Part 1 of the project has already been done. And I am looking for who can continue to do part 2 of the stock project.

The default asset allocation is 70% stocks, 10% bonds, and 10% as an ETF with the remainder in cash. The purpose of this allocation was to acquire a divestiture of securities from different sectors of the market and optimize them for growth. When constructing this fund, the goal was to create a majority passively managed fund that would still gain excess returns for our investors. I chose this strategy because of the negative correlation between increased trading activity of fund managers and realized returns. In part, this is due to miselection in securities by managers, but it also has to do with the transaction costs and capital gains tax realizations on the buying and selling of securities. That being said, there are some parameters that would warrant changes in the portfolio during the holding period. Such as sector rotation when specific sectors increase in value. A great example is the price of gas spiking during the summer months as more expensive summer-blend fuels are used.

Another example, the bond securities in the fund should be monitored closely by evaluating overall economic health (GDP Growth, S&P returns, Federal Reserve Economic sentiment). If interest rates are expected to rise, it may be within the manager’s discretion to reduce the number of bonds allocated into the fund or change the bond fund to a higher rated fund. The stocks in the fund were chosen because there is a belief according to my fundamental analysis that they are undervalued and have growth potential. However, if there was a major change in the industry or a company specific scenario that may potentially drive the price down or up (earnings reports, changes in market landscape, mergers and acquisitions, ect.) it is warranted to change the weights invested into each stock accordingly.

Looking at the United States economy over the next 3-5 years there are a variety of economic indicators that suggest a slowdown may begin after years of a bullish economy since President Trump took office in 2016. The United States is experiencing the lowest unemployment rate since 1969 at 3.8%, meaning more Americans are in the workforce producing capital, receiving income and flowing capital into the economy. Real GDP continues to grow at a healthy rate of 3.1% in 2018, supplemented by healthy difference in 1.9% inflation. According to the Federal Open Market Committee Real GDP forecasts predict a slowdown in the nation’s economic production over the following three years into 2020 at an average of -14.95% (using 3.1% as a basis) a year. The economy has very strong fundamental statistics, and the economy is still experiencing relatively low inflation, making a speculator question, when will the measures that compliment rapid economic expansion such as inflation heat up?

A dip in the market could be caused by a variety of external factors. One concern is the trade tensions with China, and their nation’s economic slowdown over the previous few business quarters. I believe their economy will continue to move forward despite the recent slowdowns through China’s governmental monetary and fiscal tools that they have not hesitated to deploy in the past. Measures such as large amounts of quantitative easing, devaluing of the yen and lowering of bank reserves as well as the rapidly expanding Asia-Pacific market will push China’s economy forward. However, trade tensions have continued to a subject of discussion and tariffs between the U.S. and China could cause market anomalies and negative investor sentiment within U.S. markets. Another is the Federal Reserve’s interest rate hikes as they attempt to reach interest rate normalization. As the economy continues to recover from the 2008-2009 recession and recover from the outlandish 0% interest rates and large quantitative easing measures, the Fed has taken a hawkish stance to try and keep pace with the growing economy. As a result of these future economic conditions, I have taken a bearish view of the current U.S. market within 3-5 years as GDP begins to slow down.

For this portfolio consideration was given to stocks, ETF’s, bonds, mutual funds and cash, of both international and domestic nature. According to the risk tolerance of the fund, 70% will be allocated in stocks, 10% in a high yield bond fund, 10% in an ETF and lastly 10% in cash for investor withdraws, additional trades and liquidity. On the topic of bonds, although the Federal Reserve has taken a hawkish stance and is speculated to increase interest, an important phenomena to observe is the recent inversion of the yield curve and bonds. When the yield curve inverts, it essentially means the bond market believes that there will be a recession in the near future. The yield curve has risen due to rate hikes by the federal reserve to match perceived U.S. economic growth, however when the yield curve inverts it means investors do not believe in the U.S. economy outperforming and believe rates will not continue to rise in the future because of a recession. From the bond investor’s perception this makes the long term bonds more preferable because of their duration and current high interest rates. Effectively meaning that the long term bonds now, will be trading at a premium within the next few years, and I believe there is evidence for this backed up by the Federal Reserve’s recent behavior of halting their hikes in interest rates.

Referring to the 70% stock allocation, the benchmarking and portfolio optimization mathematics were done with each stock being equally weighted at 10% in the portfolio. When starting the fund I believed this allocation was reasonable, as I had no particular sentiment on which stocks I truly felt would outperform my other choices. The weights however, are subject to change throughout the investment holding period at the discretion of the fund manager as specified in the Active Investment Strategy section of this document. The weights may be adjusted according to sector rotation, individual stock’s earnings reports, and mergers and acquisitions to maximize returns. Lastly, the portfolio holds on ETF that has a relatively high return and is indexed to the information technology sector. As a whole, I see this industry only growing in the time horizon of this portfolio as information technology has infiltrated practically all aspects of human life in the 21st century.

To determine the bounds of my portfolio, I utilized the portfolio optimizer tool. I uploaded the monthly close values for each of my securities, the 8 stocks, 1 ETF and 1 bond fund and found the returns dating from April 1, 2014 to April 1 , 2019. After uploading the monthly returns in the optimizer I used a risk free rate of 2.42% and referenced the returns to find the minimum (-0.797%) and maximum (2.710%) potential returns based on the historical changes in the portfolio. Next I ran an optimization to find what allocation would have provided the portfolio with the maximum Sharpe ratio over the designated period. I chose the maximum Sharpe Ratio because it would signal what security provided the best returns with low volatility (standard deviations). According to the maximum Sharpe optimization, a 100% allocation into Lululemon Athletica would have yielded the most optimal allocation over the historical period. This largely has to do with Lulu’s positive stock price run over the past three years. If I had allocated 100% of the portfolio into Lulu in 2014, the portfolio would have averaged 32.52% a year. Next, I ran an optimization for the minimum risk allocation of the portfolio to find which securities had consistent returns and low standard deviations. The optimizer then recommended an 84% allocation into the SGYAX bond fund and a 16% allocation into Procter and Gamble. This confirmed my strategy of using bonds to offset stock market value risk, and the inclusion of a consumer defensives (particularly products classified as “staples”) to also hedge against a downturn in the market. Attached below are the original portfolio weights, with both instances of optimization to compare to it. Those figures outlined in blue are minimum risk, and red for maximum Sharpe ratio.

]

A technical analysis was also run in the optimization showing that when the portfolio is maximized for the Sharpe ratio, the efficient frontier analysis reports an expected return increase of 1.5%. When adjusted for minimum risk the portfolio return decreases by 1%.

The table below represents a log of securities bought for the fund.

On the subject of benchmarking the portfolio I ran a regression analysis to determine the most correlated index to compare my portfolio to. In addition, I also ran a beta comparison analysis to find out what index had a similar beta to mine. I used the QQQ Power shares, SPY, VTI and DJI. To perform the beta analysis, I found the betas of each security and then found the summed average. With the betas equally weighted, the beta of the portfolio came out to be 1.013. Next, I compared the portfolio beta to each index and found that the Vanguard Total Market Index was most closely correlated at a value of 1.03.

After performing my beta analysis, I uploaded the monthly close prices from 2014-2019 of each security and index into an excel file and ran a statistical regression comparing the portfolio to individual indices and a weighted index that included all 4 indices. Attached below are the calculations for the portfolio and index returns.

Then once I had the average returns for the portfolio, I ran slope and correlation functions to find which index returns correlated most with my portfolio returns. The VTI came in with most correlation at 0.814, the SPY at 0.807, the QQQ at 0.783 and lastly the DIA at 0.7439. As a result, my portfolio’s beta and returns most correlated with the Vanguard Total Market Index (VTI). The results are attached below.

The second part of my benchmarking strategy was to take the statistical regression of the portfolio onto each index to observe which served as the best model.

As seen above, the VTI has the highest R-squared statistic meaning that 66.3% of the variation in the portfolio’s return values can explained by return values in the VTI. The VTI’s R-squared, beta, and correlation values suggest it the best fit as a model for benchmarking our portfolios returns against.

To do backtesting for the portfolio to evaluate the performance, I chose to compare the portfolio to the three indices as benchmarks to compare the fund’s performance to. To back test the portfolio I decided to calculate the alpha, or excess return the portfolio generates in comparison to our model indices of the VTI, SPY and QQQ. To find alpha I had to utilize the monthly return data for the portfolio and index which had already been calculated to find correlation. I was able to determine that the return of portfolio was 35.29%, or 7.05% yearly as the data dates back to 2014. To find the expected return, I needed to calculate each indices, which came in at 8.45% (VTI), 8.78% (SPY), and `5% (QQQ) yearly. Then to find alpha I took the slope of the portfolio to each indice, and multiplied that value by the summed return of the the respective index the fund is being compared to. This gave the risk adjusted return. After finding that value I subtracted it from the portfolio’s summed return to find the excess, or alpha which came out to 3.22% (VTI), 1.89% (SPY), and -11.41% QQQ. This effectively compares the portfolio returns to each indices returns over previous 5 years. When referring to the fund’s mission to beat the market with low risk characteristics, this alpha value satisfies the fund’s objective for the two funds that are most closely correlated (VTI, SPY). While the QQQ has a higher alpha than the portfolio, it takes considerably more risk which is outlined below in the risk-adjusted performance. Attached below is a chart of the calculations. The order of indices is sort by column and correlation, meaning the VTI is most correlated and the QQQ is the least correlated.

Alpha was calculated in the previous section showing an outperformance of the VTI and SPY, but an underperformance when compared to the QQQ. However, the secondary measure of performance is the Sharpe ratio of our portfolio. The Sharpe ratio is also known as the reward-to-volatility ratio and it shows how much average return a security produces when compared to its volatility (standard deviations) or the security’s risk. To calculate this statistic I pulled data from the above row labeled the Sharpe ratio, which divides the average by the standard deviation for each security. The Coefficient of Variation is also known as the Sharpe ratio and the values came to 0.111 (Portfolio), 0.2055 (VTI), 0.0001 (SPY), 0.300285 (QQQ). The lower the ratio, the better the risk/return trade off is. This shows the portfolio has a significantly better return for the portfolio’s risk when compared to each index.

Lastly, a value at risk estimation was calculated in the row labeled, “95%”. The value at risk formula is simply the average return of the security subtracted by two times the standard deviation. The equation represents the possible loss within two standard deviations of the average according to a 95% confidence interval. This essentially means that there is a 5% chance that the portfolio could experience a loss of 10.39% compared to 6.7% (VTI), 6.5% (SPY) and 8.15% (QQQ) historically based returns. This data means that our portfolio has a low risk compared to the fund’s return, but there is a wider range of potential loss than the general market (VTI, SPY, QQQ).

An outline of what policies and parameters allow rebalancing of the portfolio in the, “Active Management Strategy” section of this document. This fund has been constructed to perform adequately during market dips, and remain primarily a passive investment fund. However, the portfolio’s security weights may be adjusted in response to market conditions such as speculation on interest rates for the bond fund. This especially holds true if there is a loss predicted for a specific security due to market conditions such as earnings, technological advancement, and mergers and acquisitions. The active manipulation of the fund should not be to drastically increase returns or the composition of the portfolio, but to hedge against losses. This is primarily because the fund’s purpose is to provide an excess return to the market with a considerably lower risk, which can be observed in the fund’s Sharpe ratio.

When evaluating my construction of this portfolio, I believe I satisfied my goals of creating a risk-averse portfolio that still beats the market. One mistake I would correct however would be the inclusion of J.B. Hunt in the portfolio. After reviewing the security once again, it is apparent that is was temporarily overvalued by the market and has subsequently been taking a beating since mid-April. This was observable in the company’s high P/E ratio compared to its industry. However, I still find the company to have an extensive logistics network and the ability to continue being the one of the best companies in their business.

The other securities during the investment period have performed up to, or greater than expectations. Some of the best performing securities have been Lululemon Athletica, which has been riding a 20% rise in share price since March of 2019 and Comcast which has had 12.8% rise in share price since March as well. In addition, the Federal Reserve has take a dovish-neutral stance in recent press releases and interest rates have been flat as a result. SGYAX (bond fund) has been growing steadily in price at 6.94% since the start of the year with 7 more months to go.

Reflection on IPS:

Overall, I felt my Investment Policy Statement was produced without many issues or concerns. When choosing securities, I used fundamental analysis metrics that are readily available and public such as P/E ratios, EPS growth and revenue growth. Some other fundamental analysis tactics were employed during stock selection, primarily my speculation on the company’s products and the public perception of the company to conclude my intrinsic value evaluations that are not included in the IPS.

When reviewing whom this fund is created for, the accumulation investor typically between 22-40 years of age, I find that the fund reaches the goals of these investors. Reviewing the process of constructing the portfolio and observing the results, I found that an investor can reach their investment goals (as long as they are realistically attainable) if research and data is interpreted and applied correctly, and beat the average market return while doing so. When reviewing what drives these successful selections, it is primarily valuation and a touch of intuition to find where the market is inefficient and how to most likely beat it.