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Calculating the Risk and Interconnection with Market
Investment Risk: The Reason for Comparison between the Company and the Index
Despite the considerable number of shares’ valuation methods, the comparative approach is one of the most widespread. Many analysts consider the parallel share and index analysis as an effective one since it enables to realistically evaluate the company in the economy so that it is possible to observe how the investment the object will perform (Dopfel et al., 2018). In this case, a classical approach of historical performance analysis was used. The analyst derived the overall share and index’s returns by developing the ratio between the last and the first price of the period. After the percentage adjustment, it becomes clear that ENI stock is less volatile than the overall Italian MSCI index since the corporation’s standard deviation is almost twice lower than the market’s one. Finally, the specific ENI’s risk was measured by multiplying the company’s beta and the index’s standard deviation, representing the comparable risk measured in the market’s situation. Moreover, ENI’s specific risk is almost twice lower, mainly due to the increased volatility in the Italian MSCI index.
Beta Analysis: Eni’s Application
In ENI’s case, the analysts observe beta of 0.56. Generally, beta is the statistical representation of the different financial instruments’ correlation. This index demonstrates the possible trend for the one instrument when the other experiences a certain increase or decrease (Faisal et al., 2018). Consequently, when it comes to ENI’s case interpretation, the share price follows the Italian MSCI index with 0.56 speed. This means that on the one hand, the company drives in the same direction as the whole economy, and, on the other hand, ENI is almost two times more stable in its volatility compared to the overall Italian market. Thus, when the Italian economy rises, ENI would also be experiencing positive dynamics in the shares’ price, even with less percentage increase. At the same time, during the market decrease, the company would fall two times less than the MSCI index. These factors make the analyzed corporation a more conservative investment compared to the Italian MSCI index, which might be represented in Exchange Traded Funds (ETF) form.
How to Manage the Portfolio with Standard Deviation
Standard Deviation Principle
Standard deviation is the statistical representation of risk. Introduced by Gauss’ Bell modeling, this concept has significantly influenced portfolio management tactics. In this case, a classical strategy was used to balance the percentage of capital dedicated to provisionally risk-free assets. By adding them to the portfolio, the asset manager reduces the spread between standard deviation and the central point of risk distribution, decreasing overall portfolio volatility (Lam et al., 2021). Managing the standard deviation helps to balance the risk and return of the portfolio effectively. As a result, the most efficient asset manager would maximize the return percentage with the least possible volatility rate. Notion represents the importance of standard deviation adjustment since it positively correlates with the investors’ purposes.
Effective Rebalancing Process: Flexible Formula
In this exercise, the whole emphasis was put on the correlation between the percentage of added risk-free assets and standard deviation, which express the instrument’s volatility. At the same time, the amount of capital for the risk-free investments was measured by rationing the current standard deviation and the percentage of change needed. Moreover, the analysts detailed the method of purchase: equity was invested in decreasing the volatility, while the debt was used to increase the standard deviation of the portfolio. As a result, not only does the amount of capital dedicated for risk-free instruments matter but also the type of funding plays a significant role in defining the objective standard deviation (Juelsrud et al., 2020). More specifically, the debt usage inversed the formula by subtracting the risk-free assets’ percentage due to their liability’s origin. This aspect explains the flexibility of the formula, which consists of the two major factors that positively correlate with each other. For instance, the more risk that needs to be taken, the more capital would be dedicated to purchasing risk-free assets and. Thus, the standard deviation would simultaneously experience an increasing tendency.
References
Dopfel, F. E., & Lester, A. (2018). Optimal Blending of Smart Beta and Multifactor Portfolios. The Journal of Portfolio Management, 44(4), 93–105. Web.
Faisal, S. M., Khan, A. K., & al Aboud, O. A. (2018). Estimating Beta (β) Values of Stocks in the Creation of Diversified Portfolio – A Detailed Study. Applied Economics and Finance, 5(3), 89–100. Web.
Juelsrud, R. E., & Wold, E. G. (2020). Risk-weighted capital requirements and portfolio rebalancing. Journal of Financial Intermediation, 41. Published. Web.
Lam, W. S., Lam, W. H., & Jaaman, S. H. (2021). Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model. Entropy, 23(10), 1266–1280. Web.
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