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Chapter 38 Asset allocation and portfolio construction.


Asset allocation involves selecting the proportions of various types of assets to include in a portfolio. Proper asset allocation improves a portfolio's risk-adjusted return. The asset allocation decision is a prelude to selecting individual securities or funds for portfolio inclusion.


The relationship of return and risk is an important investment planning concept. Generally, riskier investments must offer a higher potential return to compensate for the added risk. However, combining two or more risky assets can actually reduce the risk of the overall portfolio, as long as the assets are not highly correlated. Highly correlated assets tend to move in the same direction and at a similar magnitude, while assets that are not highly correlated do not. Investors can use non-highly correlated assets to build portfolios with more favorable risk/return relationships.

Consider four potential investments that have the following expected returns for next year depending on three possible economic environments, each of which is equally likely to occur:
Economic                 Expected
Environment              Returns

               Asset A    Asset B    Asset C    Asset D

Strong            6%        15%        20%         5%
Normal            6%        10%        10%        10%
Weak              6%         5%         0%        15%
  Return          6%        10%        10%        10%
Variability    +/-0%      +/-5%      +/-10%        5%

Expected return is the asset's average return under all three business conditions. Variability (risk) is simply the range of potential outcomes relative to the expected return. Note that Asset A is a risk free asset, with an expected return of 6% and no variability. Assets B, C, and D all offer an expected return of 10% but at different levels of risk. Assets B and D are equally risky, while Asset C is riskiest. For a risk-averse investor, Assets B and D appear preferable to C because they offer lower variability for the same expected return. The choice between A, B, and D depends on whether the higher return of B or D is sufficiently enticing for the investor to take on the risk relative to A.

However, the investor doesn't have to choose only one asset. Instead, let's take a look at three potential portfolios where an equal amount of two assets (50% of each) is purchased. Note that all three portfolios offer the same 10% expected return as the individual assets.
  Business                       Expected
 Conditions                       Returns

               Portfolio BC    Portfolio CD    Portfolio BD

Strong             17.5%           12.5%           10.0%
Normal             10.0%            10%            10.0%
Weak               2.5%            7.5%%           10.0%
  Return           10.0%           10.0%           10.0%
Variability      +/-7.5%         +/-2.5%           +/-0%

The variability of Portfolio BC is the average variability of assets B and C. This portfolio does not achieve any reduction in risk because Assets B and C are perfectly correlated (a correlation coefficient of 1.0). They both do well in strong business conditions and poorly in weak conditions.

The expected variability of Portfolio CD is less than the variability for each of the individual assets. This portfolio would be preferred to individual assets B, C, and D since it offers the same expected return but lower risk. This is because assets C and D are perfectly negatively correlated. One asset is always up when the other is down.

Similarly, the Portfolio BD offers the same expected return as assets B, C, and D, but no variability. Again, this is due to the fact that B and D are negatively correlated. In fact, this portfolio offers a higher return for the same expected risk as Asset A, making it the most preferable portfolio for a risk-averse investor.

By combining assets that are not highly correlated together in portfolios, the investor can achieve a more favorable risk/return relationship. It is not necessary for assets to be negatively correlated to achieve this benefit. Small positive correlations (assets that occasionally, but not always, move in the same direction) also reduce risk.

Modern Portfolio Theory and Asset Allocation

The ability to reduce risk for a given level of return is a fundamental principal of modern portfolio theory (MPT). Properly constructed portfolios of assets that are not highly correlated achieve the highest possible return for a given level of risk. MPT measures risk as standard deviation of returns. The standard deviation for a portfolio of assets can be determined as:


The standard deviation for the portfolio, p, is based upon the weights, w, and standard deviation of each asset, a, in the portfolio and the correlation between all pairs of assets, i and j. For a two-asset portfolio, this can be expressed more simply as:


Now consider two assets with the following expected returns, standard deviations, and correlation:
                         A           B
Expected Return        10.00%      6.00%
Standard Deviation     20.00%      5.00%
Correlation             0.2

The low correlation between A and B indicates that there should be a benefit from combining these assets in a portfolio. Consider the following potential portfolios, with varying weights of A and B in the portfolio:
               Asset        Asset       Expected     Standard
Portfolio        A            B          Return     Deviation

1                   0      100.00%       6.000%        5.00%
2              10.00%       90.00%       6.400%        5.28%
3              20.00%       80.00%       6.800%        6.20%
4              30.00%       70.00%       7.200%        7.53%
5              40.00%       60.00%       7.600%        9.09%
6              50.00%       50.00%       8.000%       10.78%
7              60.00%       40.00%       8.400%       12.55%
8              70.00%       30.00%       8.800%       14.38%
9              80.00%       20.00%       9.200%       16.23%
10             90.00%       10.00%       9.600%       18.11%
11            100.00%        0.00%      10.000%       20.00%

The expected return is always the weighted average return of the underlying assets. For example, for portfolio 6 the expected return is 8% [(0.5 x 10%) +( 0.5 x 6%)]. The standard deviation of each portfolio is based upon the standard deviation formula for the two-asset portfolio above.

Note that portfolios 1 and 11 represent the individual assets. For a risk adverse investor, note that relative to portfolio 1, portfolio 2 offers a 6.7% higher return [(6.4% / 6%) - 1] but has only a 5.6% [(5.28% / 5%) - 1] increase in standard deviation. The return/risk can also be computed, which is 1.20 [6% / 5%] for portfolio 1 and 1.212 [6.4% / 5.28%] for portfolio 2. Portfolio 2 offers a greater return relative to risk compared to portfolio 1. Similarly at the other end of the spectrum, moving from portfolio 11 to portfolio 10, there is a reduction in return of 4% (1 - (9.6% / 10%)], but a 9.4% reduction [1 - (18.11% / 20%)] in standard deviation. The relationship between risk and return for these portfolios is demonstrated in Figure 38.1. An investor could choose the combination of assets A and B which provides them with their desired return while maintaining an acceptable level of risk.


Portfolios of more than two assets make the math more complex, but the basic concepts are the same. Adding assets with low or negative correlations to the rest of the portfolio improves the return/risk relationship.

Overall portfolio asset allocation decisions are made using the expected returns, standard deviation, and correlations between asset classes, rather than individual assets. An asset class is a group of securities with similar characteristics. Broadly speaking, the major asset classes include stocks, bonds, real estate, cash, commodities, and international investments. Within each asset class, however, there can be various gradations that sometimes constitute their own asset class. For example, an investor may consider growth stocks and value stocks to be separate asset classes. Likewise, treasury bonds can differ greatly from high-yield bonds in terms of their risk and return. It makes sense to consider these subsets of broad asset classes when forming a portfolio. Commercial asset allocation software packages have varying numbers of asset classes available.

Asset allocation strategies must also consider the appropriateness of each asset class for the particular investor or account. For example, municipal bonds would likely be excluded as an asset class for non-taxable or tax deferred accounts.

Using asset allocation software and the expected returns, standard deviations, and correlations of the asset classes, the advisor should attempt to generate an "efficient frontier" of potential portfolios. The efficient frontier represents those portfolios with the highest expected return for a given level of expected risk as depicted in Figure 38.2.


Portfolio A on the efficient frontier might represent a portfolio concentrated in short-term fixed income securities such as Treasury Bills. Portfolio B might represent a portfolio concentrated in risky securities such as emerging market equities. As with the simplified example for assets A and B above, the efficient frontier can be used to present to a client potential portfolios based on upon their desired returns or the level of risk they are willing to take.

Efficient frontiers can easily be generated based upon historic relationships between asset classes or expected relationships. The challenge is determining which point on the efficient frontier should be selected for a particular client. Theoretically, if the utility (satisfaction) that an investor obtains from different levels of returns relative to risk could be measured, a set of indifference curves for each investor could be drawn. Indifference curves represent the different combinations of risk and return to which an investor is indifferent. Optimally an investor would select the investor's highest indifference curve that touches the efficient frontier as depicted in Figure 38.3. This represents the point of highest utility for the investor.


Lacking a precise measure of an individual's risk/ return preferences, advisors typically use judgment to select an appropriate point on the efficient frontier based on some measure of the investor's risk tolerance (see Chapter 36, "Asset Pricing Models"). One approach involves classifying investors as conservative, intermediate, or aggressive. The investor is then placed in the relevant section of the efficient frontier as shown in Figure 38.4.


Additional degrees of risk tolerance can also be used. Some advisors use a scale such as:

* Aggressive Growth

* Growth

* Growth and Income

* Income

* Safety

The categories including growth are typically concentrated in equities, with a large portion of the expected return derived from appreciation. Income, by contrast, typically includes investments such as bonds where the current yield is an important component of return. Note that when constructing a portfolio based on a client's investment policy statement, the advisor should include the constraints and preferences (see Chapter 37, "Portfolio Management and Measurement") in determining an appropriate allocation.

Strategic Asset Allocation

A strategic asset allocation can be thought of as the long-range plan for a portfolio. Taking into consideration the long-range return requirements and risk tolerance, allocations to various asset classes are developed as described above. The strategic allocation typically is not altered to take into account short-term issues such as current market conditions (perceived current relative value of different asset classes). Instead, assets are periodically rebalanced to conform to the original allocation.

This rebalancing is necessary since the asset classes will have different returns. For example, over the long term equities usually have a higher return than bonds. If the strategic allocation is 50% stocks and 50% bonds and stocks have a higher return, over time, the portfolio will have a greater than 50% position in stocks. To bring the allocation back to the overall strategic allocation, enough stocks would have to be sold and bonds purchased to get back to the 50/50 allocation. Note that if dividends and interest are reinvested in the same asset class or held in cash, this will also impact the rebalancing. An advantage to rebalancing within a strategic asset allocation is that it enforces a discipline to sell the best performing asset classes and buy the lowest performing (buy low, sell high). Alternatively, a momentum investing strategy would suggest no rebalancing--letting the winners run.


Consider an investor who has created a strategic asset allocation of 30% bonds, 30% commodities, and 40% stocks, and who has set a guideline that the portfolio will be rebalanced when any asset class varies from the strategic allocation by more than 500 basis points (5%). Over the next five years, the assets exhibit the following return pattern:
 Year        Bonds       Commodities       Stocks

1            2.43%          13.54%          4.91%
2            4.34%          20.25%         10.88%
3            4.10%          38.59%         28.68%
4           10.25%         -15.94%        -22.10%
5            8.44%         -21.44%        -11.89%

How frequently will the portfolio be rebalanced? How will strategic rebalancing affect the ending value of the portfolio relative to letting the winners run?

First, let's consider an initial portfolio of $1 million that is allowed to run:
Year-end      Bonds     Commodities     Stocks        Total

0            300,000      300,000       400,000     1,000,000
1            307,290      340,620       419,640     1,067,550
2            320,626      409,596       465,297     1,195,519
3            333,772      567,658       598,744     1,500,175
4            367,984      477,174       466,422     1,311,579
5            399,042      374,868       410,964     1,184,873

The ending value of the portfolio is $1,184,873.

Now let's consider the percentage of the portfolio in each asset for the period:
Year-end     Bonds     Commodities     Stocks     Total

0             30%          30%          40%        100%
1             29%          32%          39%        100%
2             27%          34%          39%        100%
3             22%          38%          40%        100%
4             28%          36%          36%        100%
5             34%          32%          35%        100%

At the end of year 1, the allocations are 29% bonds, 32% commodities, and 39% stocks. No allocation has drifted by more than 500 basis points from the strategic goal, so no rebalance is necessary. The same applies at the end of year 2.

At the end of year 3, however, the allocation to bonds has fallen by 800 basis points relative to the strategic allocation, and the allocation to commodities has risen by 800 basis points. According to the guidelines, the portfolio needs to be rebalanced. Doing so results in the following adjustments:
Year-end      Bonds      Commodities      Stocks       Total

3            450,052       450,052       600,070     1,500,175
4            496,183       378,314       467,454     1,341,951
5            538,061       297,203       411,874     1,247,138

Note that by letting this newly rebalanced portfolio run, the ending value of $1,247,138 is $62,265 higher than the original ending value. However, going forward, the percentage of assets changes as follows:
 Year-end      Bonds     Commodities     Stocks       Total

3               30%          30%           40%        100%
4               37%          28%           35%        100%
5               43%          24%           33%        100%

Once again, at the end of year 4 there is an imbalance of more than 500 basis points - this time there is too high an allocation to bonds. The portfolio must be rebalanced a second time at the end of year 4, to the following allocations:
 Year-end       Bonds      Commodities      Stocks       Total

4              402,585       402,585       536,780     1,341,951
5              436,564       316,271       472,957     1,225,792

After the new rebalancing is completed and the final year elapses, the ending value of the portfolio is still $40,919 better than would have been achieved from letting the assets run from year 1 on. However, it is $21,346 lower than it would have been if the second rebalancing had not been done. This is because in year 5 bonds continued to do well, while stocks and commodities continued to fall.

Since there is no way of knowing in advance whether the trends will continue or reverse in a given year, it is generally best to decide in advance how frequently to rebalance in order to retain the desired level of risk for the client's portfolio.

Tactical Asset Allocation

In contrast to strategic allocation, tactical asset allocation attempts to capitalize on changing market conditions. In a tactical asset allocation, the overall asset allocation is frequently adjusted to take advantage of perceived opportunities in the current market. For example, in a high interest rate environment, the advisor may shift the asset allocation to favor long-term bonds. If U.S. equities are perceived to be undervalued, the advisor may overweight them. The rewards of tactical asset allocation depend on the advisor's ability to predict which asset classes will perform best in the near term. Strategic asset allocation is generally a low turnover investment strategy, while tactical asset allocation generally results in high turnover.


The investor starts with the same $1 million portfolio and strategic allocation guidelines as in the previous example. However, the advisor is given discretion to shift the allocation tactically once per year as long as the starting allocations are within 500 basis points of each asset's strategic goal. The advisor decides to follow a momentum strategy that overweights the previous year's top-performing asset by 500 basis points and underweights the worst-performing asset by 500 basis points. The resulting performance is as follows:
                     Bonds     Commodities     Stocks      Total
  of year 1         300,000        300,000    400,000    1,000,000
Performance           2.43%         13.54%      4.91%
End of year 1       307,290        340,620    419,640    1,067,550

New allocation          25%            35%        40%
  of year 2         266,888        373,643    427,020    1,067,550
Performance           4.34%         20.25%     10.88%
End of year 2       278,470        449,305    473,480    1,201,255

New allocation          25%            35%        40%
  of year 3         300,314        420,439    480,502    1,201,255
Performance           4.10%         38.59%     28.68%
End of year 3       312,627        582,687    618,310    1,513,624

New allocation          25%            35%        40%
  of year 4         378,406        529,768    605,449    1,513,624
Performance          10.25%        -15.94%    -22.10%
End of year 4       417,193        445,323    471,645    1,334,161

New allocation          35%            30%        35%
  of year 5         466,956        400,248    466,956    1,334,161
Performance           8.44%        -21.44%    -11.89%
End of year 5       506,367        314,435    411,435    1,232,238

The manager's tactical rebalancing results in a higher ending value than either the untouched portfolio or the one that was strategically rebalanced. However, the $6,446 improvement over strategic rebalancing may not have justified the higher trading costs from larger and more frequent allocation adjustments.

Controlling Volatility

Asset allocation is a means of controlling the volatility that is inherent in investment returns. Some investors may have a high degree of risk tolerance and desire to achieve the maximum possible return regardless of risk. Other investors may not sleep well at night with volatile portfolio values. Even investors who think they can tolerate risk may have circumstances where volatility can impair their ability to maintain a certain standard of living. Take for example a long-term historical return on large stocks of 10% and a long term inflation rate of 3%. An investor has $1,000,000 at retirement and desires to withdraw the maximum amount possible each year while maintaining the purchasing power of the portfolio each year and not running out of money during any foreseeable life expectancy. One might naively assume that the investor could withdraw 7% each year starting with $70,000 the first year (and increasing the dollar amount by inflation in each subsequent year). Unfortunately, it turns out that is not possible unless there is no volatility in returns. If the stock market achieved 10% returns each and every year it would be possible, but with volatility the maximum safe withdrawal rate is much lower. In fact, studies have shown that the maximum safe withdrawal rate given historic volatility of returns for a stock is less than 4%.

As noted in previous sections, creating a diversified portfolio with assets that are not highly correlated can reduce the volatility of an investment portfolio. Time also moderates volatility. If an investor has a long time horizon before investment withdrawals are to begin, the impact of volatility is dampened over time. Additionally, volatility can be managed using hedging strategies and options strategies such as a covered call (selling a call option on a currently held investment). A covered call sells some of the upside and reduces the potential downside (from the collection of the option premium), reducing the standard deviation of the portfolio. Downside risk can also be reduced through the purchase of put options. Hedging and option strategies are described in more detail in Chapter 41.

Monte-Carlo Simulation

Expected returns and standard deviation are not necessarily constant over time. Nor is the return in an individual period predictable. Returns in each asset class are probabilistic. Investors may also make periodic deposits and withdrawals, further increasing the difficulty of predicting long-term returns and risk. If an investor desires a particular level of retirement income, what is the assurance that this level of income is likely to be obtained?

Monte-Carlo simulation assesses the likelihood of an expected outcome. In a Monte-Carlo simulation, a computer program randomly chooses returns from an expected distribution of returns for each period (perhaps rebalancing the asset allocation if required under the strategy). Each run results in an ending expected portfolio value. The process is run many times (perhaps 1,000), to achieve a distribution of ending values. This process can help assess the probability of achieving a certain value or income in the future (including the possibility that an individual will outlive his retirement assets).


As noted in the prior section, volatility has a significant impact on the ability of a client to make desired retirement withdrawals during retirement. The impact of volatility on the sustainability of retirement distributions can be demonstrated using Monte-Carlo simulation. Assume that an investment adviser has the following market expectations:
               Expected Long-Term       Expected Long-Term
               Arithmetic Average     Standard Deviation of
                     Return                  Returns

Stocks                9.0%                    20.0%
Bonds                 5.5%                     9.0%

Expected long-term correlation of stocks and bonds is 0.20.

Consider a retired client who has a $1,000,000 portfolio. The client would like to withdraw as much as possible each year with that amount increasing for inflation. Ideally the client would like to withdraw about $40,000 (4% of the initial portfolio amount) adjusted for inflation each year. Using one Monte-Carlo simulation software ( using a 9% expected return for stocks, 20% expected standard deviation for stocks, and 3% inflation, the probability of success (the ability to withdraw $40,000 each year adjusted for inflation over the next 30 years without running out of money) can be computed. Running one million iterations, the Monte-Carlo simulation reveals that there is about an 80% probability of success with a 100% stock portfolio. In other words, in 20% of the 30 year time periods the client would run out of money within 30 years (not an attractive scenario for most clients). The software can also be run to output the maximum safe initial withdrawal given a certain desired probability of success. Let's say a client wants to be 95% certain not to run out of money. Monte-Carlo simulation reveals that the maximum safe withdrawal amount is about 2.6% ($26,000 rather than $40,000). Running the Monte-Carlo simulation with a 100% bond portfolio reveals that there is a 66% probability of success at withdrawing $40,000. Conversely, to achieve a 95% probability of success with bonds, the maximum safe initial withdrawal rate is about 2.9%. While the expected return on stocks was higher than that of bonds, bonds had a lower volatility. The probably of success was lower than for a stock only portfolio, but the sustainable withdrawal was higher. However, neither the stock only nor the bond only portfolios are very attractive.

By creating a portfolio of these two asset classes that are not highly correlated, the sustainability of withdrawals can be improved. Take a portfolio of 50% stocks and 50% bonds. From the above data, the expected return on the portfolio would be 7.75% with a standard deviation of 11.76%. Running Monte-Carlo simulation reveals that the combined portfolio would have an 86.5% probability of succeeding with a 4% withdrawal rate. To achieve a 95% probability of success the maximum safe withdrawal rate would be about 3.3%. The diversified portfolio outperforms both the stock only and bond only portfolios, once again demonstrating the advantages of asset allocation in controlling volatility. Note, however, that the diversified portfolio has a lower expected return than the stock only portfolio and the average expected wealth accumulation would be lower than a stock only portfolio. This is consistent with using a higher equity allocation during the pre-retirement years and switching to a more diversified portfolio during retirement years for risk adverse clients.

The preceding example was for illustrative purposes only. Each Monte Carlo software is presumably unique. Calculated results may vary.


Once the asset allocation decision has been made, the next step is to select individual securities (stocks, bonds, etc.) or mutual funds within each asset class. As with the overall asset allocation strategy, this can be either passive or active. In a passive approach, the advisor selects broad diversified portfolios (such as index mutual funds or exchange-traded index funds) representing each asset class. In an active approach, the advisor selects individual securities or traditional, actively managed mutual funds within each asset class that are expected to outperform their peers. The active approach requires more frequent monitoring, security selection, and buy/sell decisions. The choice between the active and passive approaches depends upon an advisor's beliefs about market efficiency and the advisor's ability to select, in advance, better performing securities or funds. Efficient markets generally assume rational behavior by market participants and a number of anomalies have been shown to occur. A study of behavioral finance can help the advisor understand investor behavior both in selecting investment strategies and educating clients to avoid some common pitfalls.

Market Efficiency

The Efficient Market Theory (EMT) hypothesizes that securities markets process information efficiently. This implies that new information about a security is quickly (almost instantaneously) reflected in its price.

In order for a market to be efficient, certain conditions must be met. These include a large number of profit maximizing investors competing in the market and a free, random flow of information. When these conditions are present, the market is assumed to be efficient.

Beginning with the first point, a large number of profit-seeking market participants ensures that no trader is able to manipulate the market. If a participant tries to drive the price of a security too high or too low, other investors will recognize the mispricing and sell or buy to offset the rogue trader. Note that this does not imply that prices will always adjust immediately to the "correct" price. Rather, the efficient market theory requires only an equal likelihood that prices are either too high or too low (no bias that would allow investors to earn excess returns).

An efficient market requires that news flow be random and freely available. If positive or negative announcements can be consistently predicted, a trader can take advantage of them. As long as the timing is uncertain, all traders have an equal opportunity to profit or lose from a given speculation.

Given a market with many traders, a quick reaction to information is generally a given. Still, there could be other factors limiting an investor's ability to trade. For example, exchange rules put in place after the 1987 market crash include "circuit breakers" that halt trading when the market is up or down more than a certain amount. It has been argued that these rules reduce efficiency, as investors are prevented from fully responding to important events.

Strong Form EMT

In its strong form, efficient market theory posits that all information, whether public or not, is reflected in security prices. In other words, this is a perfect market assumption. In a strong-form efficient market, no participants would be able to consistently perform better than the market other than by random luck.

Semi-Strong Form EMT

The semi-strong form of efficient market theory states that all public information is reflected in stock prices. This includes prices of securities, security volume data, financial reports, press releases, statements of company officers, newspaper articles, and the work of sell-side securities analysts, as well as many other types of information.

Weak Form EMT

To be weak form efficient, security prices need reflect only market data, such as historical trade prices, volume, and order size. In a weak form efficient market, an investor could use fundamental analysis tools such as financial statement analysis and other fundamental information to glean information that has not been efficiently priced. This information could then be used to outperform the market. However, technical analysis (charting of security price and volume data) would still fail to provide consistent out-performance, because it relies on market data.

EMT Anomalies

There are a number of research studies that show that some basic fundamental strategies can result in an investor out-performing the market. These are known as EMT anomalies.

Stocks paying high dividend yields have been shown to perform better over time than low dividend yield stocks. Similarly, stocks with low price/book, price/ sales, and price/earnings ratios have been found to outperform those stocks with higher price multiples.

The market appears to over and under react to news. Sometimes it takes several quarters for news to be fully reflected in stock prices and, in other cases, news causes an overreaction that is later corrected. Studies have been done showing that the market overreacts, and others indicate that there is persistent under-reaction. Note that unless an investor can predict whether the market reaction to news will be too much or not enough, the efficient market hypothesis would hold.

Some research has demonstrated a "January effect" under which the market tends to perform better in January. Some of this effect may be explained by the timing of tax selling. Late in the year, investors who want to minimize taxes will sell stocks that have gone down in order to incur a capital loss. Under the tax rules, if the investor desires to repurchase the security, he must wait 30 days, which can cause buying in January.

Shares of firms with small market capitalization (the size effect) or few dedicated analysts (the neglect effect) tend to perform better than others. In some ways, this is not so much an anomaly as a validation of the efficient market theory. Few analysts or a small market cap could reduce buyers' willingness to take a chance on such names. As a result, the first requirement of an efficient market--that there be many participants--is violated.

The Value Line effect is a finding that stocks ranked highest by Value Line have been found to outperform the market in certain periods. This is considered to be an efficient market anomaly, because this consistent outperformance should be recognized by the market and reflected in the stock price as soon as the ranking is published.

The existence of the various anomalies calls into question the relative efficiency of the markets. The research studies that uncovered these anomalies relate to particular time periods and, of course, do not guarantee they will work in future periods. In addition, identifying anomalies should cause traders to act on them, which in turn helps to achieve a greater level of market efficiency.

Behavioral Finance

Behavioral finance involves the study of how investors make decisions. Investors often use mental shortcuts or demonstrate biases in their decision making. Understanding these concepts can assist the advisor in understanding market behavior and anomalies and helping clients make better decisions. Some investment managers and mutual funds attempt to exploit these behavioral issues in making securities investments. This section explores some common behavioral finance issues.

Mental Accounting. Investors (and consumers) have been shown to have "categories of money" such that all dollars are not equivalent. They categorize money into buckets. Imagine that a ticket to a hockey game is purchased for $100. When the individual arrives for the game, he discovers that he has lost his ticket. Question 1--Does he spend another $100 to purchase another ticket? Consider another example where the individual has not yet purchased a ticket. When he arrives for the game, he discovers that he has lost $100 in the parking lot. Question 2 - Does he spend another $100 to purchase a ticket? Belsky and Gilovich (1999) find that most people answer no to Question 1 but yes to question 2 since in the first situation $200 would be charged to the entertainment "mental account." In the investment arena, clients may be inclined to mentally view money differently depending upon the source, such as an inheritance versus earned money, or principal versus capital gains.

Confirmation Bias. People tend to search for and select information which confirms their beliefs. This is a dangerous bias that can lead to bad investment decisions. For example, in evaluating an individual security for investment, the advisor or client may examine a half dozen analysts' reports and select the one that confirms his prior beliefs (buy, sell, or hold). Of course, the analyst writing the report may have used confirmation bias in evaluating data for purposes of reaching his recommendation based on other relationships the analyst may have with the company. Advisors need to be aware of the potential existence of this bias in evaluating reports of others and in making their own evaluations. An independent, objective frame of mind should be strived for.

Optimism/Overconfidence. People tend to believe that they are better than average (while everyone wants to be--by definition, they cannot all be above average). For example, in a survey of British drivers, 95% of them felt they were above average. Analysts, investors, and advisors may be overconfident in their abilities to forecast company earnings, future growth rates, investment returns, and similar future events. Overconfidence can be reduced by considering many different possibilities or alternatives.

Loss Aversion/Framing. Individuals are generally willing to take more risk to avoid a certain loss, but are conservative in locking in gains and avoiding risk. Aversion to losses also results in a reluctance of investors to sell losing investments. They may feel they can "avoid" the loss by not selling the investment, even though the investment has declined in value.

Herding. Individuals may engage in herding behavior. This can be demonstrated with financial analysts who forecast earnings for a company. Analysts may want to stay with the herd by having a forecast that is similar to the forecast of other analysts. Having a forecast that is much higher or lower than the "herd" can result in being out on a limb if the analyst is wrong. If an individual is wrong, but in with the herd there is no loss in reputation.

Use of Heuristics. Individuals often use heuristics (shortcuts) in making decisions. These include representativeness, availability, and anchoring. In representativeness, individuals make judgments based on similar past events. Availability involves making decisions based on how recent or vivid information is. For example, they may view the market more positively if recent returns have been positive even though market returns may actually exhibit some reversion to the mean. Similarly, they may become increasingly pessimistic if the market has declined recently. Anchoring involves individuals anchoring on a current number such as a stock price or returns and make adjustments from that number, rather than making an independent assessment of potential outcomes. These simple shortcuts ease decision making but can result in bad investment decision, such as buying investments that have recently increased in value rather than buying those that have recently declined.

Active Security Selection

If an active security selection strategy is chosen, there are two basic approaches: a top-down approach and a bottom-up approach. In a top-down approach, the advisor performs an analysis of the global and domestic economy. Based on this economic analysis, the advisor selects industries or sectors that are expected to do well under those economic conditions. Lastly, the advisor selects the best securities within those industries or sectors, perhaps based on fundamental characteristics such as earnings, cash flow, growth prospects, and risk.

In a bottom-up approach, the advisor starts at the individual security level, identifying the "best" companies according to some predefined criteria. These criteria can be based on relative valuation, fundamental analysis, or technical analysis. Chapter 39 explores fundamental and technical analysis in more detail. Chapter 33 presents valuation models useful in selecting equity securities, while Chapter 34, "Measuring Yield," presents similar models for fixed income securities.


1. 2005 Yearbook, Stocks, Bonds, Bills and Inflation (Chicago, IL: Ibbotson Associates, 2005).

2. Harold Evensky, Wealth Management, (New York,, NY: McGraw Hill, 1997).

3. James O'Shaughnessy, What Works On Wall Street, 2nd Edition (New York, NY: McGraw Hill, 1998).

4. John Stowe, Thomas Robinson, Jerald Pinto and Dennis McLeavey, Analysis of Equity Investments: Valuation (Charlottesville, VA: Association for Investment Management and Research, 2002).

5. Willam P. Bengen, Conserving Client Portfolios During Retirement, (Denver, Colorado: FPA Press, 2006).

6. Gary Belsky and Thomas Gilovich, Why Smart People Make Big Money Mistakes-And How to Correct Them, (New York, NY: Simon & Schuster, 1999).


Question--How does asset allocation reduce the risk of holding multiple risky securities?

Answer--Asset allocation looks for risky securities that behave differently from each other (have low or negative correlation.) If the overall portfolio has some assets that rise when others fall, the overall returns will be more stable (less risky).

Question--What is an advantage to controlling volatility (risk) and how can volatility be controlled?

Answer--Reducing the volatility of a portfolio can make clients feel more comfortable with their portfolio and improve the sustainability of retirement withdrawals. Volatility can be reduced through diversification, time, and the use of hedging/option strategies.

Question--Edwin Jackson is a portfolio manager who relies on in-depth research to select stocks. He examines a firm's competitiveness and position within its industry, and then evaluates management's performance using such measures as operating margin, return on equity, and its efficiency in keeping inventory and collecting accounts receivable. In a semi-strong form efficient market, are Jackson's efforts useful?

Answer--In a semi-strong form efficient market, all public information would be reflected in the stock price. This includes all of the measures Jackson uses in his investment process. Therefore, his work would be unlikely to add significant value. (Note however that there is a chicken and egg phenomenon, because if Jackson and others like him become discouraged and quit their jobs as analysts, the market would become less efficient.)

Question--A client who is willing to take more risk with the portion of his portfolio that came from recent capital gains than the amount related to his or her original principal is exhibiting what type of behavioral finance issue?

Answer--The client is using mental accounting where they are maintaining capital gains in one mental account and principal in another.
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Title Annotation:Techniques of Investment Planning
Publication:Tools & Techniques of Investment Planning, 2nd ed.
Geographic Code:1U2NY
Date:Jan 1, 2006
Previous Article:Chapter 37 Portfolio management and measurement.
Next Article:Chapter 39 Investment strategies.

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