The Effect of Black Swan Events on the Asset-Based Market
Jessica Hill
Econ 421. Intermediate Microeconomics
Wednesdays 4pm-5pm
Dr. Bedane
November 12, 2019
Abstract:
A Black Swan event is an interesting phenomenon in the
financial/finance industry because it describes an improbable event
that most people would not consider feasible until it occurs. By
studying the impact of these Black Swan events on the asset-based
market it is possible to observe how an improbable event can impact
the asset-based market and threaten investments. Through studying
the impact of three specific Black Swan events (9/11, Hurricane
Katrina and the 2008 Financial Crisis) on the asset-based market it
will inform investors of the ramifications of future Black Swan
events. By creating a time series regression using data using
variables such as S&P 500 Returns, 5 Year Treasury Bond Yields,
Retirement Pension Interest Rates, and GDP spanning from 2000 till
2016 it is possible to obtain descriptive statistics on each event
and compare the statistical significance of each variable to
calculate the impact of Black Swan events. The statistics
demonstrated that different Black Swan events impacted the
asset-based market in a variety of ways, with financially based
Black Swan events having the most negative effect and natural
disaster events having the least impact on the asset-based market.
By understanding Black Swan events and their impact, investors can
generalize how improbable events in the future will impact the
assets they select today.
Table of Contents:
Revised Introduction 2
Literature Review on Black Swans and the Asset-Based Market
3
Methodology 8
Descriptive Statistics 12
Test and Research 16
Appendix 22
Data Sources 31
Works Cited 32
1. Introduction:
The topic of the effect of Black Swan events on the asset-based
market is important because it helps researchers to understand the
impact of certain events in the past and prepare for the
ramifications from future Black Swan events. Black Swan events have
three properties according to the literature:
1. Before they occur, the general population does not imagine
them as a possibility.
2. They have a significant impact, where one event can change
the average.
3. After it occurs, even though it wasn’t predicable, humans try
and concoct explanations for it after the fact (Avenn, 2015).
There are multiple examples of Black Swan events in recent
history that I will be analyzing in this paper; the 9/11 terrorist
attack, Hurricane Katrina and the 2008 Great Recession. These
events will be analyzed in the context of the asset-based market
using data on the S&P 500, bond yields, GDP, fed funds rate and
interest rates. By educating the general public on the existence of
Black Swan events, it will be possible to recognize when Black Swan
events are in progress and the ramifications of these events on the
asset-based market, specifically the stock market and the bond
market. This understanding can be used to build a stronger
portfolio in regard to risk and reduce anxiety regarding
investments.
What is the objective of this paper? Clearly state the main
purpose of the research? What is the research question? Hypothesis
? A summary of your methodology, data and main findings is required
in the introduction section of the paper.
2. Literature Review:
Within this literature review the idea of a Black Swan event
will be discussed and broken down into digestible material.
Additionally, there will be observations of the impact of Black
Swan events on the asset-based market from the 2008 Financial
Crisis to 9/11. The goal is to discover a gap in the literature and
discover an article as a possible reference for the capstone
research paper.
First, in order to discuss Black Swan events, it is important to
recognize the importance of Nassim Taleb’s book had on the topic. A
summary of said book was as able to distill some key takeaways from
the book into a short article (Runde 2009). First, Black Swan
events are distinguished by three key identifiers as mentioned in
the introduction. To summarize, a Black Swan event must be a
surprise, have an impact and was not predictable.
Continuing, the article describes how perspective impacts how
Black Swan events develop and are categorized. Because Black Swan
events are so subjective to perspective, it might be difficult to
attempt to make a generalized categorization of what defines a
Black Swan event to make a timeline. One solution to this is to
break down the Black Swan events into different types it made them
seem more logical and easier to explain to the average person (Aven
2015).
The conditions required for a Black Swan event to develop are
relatively feasible. The minimal conditions for a Black Swan event
to form are when “Events occur, human observers exist, some events
are objects of experience for a subset of these observers, a subset
of events experienced by human observers have properties 1, 2, and
3.” (Runde 2009). This helps to balance out the difficulty of
perspective creating bias and disturbing the attempt of researchers
to be objective. Throughout my study it is important to me that I
respect Talabs interpretation of trying to identify Black Swan
events, but also understanding the importance of risk management
and offering different theories that might be used to alleviate the
risk (Aven 2015).
Furthermore, there are different subcategorizations of Black
Swan events found in the different articles. One author introduced
the idea of a ‘Black Turkey’ (Siegel 2010). This is described as an
event “that is everywhere in the data-it happens all the time-but
to which one is willfully blind” (Siegel 2010). This is an
interesting interpretation of the 2008 crisis that fits with the
ideals of a Black Swan event while following more closely with the
current literature existing on the topic. Another subcategory is
the Grey Swan events, which are Black Swan events where observers
had the opportunity to observe that there was an event coming in
the data but did not do so (Runde 2009). One example of a Grey Swan
event is Brexit. One author determined that even though Brexit was
not the typical black swan due to the amount of time that investors
had to analyze the referendum, within their case study they
observed that it still caused a shock to the market (Agyare
2016).
Having just discussed the 2008 Financial Crisis, it is important
to discuss the findings of the author, Siegel, who described the
different economic tools used that failed to predict the 2008
Financial Crisis and argued that economists do not need to
apologize for failing to predict the event (2010). It explained
history of the use of the efficient market hypothesis to explain
financial crises and how it failed due to complication of the
financial sector. Secondly, one economist had argued that there was
a belief that free market prevented such a crash; which was
rebutted by multiple economists backed with historical data. In the
end, economics is a science that makes sense of data, not an exact
science and economists don’t have to apologize for the 2008
recession was the article’s conclusion. In an additional paper, the
authors used a method to understand the effects of the 2008
financial crisis Black Swan event by examining the Term Auction
Facility (TAF) which is for banks to borrow from the Federal
Reserve quickly (Taylor 2009). This is an example of a possible
variable and influenced me to add the Federal Funds Rate (the rate
used by the Federal Reserve for interbank loans) as a variable in
my study to control for government policy.
Another interesting angle to analyze the 2008 financial crisis
is through the study of stressed financial markets. In order to
observe contagion, one article described how it needed contagion
they need to identify “an event window for the distress event”
which could be used as an interpretation of a Black Swan event
(Longstaff 2010). From here, the author used vector autoregression
(VAR) to determine if there was a relation between asset backed COD
returns and returns in other financial markets separately during
three different sample periods. This is a great example of how to
compare multiple financial markets across different sample periods,
which will be useful for studying the impact of Black Swan events.
It also found that during the 2008 financial crisis, there was a
stronger response to negative shocks in short term bond prices
compared to long term bond prices. In order to eliminate the cross
data, I will use the data on curve bond yield from the Federal
Reserve to reduce confusion between results from long-term and
short-term bonds.
An additional Black Swan event with a significant impact on the
asset-based market was the terrorist attack of 9/11. An article
explains significant indicators for the asset-based market,
specifically the impact on GDP (Blomberg 2010). It found that
resulting from 9/11, the biggest loss in GDP was due to fear
reducing air travel and tourism “amounted to $50 billion of direct
BI (business interruption) and $60 billion of indirect BI…More than
80% of the total loss of GDP due to the event”( Blomberg 2010).
This clearly displays the second characteristic of a Black Swan
event, having a significant impact. This provides an example of
what to look for in other Black Swan examples concerning GDP.
Delving deeper into the impact of Black Swan terrorist attacks
on the financial market, one article had an interesting find
(Reshetar 2011). The study took into account both international and
domestic markets and found that Swiss markets were the highest
impacted by terrorist attacks while the American financial markets
were impacted by the lowest number of events. Furthermore, the
banking sector was the least impacted by terrorist attacks
according to the study, but this might possibly be due to the
short-term nature (six days) of the study. US bond markets move on
contrast with the global markets, reacting positively to terrorist
events on the day the event occurred while the stock market moves
extremely on the day of an event with the impact declining as days
pass.
Furthermore, in addition to the financial crisis of 2008 and the
terrorist attack of 9/11, I would like to analyze the impact of a
natural disaster, Hurricane Katrina, on the asset-based market the
context of a Black Swan event. One article describes the impact of
Hurricane Katrina as fitting a trend of humans rebounding from
disasters and lists multiple historic examples. (Vigdor 2008). By
examining the asset market in an area before, during, and after the
shock of a natural disaster it is possible to estimate the impact
of the Black Swan natural disaster.
In order to develop a deeper understanding of how the stock
market responds to Black Swan events, a finding by Murry 2018
showed that when accompanied by news stories, positive news stocks
tend to overreact while negative news tended to produce
underreactions. It also showed that stocks not accompanied by news
stories are reversed. An important consideration that this article
highlighted was the difference in behaviors between institutional,
and retail investors. This has an impact on how they react to news
(or Black Swan events) in the stock market and is a factor that
should be considered when addressing shocks to the stock market
(Murry 2018).
3. Methodology:
In order to answer the question how do black swan events affect
the asset-based market, and can market data be used to recognize
future black swan events, I used a timeseries regression to compare
various measures of the asset market across a timeline revolving
around three specific events; the terrorist attack of 9/11,
Hurricane Katrina, and the Financial Crisis of 2008. These three
events are important because they represent Black Swan events that
occurred recently and had a lasting impact on the average person in
the United States. Furthermore, I specifically designed this study
to research different types of Black Swan events and by using these
three events I will analyze the impacts from: a terrorist attack,
natural disaster and financial crisis. Because these different
events are in separate categories, I used different time ranges to
estimate their impact. Considering 9/11 was such a sudden event
focused in specific geographical locations, I analyzed the two
years surrounding the event using line graphs and descriptive
statistics. Hurricane Katrina was also geographically focused and
the impact on assets primarily hit Louisiana and other states down
south, I performed another analysis for the three years surrounding
this event. Because the 2009 Financial Crisis was a national (and
global) event its impacts will be longer lasting, and I analyzed
the five years surrounding the event from the first quarter of 2007
to the last quarter of 2013. After reading multiple articles it
appears that Black Swan events in the financial crisis category
have the biggest impact on the asset-based market. In order to
account for this in my paper I included a dummy variable () which
will be 1 if the event is a financial crisis and a zero for all
other Black Swan categories.
The variables that will be used to measure the asset-based
market’s response to events will be S&P 500 Returns, GDP and
bond yields. By using the S&P 500 Returns I captured how
investors feel about the event by determining how low, or high, the
market swings in response. The S&P 500 is a market weighted
index which lends credence that it is a reasonable measure of
investor responses and changing asset values. In contrast, by using
bond yields as a variable I am able to capture the other side of
the investors that prefer to reduce risk. Longstaff (2010) stated
in their article that during times of financial, or other, crises
(Black Swan events) while the stock market drops, the bond market
improves as investors move over. Thus, as I analyzed the
asset-based market during times of stress it is important to
include a variable that responds contradictory to the S&P 500.
In order to understand the long-term and national impact of these
events (because the 9/11 terrorist attack and Hurricane Katrina
were regionalized) GPD was included as a variable. This allows me
to include the macroeconomic impact of these events specifically on
the United States.
In order to control for changes in government policy in response
to Black Swan events, the variables Fed Funds Rate and nominal
interest rates for pensions are used. The Federal Funds Rate is
controlled by the Federal Reserve and is used as an interbank loan
rate to either encourage or discourage banks from increasing their
reserves. This variable can be used to determine the monetary
response to Black Swan events. Additionally, the interest rate for
pensions is used as a variable to account for other policies, such
as 401(k) tax breaks, the government takes in the asset-based
market. By including the interest rate for pensions, it gives a
look at how households might react to the stress from the crisis in
terms of investing in the asset-based market.
In order to control for how consumers will invest in the
asset-based market I included the variable capital gains tax to
monitor how taxation might impact how consumers invest or sell
assets.
To control for the impact of information on the market’s
response to events I included the number of articles written about
the event in a specific newspaper. Throughout multiple business
classes and during my internship at a bigger bank, The Wall Street
Journal is consistently referenced as an unbiased paper with solid
reporting on the business world. Thus, I strongly believe it serves
as a standard measure of the information available to the public.
For every event I researched the number of articles that were
written in the Wall Street Journal during that time period on that
event. For example, while preparing to run a regression on the
impact of the 9/11 terrorist attack, I searched for articles
written during September through October of 2000 to see how much
exposure people had to the information.
Below are the equations to be estimated to test the effect of
Black Swan events on the asset-based market during different time
periods of stress.
Because stocks and bonds have an inverse relationship, by
completing a regression on both assets it is possible to capture
both sides of the story.
The data on quarterly GDP is collected from the Organization for
Economic Co-Operation and Development. The data for the variable on
treasury bond yields is from the US Department of the Treasury. In
order to observe the changes in the variable S&P 500 I will be
using the data available from Investing.com. Historical data on fed
funds rate is from the Federal Reserve Bank of New York going back
till July of 2000, perfectly inside my date range. The Internal
Revenue Service had data on “Weighted Average Interest Rate Table”
going back till the 90s, which is used for interest rate
variable.
Hill 2
4. Descriptive Statistics
Statistic
Average Interest Rate
S&P 500 Returns
Bond Yields
GDP % Change
Capital Gains Tax
Fed Funds
Mean
4.56629
0.00280
2.84173
0.486663
0.183968254
1.76234269
Max
5.976667
0.049067
6.05825
1.831165
0.25
6.51142857
Min
3.11
-0.0788
0.66888
-2.16381
0.15
0.073115
Standard Deviation
0.82112
0.02796
1.43142
0.635409
0.039700479
2.0197166
Table 1: Overall Descriptive Statistics
Here are the descriptive statistics for the variables I am
studying to determine if Black Swan Events have an impact on the
asset-based market. The timeline goes from the third quarter of
2000 till the first quarter of 2016 in order to capture full impact
of the three Black Swan events I picked to highlight, 9/11,
Hurricane Katrina, and the Great Recession. As you can see from the
Figure 1, the interest rates a person could expect to earn on their
retirement pension is in a steady downward slope and does not
appear to experience any major jumps or falls. Looking at the
S&P 500 returns in Figure 2 the movement of the line is erratic
and seems to trend around the zero meaning that on average the
returns are positive. Thus, it is in a graph with GDP that graphs
percent changes where it is clearly shows that the market returns
are erratic and unpredictable. Concerning Bond Yields, the graph is
downward trending with some jumps upwards (see Figure 8 for a
detailed look). My hypothesis states that during black swan periods
there would be more variety which could explain the erratic graph.
This bond is backed by the full faith of the US government, so
investors turn to it in times of stress which creates an inverse
relationship with the stock market (here represented by the S&P
500 Returns).
The percent change in GDP is surprisingly erratic in this graph
with no clear up or down trend. The biggest dip in GDP is during
the great recession and those changes were influenced by Black Swan
events. In figure 3 it seems to have an inverse relationship with
the percent changes in S&P 500 returns. It appears that the fed
funds rate change in the Effective Federal Funds Rate (EFFR) has
been on downward slope, with some hills. During the period from
2000 to 2016 there have been only 7 changes in the capital gains
tax. However, the changes have been drastic and have an impact on
how investors choose to invest in their own context, which appears
to be overwhelmed in this graph. If there is a higher capital gains
tax during a Black Swan event, it would be difficult to observe if
the lack of asset purchases was from the higher tax or the event.
One way to determine the cause would be to determine the average
S&P 500 returns and Bond Yields during the three events and
compare them. This would help to determine if it is the capital
gains tax, or the Black Swan event that determines the demand.
Mean:
Total:
9/11 Terrorist Attack
Hurricane Katrina
2008 Financial Crisis
Interest Rate
4.566294254
5.632143
4.988888417
4.262257786
S&P 500 Returns
0.002806381
-0.005045357
0.00698625
0.004370321
Bond Yields
2.841737533
4.067722357
4.07376425
2.112157432
GDP Changes
0.004866637
0.004714821
0.008953758
0.002507626
Capital Gains Tax
0.183968254
0.197428571
0.159666667
0.166428571
Fed Funds Rate
0.017432461
0.028442797
0.031783827
0.010928621
Table 2: Comparing Means Across Events
In the table above there is a comparison of the average for each
variable across the entire time series (2000 to 2016, titled
“Total” in table) and each Black Swan event. When a value for an
event was greater than the average for the total it is highlighted
in green to be easy to identify. When the average for an event was
less than the total, it is highlighted in red. During the 9/11
terrorist attack event period (from Q3 2000 to Q4 2003) the average
for Weighted Average Interest Rate, 5 Year Treasury Bond Yields,
Capital Gains Tax Rate and Fed Funds Rate were higher than the
average of the total time period. This suggests that the
asset-based market is not facing extreme duress from the Black Swan
event. However; because both the S&P 500 Returns % Change and
the Percent Change in GDP is less than the average we can infer
that the market as a whole was suffering negative side effects and
the market’s output went down.
Hurricane Katrina did not impact the asset-based market
nationally as the averages for all variables was higher than the
mean except for capital gains tax. This means that the government
was relaxing monetary policy in response to the environmental
disaster.
5. Results and Discussion
For my research I used a timeseries regression. Within
timeseries data there is a problem of unit root where the variable
is unpredictable and could exhibit a random walk. I tested for
stationarity of all the series using the Dickey-Fuller test after
running a lag on each variable. When a variables’ calculated
statistic is greater than the Dicky-Fuller table value, the
variables is stationary at level. When the test fails to reject the
null of unit root, I generated the first difference of the series
and conduct a unit root test. This process continues until we
reject the null.
Level
Calculated T-Stat
1%
5%
10%
Weighted Average Interest Rate
-1.59
-4.135
-3.493
-3.176
S&P 500 Returns % Change
-2.814
-4.135
-3.493
-3.176
5 Year Treasury Bond Yields
-2.747
-4.135
-3.493
-3.176
Percent Change in GDP
-2.419
-4.135
-3.493
-2.176
Capital Gains Tax Rate
-1.605
-4.135
-3.493
-3.176
Fed Funds Rate
-3.048
-4.135
-3.493
-3.176
Table 3: Stationary Test at Level
Here we can interpret at what difference and what table value we
can fail to reject the null hypothesis. This first table displays
the results of the Dicky-Fuller test at level. All these variables
are nonstationary at level, except for one. Percent Change in GDP
was the only variable where we can fail to reject the null
hypothesis at a critical value. (Here we can fail to reject at 10%
so the variable GDP is stationary even at level.) In the appendix
there are scatterplots showing each variable at level with a lag,
then at a difference to show stationarity. This is really clear in
the Weighted Average Interest Rate where it was a downward trend
almost in a line, then after the difference the data points were
scattered evenly across the midline.
Differences
Calculated T-Stat
1%
5%
10%
IR_diff
-3.719
-4.137
-3.494
-3.176
S&P 500_diff
-4.213
-4.137
-3.494
-3.176
BondYield_diff2
-4.273
-4.139
-3.495
-3.177
GDP_diff
-4.504
-4.137
-3.494
-3.176
tax_diff
-3.454
-4.137
-3.494
-2.176
Fed_diff2
-3.385
-4.139
-3.495
-3.177
Table 4: Stationary Test at Difference
For ease of interpreting the table at a glance, the values where
we can fail to reject the null hypothesis are highlighted in green.
The variables that passed at the first difference were: Weighted
Average Interest Rate, S&P 500 Returns Percent Change, Capital
Gains Tax Rate and Percent Change in GDP. Two variables, Fed Funds
Rate and 5 Year Treasury Bond Yields, that failed at all critical
values at the first difference and went on to the second difference
where they passed. The variable that was the most difficult to
prove stationary was the Fed Funds Rate. This variable failed at
the first difference and only passed at the 10% critical value when
it was at the second difference. 5 Year Treasury Bond Yield also
failed at the first difference so I then ran the test again at the
second difference (which is why its variable name is
BondYield_diff2, the diff2 represent that it is the 2nd
difference). Then we were able to reject the null hypothesis at all
the critical values and pass it.
Considering that I have two separate equations I ran two
regressions. After completing the Dicky-Fuller test I ran my
regression twice for each equation. First at level, then at the
second difference for each variable, with the exception of Event
and WSJ, and used the command “comma robust” to try and strengthen
outliers in the data for both regressions. From this equation, I
obtained the following results for the first equations using
S&P 500 returns as a measure for the asset market running my
regression at level:
Variable
p-value
T-statComment by Jessica Hill: Combine tables into one
5 Year Treasury Bond Yields
0.00836
-0.78
Interest Rate
-0.0432*
(-2.07)
Percent Change in GDP
-0.373
(-0.53)
Capital Gains Tax Rate
-0.242
(-1.94)
Fed Funds Rate
-0.23
(-0.50)
Event
-0.155*
(-2.05)
WSJ
0.000816*
-2.01
_cons
0.199*
-2.4
* p<0.05, ** p<0.01, *** p<0.001
Table 5: Equation 1 at Level
Here we can observe the p-values for each variable and determine
the ones that are statistically significant and positively or
negatively correlated with S&P 500 returns. Because the p-value
of Percent Change in GDP and Fed Funds Rate is greater than 0.05
these variables are not statistically significant and therefore do
not have a massive impact on the asset-based market. Also, both
these variables have a negative t-value suggesting that they are
negatively correlated to the asset-based market. Capital Gains Tax
is also not a statistically significant variable and is negatively
correlated with the asset-based market. Meaning that as the
Government raises taxes S&P500 returns decrease. The dummy
variable “Event” is statistically significant and negatively
correlated. This suggests that if a financial crisis Black Swan
event occurs it has a significant, and negative, impact on the
asset-based market compared to the base of no financial crisis
black swan event. The variable WSJ, which accounts for how news
impacts investors habits is statistically significant and
positively correlated. Thus we can conclude that as investors
receive more information it positively affects their investment in
the stock market and increases returns. Weighted Average Interest
rate is also statistically significant at the 0.05 threshold and is
negatively correlated with the asset-based market.
The results for GDP are the opposite sign from what I expected.
I had expected the percent change in GDP to be positive because as
the stock market increases GDP should increase as well. Because the
variable and data collected is specifically the Percent Change in
GDP, which moves differently than GDP as a whole so because it
moves fewer percentage points than the S&P 500 returns which
grow at a higher rate.
Variable
p-value
T-stat
D2. Treasury Bond Yields
0.0091
-0.6
D2. Interest Rate
0.0710*
-2.34
D2. Percent Change in GDP
-0.326
(-0.47)
D2. Capital Gains Tax Rate
1.139***
-7
D2. Fed Funds Rate
-2.499
(-0.93)
Event
-0.0107
(-0.16)
WSJ
0.0000665
-0.18
_cons
-0.00301
(-0.23)
* p<0.05, ** p<0.01, *** p<0.001Comment by Jessica
Hill:
Table 6: Equation 1 at Difference
After running my regression, I tested for heteroskedasticity
using the “estat hettest” command on Stata to obtain the following
results demonstrated on figure x in appendix figure I found that
the results supported the null and there was no evidence of
heteroskedasticity for Equation 1.
Below is the second equation where I used 5-year treasury bond
yields as my dependent variable once again adding the command
“comma robust” then I ran this equation at the level first and
obtained the following results:
Variable
P-value
T-stat
S&P500 Returns
0.442
0.81
Interest Rate
0
6.43
Percent Change in GDP
0.067
-1.87
Capital Gains Tax Rate
0.28
1.09
Fed Funds Rate
0
11.99
Event
0.003
3.15
WSJ
0
-3.8
_cons
2.97
0.004
* p<0.05, ** p<0.01, *** p<0.001
Table 7: Equation 2 at LevelComment by Jessica Hill: Figure out
tables for bonds
Using the same critical threshold that I used for the last
equation, 0.05, I find that four of these variables are
statistically significant. The variable weighted average interest
rate is statistically significant and positively correlated.
Another statistically significant variable is the fed funds rate
variable, which is positively correlated suggesting that as fed
funds rate increase as do bond yields. The logic behind this result
is that when the economy is doing poorly and in times of distress
investors buy bonds because they are less risky than stocks. When
the demand for bonds increases the yields decreases so during times
of economic distress, such as Black Swan events, bond yields are
lower. Additionally, when the economy is in distress the Federal
Reserve can use fiscal policy to lower the fed funds rate to incite
economic growth. Thus, as the economy recovers and flourishes both
bond yields increase as demand decreases and the fed funds rate
increases as the fed no longer needs to support the economy.
The variable WSJ, accounting for the impact of information as in
the equation 1 results. Additionally, in this case capital gains
tax is negatively correlated with bond yields which holds true to
the opposing theory that an increase in taxes would negatively
effect returns.
And once my regression was run for equation 2 I tested for
heteroskedasticity using the same “estat hettest” command in stata.
The results supported the null and there was no evidence of
heteroskedasticity. (See figure 10 in appendix).
6. Conclusion
Throughout this study I researched the definition and makeup of
Black swan events through literature reviews. To revisit the topic:
Black Swan events are subjective to different perspectives and have
three key identifiers: it must be a surprise, have an impact and
cannot be predictable. In order to analyze the impact of these
Black Swan events on the asset-based market I used these
determinates to identify three Black Swan events in recent history:
the 9/11 terrorist attack, Hurricane Katrina in 2005 and the 2008
Financial Crisis. These events all had a significant impact on
America, were a surprise to the general public and were not
predictable, with the possible exception of the 2008 Financial
Crisis. That event is being considered a ‘Grey Swan,’ (Runde 2009)
and run as a Black Swan for the sake of research.
In order to assess the impact that these variables had on the
asset-based market I selected variables that would fluctuate if the
economy was in distress. S&P 500 and 5 year treasury bond
yields are used as left hand side variables to measure the impact
of Black Swan events. Because these two variables have an inverse
relationship, I wanted to use both to capture all aspects of the
market. As right-hand side variables I used weighted average
interest rate on pensions to observe how these events impact
specific individuals and gathered the data from the IRS website.
The variable percent change in GDP is used to capture the impact on
the United States’ economy. Capital gains tax rate is used to
determine government’s monetary policy response to Black Swan
events while the Fed Funds Rate is used to analyze fiscal policy.
The dummy variable event is used because according to the
literature Black Swan events categorized as financial crises have a
more significant impact so it is used to demonstrate the impact of
the financial crisis. The final variable is the Wall Street Journal
article count to determine the impact of information availability
on investors belief in the asset-based market.
To compare the different Black Swan events I analyzed
descriptive statistics for each event and found that during the
9/11 terrorist attack the market suffered due to the average for
S&P 500 Returns and GDP growth during this period was less than
the average for the entire timeseries from 2000 to 2016. Hurricane
Katrina did not impact the asset-based market nationally as the
averages for all variables was higher than the mean except for
capital gains tax. This means that the government was relaxing
monetary policy in response to the environmental disaster. Out of
all three Black Swan events, the 2008 Financial Crisis had the most
drastic effect as all the variables except for S&P 500 returns
were below the average for the entire period.
Considering the timeseries regression, I found that on the
whole; event and wsj variables were statistically significant and
played a role on the impact of the asset-based market. I also found
that capital gains tax is negatively related to both S&P 500
returns and treasury bond yields and fed funds rate has a positive
negative relationship to the S&P 500 and positive relationship
to treasury bond yields.
The main objective of this study was XXX effect of black swan
events on asset-based market,
Findings showed that black swan events impacted the asset-based
market this way. Use descriptive statistics for supporting result…
Link figure mentioned to appendix
Fix regression table to have coefficient first column and
p-value second column, 1.64 t-stat.
Make PowerPoint presentation
· Can use note cards
· Practice presentation and time it
· Organization, use of media, content, delivery, creativity, eye
contact, ext graded on
· Shorten abstract
· Edit table of contents
· Table Titles on top
· Event negative for S&P return
·
7. Appendix:
Figure 9: Heteroskedasticity Test Equation 1.
Figure 10 Heteroskedasticity Test Equation 2.
Data Sources:
https://www.irs.gov/retirement-plans/weighted-average-interest-rate-table
https://www.investing.com/indices/us-spx-500-historical-data
https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yieldYear&year=2000
https://www.macrotrends.net/2521/30-year-treasury-bond-rate-yield-chart
https://stats.oecd.org/index.aspx?queryid=350#
https://apps.newyorkfed.org/markets/autorates/fed-funds-search-result-page
https://www.wsj.com/news/types/hurricane-katrina (Hurricane WSJ
Variables)
https://www.wsj.com/search/term.html?KEYWORDS=9%2F11%20terrorist%20attack&min-date=2000/07/01&max-date=2003/07/01&isAdvanced=true&daysback=90d&andor=AND&sort=date-desc&source=wsjarticle,wsjblogs,wsjvideo,interactivemedia,sitesearch,wsjpro&page=2
(9/11 WSJ)
https://www.wsj.com/search/term.html?KEYWORDS=2008%20financial%20crisis&min-date=2007/07/01&max-date=2012/07/01&isAdvanced=true&daysback=90d&andor=AND&sort=relevance&source=wsjarticle,wsjblogs,wsjvideo,interactivemedia,sitesearch,wsjpro
(2008 Financial Crisis)
Works Cited
Agyare, Ramous et. Al. (2016). Review of Stock Markets’ Reaction
to New Events: Evidence from Brexit. Journal of Financial Risk
Management, 5. Retrieved from
https://file.scirp.org/pdf/JFRM_2016123011255805.pdf.
Avenn, Terje.(2015). Implications of Black Swans to the
Foundations and Practice of Risk Assessment and Management.
Reliability Engineering & System Safety, 134. Retrieved from
https://www.sciencedirect.com/science/article/pii/S0951832014002440.
Blomberg, Brock, & Rose, A. (2010). Total Economic
Consequences of Terrorist Attacks: Insights from 9/11. Peace
Economics, Peace Science and Public Policy, 16. Retrieved from
https://create.usc.edu/sites/default/files/publications/totaleconomicconsequences
ofterroristattacks-insightsfrom9_0.pdf.
Longstaff, Francis. (2010). The Subprime Credit Crisis and
Contagion in Financial Markets. Journal of Financial Economics, 97.
Retrieved from https://www.sciencedirect.com
/science/article/pii/S0304405X10000127.
Murry, Frank, & Sanati, A. (2018). How does the stock market
absorb shocks. Journal of Financial Economics, 129. Retrieved from
https://www.sciencedirect.com/science/article/pii/S0304405X18300965
Reshetar, Ganna et. Al. (2011). The impact of terrorism on
financial markets: An empirical study. Journal of Banking and
Finance, 35. Retrieved from
https://www.sciencedirect.com/science/article/pii/S037842661000292X.
Runde, Jochen. (2009). Dissecting The Black Swan. Critical
Review, 21. Retrieved from
https://www.researchgate.net/publication/249051524_Dissecting_the_Black_Swan
Siegel, Laurence. (2010). Black Swan or Black Turkey? The State
of Economic Knowledge and the Crash of 2007–2009. Financial
Analysts Journal, 66. Retrieved from
https://www.researchgate.net/publication/260473430_Black_Swan_or_Black_Turkey_The_State_of_Economic_Knowledge_and_the_Crash_of_2007-2009
Taylor, John., & Williams, J. (2009). A Black swan in the
Money Market. American Economic Journal, 1. Retrieved from
https://www.frbsf.org/economic-research/files/Taylor-Williams.pdf.
Vigdor, Jacob. (2008). The Economic Aftermath of Hurricane
Katrina. Journal of Economic Perspectives, 22. Retrieved from
https://pubs.aeaweb.org/doi/pdfplus /10.1257/jep.22.4.135.
Figure 1: Asset Based Market From 2000 to 2016: Rates
Weighted Average Interest
RateQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20165.9766670000000001E-25.9400000000000001E-25.74E-25.7866670000000002E-25.7733329999999999E-25.8166669999999997E-25.5800000000000002E-25.6500000000000002E-25.6266669999999998E-25.6800000000000003E-25.2766670000000002E-25.3166669999999999E-25.2999999999999999E-25.3866669999999998E-25.1233300000000002E-25.16E-25.1499999999999997E-25.1700000000000003E-24.8800000000000003E-24.9399999999999999E-25.0266669999999999E-25.0799999999999998E-24.836667E-24.836667E-24.8399999999999999E-24.82333E-24.8333000000000001E-24.8399999999999999E-24.8399999999999999E-24.8266669999999998E-24.6800000000000001E-24.7300000000000002E-24.7166670000000001E-24.7500000000000001E-24.3666669999999998E-24.3999999999999997E-24.3900000000000002E-24.4233300000000003E-24.2633329999999997E-24.3299999999999998E-24.3033299999999997E-24.3833299999999999E-24.1599999999999998E-24.2533300000000003E-24.2299999999999997E-24.2799999999999998E-23.6600000000000001E-23.7766670000000002E-23.9266669999999997E-24.0399999999999998E-23.4500000000000003E-23.4333299999999997E-23.4833000000000003E-23.5733000000000001E-23.4566670000000001E-23.4333299999999997E-23.4099999999999998E-23.3833299999999997E-23.3099999999999997E-23.2133330000000002E-23.1666670000000001E-23.1333300000000001E-23.1099999999999999E-2Capital
Gains Tax
RateQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20160.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.1610.1610.1610.1610.1610.1610.1610.1610.1610.1610.1610.1610.1570.1570.1570.1570.1570.1570.1570.1570.1540.1540.1540.1540.1540.1540.1540.1540.150.150.150.150.150.150.150.150.150.150.150.150.250.250.250.250.250.250.250.250.250.250.250.250.25Fed
Funds
RateQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20166.5114285699999996E-26.4996774199999996E-25.6114754000000003E-24.3307812500000001E-23.4879365099999997E-22.1533871E-21.7409839999999999E-21.7565629999999999E-21.748125E-21.4554839999999999E-21.255246E-21.25375E-21.01507692E-20.011.0032259999999999E-21.0132810000000001E-21.439375E-21.9571430000000001E-22.4804840000000002E-22.9426560000000001E-23.4623439999999998E-23.9760655700000001E-24.4672580000000003E-24.9037499999999998E-25.24936508E-25.2456450000000002E-25.2619354799999997E-25.2489062500000003E-25.0903174599999997E-24.4964516099999997E-23.1688709699999998E-22.0903125000000002E-21.9580952400000001E-25.3403226E-31.8704918E-31.7859375E-31.5461538E-31.1854839000000001E-31.3672131E-31.9187500000000001E-31.8636364E-31.8936508E-31.55645E-39.3228099999999997E-48.3905999999999998E-47.5082000000000002E-41.0629000000000001E-31.54531E-31.4618999999999999E-31.6096800000000001E-31.4426199999999999E-31.1703099999999999E-38.5468999999999996E-48.5484000000000005E-47.3114999999999996E-49.0936999999999995E-48.9687000000000002E-41.01935E-31.1999999999999999E-31.25781E-31.35692E-31.6080599999999999E-33.6590199999999998E-3
Figure 3: Corralation Between S&P500 and Treasury Bond
Yields
S&P 500 Returns %
ChangeQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-2016-3.0330000000000001E-3-2.6967000000000001E-2-4.0633000000000002E-21.8967000000000001E-2-5.2166999999999998E-23.3633000000000003E-21E-4-4.7667000000000001E-2-6.1366999999999998E-22.7733000000000001E-2-1.2E-24.7732999999999998E-27.4000000000000003E-33.7633E-24.3670000000000002E-34.4330000000000003E-3-7.5329999999999998E-32.8367E-2-8.5000000000000006E-33.2669999999999999E-31.0567E-25.4999999999999997E-31.2367E-2-6.1999999999999998E-31.7000000000000001E-22.0199999999999999E-27.67E-41.9332999999999999E-25.5669999999999999E-3-1.26E-2-3.4000000000000002E-2-9.2669999999999992E-3-2.9499999999999998E-2-7.8799999999999995E-2-3.6733000000000002E-24.9067E-24.7800000000000002E-21.8467000000000001E-21.6767000000000001E-2-4.0367E-23.6332999999999997E-23.3300000000000003E-21.7967E-2-1.1000000000000001E-3-5.0033000000000001E-23.7033000000000003E-23.85E-2-1.0200000000000001E-21.8866999999999998E-2-3.3E-33.2500000000000001E-27.9670000000000001E-31.5966999999999999E-23.2066999999999998E-24.7999999999999996E-31.5433000000000001E-22.3670000000000002E-31.4500000000000001E-22.1670000000000001E-3-6.6699999999999995E-4-2.3099999999999999E-22.1999999999999999E-23.7330000000000002E-3
5 Year Treasury Bond Yields
Q3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20166.05825399999999985.55564500000000024.79048400000000024.83539700000000044.51081999999999984.08483899999999974.45950000000000024.43718799999999993.33859400000000013.01278699999999992.91180299999999992.573.13812500000000013.24467699999999982.98096799999999983.72096800000000013.50562500000000023.49371000000000013.89082000000000023.87296899999999994.03937499999999974.39114800000000034.55241900000000044.99317499999999994.84222000000000024.60177399999999984.64338699999999974.76078099999999974.51269800000000033.79758100000000012.75147500000000013.15499999999999983.11281300000000012.1727421.76131100000000012.2417462.46749999999999982.30419399999999992.42737699999999992.25484399999999981.54499999999999991.5033872.11935500000000011.85444000000000011.1336510.9526230.898387099999999990.7889060.668888999999999960.6901640.770.909375000000000041.50374999999999991.43903200000000011.6034431.6576191.70156250000000011.60096799999999991.45721300000000011.5251561.55578100000000011.58419400000000011.3698360000000001
Figure 4: Affect of 9/11
Weighted Average Interest
RateQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-20035.9766675.945.745.78666699999999965.7733335.81666699999999985.585.655.62666700000000035.685.27666699999999985.31666699999999985.35.3866670000000001S&P
500 Returns %
ChangeQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003-3.0330000000000001E-3-2.6967000000000001E-2-4.0633000000000002E-21.8967000000000001E-2-5.2166999999999998E-23.3633000000000003E-21E-4-4.7667000000000001E-2-6.1366999999999998E-22.7733000000000001E-2-1.2E-24.7732999999999998E-27.4000000000000003E-33.7633E-2
5 Year Treasury Bond Yields
Q3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-20036.05825399999999985.55564500000000024.79048400000000024.83539700000000044.51081999999999984.08483899999999974.45950000000000024.43718799999999993.33859400000000013.01278699999999992.91180299999999992.573.13812500000000013.2446769999999998Percent
Change in
GDPQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-20030.361760000000000031.83116499999999990.133555000000000010.62289000000000005-0.285177999999999990.58455599999999996-0.415038000000000020.272370999999999970.874514000000000010.605732999999999970.444570999999999990.154822999999999990.554767000000000010.86026000000000002Capital
Gains Tax
RateQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-20030.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.211999999999999990.1610.1610.1610.161Fed
Funds
RateQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-20036.51142856999999966.49967742000000035.87646529999999964.38787799999999973.74306399999999992.04767639999999981.72785870000000011.70434299999999991.73875761.25685699999999991.2906451.18046999999999990.997657539999999950.98453460000000004
Figure 5: Affects of Hurricane Katrina
Weighted Average Interest
RateQ1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-20065.12333000000000025.165.155.174.884.94000000000000045.02666699999999985.084.83666700000000034.83666700000000034.844.8233300000000003S&P
500 Returns %
ChangeQ1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-20064.3670000000000002E-34.4330000000000003E-3-7.5329999999999998E-32.8367E-2-8.5000000000000006E-33.2669999999999999E-31.0567E-25.4999999999999997E-31.2367E-2-6.1999999999999998E-31.7000000000000001E-22.0199999999999999E-2
5 Year Treasury Bond Yields
Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-20062.98096799999999983.72096800000000013.50562500000000023.49371000000000013.89082000000000023.87296899999999994.03937499999999974.39114800000000034.55241900000000044.99317499999999994.84222000000000024.6017739999999998Percent
Change in
GDPQ1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-20061.69820900000000011.14816699999999990.533746999999999970.762075999999999980.945327999999999951.0016741.10696400000000010.4615610.891333999999999960.6314631.3302870.23369999999999999Capital
Gains Tax
RateQ1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-20060.1610.1610.1610.1610.1610.1610.1610.1610.1570.1570.1570.157Fed
Funds
RateQ1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-20061.02768299999999991.03686499999999991.4768722.35787499999999992.49869699999999992.98648999999999993.65764699999999994.26588999999999974.67435470000000034.863275.24964419999999965.2309856000000003
Figure 6: Affect of the 2008 Financial Crisis
Weighted Average Interest
RateQ1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-20134.83330000000000044.844.844.82666699999999964.684.73000000000000044.71666700000000024.754.36666699999999964.40000000000000044.38999999999999974.423334.26333300000000034.334.30332999999999994.38332999999999994.164.25333000000000014.23000000000000044.283.663.77666700000000023.92666700000000014.043.453.43333000000000023.48329999999999983.5733000000000001S&P
500 Returns %
ChangeQ1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-20137.67E-41.9332999999999999E-25.5669999999999999E-3-1.26E-2-3.4000000000000002E-2-9.2669999999999992E-3-2.9499999999999998E-2-7.8799999999999995E-2-3.6733000000000002E-24.9067E-24.7800000000000002E-21.8467000000000001E-21.6767000000000001E-2-4.0367E-23.6332999999999997E-23.3300000000000003E-21.7967E-2-1.1000000000000001E-3-5.0033000000000001E-23.7033000000000003E-23.85E-2-1.0200000000000001E-21.8866999999999998E-2-3.3E-33.2500000000000001E-27.9670000000000001E-31.5966999999999999E-23.2066999999999998E-2
5 Year Treasury Bond Yields
Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-20134.64338699999999974.76078099999999974.51269800000000033.79758100000000012.75147500000000013.15499999999999983.11281300000000012.1727421.76131100000000012.2417462.46749999999999982.30419399999999992.42737699999999992.25484399999999981.54499999999999991.5033872.11935500000000011.85444000000000011.1336510.9526230.898387099999999990.7889060.668888999999999960.6901640.770.909375000000000041.50374999999999991.4390320000000001Percent
Change in
GDPQ1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-20130.154632999999999990.851864999999999980.235493000000000010.572872000000000050.543270999999999950.60784199999999999-0.574699999999999990.51638700000000004-0.54135599999999995-2.1638109999999999-1.122695-0.143990000000000010.364111000000000021.0983840.384780000000000010.922027999999999960.737210999999999950.50190299999999999-0.240440999999999990.71497299999999997-2.7779000000000002E-21.15928499999999990.782993000000000050.430201999999999970.134948000000000010.1138730.885674999999999990.12339600000000001Capital
Gains Tax
RateQ1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-20130.1570.1570.1570.1570.1540.1540.1540.1540.1540.1540.1540.1540.150.150.150.150.150.150.150.150.150.150.150.150.250.250.250.25Fed
Funds
RateQ1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-20135.20317000000000015.19768000000000015.09031745999999964.49645161000000033.168870972.09031251.958095240.534032260000000040.187049180000000010.178593750.154615380.118548390.136721310000000010.191874999999999990.186363640.189365079999999990.155645000000000019.3228099999999994E-28.3905999999999994E-27.5081999999999996E-20.106290.1545310.146189999999999990.1609680.1442620.1170318.5469000000000003E-28.5484000000000004E-2
Figure 7: S&P 500 Returns % Change 2000-2016
S&P 500 Returns %
ChangeQ1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20167.67E-41.9332999999999999E-25.5669999999999999E-3-1.26E-2-3.4000000000000002E-2-9.2669999999999992E-3-2.9499999999999998E-2-7.8799999999999995E-2-3.6733000000000002E-24.9067E-24.7800000000000002E-21.8467000000000001E-21.6767000000000001E-2-4.0367E-23.6332999999999997E-23.3300000000000003E-21.7967E-2-1.1000000000000001E-3-5.0033000000000001E-23.7033000000000003E-23.85E-2-1.0200000000000001E-21.8866999999999998E-2-3.3E-33.2500000000000001E-27.9670000000000001E-31.5966999999999999E-23.2066999999999998E-24.7999999999999996E-31.5433000000000001E-22.3670000000000002E-31.4500000000000001E-22.1670000000000001E-3-6.6699999999999995E-4-2.3099999999999999E-22.1999999999999999E-23.7330000000000002E-3
Figure 8: 5 Year Treasury Bond Yields
5 Year Treasury Bond Yields
Q3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20166.05825399999999985.55564500000000024.79048400000000024.83539700000000044.51081999999999984.08483899999999974.45950000000000024.43718799999999993.33859400000000013.01278699999999992.91180299999999992.573.13812500000000013.24467699999999982.98096799999999983.72096800000000013.50562500000000023.49371000000000013.89082000000000023.87296899999999994.03937499999999974.39114800000000034.55241900000000044.99317499999999994.84222000000000024.60177399999999984.64338699999999974.76078099999999974.51269800000000033.79758100000000012.75147500000000013.15499999999999983.11281300000000012.1727421.76131100000000012.2417462.46749999999999982.30419399999999992.42737699999999992.25484399999999981.54499999999999991.5033872.11935500000000011.85444000000000011.1336510.9526230.898387099999999990.7889060.668888999999999960.6901640.770.909375000000000041.50374999999999991.43903200000000011.6034431.6576191.70156250000000011.60096799999999991.45721300000000011.5251561.55578100000000011.58419400000000011.3698360000000001
Figure 2: Percent Change in S&P500 and GDP 2000-2016
S&P 500 Returns %
ChangeQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-2016-3.0330000000000001E-3-2.6967000000000001E-2-4.0633000000000002E-21.8967000000000001E-2-5.2166999999999998E-23.3633000000000003E-21E-4-4.7667000000000001E-2-6.1366999999999998E-22.7733000000000001E-2-1.2E-24.7732999999999998E-27.4000000000000003E-33.7633E-24.3670000000000002E-34.4330000000000003E-3-7.5329999999999998E-32.8367E-2-8.5000000000000006E-33.2669999999999999E-31.0567E-25.4999999999999997E-31.2367E-2-6.1999999999999998E-31.7000000000000001E-22.0199999999999999E-27.67E-41.9332999999999999E-25.5669999999999999E-3-1.26E-2-3.4000000000000002E-2-9.2669999999999992E-3-2.9499999999999998E-2-7.8799999999999995E-2-3.6733000000000002E-24.9067E-24.7800000000000002E-21.8467000000000001E-21.6767000000000001E-2-4.0367E-23.6332999999999997E-23.3300000000000003E-21.7967E-2-1.1000000000000001E-3-5.0033000000000001E-23.7033000000000003E-23.85E-2-1.0200000000000001E-21.8866999999999998E-2-3.3E-33.2500000000000001E-27.9670000000000001E-31.5966999999999999E-23.2066999999999998E-24.7999999999999996E-31.5433000000000001E-22.3670000000000002E-31.4500000000000001E-22.1670000000000001E-3-6.6699999999999995E-4-2.3099999999999999E-22.1999999999999999E-23.7330000000000002E-3Percent
Change in
GDPQ3-2000Q4-2000Q1-2001Q2-2001Q3-2001Q4-2001Q1-2002Q2-2002Q3-2002Q4-2002Q1-2003Q2-2003Q3-2003Q4-2003Q1-2004Q2-2004Q3-2004Q4-2004Q1-2005Q2-2005Q3-2005Q4-2005Q1-2006Q2-2006Q3-2006Q4-2006Q1-2007Q2-2007Q3-2007Q4-2007Q1-2008Q2-2008Q3-2008Q4-2008Q1-2009Q2-2009Q3-2009Q4-2009Q1-2010Q2-2010Q3-2010Q4-2010Q1-2011Q2-2011Q3-2011Q4-2011Q1-2012Q2-2012Q3-2012Q4-2012Q1-2013Q2-2013Q3-2013Q4-2013Q1-2014Q2-2014Q3-2014Q4-2014Q1-2015Q2-2015Q3-2015Q4-2015Q1-20163.6175999999999999E-31.8311649999999999E-21.33555E-36.2288999999999999E-3-2.8517799999999999E-35.8455599999999996E-3-4.1503800000000004E-32.7237099999999998E-38.7451400000000002E-36.0573299999999997E-34.4457100000000003E-31.54823E-35.5476700000000002E-38.6026000000000002E-31.6982089999999998E-21.1481669999999999E-25.3374700000000004E-37.6207599999999999E-39.4532799999999997E-31.001674E-21.106964E-24.61561E-38.9133400000000005E-36.3146299999999999E-31.330287E-22.3370000000000001E-31.5463300000000001E-38.5186499999999991E-32.3549299999999999E-35.7287199999999996E-35.4327100000000003E-36.0784200000000002E-3-5.7470000000000004E-35.1638700000000001E-3-5.4135600000000004E-3-2.1638109999999999E-2-1.1226949999999999E-2-1.4399E-33.6411099999999999E-31.098384E-23.8478000000000002E-39.2202800000000008E-37.3721100000000003E-35.0190299999999998E-3-2.4044100000000001E-37.14973E-3-2.7778999999999998E-41.159285E-27.8299300000000006E-34.3020200000000001E-31.3494799999999999E-31.1387299999999999E-38.85675E-31.2339600000000001E-37.8341499999999998E-37.9824900000000001E-3-2.8270499999999998E-31.353681E-21.2209319999999999E-25.6279399999999997E-37.8481999999999996E-37.4136000000000002E-33.30657E-3
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SP500 Returns Change Level
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SP500 Returns Change At Difference
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2000q12005q12010q12015q1
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Fed Fund Rate At Difference
