of portfolio betas over individual security beta. More reliability in the assessment of future risk in large portfolio, lack of reliability in individual security beta values with regards to the periods were key findings in his assessment. Expansion of the period twice the weeks proved nothing to do with the reliability according to his findings. Overall, they concluded past beta could be considered as a substitution to forecast future beta co-efficient.
On the whole, the systematic risk also known as market risk which indicates the sensitivity of the returns to events which is captured by beta. It is considered to be a generic risk beyond the control of the management. However, the unsystematic risk can be diversified and controlled as it possesses no relationship with the return on the market. Even though certain elements of unsystematic risk demonstrate proportion of the security return is totally independent of returns on other securities. Consequently, residual returns can net out larger number of securities. Beta coefficient is not a constant and the value should be predicted. Thus, special care should be exercised when historical data is used to evaluate future risk. Nothing could be more significant other than the prediction of beta coefficient as it determines systematic risk or in other words the lord of investment risk in a wide-ranging portfolio.
From secondary data investigation, it has been identified that previous scholar did studies on investment risk and return. Majority of the articles are based on the relationship between risk and returns. However, scholars have not assessed the ways through which risk and return can be measured. Besides this, study done by other scholars was not linked with specific exchange traded funds. Thus, by assessing such gap scholar has laid emphasis on analyzing the risk-return trade off pertaining to fund called SPDR & SPX 200. Hence, the present study will provide deeper insight to the investors about risk return trade off and its significance in decision making.
In order to measure historical return there are two approaches that are usually used by researchers in their business namely beta and standard deviation. Beta is the one of the most important tools that is used to measure risk on investment (Boyle and et.al.,, 2012). Under this approach simple percentage change that happened in share price is computed and then percentage change that happened in index is computed. By using slope function beta of two variables is computed. Beta value may be positive or negative. In case beta value is zero it is assumed that there is no relationship between dependent and independent variable or there is no relationship between return that is earned on index and stock. This can be one of the best methods that is used to measure historical risk on stock. Beta value is also used to compute cost of equity which use to calculate weighted average cost of capital in the business. Apart from this, standard deviation is another tool which can be used to measure risk that is associated with security. Standard deviation reflects an extent to which values of variables are scattering from their mean value. In case value of standard deviation is high it is assumed that values of variable are moving at slow rate. In contrast, if it is identified that there is heavy deviation, it is expected that values of variables are moving at fast rate and there is a huge gap between predicted and mean value.
Moreover, both approaches are significant for the portfolio managers because beta takes into account that percentage changes in return that happened in stock and index. On other hand, in case of standard deviation only single variable is taken into account. There is appropriateness of both approaches because if one wants to measure volatility in stock by considering single variable then in that case standard deviation is approproiate. Apart from this, if one wants to measure volatility of stock by considering multiple factors then in that case beta seems appropriate for the business firm. It depends on an individual requirements that which of historical risk tool it used to measure overall risk on investment.
In order to measure historical return there are number of approaches that can be used by the analysts in the business. Computation of percentage change in closing price of security is one of the best approaches to measure historical return on investment amount. Apart from this, in order to measure historical return in appropriate manner Sharpe and Trenyor ratios are commonly used by the business firms. Sharpe ratio basically reflects the return that is earned on each unit of standard deviation after subtracting risk free rate from return earned on investment amount. On other hand, Trenyor ratio is another approach that can be used to measure historical return as in this method, return earned on each unit of beta is measured. In Sharpe ratio method, return earned on security risk free rate is subtracted and then computed amount is divided by beta to measure performance of specific stock. In this way, historical return is measured.
Fund Performance
|
FUND |
INDEX |
|
|
1 Month |
-0.03% |
-0.01% |
|
3 Month |
-2.63% |
-2.59% |
|
YTD |
3.08% |
3.15% |
|
1 Year |
7.15% |
7.33% |
|
3 Year |
4.84% |
5.11% |
|
5 Year |
10.53% |
10.88% |
|
Inception |
7.64% |
8.00% |
The focus in SLF is only on replicating as closely as possible the performance of the A-REIT index (by holding, buying and selling stocks in S&P/ASX 200 entities that are listed property trusts). The focus is not to outperform the A-REIT index because that means the fund would be putting unit holders in the fund into a situation of enhanced risk.
The unit holders of SLF are investors (including large super funds) who are prepared to take average listed property trust market risk but no more than that. By definition of the beta of SLF cannot be the same as STW. If the STW beta is 1.0 then SLF’s can be calculated as a function of that (as STW is a proxy for what we call “M, the share of average market risk).
Ignoring the phenomenon of dividend imputation, it should be possible however to track un-grossed SLF dividend yields versus the A-REIT index as both sets of data would be available.
How to analyse historical performance in a more holistic way
As per the suggested holistic approach below in STW, if conveniently ignore dividend imputation, it must be possible to relatively easily track an investor’s return across both dividends and capital gains/losses combined. Distributions are available on published SLF web-sites and SLF unit prices are also available at regular intervals (the ASX reports them every trading day as SLF is a listed exchange traded fund). I have carried out such an investment returns analysis on SLF over a 5 year period using quarterly data and tracking “Excess Returns” being achieved quarterly returns (across both capital gains and distributions) less 10 year government bond yield (as a proxy for the risk-free rate).
Fund Performance
|
FUND |
INDEX |
|
|
1 Month |
-0.03% |
-0.01% |
|
3 Month |
-2.63% |
-2.59% |
|
YTD |
3.08% |
3.15% |
|
1 Year |
7.15% |
7.33% |
|
3 Year |
4.84% |
5.11% |
|
5 Year |
10.53% |
10.88% |
|
Inception |
7.64% |
8.00% |
The focus in STW is only on replicating as closely as possible the performance of the index (by holding, buying and selling stocks in S&P/ASX 200 companies). Market risk is quantified as a beta of 1 as both the index and STW contain a mix of stocks that when aggregated approximate the “(imaginary) share of average market risk)”.
So, in a nut-shell, Reported STW Fund performance is all about how well STW is replicating the capital gains (or losses) generated by the general share market index. If you are based in Malaysia or the UK for tax purposes then the un-grossed 4.36% is what is relevant (as this is what hits the investor’s bank accounts as paid by State Street, the manager) as overseas tax residents cannot get imputation tax credits.
Ignoring the phenomenon of dividend imputation, it should be possible however to track un-grossed STW dividend yields versus the index as both sets of data would be available.
How to analyse historical performance in a more holistic way
It is true to say that an investor cares about overall performance, so, again, if I conveniently ignore the complications of dividend imputation, it must be possible to relatively easily track an investor’s return across both dividends and capital gains/losses combined. This is “easy” as I can easily get the data. Distributions are available on published STW web-sites and STW unit prices are also available at regular intervals (the ASX reports them every trading day as STW is a listed exchange traded fund).
I have carried out such an investment returns analysis on STW over a 5 year period using quarterly data and tracking “Excess Returns” being achieved quarterly returns (across both capital gains and distributions) less 10 year government bond yield (as a proxy for the risk-free rate).
How to analyse historical risk and the risk-reward comparison
Standard deviation, beta and geomatric mean for SLF can then be compared against STW results to see if the risk-return trade-off is better or worse in SLF than investing in the top 200 entities on the share-market (by market capitalization). The comparison number generated is the reward-risk ratio. In isolation, it is typically better to invest in stocks that have higher reward/risk ratios, as that means the profit potential outweighs the risk.
The formula is, Mean achieved Excess Return / Beta
To do a rough check on the validity of results directionally would conduct a co-efficient of variation (CV) which simply takes the mean return (not mean excess return) and divides it by standard deviation of returns. The only caveat on this checker is that the risk measure used contains unsystematic risk. An investor gets no reward for taking on unsystematic risk, so in that sense it would be strongly argued by some experts that the reward-risk ratio is superior in historical risk-reward comparisons (or even in forward-looking risk-reward comparisons) than CV.
In case of SLF it can be observed that returns are fluctuating at fast rate as chart is clearly indicating that up to certain price limit of ETF. This is clearly reflecting that there is a pressure on the price of ETF. In 2017 again sharp fluctuations are observed and due to this reason it is very difficult to estimate the likely direction in which price of ETF may move. It can be said that there is huge growth potential in ETF and ETF may generate good return for the investors.
In order to compare both ETF in terms of risk and return summary table is prepared. It can be observed that in the summary table there are five components namely product of returns, geometric mean, standard deviation, beta and return profile. From the above table, it is clear that product return of SLF is -5.81 and -2.3 in STW. This reflects that return is negative in terms of product more in case of SLF than STW. If this value is considered only then it can be said that good amount of return is made on STW ETF. Geometric mean value is -0.089 in case of SLF ETF and same in case of STW ETF is -0.04. This means that negative returns are made of both stock as per results of geometric mean but little more gain is made on STW ETF. Standard deviation is very high in case of STW (3.23) than SLF (1.54 ). This reflects that returns are deviating at very fast rate of STW than SLF. In case of SLF beta is high (1.03), which reflect that ETF price is moving at fast rate than index value. This also means that ETF is more volatile than index in current time period. On other hand, beta value is 1 in case of STW throughout the whole period and this means that index and ETF are highly correlated to each other. This reflects that if index change by 5% then in that case share price will also change by same percentage. Hence, it can be said that, STW fund has a beta of 1 conversly, SLF has a beta of greater than 1, offering the possibility of higher rate of return, but also posing more risk. In terms of return it can be seen that return is 28% in case of STW ETF and same is 41% in case of SLF ETF. This means that higher return is earned in SLF than STW.
However, if geometric mean is considered for analysis purpose situation is totally different. It can be observed that value of geometric mean and standard deviation of STW is -0.04 and 3.23 respectively. On other hand, value of geometric mean of SLF is -0.08 and its standard deviation value is just 1.54. Here, trade-off is achieved as risk is low then return is also lower and if risk is high then in that case return is also high. This reflects that trade off is achieved in case of both securities. Interesting fact is that different results appear in different situations. When overall return is computed trade-off is not achieved but when geometric mean is taken into account trade-off is achieved. There are number of reasons due to which most of portfolio managers like to make use of geometric mean than annual return or average return value for making investment decisions. One of these reasons is that in geometric mean effect of compounding is taken into consideration. On other hand, in terms of mean and annual return, effect of annual return is not taken into account (Chava and Purnanandam, 2010). Hence, geometric mean is often prefered by portfolio managers to make finance decisions. This is because it is necessary to track changes that happened during certain time period in order to make better investment decisions and identifying relative amount of return that can be earned on investment if made on any security.
As previously said both measures of risk which are standard deviation and beta are appropriate for the investors. As it can be seen in the table given above that there is similarity in the results that are reflected by both securities SLF and STW. Moreover, it is not possible that one measurement tool reflects that risk is high and other tool reflects that risk is low.
However, if both techniques will be compared, it will be better to use beta than standard deviation. This is because standard deviation is no range within which values of statistic tool lies. Hence, it is difficult for one to determine whether risk is high (Difference between beta and standard deviation, 2017).
On basis of comparison one can said that risk is high in case of specific security by looking at standard deviation but chances of misinterpretation are very high.
On other hand, beta removed limitation of standard deviation as there is clear boundaries within which value of variable must lie from 0 to 1 and -1. Hence, one can make better interpretation of risk in case of beta than standard deviation.
Conclusions
On the basis of above discussion it is concluded that there are significant importance of standard deviation and beta for measuring trade-off between risk and return. Both beta and standard deviation are very important tool to measure risk and according to requirement same must be used by portfolio managers to make investment decisions. In order to measure volatility of stock multiple variables can be used like share price and index value. This feature is not available in case of standard deviation. Merely by evaluating it is not possible to perfectly measure risk but by using beta in perfect manner risk associated with security can be accesseed. This is because index is taken as parameter to measure volatility of security. Due to this reason beta seem more appriopriate risk measurement method than standard deviation.
References
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First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download