Modelcase 12: Mid Atlantic Specialty Inc. Financial Risk

Modelcase 12 Mid Atlantic Specialty Inc Financial Risk12/1/17

Model case 12 focuses on evaluating the financial risk of a medical group practice, Mid Atlantic Specialty Inc, through the analysis of various investment opportunities. The case provides probabilistic data on potential one-year returns across different economic states, historic return distributions, and calculations for standalone and portfolio risks and returns. It emphasizes understanding expected returns, variances, standard deviations, and the impact of correlations and market characteristics on investment decision-making. The core tasks involve calculating expected returns, variances, covariances, and assessing the risks associated with individual assets and diversified portfolios. Additionally, the case explores concepts such as the security market line, market beta, and the implications of market risk and return relationships on investment strategies for the healthcare sector.

Paper For Above instruction

The increase in investment opportunities in the healthcare sector necessitates a robust understanding of financial risk analysis for organizations like Mid Atlantic Specialty Inc. As a medical group practice contemplating different investment options, it is crucial to evaluate both the expected returns and the associated risks of these investments to make informed decisions that align with their financial objectives and risk tolerance.

The provided data includes probabilistic distributions of one-year returns across various economic states, which serve as a basis for calculating the expected return and risk measures for individual assets such as healthcare funds, biotech funds, the S&P 500, T-Bills, and inverse ETFs. For example, the expected return (\(E(R)\)) is derived by multiplying each possible return by its associated probability and summing these products. Using the historical data, the mean returns over five years can also be analyzed to compare the stability and consistency of different investments.

Risk assessment involves calculating the variance and standard deviation of returns, which measure the dispersion of outcomes. Variance offers insights into potential volatility, while the standard deviation provides a more interpretable measure of absolute risk. For instance, the healthcare fund has an expected return of approximately 7% annually across different states, but its variability must be quantified to assess whether the return compensates for risk.

Diversification is a key principle in risk management, as combining assets with different correlations can reduce overall portfolio risk. The correlation coefficient between assets indicates how their returns move relative to each other. A negative correlation, such as between the inverse ETF and the biotech fund, suggests that these assets may offset each other’s risks, enhancing portfolio stability. Calculating the portfolio’s expected return involves weighting individual asset returns by their proportions in the portfolio, while the portfolio risk incorporates covariances among the assets.

Market characteristics, such as the security market line (SML), translate risk into expected return using beta coefficients, which measure an asset's sensitivity to market fluctuations. A beta greater than one indicates higher volatility than the market, implying higher expected returns to compensate for additional risk. The data on market characteristic lines and the security market line offers a framework to evaluate whether projected returns align with the risk levels, guiding investment choices within the healthcare sector.

In conclusion, evaluating financial risk through expected return calculations, risk measures, correlations, and market models provides a comprehensive approach for healthcare organizations like Mid Atlantic Specialty Inc. to optimize their investment portfolios. Understanding these concepts enables organizations to balance risk and return effectively, ensuring strategic growth and financial stability in dynamic economic environments.

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