I only need a 50% on this project to pass the class. I am leaving the country so I don't have time to finish my last assignment. I attached sample document. I have all the models needed. All you gotta do is plug numbers in. I also have youtube tutorial step by step on how to complete. Don't forget, I only need a 50 to pass. just half wing this. Don't aim for perfection at all.
Risk Management Asset Allocation Project Risk Management Spring 2020 Student Name pg. 1 CLIENT DESCRIPTION: Daniel Miller, a 26-year-old primate behavioralist, wants to improve the allocation of his stock portfolio. Miller has recently inherited a large sum of money from his grandfather and wants to use the inheritance to save for retirement. Miller does not plan on retiring for another forty years and is willing to take on additional risk for higher returns. As a result, Miller has a risk tolerance of 4%. Current Portfolio: The current portfolio allocation can be seen in the figure below and has four assets classes: Large Cap growth stocks, Large Cap value stocks, Bonds, and High Yield. Miller allocated 35% in both Large Cap growth and value, 20% in bonds, and the remaining 10% in high yield. Although the client has partially diversified their portfolio, they could obtain higher expected returns at the same risk level through investing in more asset classes. The excel model evaluates if investing in previously untapped asset classes such as small cap growth and value stocks, developed markets, emerging markets, and gold can be used to optimize the portfolio allocation. Assumptions: The chart below contains all the risk and return assumptions the model utilizes for each of the asset classes provided by professor Sweet. Furthermore, the chart also shows the correlations between asset classes. The assumptions significantly impact the results of the model and therefore also the recommended portfolio composition. Sensitivity analysis should be conducted on a regular basis to ensure the assumptions are still reflective of market conditions. Efficient Frontier: The model uses the efficient frontier to optimize Miller’s portfolio. According to Investopedia: “the efficient frontier is the set of optimal portfolios that offer the highest expected return for a defined level of risk.” In other words, the efficient frontier plots a line of optimal portfolio allocations that offer the highest expected return for each risk level. The efficient frontier uses mean-variance optimization, maximizing the tradeoff between risk and return, to find the optimum portfolio allocation. Risk: Risk is the chance that the actual outcome is different than an expected outcome. Risk is measured through standard deviations. Standard deviation measures historical volatility in asset prices in comparison to their averages. Standard deviation is a measure of total risk that assumes symmetry and a normal distribution of outcomes. CONSTRAINTS: To create an optimal asset allocation strategy specific to Miller, I set several constraints to limit investment in specific asset classes. The constraints show the maximum percentage of the total portfolio the client should be willing to invest in a specific asset class. The constraints are summarized in the figure below. High Yield: Historically, high yield bonds perform well during periods of economic expansion but perform horribly during economic recessions. High yield bonds are very risky, but investors are compensated for the extra risk with higher than average returns. The client aims to maximize return over a long investment horizon and is relatively risk-seeking. Therefore, investing in high yield bonds might allow the portfolio to gain some extra returns. However, over-allocating in high yield bonds can be detrimental during economic slowdowns. I capped the investment in high yield at 15% to potentially capture some upside while limiting the downside risk. Gold: Gold is a volatile asset class traditionally used to hedge against inflation and economic downturns. Historically, when the stock market is down or inflation is increasing, gold gains value. Allocating a percentage of the portfolio into gold can mitigate some the losses when most of the asset classes are underperforming. Furthermore, low correlation with most other assets classes suggests investing in gold provides a strong diversification benefit. As a result, I would suggest only putting maximum 5% of the portfolio in gold. Emerging Markets: Emerging market stocks are favorable investment vehicles because they provide diversification outside of the United States. Emerging market stocks have higher risk and higher returns than US market stocks. For diversification purposes, I would limit investment in emerging market stocks to a maximum of 15%. International Stocks: The final constraint is on international stocks in general. When in it comes to US investors, we tend to invest in stocks that we think we are familiar with. This phenomenon is also called the equity home bias. The bias can negatively impact our investment strategy and result in unoptimized portfolios. To negate home bias and provide means for diversification, I generously set the limit for international stocks at 50%. SUMMARY OF ANALYSIS: Through the afore mentioned assumptions and constraints, I used the excel model to build an efficient frontier. The goal of the efficient frontier is to maximize returns while maintaining a set level of risk. To find the efficient frontier, I used the excel solver to optimize the portfolio for specific risk levels. Starting at the 4.5% risk level and incrementally increasing it by 0.5%. The final risk level calculated was 17%. The optimized return portfolios were then plotted on a risk versus return graph. Lastly, a trendline was drawn to create our efficient frontier. The finalized efficient frontier is displayed below. Portfolio Reallocation: As we can see from the chart, Miller’s current portfolio is below the efficient frontier. A portfolio below the efficient frontier indicates that the client has not optimized their expected return for their current risk exposure. If Miller can adjust the allocation to be on the efficient frontier, he can obtain higher returns for the same level of risk. The current portfolio has risk of 10.74% and return of 6.87%. The recommended portfolio provided by the efficient frontier has risk of 10.74% but has an expected return of 7.20%. A reallocation of the portfolio could increase returns by 33 basis points. Overall, switching the allocation can have great benefits for the client. Value at Risk Analysis: Next, I performed a historical and parametric Value at Risk (VaR) analysis. VaR is the measure of the amount of loss expected at a given probability or risk tolerance. The risk tolerance for Miller was 4%. The VaR analysis for the current and recommended portfolio are summarized below. Historical Portfolio Return Comparison: The current portfolio has an historical average annual return of 10.30% whilst the recommended portfolio stands at 9.91%. If Miller would have held his current portfolio from 1988 to the present, he would have performed better than the recommended portfolio over the same time period. The difference can be explained by bonds overperforming historically. Because Miller originally had 20% allocated in bonds it skews the historical returns. Furthermore, international stocks have underperformed historically. The model assumes international stocks to do better and bonds to do worse in the future. When calculating the average return using our assumptions, the average return on the recommended portfolio is higher. The recommend portfolio has 7.20% return versus the 6.87% return of the current portfolio. Historical VaR Comparison: Historical VaR runs simulations from selected points in history and evaluates the mean loss in these simulations. The current portfolio has a historical VaR of 12.00% whilst the recommended portfolio has a historical VaR of 8.27%. With 96% confidence, the recommended portfolio will not lose more than 8.27% of its value. Reallocating the portfolio significantly decreases the potential size of losses. Parametric VaR Comparison: On the other hand, parametric VaR calculates the expected loss based on the number of standard deviations. Parametric VaR is calculated with the formula: Mean - # of St Dev * St Dev. The parametric VaR of the current portfolio is 11.93% while the parametric VaR for the recommended portfolio is 11.61%. In other words, under the recommended portfolio allocation 4% of the time we expect a 11.61% loss or more. Or expect to lose no more than the VaR 96% of the time. In summation, reallocating the portfolio marginally improves the both the historical and parametric VaR. VaR Inconsistency: As seen in the chart, there is a large discrepancy between the parametric and historical VaR. This occurs because the risk is not as symmetrical as parametric VaR would assume. A possible explanation for non-symmetrical risk could be fatter tail risk due to market contagion. When the entire market is stressed it increases the failure of all components which makes the distribution of risk asymmetrical. FINAL RECOMMENDATION: The recommended portfolio consists of 5 assets classes: large cap growth, large cap value, developed market stocks, emerging market stocks, and bonds. Furthermore, the recommended portfolio considers the client’s risk tolerance and investment horizon. Diversifying the portfolio allows Miller to obtain higher expected return while maintaining the same level of risk. The specific asset class allocations can be seen on the pie chart below. The recommended portfolio invests in more asset classes to gain diversification benefits. Furthermore, the portfolio makes use of international stocks to increase returns. Lastly, allocating a larger percentage in bonds allows the client to have stable long-term returns. Overall, the recommended portfolio maximizes returns while maintaining the same level of risk. STYLE ANALYSIS: The client asked me if one of their preferred mutual funds would work with the recommended portfolio. Miller specifically wanted to see if Vanguard Wellington Fund Investor Shares (VWELX) was in line with the recommended portfolio. To evaluate if the mutual fund aligns with the recommended portfolio, we can conduct a style analysis. A style analysis compares the returns of the mutual fund to the returns of our asset classes to infer how much the mutual funds has invested in each asset class. We use excel solver to find the highest R-squared value and get the best possible guess for the mutual fund allocation. With a R-squared of 63%, the allocation estimate of the mutual fund can be seen below. R-Squared Analysis: The 63% R-squared value is not as high as I would like it to be. Ideally the R-squared value would be above 90%. However, it appears Vanguard has changed allocation mid-stream, and you can see the departure in the graph below. Thus, causing a lower than usual R-squared of 63%. Mutual Fund versus Recommended: When comparing the mutual fund allocation and the recommended fund allocation, we can see large discrepancies. The Vanguard fund is invested in four asset classes whilst the recommended portfolio has 5 asset classes. Most of the Vanguard fund is allocated to bonds and large cap value stocks. Furthermore, the mutual fund has no investments in international stocks. In summation, the mutual fund is not a good match for our recommended portfolio because it is too US focused and does not match our allocation. Furthermore, the Vanguard fund drastically underperformed compared to the benchmark. I do not recommend using the mutual fund suggested by our client. MANAGER ATTRIBUTION: Lastly, I did a manager attribution of a fund recommended by the client. Miller