Options for Investment in Equity Market
7101 words (28 pages) Essay in Finance
23/09/19 Finance Reference this
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SECURITIES, FUTURES AND OPTIONS
Table of Contents
Portfolio Theory and The Efficient Frontier
Reasons to invest in US stocks
Efficient Frontier with NO Short Selling
Efficient Frontier with Short Selling
Market Portfolio with ShortSelling
Procedure for testing the CAPM
Testing the CAPM via FamaMacbeth two step Methodology
Results comparison on CAPM tests
Explanation for the invalidation of CAPM model
List of Equations
Equation 1……………………………………………….
Equation 2……………………………………………….
Equation 3……………………………………………….
Equation 4……………………………………………….
Equation 5……………………………………………….
Equation 6……………………………………………….
List of Figures
Figure 1: World’s largest stock markets……………………………..
Figure 2: GDP Growth of top 4 developed Countries………………………
Figure 3: Efficient Frontier(Blue) and Capital Allocation Line(Orange) with no shortselling.
Figure 4: Efficient Frontier(Blue) and Capital Allocation Line(Orange) with shortselling.
Figure 5: Regression results of Step 2
List of Tables
Table 1: Weights of the assets invested in the Market Portfolio
Table 2: Composition of the Minimum Variance Portfolio and Optimum Portfolio
Table 3: Weights of the assets invested in the Market Portfolio
Table 4: Composition of the Minimum Variance Portfolio and Optimum Portfolio
Table 5: Equally weighted portfolio of stocks and marketcapitalisation weighted portfolio and Risk Adjusted Returns performance of four portfolios
Table 6: Estimation of _{i} of 20 stocks
QUESTION 1
Portfolio Theory and The Efficient Frontier
Introduction
The client is currently looking for a 5year investment in an equity market and would like a diversified portfolio. The client also wants to invest his money in 20 different stocks over 5 sectors. Our recommendation follows.
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The Market – S&P 500
We have decided to invest our client’s money in the US Equity market, particularly in the stocks that are a part of the S&P 500. The S&P 500 covers a very wide range of industries (unlike NASDAQ or Dow Jones) and focuses on the 500 largest companies in America based on market capitalization.
Reasons to invest in US stocks
 The US is the most liquid and biggest stock market in the world.
Figure 1: World’s largest stock markets
Source: Bloomberg, April 2018
As can be seen in Figure 1, the sheer size of the American stock market poses the least liquidity risk for our investor compared to all other available markets.
 Amongst the developed nations, USA promises the highest growth rate.
 Figure 2: GDP Growth of top 4 developed Countries
Source: IMF DataMapper
From Figure 2, we can observe that the United States has the highest GDP growth rate (roughly 2.9%) compared to the other developed nations. The projections of the IMF also show that the United States is likely to continue having a GDP growth rate that is higher than the other developed nation’s growth rate. Since the performance of the stock market is closely tied to the macroeconomic performance of the nation, the current projections are likely to result in favorable returns on investments.
 Lower corporate tax rates President Trump’s election promise was to lower corporate tax rates. In 2017, the Statutory Corporate Income Tax Rate in the United States was 38.9%[1]. After the Tax Cuts and Jobs Act 2017 was introduced, the Federal Tax rate was reduced to 21%, and after including a weighted average of state specific corporate tax rates to the Federal Tax rate, the average tax rate paid by US Corporations was 25.7%[2]. This drastic reduction in Corporate Tax rates was welcomed by the markets and companies listed on the stock market now provide higher returns for investors.
The Sectors
Sector Investing offers targeted exposure to the stocks of companies in specific segments of the economy and can help pursue growth, diversify portfolios, and manage risks. For this purpose, we have selected the following sectors:
 Financial Sector
 Rising interest rates: Financial companies benefit from earning higher interest on the loans that they’ve given
 Increasing consumer finances: With the pay budgets of companies increasing by 3.1% in 2019[3], the debt burden of consumers is likely to reduce and at the same time allows consumers to afford more debt
 Regulatory burden: While the number of regulations is increasing, financial sector companies are staying one step ahead by preparing for changes in regulation, thus increasing efficiency and profitability[4]
The U.S. Financial sector is the best performing sector in the U.S, with a market capitalization of $7.17 trillion. The market weight for U.S. Financial services is 13.58% and is ranked 3^{rd}. The U.S. financial sector is outperforming the overall market and thus it is one of the sectors to invest in.
 Information Technology Sector
 Lack of sensitivity to oil prices and interest rates makes returns of the IT Sector more stable than other sectors
 Implementation of 5G wireless technology in the near future has large potential for high returns on investment
 Rising consumer expenditure[5] in technology related categories such as personal computers and software is a good indicator of higher sales revenue in the future
The IT Industry is the secondbest performing industry in the U.S with a market capitalization of $7.14 trillion. The market weight for the U.S. I.T. sector is 20.51%, and is ranked 1^{st}.
 Healthcare Sector
 Healthcare sector tends to be more stable than other sectors
 America faces an ageing population. The population aged 65 and over will increase from 46 million in 2014 to 98 million by 2060[6]. Ageing population translates to higher healthcare expenditure
 Healthcare stocks are currently inexpensive compared to the rest of the market[7]
The U.S. Healthcare sector is the third best performing industry and has a market capitalization of $5.58 trillion. The market weight for the U.S. health sector is 15.05% and is ranked 2^{nd}.
 Industrial Sector
 This sector tends to be more economically sensitive. Its performance is closely linked to the performance of the economy. With data showing that the American economy is expected to grow and perform well, this sector will generate positive returns
 Since March 2009, the Industrial Sector stocks of the S&P 500 went up by 149 percent[8]. Strong recovery from the 2008 financial crisis is expected to continue
 Majority of the companies in this sector have large cash reserves and have expressed their desire to invest more of this cash in projects and R&D
The Industrial Sector has a market capitalisation of $3.94 trillion. The sector is ranked 6^{th} in the economy based on market weight.
 Communications Sector
 Telecom sector dividend yields are one of the highest in the S&P 500
 Increasing wireless demand in America promises higher sales revenue for Telecom companies. 5G Technology is also due to be used in the coming years, further enhancing revenue growth for companies
 Telecom companies are diversifying into the content sector, thereby increasing returns. For example, AT&T’s merger with Time Warner
The U.S. Communications services sector is the fifth best performing industry, and has a market capitalization of $4.53 trillion. The market weight for the U.S. Communications sector is 10%, and ranked 5^{th}.
The Stocks
The 20 stocks were chosen based on their market capitalization, P/E Ratio and Sharpe Ratio.
Proxy for the RiskFree Asset
We chose the 5year treasury note as a proxy. It provides a coupon rate of 2.875 percent and its yield is relatively stable. It is a AAA rated note and is therefore considered to be a safe bond with negligible default risk.
Efficient Frontier with NO Short Selling
To form the efficient frontier, we carried out the following steps:
Step 1: Calculation of daily return:
$\frac{\textcolor[rgb]{}{\mathit{New}}\textcolor[rgb]{}{}\textcolor[rgb]{}{\mathit{Price}}\textcolor[rgb]{}{\u2013}\textcolor[rgb]{}{\mathit{Old}}\textcolor[rgb]{}{}\textcolor[rgb]{}{\mathit{Price}}}{\textcolor[rgb]{}{\mathit{Old}}\textcolor[rgb]{}{}\textcolor[rgb]{}{\mathit{Price}}}\textcolor[rgb]{}{*}\textcolor[rgb]{}{\mathit{100}}$
Equation 1: Daily Return
Step 2: Construction of VarianceCovariance Matrix by using the data analysis addin on Excel
Step 3: Calculation of annualized average returns:
$\frac{\textcolor[rgb]{}{\mathbf{\Sigma}}\textcolor[rgb]{}{\mathit{x}}}{\textcolor[rgb]{}{\mathit{n}}}\textcolor[rgb]{}{*}\textcolor[rgb]{}{\mathit{252}}$
Equation 2:Annualized Average Return
(where ‘x’ is the daily returns)
Step 4: Calculation of annualized standard deviation:
$\sqrt{\textcolor[rgb]{}{\mathit{Variance}}}$
*
$\sqrt{\textcolor[rgb]{}{\mathit{252}}}$Equation 3: Annualized Standard Deviation
Step 5: Construction of minimum variance portfolio using Solver on Excel, setting the objective as minimum variance
Step 6: Creation of optimum portfolio by using Solver on Excel and setting the objective to maximize the sharp ratio
Step 7: Creation of a maximum return portfolio by using Solver on Excel
Step 8: Using Solver, the objective was set for particular levels of returns
Step 9: Plotted the Efficient Frontier using the returns and standard deviations
Figure 3: Efficient Frontier(Blue) and Capital Allocation Line(Orange) with no shortselling.
Source: As calculated on Excel
Market Portfolio
We have assumed that the market portfolio is the yellow point where the capital allocation line is tangent to the efficient frontier in Figure 3. The optimum portfolio is the market portfolio. This portfolio is most preferred as risk and return is in line with the utility function of the investor. The capital allocation line is the line that shows the various risktoreturn combinations available. The efficient frontier is the curve that shows the combination of the expected returns and standard deviations of the 20 stocks.
Optimum portfolio (Y) 
Weights 
Expected return 
29.65% 
BAC 
0.00% 
Expected Var 
2.68% 
JPM 
0.00% 
Expected SD 
16.36% 
JNJ 
0.00% 
Excess return 
26.78% 
MRK 
0.00% 
Sharpe ratio 
163.67% 
PFE 
0.00% 

UNH 
43.42% 

T 
0.00% 

VZ 
0.00% 

FB 
3.92% 

GOOGL 
0.00% 

AMZN 
26.07% 

AAPL 
13.19% 

MSFT 
0.12% 

MMM 
0.00% 

HON 
0.00% 

BA 
5.53% 

INTC 
0.00% 

UNP 
0.00% 

BRK/A 
0.00% 

V 
7.76% 

100.00% 
Table 1: Weights of the assets invested in the Market Portfolio
Source: As calculated on Excel
We believe that the composition of the market portfolio in Table 1 is practical for investment. It is possible to invest a corpus per these weights and in these proportions. The Sharpe Ratio is 163.67% which is considered acceptable and good by investors[9].
Minimum Variance Portfolio
Minimum Variance 
Weights 
Optimum portfolio 
Weights 
BAC 
0.00% 
BAC 
0.00% 
JPM 
0.00% 
JPM 
0.00% 
JNJ 
17.81% 
JNJ 
0.00% 
MRK 
2.11% 
MRK 
0.00% 
PFE 
11.32% 
PFE 
0.00% 
UNH 
4.33% 
UNH 
43.42% 
T 
17.11% 
T 
0.00% 
VZ 
11.55% 
VZ 
0.00% 
FB 
0.19% 
FB 
3.92% 
GOOGL 
0.00% 
GOOGL 
0.00% 
AMZN 
2.00% 
AMZN 
26.07% 
AAPL 
6.51% 
AAPL 
13.19% 
MSFT 
0.00% 
MSFT 
0.12% 
MMM 
5.07% 
MMM 
0.00% 
HON 
2.89% 
HON 
0.00% 
BA 
0.00% 
BA 
5.53% 
INTC 
0.00% 
INTC 
0.00% 
UNP 
0.75% 
UNP 
0.00% 
BRK/A 
16.13% 
BRK/A 
0.00% 
V 
2.25% 
V 
7.76% 
Total 
100.00% 
100.00% 
Table 2: Composition of the Minimum Variance Portfolio and Optimum Portfolio
Source: As calculated on Excel
In the minimum variance portfolio, the purpose is to minimize the variance (or risk) by choosing the least risky stocks. In the optimum portfolio, our objective is to maximize our return per unit of risk. Therefore, we invest a higher proportion of the funds in the stocks that are highlighted in blue in Table 2 in the minimum variance portfolio compared to the same stocks in the optimum portfolio because the variance of these stocks is the lowest in our portfolio. We invest a higher proportion of our funds in stocks that are highlighted in yellow in Table 2 in our optimum portfolio compared to the minimum variance portfolio because they give us the highest return per unit of risk.
Efficient Frontier with Short Selling
The same steps are followed in the construction of the efficient frontier with and without short selling. In the construction of the efficient frontier with short selling, we allow for nonnegative variables in Solver. In this case, the change in the portfolio is that our investment is in all 20 stocks, long on some stocks and short on the other.
Figure 4: Efficient Frontier(Blue) and Capital Allocation Line(Orange) with shortselling.
Source: As calculated on Excel
Market Portfolio with ShortSelling
We have assumed that the market portfolio is the green point where the capital allocation line (Orange Line) is tangent to the efficient frontier (Blue curve) in Figure 4. The change in our holdings imply that we are taking advantage of the stocks that have high returns and volatility by hedging this risk through investment in stocks with lower volatility.
The following table shows the weights of the assets invested in the Market Portfolio:
Optimum portfolio 
Weights 


BAC 
28.64% 
Expected return 
66.20% 
JPM 
16.40% 
Expected var 
10.98% 
JNJ 
19.73% 
Expected SD 
33.14% 
MRK 
10.28% 
Excess return 
63.32% 
PFE 
52.09% 
Sharpe ratio 
191.08% 
UNH 
101.04% 

T 
65.45% 

VZ 
3.23% 

FB 
17.31% 

GOOGL 
37.53% 

AMZN 
49.79% 

AAPL 
29.08% 

MSFT 
30.84% 

MMM 
25.32% 

HON 
16.64% 

BA 
33.54% 

INTC 
15.42% 

UNP 
7.43% 

BRK/A 
34.42% 

V 
33.94% 

100.00% 
Table 3: Weights of the assets invested in the Market Portfolio
Source: As calculated on Excel
Minimum Variance Portfolio
Minimum Variance Portfolio 
Weights 
Optimum portfolio 
Weights 
BAC 
3.00% 
BAC US Equity 
28.64% 
JPM 
7.09% 
JPM US Equity 
16.40% 
JNJ 
16.48% 
JNJ US Equity 
19.73% 
MRK 
2.56% 
MRK US Equity 
10.28% 
PFE 
11.64% 
PFE US Equity 
52.09% 
UNH 
5.18% 
UNH US Equity 
101.04% 
T 
16.71% 
T US Equity 
65.45% 
VZ 
11.94% 
VZ US Equity 
3.23% 
FB 
0.54% 
FB US Equity 
17.31% 
GOOGL 
0.22% 
GOOGL US Equity 
37.53% 
AMZN 
2.94% 
AMZN US Equity 
49.79% 
AAPL 
7.57% 
AAPL US Equity 
29.08% 
MSFT 
4.45% 
MSFT US Equity 
30.84% 
MMM 
5.49% 
MMM US Equity 
25.32% 
HON 
6.10% 
HON US Equity 
16.64% 
BA 
2.55% 
BA US Equity 
33.54% 
INTC 
0.20% 
INTC US Equity 
15.42% 
UNP 
2.26% 
UNP US Equity 
7.43% 
BRK/A 
23.48% 
BRK/A US Equity 
34.42% 
V 
4.17% 
V US Equity 
33.94% 
Total 
100.00% 
100.00% 
Table 4: Composition of the Minimum Variance Portfolio and Optimum Portfolio
Source: As calculated on Excel
Here, we invest a higher proportion of the funds in the stocks with lower variances (highlighted in green in Table 4) in the minimum variance portfolio compared to the same stocks in the optimum portfolio. For some stocks in the optimum portfolio (highlighted in orange in Table 4) we hold a high long position, but in the minimum variance portfolio, we hold a short position. We observe that in the Optimum portfolio we hold a long position in Tech stocks as they have a high rate of return but at the cost of high risk.
Risk Adjusted Performance
Minimum Variance Portfolio (No shortselling) 
Market Portfolio (No shortselling) 
MarketCapitalisation Weighted 
Equally Weighted Portfolio 

Weights 
Weights 
Weights 
Weights 

BAC 
0.00% 
0.00% 
3.65% 
5.00% 
JPM 
0.00% 
0.00% 
4.90% 
5.00% 
JNJ 
17.79% 
0.00% 
5.25% 
5.00% 
MRK 
2.01% 
0.00% 
2.65% 
5.00% 
PFE 
11.53% 
0.00% 
3.37% 
5.00% 
UNH 
4.30% 
43.42% 
3.49% 
5.00% 
T 
16.96% 
0.00% 
2.95% 
5.00% 
VZ 
11.74% 
0.00% 
3.33% 
5.00% 
FB 
0.13% 
3.92% 
5.38% 
5.00% 
GOOGL 
0.00% 
0.00% 
9.92% 
5.00% 
AMZN 
1.97% 
26.07% 
10.44% 
5.00% 
AAPL 
6.50% 
13.19% 
12.30% 
5.00% 
MSFT 
0.00% 
0.12% 
11.21% 
5.00% 
MMM 
5.11% 
0.00% 
1.63% 
5.00% 
HON 
3.06% 
0.00% 
1.47% 
5.00% 
BA 
0.00% 
5.53% 
2.56% 
5.00% 
INTC 
0.00% 
0.00% 
2.99% 
5.00% 
UNP 
0.78% 
0.00% 
1.49% 
5.00% 
BRK/A 
15.81% 
0.00% 
7.21% 
5.00% 
V 
2.30% 
7.76% 
3.79% 
5.00% 
Excess Return (Risk Adjusted) 
10.38% 
26.78% 
17.43% 
14.30% 
Source: As calculated on Excel
As can be seen in Table 5, the optimum portfolio gives the highest risk adjusted return of 26.78%. The minimum variance portfolio gives us the lowest risk adjusted return of 10.38%. The equally weighted portfolio gives a relatively low excess return because we also invest in stocks that provide lower rates of return. In the marketcapitalization weighted portfolio gives a higher return than equally weighted portfolio as in this we invest higher proportion in the tech stocks that provide high return.
QUESTION 2
Testing the CAPM
Procedure for testing the CAPM
To test the CAPM we obtain,
 Last price monthly data for 10 years (2008.112018.11) of the 20 stocks
 10 years of onemonth treasury bills rate data as the proxy for US risk free rate R_{ft}
 Same period for Standard & Poor’s 500 index data and calculate its monthly return. This was used as the proxy for market return R_{mt}
Risk Premium can be derived by applying:
$\textcolor[rgb]{}{R}\textcolor[rgb]{}{\mathit{mt}}\textcolor[rgb]{}{\u2013}\textcolor[rgb]{}{R}\textcolor[rgb]{}{\mathit{ft}}$
Equation 4: Risk Premium
Testing the CAPM via FamaMacbeth two step Methodology
Step 1: Run time series regression of the excess return of each stock on a constant and the excess return on the market portfolio for 20 stocks. Regression formula proposed by Fama and MacBeth (1973):
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View our services$\textcolor[rgb]{}{R}\textcolor[rgb]{}{\mathit{it}}\textcolor[rgb]{}{\u2013}\textcolor[rgb]{}{\mathit{R}}\textcolor[rgb]{}{\mathit{ft}}\textcolor[rgb]{}{=}\textcolor[rgb]{}{}\textcolor[rgb]{}{\uf061}\textcolor[rgb]{}{\mathit{i}}\textcolor[rgb]{}{+}\textcolor[rgb]{}{}\textcolor[rgb]{}{\uf062}\textcolor[rgb]{}{\mathit{i}}\textcolor[rgb]{}{(}\textcolor[rgb]{}{R}\textcolor[rgb]{}{\mathit{mt}}\textcolor[rgb]{}{\u2013}\textcolor[rgb]{}{\mathit{R}}\textcolor[rgb]{}{\mathit{ft}}\textcolor[rgb]{}{)}\textcolor[rgb]{}{}\textcolor[rgb]{}{+}\textcolor[rgb]{}{\mathit{e}}\textcolor[rgb]{}{\mathit{it}}$
Equation 5: FamaMacBeth Regression
Results of Regression:
Stock 
_{i} 
APPLE 
0.9968 
MSFT 
1.0351 
VUN 
0.6846 
JNJ 
0.6280 
UNH 
0.8055 
PFE 
0.8826 
MRK 
0.6803 
BRK 
0.6891 
JPM 
1.5927 
BAC 
2.4346 
GOOGL 
0.9770 
VZ 
0.4543 
AMZN 
0.8729 
INTC 
0.9880 
MMM 
1.0890 
BA 
1.2714 
UNP 
1.1395 
HON 
1.1839 
T 
0.4440 
FB 
0.8520 
Table 6: Estimation of _{i} of 20 stocks
Source: As calculated on EViews
Step 2: Run 20 regressions based on the formula:
$\textcolor[rgb]{}{\mathit{\lambda}}\textcolor[rgb]{}{0}\textcolor[rgb]{}{+}\textcolor[rgb]{}{\lambda}\textcolor[rgb]{}{1}\textcolor[rgb]{}{\uf062}\textcolor[rgb]{}{i}\textcolor[rgb]{}{+}\textcolor[rgb]{}{\epsilon}\textcolor[rgb]{}{i}$
Equation 6: Regression
Here, R_{i }(i=1.2.3…20) is the simple average return on stock i.
Figure 5: Regression results of Step 2
Source: As calculated on EViews
As shown in Figure 5, λ_{0 }is 1.4176, it’s tstatistic is 3.1087 which is very significant. Hence, we reject the null hypothesis: λ_{0 }= 0. The value of λ_{1} is 0.1235, its tstatistic is 0.2909 which is not significant.
After calculating the average return for R_{f}_{ }using the data we collected previously, we can conclude that the value of 0.2876 is not equal to λ_{0}. Meanwhile, the average risk premium calculated (average market return less the riskfree rate) of 0.7154 is not equal to λ_{1}, so we come to the conclusion that CAPM is not valid.
Results comparison on CAPM tests
According to our regression on the 20 stocks, the CAPM is not valid to explain the relation between risk and return. This result is not consistent with early empirical study. Sharp (1964), Lintner (1965), Black (1972) point out that the return of stocks is only linearly related to the systemic risk of the whole stock market. However, Fama and French (1992) consider that β of the stock market cannot explain the difference of stock returns, while the market value, booktomarket value ratio and pricetoearnings ratio can explain the difference of stock returns in US equity market. The study of Boutabba (2015) and Esteban (2015) both proved that the threefactor model is valid for 25 portfolios in US market. Chaudhary (2017) ran the regression of 25 portfolios in the US market applying single factor and multifactor models. In the single factor model, the crosssectional returns of portfolio can still be expressed through the linear relationship of beta, which differs from the early literature and our result.
Explanation for the invalidation of CAPM model
There are three possible reasons why our result shows that the CAPM is not valid.

In terms of models, we only use the single factor CAPM model rather than threefactor model, which is not efficient for explaining the relation between risk and return.

In terms of regression methods, stock returns are usually crosssectional correlated and heteroscedastic. The regression results of this regression method are misleading. For instance, when beta becomes larger, stock returns become higher, but its regression error term will also become larger, which makes the variance of the error term of the observed value of different stock returns different. Hence, heteroscedasticity may make parameter estimators or significant test ineffective.
 From the aspect of sample selection, we only choose 20 stocks whereas Chaudhary (2017), Boutabba (2015) and Esteban (2015) both select 25 portfolios, which are more representative than our sample. Furthermore, we choose stocks mainly based on their market value, leading to the result that these stocks have close range of β and returns. Bilinski and Pawel (2014) find that there is a Ushaped relationship between β and yield. When β is between 0 and 1, return of stock is concentrated between – 3.13% and 4%. But when β is over 1.2, the return of stocks can reach 20% to 30%. With regard to our data, the average β of 20 stocks range from 0.4 and 1, which makes their observations of β and returns centralize at the bottom of the Ushaped curve and the relation tends to be flat.
References
 Data obtained from Bloomberg
 Black, F., M. Jensen, and M. Scholes (1972), The Capital Asset Pricing Model: Some Empirical Tests, in M. Jensen (ed.), Studies in the Theory of Capital Markets, Praeger, Connecticut.
 Bilinski, P. (2014). Risk Interpretation of the CAPM’s Beta: Evidence from a New Research Method. Abacus, 50(2), 203226
 Boutabba, I. (2015). An empirical validation of fama and french threefactor model (1992, A) on some US indices. Asian Economic and Financial Review, 5(7), 915925.
 Chaudhary, P. (2016). Test of CAPM: A study of india and US. International Journal of Financial Management, 6(2), 5158
 Esteban, M. V. (2015). Nonparametric methods for estimating and testing for constant betas in asset pricing models. Applied economics, 47(25), 25772607
 Fama, E., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy,71, 607636.
 Fama, E. F. and K. R. French (1992),‘The Crosssection of Expected Stock Returns’, Journal of Finance, Vol. 47, No. 2, pp. 427–65.
 Lintner, J. (1965),‘The Valuation of Risk Assets and Selection of Risky Investments in Stock Portfolios and Capital Budgets’, Review of Economics and Statistics,Vol. 47, No. 1, pp. 13–37.
 Sharpe, W. F. (1964), Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance,Vol. 19, No. 3, pp. 425–42.
[1] https://taxfoundation.org/uscorporateincometaxmorecompetitive/, Tax Foundation
[2] Kyle Pomperleau (Author) Calculations, Tax Foundation
[3] ‘Salaries Expected to Rise 3.1% of Payroll’, AON Media Center
[4] 2018 Banking Regulatory Outlook, Deloitte
[5] ‘The Pros of Investing in the Technology Sector’, USNews
[6] Population Bulletin, Population Reference Bureau Vol. 70 No. 2
[7] ‘Two Reasons to Invest in Healthcare Stocks Now’, Forbes
[8] ‘Industrial stocks: A big bet on industrial growth’, CNBC
[9] What is a good Sharpe ratio? By J.B. Maverick, Investopedia
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