FINAL MICRO PAPER

Determinants of Stock Market Participation and Risky Asset
Allocation among Canadian Households
Greg Poapst
Carleton University
April 8th
2014

Determinants of Stock Market Participation and
Risky Asset Allocation among Canadian Households
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Motivation
As an avid financial economist student, I find it very interesting that some people will
invest in the by buying individual stocks, and some people will only invest in mutual funds,
bonds, real estate, or not at all. What kind of person invests a large amount of money in stocks?
Is it pure personality and risk aversion, or is it based on other variables? This study should be
able to further our understanding of individual investor behaviour and its role in stock market
participation.
Some of the possible effects of holding a large amount of stock in your portfolio could
be: prolonged working career, lower consumption level or spending, increased volatility in
wealth. The increased volatility in wealth could possibly lead to bankruptcy in an extreme
circumstance.
Literature Review
What kind of household invests a high portion of their investment portfolio/net worth in
stocks? Why would an individual allocate a large portion of their money in something so risky?
Behavioural financial economics and asset allocation studies have increased in popularity in the
past 15 years. The rise of behavioural financial economics as a discipline of study as well as new
micro data availability can help to explain the rising trend of interest. Economists have studied
the determinants of risk aversion, and portfolio allocation in many different ways. Within this
world of risk aversion, we see studies specifically from the financial side, behaviour theory, or
economics. In this paper, I will look at risk aversion and stock market participation from all three

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angles of attack. Using theoretical methods from the infamous ‘Microeconomic Analysis -
Varian 1992’ textbook and empirical analysis of equity holdings (Survey of Financial Security –
Canada 2005) I will explore the determinants of stock holdings in Canada.
There are many variables that may cause someone to hold more or less of their portfolio in
stock. A common “rule of thumb” in portfolio theory is the rule of 100. I first read about it in the
Intelligent Investor by Benjamin Graham. This rule of 100 states that if you subtract your age
from 100 that should be the proportion of stocks in your portfolio (given only the choice of
stocks and bonds), due to the assumption that you should be less risky as you get older. I
personally don’t think this rule captures an investor’s optimized level of risk, but it does leave us
with age as a possible cause of increased proportion of stocks to net worth.
One recent study of portfolio allocation is Faig Miquel and Shum Pauline, 2006, “What
Explains Household Stock Holdings?” Journal of Banking and Finance, 30, 2579-2597. Shum
and Faig explore the determinants of stock holdings using micro data from the United States, and
attempt to predict who holds stocks using several explanatory variables. They found that they
could predict who holds stocks with a good degree of confidence, but it was much harder to
predict how much equity the participants actually held. Using a rich source of information they
were able to capture a more representative sample of the US economy in contrast with the more
narrow focused samples of previous studies. This solidifies my hypothesis of micro data
availability increasing in recent years. One benefit of using the ‘Survey of Consumer Finances in
the United States’ was the wide range of explanatory variables available. The survey of financial
security in Canada 2005 has some similar variables to SCF – United States, but not all.
Hopefully the use of a different data set will give us some variety in this topic’s conclusions.

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Traditional risk aversion theory assumes that everyone has a different level of risk aversion,
which may or may not be quantifiable. Studies such as “Nature or Nurture: What Determines
Investor Behavior?” by Amir Barnea, Henrik Cronqvist, and Stephan Siegel, Journal of
Financial Economics, 2010 have attempted to explain whether this risk aversion is due to genetic
disposition or events in one’s life. Barnea, Cronqvist and Siegel use financial portfolio data of
twins in Sweden to determine how much of the variance in portfolio allocation can be explained
by genetics. They use cross-sectional data, and determine that about 30% risk aversion is due to
genetics (using portfolio allocation and variance in returns as a proxy for risk). This could
explain the difficulties Chum and Faiq faced when attempting to explain the variance in amount
of portfolios invested in equity. The unexplainable portion of stock holdings could be partially
attributed to genetic disposition.
The paper by Barber, Brad M. and Terrance Odean, 2001, “Boys Will be Boys: Gender,
Overconﬁdence, and Common Stock Investment,” reveals some interesting theories on gender
and the role it plays in the financial investment world. Barber and Odean use a large sample of
35,000 investors from a major brokerage firm to explain that in general males are more likely to
trade more frequently. They attribute this high level of trades made to overconfidence.
According to Barber and Odean, there have been many studies performed on gender and the role
it takes in overconfidence. The theory developed by [Lundeberg, Fox, and Punc´ochar´ 1994]
and Deaux and Farris [1977] is that men claim to be better at traditionally masculine tasks (Such
as financial responsibilities) and therefore hold a false sense of confidence in their own abilities.
This overconfidence in investing ability could explain why males invest a higher portion of their

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portfolio in risky assets as well. If men feel like their chances at making money in the stock
market are higher generally than women, they will indeed invest more.
The problem of portfolio allocation has been attacked from many angles. This paper will
look into determinants of stock market participation and risky asset allocation among Canadian
households, specifically how gender, age, education level, retirement, business ownership, and
home ownership status affects the portion of wealth invested in stocks and the effects this has.
Economic Model
Every consumer has a choice. They have a choice of whether to invest or consume their
accumulated wealth. Within this decision they have a secondary decision of where to allocate
their money. If the consumer wishes to spend all of their income, they have to decide what to
spend it on, just as the investor has to decide where to invest his/her money. My research deals
with the decision of investment allocation.
Within this world of investments we see a trade off between Risky assets and Low-
risk/Risk free assets. Realistically there are many other choices involved. A consumer could
invest in stocks, bonds, pension plans, mutual funds, other types of funds, housing. The list is
endless. The simple model I am proposing is between risky assets, and risk free. Equity is
inherently a more risky investment when compared to debt. Stock prices are historically more
volatile than high grade bonds, and when a company goes bankrupt they must pay out their debt
before equity holdings. Another alternative to individual stock holdings is mutual funds. In my

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analysis I only look at stocks in non-registered savings plans, excluding mutual funds. I exclude
registered savings plans due to a lack of variables available in my data set, and the TFSA
accounts not being available at the time of this survey. Essentially there is a barrier to entry in
the stock market if the investor values diversity. To be diversified among stock holdings the
investor would need a substantial amount of money, whereas if they want to invest a relatively
small amount in the stock market they may choose invest in a mutual fund in order to attain a
decent level of diversity. If the investor has substantial capital to invest they would be able to
diversify to some extent within the world of equity (Markowitz, 1959).
The objective of the individual investor is to maximize the utility they receive from their
investments. Utility in this perspective is a function of the expected returns of the investment,
and the level of risk aversion of the individual. I compare this risk aversion to a roller coaster.
There are people who love roller coasters and get a large amount of utility out of adrenaline
filled amusement park rides. There are also people who stay as far away from them as possible
for the fear manifested in them possibly from a previous experience. Where things get interesting
is in the grey area. I'm sure there are some people who are indifferent between these rides (Risk
neutral), and the slight push they need may be due to peer pressure or some other minor force of
utility (such as the right to claim that they have survived it).
There is a constraint to the investment criteria as well. In order to invest money you have
to have some amount of liquid wealth. I am assuming you cannot invest what you don't have, but
in fact you can. If someone were to trade on margin (borrow money in order to invest it), they
would be able to invest regardless of their wealth. This is generally seen as extremely high risk.
There is potential for great returns when trading on margin, but also great losses. The utility they

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would get from the investment’s expected returns would have to outweigh the loss of utility from
the increase in risk. If the investor is willing to trade on margin, they would value marginal
expected returns more than marginal risk measured using variance or standard deviation. I will
come back to this idea when modelling the trade off below.
[Figure 1]
There are three types of consumer utility functions illustrated. The first is the risk loving
investor. The risk loving investor's utility gained from the lottery is greater than the utility gained
from the expected value of the lottery (Varian : Microeconomic analysis 3rd
edition, 1992). This
risk loving investor would always prefer a higher risk investment, sometimes even one with a
lower expected return. The second consumer is a risk averse consumer. The risk averse
consumer's utility gained from the lottery is less than the utility gained from the expected value
of the lottery (Ibid). The risk averse investor would prefer an investment with low risk,
sometimes even if that means giving up expected returns. The risk neutral investor puts equal
weight on risk and expected return (Robert Jaeger : All about hedge funds, 2003).
Given a simplified example of a 50-50 chance investment, we can see that the concavity
of the 3 investors illustrates each level of aversion. Given the probability of success we can
explain two things; the utility gained from the lottery itself, and the utility of the expected value
of the lottery (Varian: Microeconomic Analysis 3rd
edition, 1992). The functions for these are:
0.5U(failure) + 0.5U(success) for the former (Point A for the Risk Averse investor), and

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U(0.5(failure) + 0.5(success)) for the latter( Point B for the Risk Averse investor) (Ibid). We see
that due to concavity, the risk averse investor's utility of the expected value is higher than the
utility from the risk/gamble (Ibid). The spread between the two measures of the risk averse
investor’s utility represents the amount of risk aversion of that investor. As you can probably
imagine, the curve of the risk aversion line will determine the spread at different levels of payoff.
Everyone has a different level of risk aversion, and the Canadian Survey of Financial Security I
will attempt to estimate this level based on explanatory variables and using stock holdings as a
proxy to risk aversion.
Using the underlying principles of this model, I believe it is possible to estimate an
individual’s risk aversion level by using attributes such as age, gender, housing finance, use of
credit cards etc. In addition to the predictability of risk aversion, we can also make a hypothesis
with regards to diversification. In order to properly diversify stock holdings without the use of
mutual funds (or similar instruments), the investor needs a fairly large amount of wealth to
invest. I predict that at the low levels of wealth, we should see a significant increase in stock
holdings due to a barrier to entry caused by investors not being able to fully diversify unless in a
mutual fund or other financial instrument. I plan on using the Survey of Financial Security
(Canada – 2005) to regress various attributes on stock holdings to show a general level of risk
aversion, and consequently determine how and why this deviates from what we theoretically
know is true. My prediction is that a higher age would lead to a more risk averse portfolio, which
may be linked to past traumatic experiences or large shocks to their portfolio in the past (“The
Long-Run Impact of Traumatic Experience on Risk Aversion” by Young-Il Kim and Jungmin
Lee, Sogang University, South Korea, working paper, July 2013 ). This prediction can be

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modelled as shown in Figure 2. Two individuals – In this case old and young – can have
different risk preferences which lead to different utility spreads (Point A and Point B), but with
the same probabilities and identical investments. This would force the older investor to shift their
holdings to a less risky investment in order to maximize utility.
[Figure 2]
As noted above, the standard investment advice is to avoid risk as you get older. The rule
of 100 is one of the ways this is explained to us. I personally believe that an investor’s optimal
level of risk depends on several factors. If the investor is in a position where losing money in the
stock market would significantly hurt their net worth, I feel it would be a bad decision. An
example of this would be a new graduate from a post-secondary institution with student debt.
Ignoring the fact that paying off debt would be a guaranteed risk-free investment (similar to
mortgage payments), a loss in the stock market would hurt this student significantly in the long
run. The conclusion is that age cannot be solely responsible for stock market participation.
Description of data set
The SFS was collected from May to July 2005 using a clustered approach. The sample
included anyone over 15 years of age in all 10 provinces, but excluded the territories. There were
two major sources within their 9000 household sample of collection data. The main source was

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7500 households, and collected using a “stratified, multi-stage sample selected from the Labour
Force Survey (LFS) sampling frame.” (Survey of Financial Security Study Documentation –
Canada 2005) Using the labour force survey’s cluster approach (2001 census data), they
surveyed in three stages of clusters. The first cluster comprised of geographical areas. After the
surveyors/statisticians determined a good sample area geographically they would pick several
representative clusters of households within this small geographical area. These households had
not previously participated in any similar surveys. The second source was specifically gathered
from areas with a large percentage of high-net-worth families, as defined by either over $200,000
annual family income, or $50,000 annual family investment income as to exclude the possibility
of households with low yearly income, and high level of investment income. The survey
excluded several niche areas of Canada, such as the territories, military personnel living on base,
and students on campus to avoid double counting.
Summary Statistics
Several issues consistently arise when coding variables from a survey. Many of the
variables used in my model are categorical, and therefore have to be recoded into binary or
dummy variables.
Age – continuous variable with no recoding required.
Gender – Gender of the respondent. Recoded to 0 if female, and 1 if male.

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Business Indicator – Recoded to 0 if no one in the household owns a business and 1 if
they do, dropping the 14 that answered “I do not know” to the survey question.
House ownership status – Recoded to 0 if does not own a house, 1 if own but mortgaged
and 2 if owned without a mortgage.
Household after tax income – Continuous variable but recoded to be reported as
ln(income) in order to reshape the data to be more normally distributed.
Has investment real estate – Continuous variable recoded to be a binary variable for
house ownership outside of principle residence.
Education – Dummy variable set up for highest education level achieved by the
respondent.
The
Summary of Regression Models 1 and 2:
Variable Observations Mean Std. Dev.
Minimum
Value
Maximum
Value
Holds Stocks 5204 0.1412375 0.3482999 0 1
Age 5204 50.10684 16.28642 17 80
Age^2 5204 2775.892 1702.911 289 6400
Has Retired Before 5204 0.2505765 0.4333867 0 1
Business Indicator 5204 0.2021522 0.4016437 0 1
House Ownership 5204 1.025173 .8045257 0 2
Highest Education level achieved 5204 2.623367 1.1042 1 4
After Tax Income (thousands) 5193 65.33769 89.74926 0.125 1386.275
Gender 5204 .6133743 .4870234 0 1

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Summary of Regression Models 3 and 4:
Regression Model
The first two preliminary regression models attempt to explain who holds stocks at all.
As seen in our summary statistics about 14% of our sample holds stocks in a non-registered fund.
My preliminary results show that age, retirement, business ownership, education and income are
positively correlated with stock ownership while gender and house ownership are negatively
correlated.
Variable Observations Mean Std. Dev.
Minimum
Value
Maximum
Value
Stock holdings/Net worth including
pension (As percent) 728 10.20797 13.93279 0.0035116 95.09782
Age 728 53.34341 14.02808 21 80
Age^2 728 3042.036 1521.76 441 6400
Has Retired Before 728 0.2582418 0.4379685 0 1
Business Indicator 728 0.3887363 0.4877984 0 1
House Ownership 728 1.482143 0.6421335 1 3
Highest Education level achieved 728 3.199176 0.9700604 1 4
After Tax Income (thousands) 725 136.1061 163.1 0.225 1386.275
Gender 728 0.7101648 0.4539976 0 1

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The problem with this model is that there are some variable defined at the household
level, and others asked about the major income earner of the household or specifically the
respondent, which would not give accurate results. Age and gender are defined as the age and
gender of the major income earner therefore do not give significant results. Given the lack of
variables defining RRSP holdings, and all other financial variables being defined at the
household level, I cannot use the gender or age variables to explain stock participation/holdings.
Similarly, the retirement and education variables are asked to the respondent only and therefore
cannot be used specifically either. I did end up using highest education level earned because if
the respondent was randomly selected from the households then we should see an effect of
households with higher education levels, even if it is not the major income earner that is
educated. These findings about gender, age, retirement and education are consistent with the 3rd
and 4th
preliminary models as well.
After determining that gender, age, retirement and education are individual and
respondent level variables I decided to revise my regression model. Without getting into some
really advanced econometrics and data manipulation it would be almost impossible to use these
variables correctly with the exception of education. I have left education as an explanatory
variable for 1 reason. If the respondent was randomly selected from the households then we
should see an effect of households with higher education levels, even if it is not the major
income earner that is educated. I attempt to explain the effects of gender and marital status using
several constrained regressions. I run the same regression several times, excluding parts of the
sample data. As seen in the final regression section below.

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The primary regression results for stock market participation show very good results. All
of the variables are statistically significant, and therefore contribute to the overall model. On
average if someone in the household owns a business that household is 7.89% more likely to
own stocks. The model also shows that if you mortgage a house you are 8.41% more likely to
own stocks than someone who does not own a house. If you own a house with no mortgage you
are 2.91% less likely to own stocks. These results are somewhat difficult to interpret. While it
makes sense that if you own a house you would own stocks, it does not make sense that you
would be less likely to own stocks if you have paid that mortgage off. One of the reasons for this
result could be that people who are purchasing more expensive homes are more likely to have a
mortgage, and therefore invest in stocks before paying off the mortgage due to higher returns as
seen in figure 4 below.
[Figure 4]
The results for education state that even though you are more likely to invest in stocks
with a high school diploma or college level diploma than someone who hasn’t graduated high
school, you are significantly more likely to invest in stocks with a university level certificate.
Even though this is a respondent level question, we still see a strong significant due to random
selection and a possible correlation of education levels within those households. This could be
due to the fact that educated people are more likely to be around peers and friends investing in
the stock market, and therefore feel the pull to invest (Amir Barnea, Henrik Cronqvist, and
Stephan Siegel, 2010, “Nature or Nurture: What Determines Investor Behaviour”). There is a

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positive relationship between ln of after tax income and stock market participation. This finding
solidifies our hypothesis that someone with more liquid wealth would be able to push past the
boundary of diversification in the stock market. Last but not least we see that if someone owns
real estate beyond a primary residence they are 5.24% more likely to own stocks. One possibility
to explain this would be that people who own real estate beyond their primary residence either
use it as an investment medium by renting, or have several vacation homes due to higher net
worth. Either way we would assume that there would be a positive correlation between the two.
The same regression model run only for single males shows less significant results, but
similar direction of coefficients, with the exception of owning a house without mortgage. This
may be due to single males owning houses with no mortgage at a similar cost to the houses with
a mortgage. Single females show similar results to the main regression model, but extremely low
significance (high P values) for business ownership and real estate investment ownership. This
may be due to a smaller sample base with fewer females owning businesses and multiple homes.
The couples vs. singles regressions don’t show much different in terms of coefficient direction
but do vary in magnitude. The significance of these values are in question, but not in the original
model which may lead us to believe that certain variables are more prominent among gender and
marital status.
In the second set of models we attempt to explain how much stock someone holds as a
proportion to their net worth. In the preliminary regressions I included housing and pension
values in our net worth observation. In the final regressions I exclude pension plan values, and
for the second model the primary residence value in order to isolate the value of assets usable for
stock investment only.

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The first model shows significant results for house ownership, university level education,
ownership of multiple houses (vacation homes or rental units) and income. This leaves business
ownership and high school/non-university certificate education levels statistically insignificant.
The results show that on average if you own a house with or without a mortgage you own 13%
and 19% respectively less stock as a proportion to net worth. While this is quite a surprising
result, I believe it may be due to a trade-off. Some people might choose to invest more or extend
their primary residence purchase to encompass some kind of investment over time and therefore
lose the liquidity needed to overcome the capital requirements of stock investing. People with a
mortgaged residence have a significantly higher after tax household income than those without a
mortgage as seen in figure 5 below. This may lead us to believe that someone who pays off their
mortgage might be highly risk averse, and prefer the money as cash in the bank after paying off
the mortgage.
[Figure 5]
Despite insignificant results from lower education levels, we observe that someone who
holds a university degree holds 4.291% more stock as a proportion to net worth on average. This
solidifies the theories defined and discussed throughout the paper. A less significant result, but
still somewhat significant would be the after tax income variable. Generally we see that as
income increases so does stock ownership. This result is also discussed throughout the research
above.
The second model shows significant differences in explanatory variables given the

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exclusion of house value in the dependent variable calculation. With the exclusion of primary
residence value we see that business ownership is now significant and income is not. On average
households with business owners own 3.4% less stock than those without. Multiple real estate
ownership and university level education have a similar but stronger effect on stock allocation.
Primary residence ownership has a diminished effect on stock ownership as a proportion of net
worth.
Overall our model confirms one of the main findings in Faig Miquel and Shum
Pauline, 2006, “What Explains Household Stock Holdings?” Journal of Banking and Finance,
30, 2579-2597. It is relatively easy to determine who holds stock, but difficult to determine how
much stock those people hold. The explanatory level of stock market participation drastically
overshadows the level found in portfolio allocation. At the personal and categorical level, it is
very difficult to develop significant findings.

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Preliminary Regression Results
Determinants of stock market participation
Dependent Variable: Has stocks
Data Source: 2005 Financial Security Survey, Canada
Regression Technique: Simple Ordinary Least Squares
Model 1 - Linear Model 2 – Age^2
Variable P Vals P Vals
Age 0.000507 0.23 0.00127 0.492
-1.2 -0.69
Age^2 -0.00000786 0.672
(-0.42)
Has retired before 0.0116 0.443 0.0143 0.384
-0.77 -0.87
Owns a business (incorporated or not) 0.0814 0 0.0812 0
-6.88 -6.86
Owns a house (Mortgaged) -0.0957 0 -0.0961 0
(-8.08) (-8.09)
Owns a house (No Mortgage) -0.108 0 -0.107 0
(-8.47) (-8.36)
Graduated High School 0.0671 0 0.0669 0
-4.85 -4.83
Non-University post secondary certificate 0.0643 0 0.0637 0
-4.68 -4.62
University Certificate 0.147 0 0.146 0
-10.38 -10.32
After Tax Income (reported in thousands) 0.000832 0 0.000831 0
-14.94 -14.91
Male -0.014 0.136 -0.0139 0.14
(-1.49) (-1.48)
Constant Term 0.052 0.074 0.0353 0.47
-1.79 -0.72
R^2 0.1526 0.1526
Adjusted R^2 0.1509 0.1508
N 5193 5193
T-Statistics reported below coefficients.
Base Cases:
Has retired before Has retired
Business Indicator Doesn’t own a company
Housing Ownership Doesn't own a house
Highest Education Level No High School

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Determinants of equity holdings as a proportion of net worth among Canadian households
Dependent Variable: Stocks/Net Worth including pension plan (in percent)
Model 3 - Linear Model 4 – Age^2
Variable P Vals P Vals
Age 0.0301 0.545 -0.955 0
-0.61 (-3.87)
Age^2 0.00949 0
-4.07
Has retired before 3.606 0.019 1.558 0.332
-2.34 -0.97
Owns a business (incorporated or not) 0.372 0.725 0.694 0.507
-0.35 -0.66
Owns a house (Mortgaged) -1.588 0.184 -2.108 0.077
(-1.33) (-1.77)
Owns a house (No Mortgage) 14.71 0 12.92 0
-7.62 -6.6
Graduated High School 1.109 0.622 1.037 0.641
-0.49 -0.47
Non-University post secondary certificate -0.133 0.953 0.25 0.91
(-0.06) -0.11
University Certificate 4.116 0.053 4.215 0.045
-1.94 -2
After Tax Income (reported in thousands) 0.00675 0.04 0.00693 0.033
-2.06 -2.14
Gender -2.005 0.068 -1.73 0.112
(-1.83) (-1.59)
Constant Term 4.971 0.144 29.03 0
-1.46 -4.27
R^2 0.131 0.151
Adjusted R^2 0.1192 0.138
N 725 725
Base Cases:
Has retired before Has retired
Highest Education Level No high school

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Final Regression Results
allrespondentssinglemalesinglefemalecouplesingle
VariablePValuesPValuesPValuesPValuesPValues
Ownsabusiness(incorporatedornot)0.078900.04050.1460.008090.7880.085200.03660.065
-6.72-1.46-0.27-5.73-1.85
Ownsahouse(Mortgaged)0.084100.0340.1540.087600.11400.06680
-6.82-1.43-5.11-5.63-4.74
Ownsahouse(NoMortgage)-0.02910.020.02650.3-0.03920.047-0.009710.624-0.01020.517
(-2.33)-1.04(-1.99)(-0.49)(-0.65)
GraduatedHighSchool0.050300.04960.0530.07700.03260.1260.06430
-3.71-1.94-4.08-1.53-4.18
Non-Universitypostsecondarycertificate0.04090.0020.04280.1030.04270.0280.03660.0740.04190.008
-3.05-1.63-2.2-1.79-2.65
UniversityCertificate0.12700.17800.078800.10900.1190
-9.08-6.12-3.73-5.2-6.85
lnofaftertaxhouseholdincome0.079100.037700.053900.12200.04560
-13.63-3.77-5.43-12.25-6.51
Ownsrealestateotherthanprimaryresidence0.052400.08980.0020.02080.3440.04790.0020.04830.007
-4.49-3.09-0.95-3.14-2.72
ConstantTerm-0.8070-0.3810-0.540-1.2960-0.4580
(-14.14)(-3.89)(-5.65)(-12.69)(-6.75)
r20.150.1130.0930.1590.089
AdjustedRSquared0.14890.10530.08730.15710.0852
N5193880121031032090
T-Statisticsreportedbelowcoefficients.
BaseCases:
BusinessIndicatorDoesn’townacompany
HousingOwnershipDoesn'townahouse
HighestEducationLevelNoHighSchool
Determinantsofstockmarketparticipation
DependentVariable:Hasstocks
DataSource:2005SurveyofFinancialSecurity,Canada
RegressionTechnique:SimpleOrdinaryLeastSquares

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Determinants of equity holdings as a proportion of net worth among Canadian households
Dependent Variable: Stocks/Net Worth excluding pension plan (in percent)
Dependent Variable Includes house value Excludes house value
Variable
P
Values
P
Values
Owns a business (Incorporated or not) -0.842 0.425 -3.434 0.013
(-0.80) (-2.49)
Owns a house (Mortgaged) -12.93 0 -6.175 0.015
(-6.68) (-2.44)
Owns a house (No Mortgage) -19.23 0 -12.28 0
(-9.65) (-4.72)
Graduated High School 1.68 0.458 2.693 0.362
-0.74 -0.91
Non-University post secondary certificate -0.119 0.958 0.468 0.873
(-0.05) -0.16
University Certificate 4.291 0.046 6.011 0.032
-2 -2.15
ln After Tax Income 0.907 0.141 0.776 0.335
-1.47 -0.96
Owns other real estate -2.81 0.008 -5.73 0
(-2.68) (-4.19)
Constant Term 12.92 0.05 14.06 0.103
-1.96 -1.63
R^2 0.15 0.089
Adjusted R^2 0.1406 0.0789
N 725 725
Base Cases:
Highest Education Level No high school

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22 | P a g e
References
Alison Booth and Patrick Nolen, 2012, “Gender Differences in Risk Behaviour: Does
Nurture Matter,” The Economic Journal
Amir Barnea, Henrik Cronqvist, and Stephan Siegel, 2010, “Nature or Nurture: What
Determines Investor Behaviour,” Journal of Financial Economics
Barber, Brad M. and Terrance Odean, 2001, “Boys Will be Boys: Gender,
Overconﬁdence, and Common Stock Investment,” Quarterly Economic Review, 116, 261-292
Deaux, Kay, and Elizabeth Farris, 1977, “Attributing Causes for One’s Own
Performance: The Effects of Sex, Norms, and Outcome,” Journal of Research in Personality XI,
59–72
Benjamin Graham “The Intelligent Investor”
Faig Miquel and Shum Pauline, 2006, “What Explains Household Stock Holdings?”
Journal of Banking and Finance, 30, 2579-2597.
Lundeberg, Mary A., Paul W. Fox, Judith Punccohar, 1994, “Highly Confident but
Wrong: Gender Differences and Similarities in Confidence Judgements,” Journal of Educational
Psychology, 114-121
Markowitz H., 1959, “Portfolio Selection: Efficient Diversification of Investments”
Robert Jaeger, 2003, “All about hedge funds”

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23 | P a g e
Young-Il Kim and Jungmin Lee, 2013, “The Long-Run Impact of Traumatic Experience
on Risk Aversion,” University of South Korea working paper
Varian, 1992 “Microeconomic analysis,” 3rd
edition

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Figure 1
(Modified from Varian: Microeconomic Analysis, 1992 – Chapter 11)

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Figure 2
(Modified from Varian: Microeconomic Analysis, 1992 – Chapter 11)

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Figure 3
Model 1:
has_stocks = αt + β1age + β2hasretired + β3busi_ind + β4house_ownership + β5education
+ β6incAT_thousands + β7gender + e
Model 2:
has_stocks = αt + β1age + β2age²+ β3hasretired + β4busi_ind + β5house_ownership +
β6education + β7incAT_thousands + β8gender + e
Model 3:
stocksratio_percent = αt + β1age + β2hasretired + β3busi_ind + β4house_ownership +
β5education + β6incAT_thousands + β7gender + e
Model 4:
stocksratio_percent = αt + β1age + β2age²+ β3hasretired + β4busi_ind +
β5house_ownership + β6education + β7incAT_thousands + β8gender + e

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27 | P a g e
Figure 4
Figure 5
2 76997.81 1877.085 73317.93 80677.69
1 87058.22 2847.413 81476.1 92640.35
0 28244.64 516.3453 27232.39 29256.9
inc_aftertax
Over Mean Std. Err. [95% Conf. Interval]
2: house_ownership = 2
Mean estimation Number of obs = 5193
2 109408 5841.58 97939.59 120876.5
1 162698.4 9360.093 144322.3 181074.6
0 46380.51 4818.843 36919.93 55841.08
inc_aftertax
Over Mean Std. Err. [95% Conf. Interval]
Mean estimation Number of obs = 725

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DO FILE
destring, replace
tab rtretire
tab RTRETIRE
generate hasretired = RTRETIRE
replace hasretired = 0 if (hasretired = 9) & (hasretired = 7) & (hasretired = 0)
tab RTRETIRE
tab hasretired
replace hasretired = 0 if hasretired==9 & hasretired==7 & hasretired==0
tab hasretired
recode hasretired 2 9 = 0
tab hasretired
tab ECPAGE
generate has_stocks = WASTSTCK
sum WASTSTCK
tab WASTSTCK

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jreplace has_stocks = 1if has_stocks>5
replace has_stocks = 1if has_stocks>5
replace has_stocks = 1 if has_stocks>5
tab has_stocks
reg has_stocks hasretired
twoway (scatter has_stocks hasretired)
reg WASTSTCK hasretired
generate business_ind = BUSIND
replace business_ind = 0 if business_ind = 2
tab has_stocks
recode business_ind 2 = 0
tab business_ind
NEW DAY
use "C:UsersGregDocumentsMicro ThesisRECODEDMARCH.dta"

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tab ECPAGE
gen age = ECPAGE
sum age
gen age_squared = age^2
sum age_squared
tab ATTSPD
gen stocks_networth = WASTSTCK/WNETWPT
tab stocks_networth
gen stocks_networth_new = stocks_networth if stocks_networth < 1.01
gen invsine_stocksratio = log(stocks_networth_new + sqrt(stocks_networth_new ^
stocks_networth_new + 1))
hist invsine_stocksratio
gen no_stocks = WASTSTCK
sum no_stocks
tab has_stocks
drop no_stocks
logit has_stocks age age_squared hasretired

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31 | P a g e
mfx
logit has_stocks age
mfx
reg has_stocks age
twoway (scatter has_stocks age)
reg has_stocks age age_squared hasretired
gen stock_amount = WASTSTCK if WASTSTCK>0
tab WASTSTCK
tab stock_amount
reg stock_amount age age_squared
reg stock_amount age hasretired
twoway (scatter stock_amount age)
twoway (scatter stock_amount hasretired)
tab hasretired
drop if hasretired > 6
gen busi_ind = BUSIND

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drop if busi_ind > 6
tab busi_ind
recode busi_ind 2 = 0
tab busi_ind
kdensity stock_amount
hist stock_amount
gen stock_amount_netw = stock_amount/WNETWPG
tab stock_amount_netw
drop if stock_amount_netw > 1
tab stock_amount_netw
reg stock_amount_netw age hasretired business_ind
vce
drop invsine_stocksratio
drop if stock_amount_netw <0
gen invsine_stocksratio = log(stock_amount_netw + sqrt(stock_amount_netw ^
stock_amount_netw + 1))
hist invsine_stocksratio

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33 | P a g e
hist stock_amount_netw
reg stock_amount_netw
reg invsine_stocksratio age
twoway (scatter invsine_stocksratio age)
twoway (scatter invsine_stocksratio age)
save "C:UsersGregDocumentsMicro ThesisRECODEDMARCH.dta", replace
gen stocksratio_percent = stock_amount_netw * 100
reg stocksratio_percent age hasretired
reg stocksratio_percent age hasretired
reg stocksratio_percent age hasretired age_squared
xi: regress stocksratio_percent age hasretired i.busi_ind
gen house_ownership = DVFTENUR
tab DVFTENUR
xi: regress stocksratio_percent age hasretired busi_ind i.house_ownership
tab house_ownership
xi: regress stocksratio_percent age hasretired busi_ind i.house_ownership i.DVPHLV2G

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34 | P a g e
vce
tab ATINC27
gen inc_aftertax = ATINC27 if ATINC27 >0
tab inc_aftertax
inc_aftertax
gen incAT_thousands = inc_aftertax/1000
incAT_thousands
gen education = DVPHLV2G
replace education = DVPHLV2G if DVPHLV2G < 6
tab education
drop if education > 6
tab busi_ind
xi: regress stocksratio_percent age i.hasretired i.busi_ind i.house_ownership i.education
incAT_thousands
estimates store model1

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35 | P a g e
xi: regress stocksratio_percent age age_squared i.hasretired i.busi_ind i.house_ownership
i.education incAT_thousands
incAT_thousands
ssc install estout
estout * using exceltrial1.csv, cells(b(fmt(a3)) t(fmt(2) par)) stats(r2 N, fmt(3 0))
sum stocksratio_percent
tabstat stocksratio_percent HCSEX_R age hasretired house_ownership busi_ind education
incAT_thousands
xi: regress has_stocks age age_squared i.hasretired i.busi_ind i.house_ownership i.education
incAT_thousands
tab has_stocks
twoway (scatter incAT_thousands stocksratio_percent)
sum stocksratio_percent age age_squared hasretired busi_ind house_ownership education
incAT_thousands
gen gender = HCSEX_R

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tab gender
recode gender 2 = 0
sum gender
xi: regress stocksratio_percent age i.hasretired i.busi_ind i.house_ownership i.education
incAT_thousands gender
i.education incAT_thousands gender
estout * using excelgenderadd.csv, cells(b(fmt(a3)) t(fmt(2) par)) stats(r2 N, fmt(3 0))
NEW DAY
use "C:UsersGregDocumentsMicro ThesisRECODEDMARCH.dta", clear

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37 | P a g e
sum WNETWPG
sum WNETWPT
gen stocksassets = WASTSTCK/WATOTPG
gen assetsexclpen = WATOTPG - WARPPG
gen stocksassets_exclpen = WASTSTCK/assetsexclpen
gen newdep = stocksassets_exclpen * 100
tab newdep
xi: regress newdep age age_squared i.hasretired i.busi_ind i.house_ownership i.education
vif
xi: regress newdep age_squared i.hasretired i.busi_ind i.house_ownership i.education
vif
findit excel
help outreg2
outreg2

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38 | P a g e
outreg2 using ols_finance, excel
seeout using "ols_finance.txt"
outreg2 using ols_finance, ci
outreg2 using ols_finance, ci
outreg2 using ols_finance, pval
outreg2 using ols_finance, pval
gen yearsuntilretire = RTPLNAGE - ECPAGE
sum yearsuntilretire
drop yearsuntilretire
gen yearsuntilretire = RTPLNAGE - ECPAGE if RTPLNAGE < 95
sum yearsuntilretire
xi: regress stocksratio_percent age_squared i.hasretired i.busi_ind i.house_ownership i.education

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39 | P a g e
outreg2 using ols_summary, excel pval
seeout using "ols_summary.txt"
seeout using "ols_summary.txt"
gen budget = ATTBUD
gen budget = ATTBUD
recode budget 2=0
sum budget
tab budget
xi: regress stocksratio_percent age_squared i.hasretired i.busi_ind i.house_ownership i.education
i.budget incAT_thousands gender
xi: regress newdep age_squared i.hasretired i.busi_ind i.house_ownership i.education i.budget

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40 | P a g e
i.education incAT_thousands gender
i.education i.budget incAT_thousands gender
i.education i.budget incAT_thousands gender
i.budget incAT_thousands gender
vif
twoway (scatter newdep age)

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41 | P a g e
twoway (scatter newdep age) (scatter stocksratio_percent age)
twoway (scatter newdep age_squared) (scatter stocksratio_percent age_squared)
twoway (scatter age age_squared)
xi: regress newdep age age_squared i.hasretired i.house_ownership i.education i.budget
xi: regress newdep age age_squared i.hasretired i.house_ownership i.education incAT_thousands
gender
gen postsecond = education
recode postsecond 3=0 2=0 1=0
tab postsecond
recode postsecond 4=1
xi: regress newdep age age_squared i.hasretired i.house_ownership i.postsecond
regress newdep age age_squared
regress newdep age

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42 | P a g e
regress newdep age
outreg2 using age, excel pval
outreg2 using age, excel pval
NEW DAY
xi: regress newdep age_squared i.hasretired i.busi_ind i.house_ownership i.education i.budget
gen stocksassets = WASTSTCK/WATOTPG
gen assetsexclpen = WATOTPG - WARPPG
gen stocksassets_exclpen = WASTSTCK/assetsexclpen
gen newdep = stocksassets_exclpen * 100
tab newdep

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43 | P a g e
reg newdep age age_squared
incAT_thousands gender, if gender==0
help reg
gen single = DVFMCOMP
gen couple = DVFMCOMP
replace couple 1 4 5 = 0
replace couple 1 = 0
recode couple 1 4 5 = 0
recode couple 2 3 = 0
recode single 2 3 = 0
recode single 1 4 5 = 1
incAT_thousands gender if single==1

2014
44 | P a g e
xi: regress newdep age squared i.hasretired i.busi_ind i.house_ownership i.education
xi: regress newdep age age_squared i.busi_ind i.house_ownership incAT_thousands gender if
single==0
incAT_thousands gender if single==1 & gender = 1
incAT_thousands gender if single==1 & gender ==1
incAT_thousands if single==1 & gender ==1
incAT_thousands if single==1 & gender ==0
xi: regress newdep age age_squared i.hasretired i.house_ownership i.education incAT_thousands
if single==1 & gender ==0
sum WARRSPL

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45 | P a g e
histogram WARRSPL
histogram incAT_thousands
histogram ln(inc_aftertax)
gen lnincomeat ln(inc_aftertax)
gen lnincome_at = ln(inc_aftertax)
histogram lnincome_at
lnincome_at gender
findit outreg2
save "G:MICRO FINALRECODEDMARCH.dta", replace
Different File
use "G:MICRO FINALhas_stocks.dta", clear
sum gender
recode gender 2= 0
sum gender

2014
46 | P a g e
sum house_ownership
recode house_ownership 3=0
tab house_ownership
sum house_ownership
gen lnincome = ln(inc_aftertax)
tab education
lnincome gender
gen has_investrealestate = WASTREST
replace has_investrealestate = 1 if has_investrealestate > 0
tab has_investrealestate
lnincome has_investrealestate gender
sum has_stocks
gen has_stockspercent = has_stocks*100
xi: regress has_stockspercent age age_squared i.hasretired i.busi_ind i.house_ownership
i.education lnincome has_investrealestate gender

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47 | P a g e
xi: regress has_stockspercent i.busi_ind i.house_ownership i.education lnincome
has_investrealestate
gen single = DVFMCOMP
recode single 2 3 = 0
recode single 1 4 5 = 1
has_investrealestate if single==1 & gender==1
has_investrealestate if single==1 & gender==0
has_investrealestate
ssc install estout
xi: regress has_stocks i.busi_ind i.house_ownership i.education lnincome has_investrealestate

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48 | P a g e
xi: regress has_stocks i.busi_ind i.house_ownership i.education lnincome has_investrealestate if
single==1 & gender==1
single==1 & gender==0
single==0
estout * using regressionfinalresults.csv, cells(b(fmt(a3)) t(fmt(2) par)) stats(r2 N, fmt(3 0))
single==1
estout * using regressionfinalresults2.csv, cells(b(fmt(a3)) t(fmt(2) par)) stats(r2 N, fmt(3 0))
NEXT FILE (STOCK ALLOCATION)
xi: regress newdep i.busi_ind i.house_ownership i.education lnincome_at
gen has_investrealestate = WASTREST

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49 | P a g e
replace has_investrealestate = 1 if has_investrealestate > 0
xi: regress newdep i.busi_ind i.house_ownership i.education lnincome_at has_investrealestate
xi: regress newdep i.busi_ind i.house_ownershiplnincome_at has_investrealestate
xi: regress newdep i.busi_ind i.house_ownership lnincome_at has_investrealestate
tab house_ownership
sum house_ownership
recode house_ownership 3=0
tab gender
xi: regress newdep i.busi_ind i.house_ownership lnincome_at has_investrealestate
gen denominator = assetsexclpen - WAPRVAL
gen newdep2 = (WASTSTCK/denominator)*100
xi: regress newdep2 i.busi_ind i.house_ownership lnincome_at has_investrealestate
xi: regress newdep2 i.busi_ind i.house_ownership i.education lnincome_at has_investrealestate
BACK TO STOCK PARTICIPATION
use "G:MICRO UPLOAD FINAL DRAFThas_stocks.dta", clear

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50 | P a g e
mean inc_aftertax, over(house_ownership)
tab house_ownership
recode house_ownership 3= 0
mean inc_aftertax, over(house_ownership)
BACK TO STOCK ALLOCATION
Computer crashed and lost the last bit of do file. (maybe 20 lines)

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