Risk and return

Risk is a complex topic. There are many types of risk, and many ways to evaluate and measure risk. In the theory and practice of investing, a widely used definition of risk is:

Even though this definition of risk sounds simple, questions arise:
 * “Risk is the uncertainty that an investment will earn its expected rate of return.”


 * Why is risk defined in terms of the uncertainty of investment return?
 * The definition above does not distinguish between gain and loss. Isn't risk just the possibility that the investor will lose money on the investment?
 * What is the relationship between risk and return?
 * How is risk measured?
 * What is "expected rate of return", and how is it evaluated?
 * What can be done to manage risk?
 * How does an investor determine how much risk and what types of risk to take?
 * Are there differences between short-term risk and long-term risk?

To make wise investment decisions, an investor must spend some time studying the answers to these questions.

Risk as the uncertainty of returns
There is very high certainty in the return (short for "rate of return") that will be earned on an investment in a 30-day Treasury bill (T-Bill) or short-term Certificate of Deposit (CD). Similarly, there is fairly high certainty in the return that will be earned over a short period in a money market fund.

Even over longer time periods, the returns earned by T-Bills, CDs and money market funds fall into a relatively narrow range.



As seen in the top chart in Figure 1 (covering the years 1928 through 2011), the tallest bar shows that annual returns (horizontal axis) on 3-month T-Bills have fallen in the range of 0% to 5% in 59 years (vertical axis). Returns have been between 0% and 10% in most years (tallest two bars), and between 0% and 15% in all years (all three bars).

T-Bills and CDs are among the investments referred to collectively as money market securities. For an individual investor, a federally-insured bank account also provides a high degree of certainty in the short-term return. The term cash often is used to refer to money market securities and money in bank accounts. Vanguard refers to these types of assets as short-term reserves.

The return on bonds is less certain, and the return on stocks is even more uncertain. Thus, bonds are considered riskier than money market securities (cash), and stocks are considered riskier than bonds.

The middle chart in Figure 1 shows the range of annual returns on 10-Year Treasury Bonds from 1928 through 2011. Note the larger range (dispersion) of returns--from about -11% to +33%.

The bottom chart in Figure 1 shows the range of annual returns on stocks in the S&P 500 from 1928 through 2011. Note the much larger dispersion of returns--from about -44% to +53%.

Risk as the possibility of loss
Note that the definition of risk as the uncertainty of investment return does not distinguish between loss and gain. Typically, individual investors think of risk as the possibility that their investments could lose money. They are likely to be quite happy with an investment return that is greater than expected (a “positive surprise”). However, since risky assets generate negative surprises as well as positive surprises, defining risk as the uncertainty of the return is reasonable. Greater uncertainty results in greater likelihood that the investment will generate gains as well as losses. The long-term consequence is that there is greater uncertainty in the value of accumulated wealth. This is referred to as terminal wealth dispersion.

In financial planning, the investment goal must be considered in defining risk. Once a goal is established, for example the goal of providing retirement income, an investment portfolio is constructed to generate an expected return that is sufficient to meet the investment goal. But because there is uncertainty that the portfolio will earn its expected long-term return, the long-term realized return may fall short of the expected return. This raises the possibility that the investor's available funds could fall short of the need, and that the investor might outlive the investment portfolio. This is an example of "shortfall risk". The magnitude and consequences of potential shortfall deserve special consideration from investors. However, since the uncertainty of return could result in a realized return that is higher than the expected return, the investment portfolio might "outlive" the investor. Therefore, considering risk as the uncertainty of investment return subsumes considerations of shortfall risk.

Some financial theorists have proposed using measures of risk that only account for the risk of loss (e.g., semi-variance instead of variance, to be discussed later). However, most academic investment theory, especially portfolio theory, is based simply on the uncertainty of return, considering both positive and negative outcomes relative to expectations. Justifications include the observation that long-run investment returns are reasonably symmetrical, and that long-run returns of broadly diversified investment portfolios are reasonably normally distributed. (add references). Although these observations have been challenged (according to Wikipedia article on MPT, etc.), this article is based on standard investment theory based on the stated observations.

Relationship between risk and return
Investors are risk averse; i.e., given the same expected return, they will choose the investment for which that return is more certain. Therefore, investors demand a higher expected return for riskier assets. Note that a higher expected return does not guarantee a higher realized return. Because by definition returns on risky assets are uncertain, an investment may not earn its expected return.

Although the charts in Figure 1 show historical (realized) returns rather than expected (future) returns, they are useful to demonstrate the relationship between risk and return. Note that the mean (average) annual return increases as the dispersion of returns increases.

This demonstrates one of the most fundamental axioms of investing: ''Risk and return are inextricably related. Higher returns generally can be achieved only by taking more risk, but because the risk exists, the higher expected returns may not result in higher realized returns.''



"Risk free" assets


Money market securities are often referred to as risk-free assets, especially the shorter-maturity securities such as 30-day T-Bills. This is because the short-term return is known with relative certainty at the time the investment is made. There is absolute certainty in the nominal return of a T-Bill (assuming the U.S. government does not default on its obligations), and it is unlikely that unexpected inflation will have significant impact on the real return over a short time period.

If longer time periods are considered, even money market securities have some risk. This is because the effect of unexpected inflation on returns is uncertain over longer time periods. Although money market security rates usually respond relatively quickly to changes in inflation, this is not always the case.

Figure 2 illustrates the longer-term uncertainty of real returns on 90-day T-Bills. Also, note that the relative certainty of return does not mean that the real return necessarily is positive. It may be known with certainty that a 90-day T-Bill will earn a nominal annualized return of 1% over its 90-day term (i.e., if held to maturity). However if inflation over the 90-day term is expected to be 3% annualized, the relatively certain, expected real return is -2% annualized. T-bill data source comes from Annual Returns on Stock, T.Bonds and T.Bills: 1928 - Current, Damodaran Online; inflation data comes from Consumer Price Index 1913 -, Minnesota Federal Reserve]. Retrieved 1 April 2012

Uncertainty in real returns can be eliminated by investing in inflation-indexed securities, such as Treasury Inflation Protected Securities (TIPS) and Series I Savings Bonds (I Bonds). Of course in return for this reduction in uncertainty, investors must accept lower expected returns. Marketable inflation-indexed securities also have other risks, such as interest rate risk (i.e., prices decline when interest rates rise) and liquidity risk, as was made evident in late 2008 (September 12 - October 31) when the Vanguard Inflation-Protected Securities fund declined in value by almost 14%. During this same time period other U.S. treasury securities increased in value.

Measuring risk: historical returns
Historical returns often are used as a starting point in estimating expected returns and uncertainty of returns (risk). Although evaluating historical returns may not enable an accurate or comprehensive estimation of risk, evaluating the dispersion of historical returns provides a convenient way to develop an understanding of commonly used risk measures.

The risk of an investment is related to the uncertainty of its investment return. This was illustrated graphically in Figure 1, which showed that dispersion of returns is a way to characterize the riskiness of an investment. Many investors like to summarize return and risk with numbers that quantify the average return and the dispersion of returns around the average. This facilitates comparing the return and risk of different investments.

Calculating the average of a set of historical annual returns is straightforward: simply add the annual returns and divide by the number of annual returns. The technical term for this type of average is the arithmetic mean, usually referred to simply as the mean. Statisticians use the term expected value to refer to the mean. In finance theory the term expected value of the return, or just expected return is commonly used in referring to the average of a set of returns.

What about calculating a number to quantify the uncertainty or dispersion of returns? Basic statistics and probability theory provide two measures to quantify the dispersion of a set of numbers: variance and standard deviation. Starting in the early 1950s, finance academics began using these statistical measures to quantify risk.

Historical returns are commonly used as a starting point in evaluating the relative riskiness of different assets. A subset of the historical data shown graphically in Figure 1 provides a simple example to begin developing an understanding of standard deviation as a measure of risk.

Consider the S&P 500 stock returns for 2008-2011:


 * 2008: -36.55%
 * 2009: +25.94%
 * 2010: +14.82%
 * 2011: +2.07%

The mean, or expected value, E(r), of this set of returns is calculated below (for simplicity, the percent signs are dropped):


 * $$ \operatorname{E}(r) = \frac{-36.55 + 25.94 + 14.82 + 2.07}{4} = 1.57 $$

The expected value of the annual return is 1.57%.

An alternate way to calculate expected value is to multiply each value by the probability of that value occurring, then sum the results. This formulation will be useful when calculating the expected value of estimated future returns. When evaluating historical returns, each return is considered to have equal probability, so the calculation using this method is:



\operatorname{E}(r) = \frac 14(-36.55)+\frac{1}{4}(25.94)+\frac{1}{4}(14.82) + \frac{1}{4}(2.07) = 1.57 $$

The average of the annual returns is useful information, but it doesn't indicate anything about the dispersion of returns; i.e., how the returns are distributed around the mean return.

It is intuitive that a useful measure of dispersion of returns around the average return would involve calculating the differences (deviations) between the individual returns and the average return, and perhaps averaging these deviations. For example the deviation for the 2009 return is 25.94 - 1.57 = 24.37, whereas the deviation for the much smaller 2011 return is only 2.07 - 1.57 = 0.50.

The problem with this is that there will be both positive and negative deviations that will tend to cancel each other out, resulting in a misleading value that understates the dispersion of returns around the mean; in fact, the average of the deviations from the mean will always be 0. . This can be verified by calculating the average of the deviations from the mean for all four years in the example. Therefore, this value indicates nothing about dispersion.

This problem is resolved by squaring the deviations from the mean (which results in all positive numbers), then calculating the average of the squared deviations. The term variance is used to describe the average of the squared deviations. Here is the calculation of the variance of the annual returns using the returns from 2008-2011:



\operatorname{Var}(r) = \sigma^2 = \frac{ (-36.55-1.57)^2+(25.94-1.57)^2+(14.82 - 1.57)^2+(2.07-1.57)^2 } {4} = 556 $$

Note that the symbol &sigma;2 is commonly used to represent variance, so Var(r) = &sigma;2 = 556.

This formula can be generalized as:


 * $$\operatorname{Var}(r) = \sigma^2 = \frac{1}{n} \sum_{i=1}^n (r_i - \operatorname{E}(r))^2$$

(When calculating the variance of a sample of the full population of values (as in the example here), statisticians often substitute $$\tfrac{1}{n-1}$$ for $$\tfrac{1}{n}$$ in the above formula. There are arguments for and against this. In this article this substitution will not be made.)

To get a value with the same units as the rate of return (percent), the square root of the variance is calculated, resulting in the standard deviation of the annual returns:


 * $$ \operatorname{SD}(r) = \sigma = \sqrt{556} = 23.6 $$

Note that the symbol &sigma; is commonly used to represent standard deviation, so SD(r) = &sigma; = 23.6 pp (technically, the units of the standard deviation of returns are percentage points (pp), but standard deviation of returns often is displayed using the % symbol, e.g., 23.6%).

Note that the resulting value of 23.6 pp (or 23.6%) for the standard deviation seems somewhat reasonable as an indicator of the dispersion of returns around the mean value of 1.57%, considering that the range of values is between -36.55% and +25.94%.

Standard deviation has some standard statistical interpretations for a large number of values with a normal distribution (the ubiquitous bell curve). For example, about 68% of all values fall within +/- one standard deviation, and about 95% of all values fall within +/- two standard deviations.

There are too few values in our simple example for these statistical attributes to apply, but consider the 84 annual returns for 10-Year treasury bonds represented in the second chart in Figure 1.

First note that the data somewhat resembles the bell-shape curve characteristic of a normal distribution. The mean value of the 84 annual returns for 10-Year treasury bonds is about 5%, and the standard deviation is about 8% (pp). Two standard deviations is about 2 x 8 = 16, so we'd expect most of the annual returns to fall within the range of about 5% +/- 16 pp or between about -11% and +21%. Eyeballing the chart, this looks about right.

For the third chart in Figure 1, representing 84 annual returns for the stocks in the S&P 500, the mean is about 11% and the standard deviation is about 20%, so we'd expect most values to fall within a range of about 11% +/- 40 pp, or between about -29% and + 51%. Again, this looks about right.

So standard deviation seems to be a reasonable measure for the dispersion of annual returns, and the standard statistical interpretation seems to apply pretty well.

Expected return
Although in finance theory, expected return is used as a synonym for the average of a set of returns, in investing, expected return more often refers to future returns than to historical returns. From here on this will be the context in which the term is used. In this context, expected return can be thought of informally as the return investors expect to receive on an investment. Indeed, the term often is used this way by authors of investment books (as seems to be the case, for example, in Expected Returns by Ilmanen. ) However, in investment theory expected return has a more precise definition.

An investment's expected return, $$\operatorname{E}(r)$$, is calculated as follows:


 * 1) Various economic scenarios, $$s$$, are defined.
 * 2) Estimates are developed for the probability, $$\operatorname{p}(s)$$, of each scenario occurring and the return, $$\operatorname{r}(s)$$, for that scenario.
 * 3) The probability and return for each scenario are multiplied together:  $$\operatorname{p}(s)\operatorname{r}(s)$$
 * 4) The results are summed across all scenarios: $$\operatorname{p}(s_1)\operatorname{r}(s_1) + \operatorname{p}(s_2)\operatorname{r}(s_2) + \cdots + \operatorname{p}(s_n)\operatorname{r}(s_n)$$

Or using summation notation:


 * $$\operatorname{E}(r) = \sum_{i=1}^n \operatorname{p}(s_i)\operatorname{r}(s_i)$$

Thus, expected return is the probability-weighted average of returns across all possible scenarios.

First consider an investment in a 1-year T-Bill. Since the nominal return is unaffected by economic factors, the nominal return is certain; i.e., the probability, $$\operatorname{p}(s)$$ = 1, and the return, $$\operatorname{r}(s)$$, is the rate of return on the bill. If the rate of return is 1%, the expected nominal return is calculated as:



\operatorname{E}(r) = \operatorname{p}(s)\operatorname{r}(s) = (1.0)(0.1) = 0.01 = 1% $$

As a slightly more complex, but still simple, scenario analysis, consider a one-year investment in a total stock market index fund, with three possible economic scenarios for the year: recession, normal growth, and strong growth. Assume the following estimates for the three scenarios:


 * Recession: probability $$\operatorname{p}(s_1)=0.20$$, estimated return $$\operatorname{r}(s_1)= -10%$$
 * Normal growth: probability $$\operatorname{p}(s_2) = 0.60$$, estimated return $$\operatorname{r}(s_2) = 5%$$
 * Strong growth: probability $$\operatorname{p}(s_3) = 0.20$$, estimated return $$\operatorname{r}(s_3) = 10%$$

With these scenario estimates, the expected return,$$\operatorname{E}(r)$$, is calculated as:



\operatorname{E}(r) = \operatorname{p}(s_1)\operatorname{r}(s_1) + \operatorname{p}(s_2)\operatorname{r}(s_2) + \operatorname{p}(s_3)\operatorname{r}(s_3) $$



\operatorname{E}(r) = (0.20)(-0.10)+(0.60)(0.05)+(0.20)(0.10) = 0.03 = 3% $$

Note that the expected return of 3% is not equal to the most probable return of 5%.

Extensive research has demonstrated that there are no good forecasters. It follows that scenario analysis itself is fraught with uncertainty. To put it bluntly, expected returns are not directly observable; they can only be estimated. Nevertheless, financial academics and practitioners develop and publish their estimates of expected returns.

A concept that will be useful in subsequent sections is the expected return of a portfolio, which is simply the weighted average of the expected returns for the individual assets in the portfolio. This is expressed mathematically as:


 * $$\operatorname{E}(r_p) = \sum_{i=1}^n (w_i)\operatorname{E}(r_i)$$

where wi is the percent of the portfolio in asset i, and E(ri) is the expected return of asset i.

Measuring risk: estimated returns
Variance of returns was illustrated using historical returns in a prior section. The calculation of the variance of estimated future returns is similar, except that the probabilities of the returns are not equal, but are the probabilities assigned to various scenarios.

Variance of estimated future returns, Var(r), is calculated as follows:


 * 1) Subtract the expected return, E(r), from the estimated return, r(s), for the scenario: [r(s) - E(r)]
 * 2) Square the result: [r(s) - E(r)]2
 * 3) Multiply the result by the probability, p(s), of the scenario: p(s) x [r(s) - E(r)]2
 * 4) Sum the resulting values across all scenarios:



\operatorname{Var}(r) = \sigma^2 = \operatorname{p}(s_1)[\operatorname{r}(s_1) - \operatorname{E}(r)]^2 + \operatorname{p}(s_2)[\operatorname{r}(s_2) - \operatorname{E}(r)]^2 + \cdots + \operatorname{p}(s_n)[\operatorname{r}(s_n) - \operatorname{E}(r)]^2 $$

Or using summation notation:


 * $$\operatorname{Var}(r) = \sigma^2 = \sum_{i=1}^n \operatorname{p}(s_i)(r_i - \operatorname{E}(r))^2$$

To deepen the intuitive understanding of the calculation of variance of estimated returns, consider the purpose of each step in the calculation.


 * 1) Step 1 yields a number that is larger for a scenario return that is further from the expected return.
 * 2) Step 2 eliminates negative values, so that negative values do not offset positive values; i.e., the measure should increase whether a scenario return is less than or greater than the expected return.
 * 3) Step 3 gives a greater weight to a higher-probability scenario.
 * 4) Step 4 adds the individual, probability-weighted, squared differences to yield a single number.

Note that the purposes of steps 1, 2 and 4 are basically the same as for the calculation of the variance of historical returns. Step 3 introduces probability weighting.

As with historical returns, the standard deviation of estimated returns is calculated as the square root of the variance of the estimated returns.

For the case of the nominal return on a 1-year T-Bill yielding 1%, the expected return, E(r), = 1%, and there is only one scenario, for which the nominal return is a certain (p(s) = 1). Therefore, the the variance is calculated as:



\operatorname{Var}(r) = \sigma^2 = \operatorname{p}(s_1)[\operatorname{r}(s_1) - \operatorname{E}(r)]^2 $$


 * $$ \operatorname{Var}(r) = \sigma^2 = (1.0)(0.01 - 0.01)^2 $$


 * $$ \operatorname{Var}(r) = \sigma^2 = 0 $$

Since $$\sqrt{0} = 0$$, the standard deviation is 0 pp. Note the intuitive sense of this: a standard deviation of 0 indicates zero uncertainty in the return.

Recall the three-scenario case discussed earlier:


 * Recession: probability, p(s1) = 0.20; estimated return, r(s1) = -10%
 * Normal growth: probability, p(s2) = 0.60; estimated return, r(s2) = 5%
 * Strong growth: probability, p(s3) = 0.20; estimated return, r(s3) = 10%

The expected return, E(r), was calculated as 3%. The variance is calculated as:



\operatorname{Var}(r) = \sigma^2 = \operatorname{p}(s_1)[\operatorname{r}(s_1) - \operatorname{E}(r)]^2 + \operatorname{p}(s_2)[\operatorname{r}(s_2) - \operatorname{E}(r)]^2 + \operatorname{p}(s_3)[\operatorname{r}(s_3) - \operatorname{E}(r)]^2 $$



\operatorname{Var}(r) = \sigma^2 = (0.20)(-0.10 - 0.03)^2 + (0.60)(0.05 - 0.03)^2 + (0.20)(0.10 - 0.03)^2 $$



\operatorname{Var}(r) = \sigma^2 = (0.20)(-0.13)^2 + (0.60)(0.02)^2 + (0.20)(0.07)^2 $$


 * $$ \operatorname{Var}(r) = \sigma^2 = 0.0046 $$

And the standard deviation is:


 * $$ \operatorname{SD}(r) = \sigma = \sqrt{0.0046} $$


 * $$ \operatorname{SD}(r) = \sigma = 0.07 = 7% $$

Although three samples is not enough to apply the statistical implications of standard deviation, note that two of the returns fall within E(r) +/- one standard deviation (3% +/- 7 pp), and all returns fall within E(r) +/- two standard deviations (3% +/- 14 pp). Again, standard deviation seems to provide a reasonable measure of the dispersion of returns (risk).

Managing risk
This article focuses primarily on investment risk and return. However, managing risk also can be viewed from a broader perspective. The next section focuses on managing investment risk--specifically portfolio risk. The section after that provides an overview of managing financial risk from a broader perspective.

Managing portfolio risk
A portfolio is the investor's collection of financial assets, e.g., stocks, bonds and cash. It is intuitive that holding a greater portion of low-risk assets and a smaller portion of a high-risk assets will reduce overall portfolio risk. So a fundamental aspect of managing portfolio risk is to decide on an appropriate mix of high-risk assets (such as stocks) and low-risk assets (such as cash and bonds).

Portfolio theory reduces this to an even more fundamental level: establishing the level of risk by combining a portfolio of risky assets with a risk-free asset. A 30-day T-bill conventionally is used as the risk-free asset. Portfolio risk is proportional to the ratio of risky assets to risk-free assets. Risky assets include bonds as well as stocks.

Determining the risk of the portfolio of risky assets is where more complicated concepts are introduced.

Variance is one way to characterize the risk of a single asset. Another way to characterize the risk of an asset is to evaluate its co-movement with the market portfolio, where the market portfolio is the collection of all assets within a specified market; e.g., all stocks in the US stock market. This co-movement is measured by the asset's covariance with the market portfolio, and is referred to as systematic risk. The sources of systematic risk are the economic factors that affect the the overall market. For example, the level of economic growth affects all companies to some extent, and therefore affects the price movement of all stocks to some extent. The term beta often is used to describe the extent to which an individual asset's return is related to the market portfolio's return.

The portion of an asset's return variation that is not related to the market portfolio is referred to as unsystematic risk. The sources of unsystematic risk are the firm-specific risks of individual companies, as opposed to the broader economic factors that have some effect on all companies.

Unsystematic risks can be eliminated, or "diversified away", by owning the entire market portfolio. So one way to manage risk is to reduce or eliminate unsystematic risk by holding a large number of securities--ultimately by holding all securities in the market. This is the theoretical foundation for owning a total market mutual fund, such as a total stock market or total bond market index fund.

Covariance and correlation of returns. Reilly, p.245-250

Standard deviation of a portfolio. Reilly, pp. 250-258

The efficient frontier. Reilly, pp. 258-261

Managing financial risk
Investment risk, or financial-asset risk, is only one type of financial risk facing households. Other major categories of risk are:


 * Sickness, disability, and death.
 * Unemployment risk.
 * Consumer-durable asset risk: e.g., the risk of loss from fire, theft or obsolescence related to owning a car, house, or other consumer-durable asset.
 * Liability risk: e.g., the possibility of getting sued.

Managing these types of risks, as well as managing investment risk, from a broader financial planning perspective is the topic of this section.

Determining risk exposure
The purpose of this section is to present different frameworks the investor can use to determine the appropriate amount of risk and types of risk to take. Although the term risk tolerance is more common, and is used by some authors to describe all aspects of determining how much risk to take, many authors use the term risk tolerance to describe only the psychological and emotional considerations of determining how much risk to take. Hence the more generic term risk exposure is used here to describe all aspects of determining how much risk to take.

Below are ideas and references for this section. Feel free to flesh it out. The intention is to avoid bias and specific recommendations, but to simply present the frameworks objectively based on the references.

Some authors present risk evaluation as inextricably linked to portfolio construction. Others present it more as a standalone topic.

Following are some references for different frameworks. Consider combining the ones that have a lot of overlap, and/or explaining the commonality and differences; e.g., Swedroe's "willingness" is what most refer to as "risk tolerance".

Boglehead's Guide To Investing
The Bogleheads' Guide To Investing discusses determining appropriate risk exposure in the chapter on Asset Allocation. The context is designing an efficient portfolio and "staying the course".

Four areas are explored:


 * Goals
 * Time frame
 * Risk tolerance
 * Personal financial situation

Examples of goals are saving for a home, a child's education, or retirement.

Stocks are suitable for long time frames, not short time frames (less than five years)

Risk tolerance is about the psychological and emotional ability to stick with the investment plan during large market declines. Whether or not one thinks one could sleep well at night (the sleep test) with one's current asset allocation (proportions of stocks, bonds and cash) is one indicator of risk tolerance.

The investor's personal financial situation affects how much risk and what types of risk are appropriate. Stability of income and net worth affect the need to take risk. Individuals with higher guaranteed retirement incomes (e.g., a pension or social security) or high net worth do not need to take as much risk, e.g., by investing in stocks.

Three "tools, along with the investor's own experience, are recommended to help factor risk into the one's asset allocation:
 * John Bogle, founder of Vanguard, suggests that a rough guide is to hold one's age in bonds.
 * Consider the maximum decline to expect with various stock/bond ratios (the book presents maximum declines in the 2000 to 2002 bear market, but the maximum declines in the 2008/2009 bear market were worse; a common rule of thumb is to be prepared for a 50% loss in the stock portion of one's portfolio).
 * Vanguard provides an online questionnaire and suggested asset allocations. The book includes a version of the questionnaire in Appendix IV, but see the Vanguard website for the latest version.

Various sample portfolios are presented based on these "stages in life":


 * Young investor
 * Middle-aged investor
 * Investor in early retirement
 * Investor in late retirement

Swedroe

 * Ability, willingness and need to take risk.

Ferri
Ferri, All About AA, Chapter 12: Early savers, Mid-life accumulators, Preretirees and active retirees, Mature retirees. Assets, future liabilities, risk tolerance (questionnaires, AA stress test). Risk avoidance.

CFA Institute
CFA Institute (Bodie et all, 2008, chapter 21): Investors and Objectives, Investor constraints (Liquidity, Investment Horizon, Regulations, Tax Considerations, Unique Needs.

Bernstein
Bernstein (2010), The Investor's Manifesto, pp. 75-80: Age (human capital considerations), risk tolerance (equipoise point). Bernstein used "risk tolerance" in the sense of willingness to take risk.

Short term risk vs. long term risk
Mentioned in "Risk as the possibility of loss" section, perhaps here we go into more detail on terminal wealth dispersion, how small differences in annualized return can result in large differences in terminal wealth, stocks are risky in the long run, etc.. This is covered in Bodie et al, 2008.

See Bernstein's "The 15 Stock Myth" for more on terminal wealth dispersion, but there he focuses on the danger of a portfolio with only a small number of stocks. He does emphasize that such portfolios, although they may have low standard deviation of monthly returns, also have a large dispersion of terminal wealth.