investment risk and return

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Investment risk and return

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You might be familiar with the concept of risk-rewardwhich states that the higher the risk of a particular investment, the higher the possible return.

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Hurtta polar vest sizing Using the concepts of this theory, assets are combined in a portfolio based on statistical measurements such as standard deviation and correlation. It may turn out that fairly large changes in some factors do not significantly affect the outcomes. You can use actual results and estimated returns to evaluate various assets, such as stocks and bonds, as well as different securities within each asset category. This concept has already been applied to investment portfolios. There is no right or wrong amount of risk; it is a very personal decision for each investor. Fundamental analysis books forex trading
Halalnya forex Using this approach, management takes the various levels of possible cash flows, return on investment, and other results of a proposed outlay and gets an estimate of the odds for each potential outcome. With the investment analysis, we obtain the tabulated and plotted data investment risk and return Exhibit V. Therefore, I use a method combining the variabilities inherent in all the relevant factors under consideration. Bonds generally provide higher returns with higher risk than savings, and lower returns than stocks. Financial Planning. For instance, the lowest in the range of prices might be combined with the highest in the range of growth rate and other factors. If students already have selected a stock that they are following, have them chart how the stock has performed for the past two years, five years and 20 years.
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The firm must compare the expected return from a given investment with the risk associated with it. Higher levels of return are required to compensate for increased levels of risk. In other words, the higher the risk undertaken, the more ample the return — and conversely, the lower the risk, the more modest the return. This risk and return tradeoff is also known as the risk-return spectrum.

There are various classes of possible investments, each with their own positions on the overall risk-return spectrum. The general progression is: short-term debt, long-term debt, property, high-yield debt, and equity. The existence of risk causes the need to incur a number of expenses. For example, the more risky the investment the more time and effort is usually required to obtain information about it and monitor its progress.

Moreover, the importance of a loss of X amount of value can be greater than the importance of a gain of X amount of value, so a riskier investment will attract a higher risk premium even if the forecast return is the same as upon a less risky investment. Risk is therefore something that must be compensated for, and the more risk the more compensation is required. When a firm makes a capital budgeting decision, they will wish, as a bare minimum, to recover enough to pay the increased cost of investment due to inflation.

However, since interest rates are set by the market, it happens frequently that they are insufficient to compensate for inflation. Inflation : Inflation is a rise in the general level of prices of goods and services in an economy over a period of time. Risk aversion is a concept based on the behavior of firms and investors while exposed to uncertainty to attempt to reduce that uncertainty.

Risk aversion is the reluctance to accept a bargain with an uncertain payoff rather than another bargain with a more certain, but possibly lower, expected payoff. For example, a risk-averse investor might choose to put his or her money into a bank account with a low but guaranteed interest rate, rather than into a stock that may have high expected returns, but also involves a chance of losing value.

Risk aversion can be thought of as having three levels:. Risk is the chance that your actual return will differ from your expected return, and by how much. You could also define risk as the amount of volatility involved in a given investment. There are four major asset classes that make up most portfolios: equity, bonds, cash, and alternative investments. While each of those broad categories includes a wide range of investments, typically those are the ones you look at to balance your level of risk with the returns you want to earn.

Equities are any investment that represents an ownership stake in a company, which are commonly referred to as shares. That might mean holding shares directly, but it could also be ETFs or mutual funds that hold shares in companies.

Typically, equities come with a higher level of risk and a higher expected return. You might earn those returns as capital gains, when the price of the shares you own goes up, or through dividends paid to shareholders when the company is profitable. This is the wildcard category, because it covers everything from investing in real estate, to commodities, to private equity want to be an angel investor in a startup?

These investments can be higher risk than both stocks and bonds, but their expected returns follow different patterns than both stocks and bonds, which is what can make them a good diversification tool for an already well-rounded portfolio. Cash can sometimes mean what it sounds like—holding money in cash in your portfolio—but it can also represent short-term, liquid investments in high-quality securities like US treasury bonds. In this chart you can see the average annual risk and returns for three different investments.

One is equity, one is fixed income aka bonds , one is cash, and one is an alternative investment in commodities. You can clearly see that the highest return came from equity, but it also came with the second-highest level of risk. To reduce your risk a bit, you might have included some of the fixed income category in your portfolio.


Mutual Funds. Portfolio Construction. Alternative Investments. Financial Planning. Investing Essentials. Your Money. Personal Finance. Your Practice. Popular Courses. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation. Related Articles. Alternative Investments 5 Alternative Investments for Financial Planning I've come into a large amount of money.

Should I invest it or pay off my mortgage? Partner Links. Related Terms Investment Pyramid Definition An investment pyramid is a strategy used by investors by layering smaller weights of more risky assets on top of larger allocations to more conservative assets.

Risk Risk takes on many forms but is broadly categorized as the chance an outcome or investment's actual return will differ from the expected outcome or return. What Is Risk Tolerance? Risk tolerance is the degree of variability in investment returns that an individual is willing to stand. It is an important component in investing.

Inefficient Portfolio An inefficient portfolio is one that delivers an expected return that is too low for the amount of risk taken on. Capital Growth Strategy A capital growth strategy seeks to maximize long-term capital appreciation of a portfolio via an allocation geared to assets with high expected returns. Investment Ideas Investment ideas are specific views, plans, or ideas on ways to invest money effectively.

They have come to the conclusion that five factors are the determining variables: advertising and promotion expense, total cereal market, share of market for this product, operating costs, and new capital investment. This future, however, depends on whether each of these estimates actually comes true. The decision makers need to know a great deal more about the other values used to make each of the five estimates and about what they stand to gain or lose from various combinations of these values.

This simple example illustrates that the rate of return actually depends on a specific combination of values of a great many different variables. But only the expected levels of ranges worst, average, best; or pessimistic, most likely, optimistic of these variables are used in formal mathematical ways to provide the figures given to management. Thus predicting a single most likely rate of return gives precise numbers that do not tell the whole story. The expected rate of return represents only a few points on a continuous cure of possible combinations of future happenings.

It is a bit like trying to predict the outcome in a dice game by saying that the most likely outcome is a 7. The description is incomplete because it does not tell us about all the other things that could happen. In Exhibit I, for instance, we see the odds on throws of only two dice having 6 sides. Now suppose that each of eight dice has sides.

Nor is this the only trouble. Our willingness to bet on a roll of the dice depends not only on the odds but also on the stakes. Since the probability of rolling a 7 is 1 in 6, we might be quite willing to risk a few dollars on that outcome at suitable odds. In short, risk is influenced both by the odds on various events occurring and by the magnitude of the rewards or penalties that are involved when they do occur. Suppose that getting no return at all would put the company out of business.

Then, by accepting this proposal, management is taking a 1-in-3 chance of going bankrupt. If only the best-estimate analysis is used, however, management might go ahead, unaware that it is taking a big chance. If all of the available information were examined, management might prefer an alternative proposal with a smaller, but more certain that is, less variable expectation. Such considerations have led almost all advocates of the use of modern capital-investment-index calculations to plead for a recognition of the elements of uncertainty.

Perhaps Ross G. How can executives penetrate the mists of uncertainty surrounding the choices among alternatives? A number of efforts to cope with uncertainty have been successful up to a point, but all seem to fall short of the mark in one way or another. Reducing the error in estimates is a worthy objective. But no matter how many estimates of the future go into a capital investment decision, when all is said and done, the future is still the future. Therefore, however well we forecast, we are still left with the certain knowledge that we cannot eliminate all uncertainty.

Adjusting the factors influencing the outcome of a decision is subject to serious difficulties. And in any case, what is the basis for adjustment? We adjust, not for uncertainty, but for bias. For example, construction estimates are often exceeded.

This is a matter of improving the accuracy of the estimate. In so doing, it is possibly missing some of its best opportunities. Selecting higher cutoff rates for protecting against uncertainty is attempting much the same thing. Management would like to have a possibility of return in proportion to the risk it takes.

Where there is much uncertainty involved in the various estimates of sales, costs, prices, and so on, a high calculated return from the investment provides some incentive for taking the risk. This is, in fact, a perfectly sound position. The trouble is that the decision makers still need to know explicitly what risks they are taking—and what the odds are on achieving the expected return. A start at spelling out risks is sometimes made by taking the high, medium, and low values of the estimated factors and calculating rates of return based on various combinations of the pessimistic, average, and optimistic estimates.

These calculations give a picture of the range of possible results but do not tell the executive whether the pessimistic result is more likely than the optimistic one—or, in fact, whether the average result is much more likely to occur than either of the extremes. So, although this is a step in the right direction, it still does not give a clear enough picture for comparing alternatives. Various methods have been used to include the probabilities of specific factors in the return calculation.

Grant discussed a program for forecasting discounted cash flow rates of return where the service life is subject to obsolescence and deterioration. He calculated the odds that the investment will terminate at any time after it is made depending on the probability distribution of the service-life factor. After having calculated these factors for each year through maximum service life, he determined an overall expected rate of return. Edward G. Bennion suggested the use of game theory to take into account alternative market growth rates as they would determine rate of return for various options.

He used the estimated probabilities that specific growth rates would occur to develop optimum strategies. Bennion pointed out:. Note that both of these methods yield an expected return, each based on only one uncertain input factor—service life in the first case, market growth in the second.

Both are helpful, and both tend to improve the clarity with which the executive can view investment alternatives. Since every one of the many factors that enter into the evaluation of a decision is subject to some uncertainty, the executives need a helpful portrayal of the effects that the uncertainty surrounding each of the significant factors has on the returns they are likely to achieve. Therefore, I use a method combining the variabilities inherent in all the relevant factors under consideration.

The objective is to give a clear picture of the relative risk and the probable odds of coming out ahead or behind in light of uncertain foreknowledge. A simulation of the way these factors may combine as the future unfolds is the key to extracting the maximum information from the available forecasts.

In fact, the approach is very simple, using a computer to do the necessary arithmetic. To carry out the analysis, a company must follow three steps:. Estimate the range of values for each of the factors for example, range of selling price and sales growth rate and within that range the likelihood of occurrence of each value. Select at random one value from the distribution of values for each factor. Then combine the values for all of the factors and compute the rate of return or present value from that combination.

For instance, the lowest in the range of prices might be combined with the highest in the range of growth rate and other factors. The fact that the elements are dependent should be taken into account, as we shall see later. Do this over and over again to define and evaluate the odds of the occurrence of each possible rate of return. Since there are literally millions of possible combinations of values, we need to test the likelihood that various returns on the investment will occur.

This is like finding out by recording the results of a great many throws what percent of 7s or other combinations we may expect in tossing dice. The result will be a listing of the rates of return we might achieve, ranging from a loss if the factors go against us to whatever maximum gain is possible with the estimates that have been made. For each of these rates we can determine the chances that it may occur.

Note that a specific return can usually be achieved through more than one combination of events. The more combinations for a given rate, the higher the chances of achieving it—as with 7s in tossing dice. The average expectation is the average of the values of all outcomes weighted by the chances of each occurring. We can also determine the variability of outcome values from the average. This is important since, all other factors being equal, management would presumably prefer lower variability for the same return if given the choice.

This concept has already been applied to investment portfolios. When the expected return and variability of each of a series of investments have been determined, the same techniques may be used to examine the effectiveness of various combinations of them in meeting management objectives. To see how this new approach works in practice, let us take the experience of a management that has already analyzed a specific investment proposal by conventional techniques. Taking the same investment schedule and the same expected values actually used, we can find what results the new method would produce and compare them with the results obtained by conventional methods.

As we shall see, the new picture of risks and returns is different from the old one. Is this investment a good bet? In fact, what is the return that the company may expect? What are the risks? We need to make the best and fullest use of all the market research and financial analyses that have been developed, so as to give management a clear picture of this project in an uncertain world.

The key input factors management has decided to use are market size, selling prices, market growth rate, share of market which results in physical sales volume , investment required, residual value of investment, operating costs, fixed costs, and useful life of facilities. These factors are typical of those in many company projects that must be analyzed and combined to obtain a measure of the attractiveness of a proposed capital facilities investment.

How do we make the recommended type of analysis of this proposal? Our aim is to develop for each of the nine factors listed a frequency distribution or probability curve. The information we need includes the possible range of values for each factor, the average, and some idea as to the likelihood that the various possible values will be reached.

It has been my experience that for major capital proposals managements usually make a significant investment in time and funds to pinpoint information about each of the relevant factors. An objective analysis of the values to be assigned to each can, with little additional effort, yield a subjective probability distribution. Specifically, it is necessary to probe and question each of the experts involved—to find out, for example, whether the estimated cost of production really can be said to be exactly a certain value or whether, as is more likely, it should be estimated to lie within a certain range of values.

Management usually ignores that range in its analysis. The range is relatively easy to determine; if a guess has to be made—as it often does—it is easier to guess with some accuracy a range rather than one specific value. I have found from experience that a series of meetings with management personnel to discuss such distributions are most helpful in getting at realistic answers to the a priori questions. The term realistic answers implies all the information management does not have as well as all that it does have.

The ranges are directly related to the degree of confidence that the estimator has in the estimate. Thus certain estimates may be known to be quite accurate. Thus we treat the factor of selling price for the finished product by asking executives who are responsible for the original estimates these questions:.

Managements must ask similar questions for all of the other factors until they can construct a curve for each. Experience shows that this is not as difficult as it sounds. Often information on the degree of variation in factors is easy to obtain.

For instance, historical information on variations in the price of a commodity is readily available. Similarly, managements can estimate the variability of sales from industry sales records. Even for factors that have no history, such as operating costs for a new product, those who make the average estimates must have some idea of the degree of confidence they have in their predictions, and therefore they are usually only too glad to express their feelings.

Likewise, the less confidence they have in their estimates, the greater will be the range of possible values that the variable will assume. This last point is likely to trouble businesspeople. Does it really make sense to seek estimates of variations?

It cannot be emphasized too strongly that the less certainty there is in an average estimate, the more important it is to consider the possible variation in that estimate. Further, an estimate of the variation possible in a factor, no matter how judgmental it may be, is always better than a simple average estimate, since it includes more information about what is known and what is not known.

This very lack of knowledge may distinguish one investment possibility from another, so that for rational decision making it must be taken into account. This lack of knowledge is in itself important information about the proposed investment. To throw any information away simply because it is highly uncertain is a serious error in analysis that the new approach is designed to correct. The next step in the proposed approach is to determine the returns that will result from random combinations of the factors involved.

This requires realistic restrictions, such as not allowing the total market to vary more than some reasonable amount from year to year. Of course, any suitable method of rating the return may be used at this point. In the actual case, management preferred discounted cash flow for the reasons cited earlier, so that method is followed here. A computer can be used to carry out the trials for the simulation method in very little time and at very little expense.

The resulting rate-of-return probabilities were read out immediately and graphed. The process is shown schematically in Exhibit II. For a given combination of these factors sales revenue may be determined for a particular business. Being tied to the kinds of service-life and operating-cost characteristics expected, these are subject to various kinds of error and uncertainty; for instance, automation progress makes service life uncertain.

These categories are not independent, and for realistic results my approach allows the various factors to be tied together. Thus if price determines the total market, we first select from a probability distribution the price for the specific computer run and then use for the total market a probability distribution that is logically related to the price selected.

We are now ready to compare the values obtained under the new approach with those obtained by the old. This comparison is shown in Exhibit III. Exhibit III. That is, there is only a 1-in chance that the value actually achieved will be respectively greater or less than the range.

How do the results under the new and old approaches compare? In this case, management had been informed, on the basis of the one-best-estimate approach, that the expected return was When we run the new set of data through the computer program, however, we get an expected return of only This surprising difference results not only from the range of values under the new approach but also from the weighing of each value in the range by the chances of its occurrence.

Our new analysis thus may help management to avoid an unwise investment. In fact, the general result of carefully weighing the information and lack of information in the manner I have suggested is to indicate the true nature of seemingly satisfactory investment proposals. If this practice were followed, managements might avoid much overcapacity.

The computer program developed to carry out the simulation allows for easy insertion of new variables. But most programs do not allow for dependence relationships among the various input factors. Further, the program used here permits the choice of a value for price from one distribution, which value determines a particular probability distribution from among several that will be used to determine the values for sales volume.

The following scenario shows how this important technique works. Suppose we have a wheel, as in roulette, with the numbers from 0 to 15 representing one price for the product or material, the numbers 16 to 30 representing a second price, the numbers 31 to 45 a third price, and so on.

Now suppose we spin the wheel and the ball falls in Most significant, perhaps, is the fact that the program allows management to ascertain the sensitivity of the results to each or all of the input factors. Simply by running the program with changes in the distribution of an input factor, it is possible to determine the effect of added or changed information or lack of information. It may turn out that fairly large changes in some factors do not significantly affect the outcomes.

In this case, as a matter of fact, management was particularly concerned about the difficulty in estimating market growth. In addition, let us see what the implications are of the detailed knowledge the simulation method gives us. Under the method using single expected values, management arrives only at a hoped-for expectation of