1. finance
2. investing
3. stocks

Volatility
and
How To Handle It
By
Dr. Robert C. Smithson
Anava Capital Management LLC
2 North First St., 4th Floor
San Jose, CA 95113
408-918-9333
Please Note: Individual companies shown or discussed in this presentation have been used as examples only and are not intended as
recommendations of any kind by Anava Capital Management LLC or any of its representatives.
An investment involves buying something with the hope, but not the guarantee, that its future value will be greater than
the price you paid for it. Volatility is the risk that the future value will not be what you think it will be. In return for
taking that risk, the investor expects to receive a higher average return than would be received from a less risky
investment. Conventional wisdom, then, is that investors that make high profits are also highly likely to lose money
instead. This idea, however, is vastly over-simplified. Investors who understand volatility and risk can actually achieve
better returns and lower risk than those who simply select low-risk investments.
Before we can talk about investing and markets, however, we have to be able to recognize when we are doing better or
worse. So, we’ll first define what value actually is and what money actually is. Once we know that, we can tell if the
value of our investments is actually increasing or decreasing. That’s what we really want to know, and it’s not the same
thing as how much money they represent. As we shall see, value is created in the minds of people, and the same thing can
be valued very differently by different people. We will also see that money is not a physical thing. Money simply
represents promises, promises that can be made and broken with equal ease. Because of the subjective nature of both
value and money, it is impossible to define a specific “correct” price for anything. Because there is no single price that is
correct for everyone, it is possible in principal for all investors to “beat the market.” For example, people who value the
immediate safety of their funds will be glad to trade possibly large long-term profits for lower short-term volatility. The
investor that takes the other side of the deal will also be happy because in the long run he makes more money, money for
which he has no short-term need. This is a win-win situation because the definition of value is different for the two
people. Financial markets have the means in place to allow individual investors, within limits, to optimize their risks and
returns over time. It’s no exaggeration to say that what financial markets are really for is to transfer risk from people who
don’t want it to people who do. Investors reduce the risk for businesses in return for an uncertain share in profits.
How do you optimize your investments to meet your particular requirements? We’ll look at how the stock market has
behaved in the past and why. Then we’ll discuss several optimization strategies that have stood the test of time, and are
appropriate for different kinds of investors. By the way, the best portfolios are rarely, if ever, found by random selection
of stocks on the theory that the market has efficiently found the correct price for all stocks. An “efficient market” is
impossible because there is no one correct price for everyone. You can use this to your advantage. Finally we’ll discuss
some actual applications of these ideas and the results obtained.
1
A LOOK AT THE MARKET
The S&P 500
S&P 500 Price Changes 1871 - 2005
Stock Data from Robert J. Shiller, Yale University
(http://www.econ.yale.edu/~shiller/data.htm)
1871 Dollars
1000
100
S&P Real
S&P Nom.
10
1
0.1
1850
1900
1950
2000
2050
Year
The S&P 500 was started in 1923 when it was introduced as the “Standard and Poor’s Index” containing 233 companies.
In 1957, the present index, the S&P 500 began, with 500 companies representing about 80% of the total market value of
U.S. stocks. The composition varies, and is intended to represent not only the largest, but the most representative
companies in each important market sector. The index is weighted by market capitalization, so it is dominated by largecap stocks. It is probably the most widely-used stand-in for the entire market in studies of market characteristics, and in
measuring the effectiveness of investing techniques and managers. We people say they are trying to “beat the market”
they usually mean they are trying to get better percentage returns than the percentage change in the S&P 500 over the
same period.
One thing to notice about this chart is that something dramatic happened to the S&P around 1950. Prior to that time, there
were wild swings in the real (inflation adjusted) index that were not duplicated by the nominal (not inflation adjusted)
index. This was caused by large changes in the rate of inflation In 1921, inflation drove the real value of the S&P to a
level last seen in 1878. The nominal index was only down a little at the same time. At the time of the depression, there
was actually deflation, and the market remained long term flat with a slight downward trend and wild shot-term swings
until the start of WWII. At the end of the war, the market showed a marked reduction in volatility. The large swings in
value prevalent before the war were replaced by an unprecedented bull market with only minor short-term wiggles. This
general trend was interrupted once by the “stagflation” of the 70s and 80s, a 10 to 20 year interlude, and has been
interrupted again by the tech bubble boom and bust, after which the upward trend seems to be back where it would have
been had the tech bubble not occurred.
The next few slides will show what happened in some detail. The important thing to see here is that the market since
WWII is very different in character than the market was before that time.
2
GROSS DOMESTIC PRODUCT
The Gross Domestic Product from 1928 to 2005
GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov)
1000
1928 Dollars
100
GDP Real
GDP Nom.
10
1
0.1
1920
1940
1960
1980
2000
2020
Year
The gross domestic product (GDP) measures all the goods and services produced within the borders of the United States,
regardless of who produced them. It differs from the earlier gross national product (GNP) which measures all the goods
and services produced by United States citizens, regardless of where they were produced. Both are commonly used as
measures of the entire economic output of the country.
This chart shows only the period since 1928. The first thing that strikes the eye is that after about 1947, there is an almost
complete absence of large fluctuations compared to what was seen in the S&P graphs. Even the stagflation of the 70s and
80s shows up in the nominal GDP graph as only a gentle increase of the slope, followed by an equally gentle return to the
earlier slope seen in the 50s.
Looking at these two graphs might well make one wonder if it’s really true that the S&P efficiently reflects the “correct”
value of the 80% of the economy it represents. Let’s look a little closer.
3
S&P VS GDP SINCE 1928
S&P Index vs GDP from 1928 to 2005
S&P Data: Robert J. Shiller, Yale University (www.econ.yale.edu/~shiller/data.htm)
GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov)
1000
1928 Dollars
100
S&P Real
S&P Nom.
GDP / Real
GDP Nom.
Expon. (S&P Real)
Expon. (GDP / Real)
10
1
0.1
1920
1940
1960
1980
2000
2020
Year
It appears that since 1928, the best that can be said for the S&P is that it is approximately long-term efficient. From 1928
to 2005 the real S&P 500 best-fit exponential trend line earned 3.09% per year, while the trend line for the GDP gained
3.5% annually. The fluctuations away from the line for the S&P are so large that it is apparent that something completely
unrelated to the underlying value of the assets is affecting the market’s pricing. However, if you look very closely at the
fluctuations around the trend line for the real GDP, you will see very small fluctuations that are similar to the much larger
swings in the real S&P.
We have found the first rule of market inefficiency. The market can be counted on to over-react to both good and bad
events. Let’s check on this. We’ll use the stable post-war period.
4
S&P VS GDP SINCE 1950
S&P Index vs GDP from 1950 to 2005
S&P Data: Robert J. Shiller, Yale University (www.econ.yale.edu/~shiller/data.htm)
GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov)
1950 Dollars
100
S&P Real
S&P Nom.
GDP Real
GDP Nom.
Expon. (GDP Real)
Expon. (S&P Real)
10
1
1940
1960
1980
2000
2020
Year
The efficiency of the long-term market trend line is somewhat worse in this time period. The S&P has an geometric mean
yearly return of 2.70%% compared to 3.33% for the GDP. However, in this period there are times when the S&P vastly
outperforms and periods when it vastly underperforms the growth of the economy. There is no rational reason why this
should be true, but there are some compelling irrational ones.
Let’s look a little closer.
5
REAL S&P VS GDP RESIDUALS
S&P Residuals vs 10 X GDP Residuals from 1950 to 2005
S&P Data: Robert J. Shiller, Yale University (www.econ.yale.edu/~shiller/data.htm)
GDP Data: U.S. Bureau of Economic Analysis (www.bea.gov)
7
1950 Dollars
6
5
4
S&P
10 X GDP
Best Fit
GDP
3
2
1
0
-1
-2
-3
1940
1950
1960
1970
1980
1990
2000
2010
Year
This chart shows the departure of the real S&P 500 and the real GDP from a best exponential fit. Separate fits were done
for the two curves. For the S&P, the best fit was for an annual increase of 2.70%. For the GDP the annual best fit
increase was 3.33%.We want to just look at it and see if we can see any correlations between the two. For clarity, the
best fit zero line is plotted in heavy black and the GDP residuals are plotted twice, using yellow for the actual values, and
magenta to show ten times the actual values. This allows us to easily see the relative magnitude of the volatility of the
GDP (yellow) and that of the S&P (dark blue). The ten-times magnified GDP plot (magenta) allows us to easily see if
there are any correlations between the two that might be of predictive value.
The first thing we see is just how uniform the economic growth has been during the last 55 years. In that time the largest
deviation from the 3.33% annual increase was less than 26 cents in 1950 dollars. By comparison the S&P 500 at the peak
of the tech boom was nearly $6.00 off the best fit of 2.70% per year.
The stable increase in the S&P is no accident. The Federal Reserve began operations in 1914, but its original purpose was
to help the Treasury sell bonds at a high price, not to control the economy. However, the Fed was directed to control
employment, inflation, and economic growth in 1946 and again in 1978. The depression was a traumatic event, and it is
not clear we would have recovered from it even now if it had not been for the massive government spending required
during World War II. After the war, steps were taken to reduce or eliminate the possibility of another depression. So far
it has worked in this country. Economic growth is so stable because it is carefully controlled to be that way.
Please notice that the Fed is not required to control stock prices.
Given that economic growth is now deliberately controlled to remain at a sustainable rate, why does the stock market
fluctuate so much? Look at the chart and notice the almost one for one correspondence of the year-to-year variations.
The difference is that the S&P varies around ten times as much as the GDP. Over a period of about ten years, there are
fluctuations in both that are fairly large compared to the yearly changes. They are loosely correlated, but the only
consistent correlation is that the two move together. You might make an argument that in the 60s and 70s the S&P led the
market. This fits conventional wisdom, but since about 1982 the two have no obvious correlation or move together. I can
assure you that on the basis of this chart there is no reason to believe that there is any statistically significant predictive
ability shown. A much more significant fact is the similar movements of the two, and the fact that the S&P moves so
much more than the GDP.
6
INVESTING IN THE S&P 500
Real S&P Index Best Fit to Real GDP Trend from 1950 to 2005
S&P Data: Robert J. Shiller, Yale University (w ww.econ.yale.edu/~shiller/data.htm)
GDP Data: U.S. Bureau of Economic Analysis (www .bea.gov)
9
8
1950 Dollars
7
6
5
S&P Real
GDP Real Best Fit
4
3
2
1
0
1940
1960
1980
2000
2020
Year
Here’s a plot showing the best fit of the last 50 years of the S&P to the best fit trend line for the GDP. If we define the
GDP as “reality” then this plot shows the variations of the S&P that occur for irrational or at least unpredictable reasons.
If you like diversifying by buying S&P index funds, it would not be unreasonable in the modern era to simply learn to
trust the “invisible hand” of the market to be long-term efficient. It is easier to do this if you trust and love the Fed. If
you do, then you know that “efficient” means the 3.33% per year rate the economy is following so closely. Then buy
when the real value of a dollar invested in the S&P 500 in 1950 is a dollar or so below it’s value predicted by the annual
3.33% since 1950, there is good news, and the market is is at least 2 years into the recovery. If you trust the Fed to keep
the market drop from messing up the economy too much, there will be no particular reason for the market to be down
there, and it will eventually go up – but you will probably have to wait a decade or two. You might sell when it goes up
to the same amount above the trend line, there’s bad news, and the market has been dropping for a couple of years.
During bad times to hold stocks because the market is too high, keep most of your money in something like treasuries.
Modern Portfolio Theory provides a systematic way to do this.
Will this always work? There are probably other systems that will pay better, and this one assumes that in the future the
Fed will still be able to control economic growth almost perfectly, and that the market will still refuse to believe that the
Fed will be able to do it this time. It depends on the tendency of the market to exaggerate the economic impact of news,
and on the longer term tendency for the market to revert to reality. It also assumes the best definition of reality is what’s
been so reliable in the post- WWII era. Any of those things could change, but they probably won’t very soon, so it’s not
an unreasonable strategy.
By this line of reasoning, it appears that the market at the end of 2005 was a little below the economy’s trend line, but
probably not enough to be significant. Over the next decade or so it’s most likely to move along a bit above the 3.33%
trend line like it did in the late 50s and early 60s. Hopefully we learned something from the tech bubble and won’t do
anything that dumb until a new generation of money managers who have seen only prosperity appears. If we haven’t
learned, then the present recovery could end with a drop as everyone bails out of, say, energy stocks. That would be a
buying opportunity. I’d keep some money in treasuries if this were my only strategy.
7
MODERN PORTFOLIO THEORY
High Risk & Return vs. Low Risk & Return
$30
$25
Price
$20
High Risk
$15
Low Risk
$10
$5
97
91
85
79
73
67
61
55
49
43
37
31
25
19
13
7
1
$0
Trading Days
We won’t spend too much time on Modern Portfolio Theory. There are many books on how to use it,
and most investment advisors use it, especially for conservative, loss-intolerant customers. It’s
conservative because you can control your investment risk very easily by creating a portfolio of
appropriate amounts of both high-yield investments like stocks and low-risk or “risk-free” securities
like treasuries. Of course, the lower risk comes with lower returns. It’s important to do your
calculations with after-tax inflation-adjusted returns, because with this method your earnings will
sometimes be low enough that you can’t just assume that these things don’t matter.
The basic idea is simple enough. If your portfolio contains two assets, one very risky (stocks) but
with a high expected return and the other with low risk (treasuries) and a low expected return, then
you can choose from a range of possible risks and returns by varying the proportions of the two in
your portfolio. Obviously the highest expected return you can achieve is with all stocks, and the
lowest would be all treasuries. What is not obvious is that unless the variations in value of the two
are exactly correlated, the lowest risk does not happen with an all-treasuries portfolio. Because the
values of the two investments move differently, adding a small amount of stocks to a portfolio of
treasuries actually reduces the risk and increases the return at the same time.
This graph shows two securities, one very volatile, one not. They might be a stock fund and a bond
fund. Notice that the high risk security went up more than the low risk one, but that was clearly just
luck. During the 100 trading day holding period its price varied more than a factor of two, and if
there’s an overall upward trend it’s not clear. The low risk security varied a few percent at most, and
has a clear, if small, upward trend.
Can you improve your risk/return ratio with an appropriate mixture of the two?
8
RESULTS OF BLENDING
Low Risk vs. Blended Portfolio
$26
$26
$25
Price
$25
$24
Low Risk
$24
9% High Risk
$23
$23
$22
97
91
85
79
73
67
61
55
49
43
37
31
25
19
13
7
1
$22
Trading Days
If you didn’t peak at your handout, this may surprise you. The magenta line is the same low risk
security, plotted on a magnified scale so you can see the wiggles better. Notice that they are a mirror
image of the high risk security. The two are highly anti-correlated. A 91% low-risk and 9% highrisk portfolio reduces the risk to almost zero, and at the same time, increases the return to 5.07%, a
bit better than the 4.74% produced by the low-risk security alone.
This was manufactured data. The “securities” shown are a random walk superimposed on a linear
up-trend, which is about 24% in 100 trading days for the high risk security, and a little over 4% for
the low risk one. The point of using this method was to show how realistic a random-walk model of
stock prices can look, and to illustrate in principle how adding a little high-risk stock to a portfolio
can reduce risk. This reduction also happens if the two securities are merely uncorrelated, but it’s not
as easy to see.
Now let’s look at an example using real data. It was done done with the benefit of hindsight, but it is
sufficiently obvious that it was probably used by a few people at least.
9
REAL STOCKS
This is a portfolio for the aggressive investor. I held Continental during most of this period, but sold
it a while ago. I’ve never held Chevron. CAL was a lucrative investment, but as you can see,
volatile. I considered at the time balancing it with some petroleum stocks. They were also rising, but
not as fast. I didn’t do it, but was therefore running more risk that the high price of oil would hit the
stock price very hard. An investor in oil runs the same risk, but in the opposite direction. Then it
might be a good idea to add a little volatile CAL to reduce the risk of lower oil prices to a holding in
CVX. The next slide shows the results.
10
CAL & CVX
Portfolio 20% CAL & 80% CVX
3
2.5
Dollars
2
CVX
1.5
Porfolio
CAL
1
0.5
0
6
l- 0
Ju
26
-0
ay
-M
13
6
-0
ar
-M
24
06
bFe
25
-0
ec
-D
14
5
-0
ct
-O
25
05
pSe
55
-0
ul
-J
17
5
-0
ay
-M
28
5
r-0
Ap
8-
Date
These are real stocks. This shows CAL and CVX for the last year. (The previous chart showed two
years.) By adding 20% of a risky but anti-correlated stock (CAL) to a safer and less volatile holding
(CVX), volatility is reduced and return is nearly doubled. Similar but less dramatic results are
possible with stocks that are merely uncorrelated. This means that picking stocks whose price
movements are unrelated produces a reduction in volatility. It can be shown that with completely
uncorrelated random fluctuations, combining some number, N, of the stocks in equal dollar amounts
reduces the volatility of the portfolio by an amount equal to 1/√N. In general, a 4-stock portfolio will
have half the volatility of a single stock, a 16-stock portfolio a quarter the volatility of one stock, and
so on.
In the example shown in the chart, we have achieved a reduction of volatility of more than a factor of
two with only two stocks because their short-term price fluctuations are anti-correlated. The opposite
is true as well, combining stocks with strongly correlated price movements, typically those in the
same or similar industries, produces little or no reduction in volatility. Those who invested heavily
in internet stocks before the bubble burst learned that lesson.
11
BUY AND HOLD – GOOD?
Percent Return
Dow Industrials Per-Year Nominal Returns - Hold Ten Years
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
-10.00%
-20.00%
-30.00%
-40.00%
-50.00%
-60.00%
-70.00%
-80.00%
Year
People often say that buy and hold strategies work well. Here’s why. This chart shows what your
annual rate of return would be if you had bought the Dow Industrials in a given year, and sold after
ten years. Impressive isn’t it? You would have made money almost every time, often over ten
percent annually. Who could argue with that? It’s easy and it works! Are there any problems? See
the next slide.
12
BUY AND HOLD – BAD?
Dow Industrials Per-Year Real Returns - Hold Ten Years
30.00%
25.00%
20.00%
Percent Return
15.00%
10.00%
5.00%
0.00%
-5.00%
-10.00%
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
19
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
-15.00%
-20.00%
-25.00%
Year
When you look at real, inflation-adjusted returns, you lose money after ten years, sometimes a lot of
money, about a quarter to a third of the time. This is with the Dow, a selection of very high quality
and stable companies. Buy and hold is not so easy after all. You still have to watch your
investments because market conditions and company fortunes change. There have been decade-long
periods in the past when just holding on was not a very good strategy.
The stagflation period in the sixties and seventies is the most obvious example of the danger in buyand-hold. The market was flat then and inflation was high. So were interest rates. You would have
been much better off putting money in treasuries or bonds as recommended by Modern Portfolio
Theory.
13
SECTOR SPDRS
70
70
60
60
50
50
40
Cons. Disc.
Cons. Stap.
Energy
30
40
20
20
10
10
0
24-Jul-98
Fin.
Hlth. Care
Ind.
30
0
6-Dec-99
19-Apr-01
1-Sep-02
14-Jan-04
28-May-05
10-Oct-06
24-Jul-98
6-Dec-99
19-Apr-01
1-Sep-02
14-Jan-04
28-May-05
10-Oct-06
70
60
50
40
Mater.
Tech.
Util.
30
20
10
0
24-Jul-98
6-Dec-99
19-Apr-01
1-Sep-02
14-Jan-04
28-May-05
10-Oct-06
Sectors don’t necessarily move together, but most of the time they tend to cluster closer to each other
than individual stocks do. However, when sectors get hot, they can be very dangerous to be heavily
invested in. For example, energy looks like the tech boom did just before the bust. Yes, energy is up
for good reasons. Booms always have good reasons. Remember that the market always overreacts to
events. It’s a good idea not to have everything on the same bandwagon. In the last two years just
about every sector has been going up, particularly if you invest in well-run companies. Don’t put
everything into a hot sector. Diversify across sectors.
14
CAPITALIZATION
Russell Indices
900
800
700
600
500
Total Market
Large Cap
400
Small Cap
300
200
100
0
6
-0
ct
9
l -0
Ju
6-
-O
10
4
-0
an
-J
14
8
-9
ul
5
-9
ct
90
1
-0
pr
-A
19
-J
24
-O
28
ay
3
-9
an
-J
31
M
7-
Large caps (market cap above $5B) move with the market because they are the market. Small caps
(market cap below $1B) are different. They are more variable in business quality than large caps,
which means that it’s necessary to do more study before investing in a particular company.
However, a good small cap has room to grow, and makes an excellent investment. Small caps can
also be quite safe when portfolios are properly diversified. Mid caps (market cap between $1B and
$5B) can also grow and are generally more stable, but they are harder to find bargains in because
they are more closely watched. Micro caps (market cap below $500M) can be excellent investments,
but there is often little information available concerning them. If you invest in micro caps, expect to
work harder to find out about the company. It’s best to confine yourself to businesses you
understand, and this is particularly true with micro caps.
15
HOW TO CONTROL VOLATILITY
Buy good companies with established value as businesses.
Buy companies which are undervalued as businesses by
the market, but which are beginning to be noticed. The
combination of an undervalued company and an upward
price trend is a mark of a good investment
Don’t wait until a company is widely publicized. Do your
own screening and find the companies the experts will be
talking about next month.
Diversify both among companies and sectors. Try to find
growing companies that respond differently to market
forces.
Never buy a bad company just to diversify.
16
WHAT YOU ACTUALLY DO
Use a good stock screener like Stock Investor Pro to find
companies that meet your criteria. (Hard screening)
Check news, recent prices, and opinions of experts and
other investors. Decide if the company is as good as it
looks. Many won’t be.
Try to have at least ten, and preferably twenty companies
in your portfolio spread over at least five sectors. If
necessary buy fewer shares to accomplish this. If your
screens aren’t finding enough candidates, loosen your
criteria a bit. Would you really refuse to buy a company
because its price-to-earnings is 10.1 rather than 9.9? Stock
Investor Pro will refuse if you set the threshold at 10.
17
SOFT SCREENING
Screening with hard thresholds will cause companies
near a threshold to jump on and off your list as normal
variations in price, etc. occur. You can buy something
one month, sell the next, then buy the next, and so on.
This is not good for the investor, but brokers love it.
To avoid this, use less strict criteria for selling than for
buying. For example, you might buy with the P/E less
than 10, but hold the stock unless it goes above 12.
We have developed software that works with the Stock
Investor Pro database that doesn’t use thresholds at all.
Instead, it tells you which companies in the database
are closest to the companies that passed a screen. It’s
also a great help with diversification.
18
WHY NOT FOLLOW THE
EXPERTS?
After all, they know more than you do, don’t they?
They probably do, so don’t try to outsmart them. But
don’t take their advice either, at least not if you want
better than average results. Recommendations given on
TV are instantly known to millions of people, and have
probably already been acted on by thousands of
professionals, newsletter subscribers and such. It’s better
to do what they do and find the same companies before
they recommend them.
Do things they can’t do. In addition to any special
knowledge they may have about a particular business or a
local company, small investors have major advantages.
They are described on the next slide.
19
SMALL INVESTOR ADVANTAGES
Small investors can wait. In the normal course of business,
good companies go through periods of decreased earnings
in order to expand, or to meet changing business conditions.
Investment managers need to make money every quarter.
If you are able to tell the difference between normal
business fluctuations and a serious problem, you can buy
when earnings and the stock price are low. Then wait until
both recover.
Small investors can take advantage of excellent
opportunities that are too small to interest institutions. A
large fund has to invest in big companies because it has big
money to invest.
20
HOW WELL CAN YOU DO?
The portfolios shown on this slide are maintained by Anava Capital Management LLC as a pool of companies from which we can draw
when forming client portfolios. Client results can vary widely from these portfolios depending on their personal investment objectives.
Past performance does not guarantee future success, and does not protect against the possibility of loss.
Using the techniques described in this talk, we have been successful in outperforming the market
substantially over the past year. We believe that individual investors can do as well. However,
success in investing, like success in any other field, requires an understanding of the principles
involved, and diligence in applying them. There are no guarantees than stocks in general, or any
given portfolio, will produce profits for investors, or that losses, including loss of capital, will not
occur. There are no quick and easy ways to wealth, but it is quite reasonable to expect that individual
effort will produce better returns and greater safety than is available otherwise.
21