Portfolio Risk Analysis - Assignment Example

Portfolio Risk Analysis Student’s Name Institutional Affiliation Table of Contents Table of Contents 2 1.0 Introduction 3 1.1 Altman’s Z-score as an investment value indicator 3 1.2 Reasons for big housing bull market around the world over the last 25 years 5 1.3 The Monte Carlo simulation for retail business model 8 2.0 Part II 10 2.1 The investment strategies of hedge funds. 10 2.2 Quantitative Aspect of hedging 13 2.3 Role of risk analysis in portfolio protection and how such analysis should be implemented in practice 14 2.4 The coherence definition for risk functions 15 3.0 Conclusion 16 Reference 17 1.0 Introduction Many companies have taken a lot of precaution when it comes to trading in the stock market and asset investment in general. Due to market uncertainties, risk analysis has become vital part of management strategies. This paper investigates the various risk analysis methods like The Altman’s Z-score can be describes as an indicator for the probability of a company to be in a bankruptcy in the next 2 years. Reasons for big housing bull market around the world over the last 25 years. Hedge funds can be defines as investments that uses pooled funds to invest in alternative asset or strategies. These alternatives main include but not limited to derivative markets and other leverage investments both in local and international markets. The primary objective of hedging in quantitative is to help in understanding the shock and the options which have the negative correlation in the statistical then quantified the proportion of the coefficient factors between the stocks and the options. The last aspect of this discussion is on the importance of risk assessment and definition of the coherent risk and risk measurement formula. 1.1 Altman’s Z-score as an investment value indicator The Altman’s Z-score can be describes as an indicator for the probability of a company to be in a bankruptcy in the next 2 years (Syamala, Reddy & Goyal, 2014). The rule under Altman’s Z-score is that the higher the value the lower the probability of bankruptcy. Z-score more than 3 indicates that the bankruptcy is unlikely to happen while a Z-score of 1.8 indicates higher chances of bankruptcy taking place within a company. The Altman’s Z-score is determined using the formula:- Z-score = 1.2 * (Working Capital/ Tangible Assets) + 1.4* (Retained Earnings/Tangible Assets) + 3.3* (EBIT/Tangible Assets + 0.6*(Market value of Equity/Total Liability) + (Sales/Tangible Assets). This ratio was first introduced by Altman in 1968 when evaluating the financial status and also to help in predicting the financial distress of a corporation financial status. In order to measure risk by in Z-score, need to change the equation by measuring a single variable. The two types of Z-score models are for the publicly listed companies and the one. There are very many people who have criticized the Z-score as one which is quite inadequate. They claim that the different and purportedly better ways to help companies to predict their bankruptcy. Nevertheless, the Altman Z-score has withstood test of time and many scholars are still using the model in determining the cooperate level of bankruptcy (Syamala, Reddy & Goyal, 2014). Some of the key or primary use of the Z-score include lenders and creditors. For general management use and internal control system, they uses the Z-score for their business clients. A well informed investors frequently uses Z-score to assess the financial strength and health of the business before they could commit their money (Lai & Wang, 2014). Altman’s Z-score is a good indicator for investment value. We are concerned with business survivability and growth using tools that anticipate potential problems. The Altman Z-Score expressions are as follow: Z-Score = (X1*1.2) + (X2*1.4) + (X3*3.3) + (X4*0.6) + (X5*0.999) for public manufacturing businesses Z’ Score = (X1*0.717) + (X2*0.847) + (X3*3.107) + (X4*0.42) + (X5*0.998) for private industrial businesses Z” Score = (X1*6.56) + (X2*3.26) + (X3*6.72) + (X4*1.05) for private non-manufacturing companies Where: X1 = Working Capital / Total Assets X2 = Total Retained Earnings / Total Assets X3 = E.B.I.T. / Total Assets X4 = Market Value Equity / Total Debt for Public Companies or Owners’ Equity / Total Liabilities for Private Businesses X5 = Net Revenue / Total Assets 1.2 Reasons for big housing bull market around the world over the last 25 years One of the main reasons as to why the hose prices has gone up for the past 25 years can be attributed to the fact that fixed asset has become a tool to help in offsetting the influence of the inflation in the market. Furthermore fixed assets has been used by investors to help in the risk transfer from the financial system (Shiller, 2014). Population increase which has results to increase in demand in major cities across the globe as also help in pushing up the prices of the houses since the supply remains limited. For instance, if the house prices has been having the stable behaviour for the past 25 years, it is believed that even though they are some of the factors that can lead to change in price, the supply and demand of houses have very limited influence on the prices of houses since it is a basic good and in case of such behaviour, the demand curve would have flat line. In UK for instance, the house prices can be presented in this graph:- Figure 1: House price movement It is believed that the rate at which the house prices increases is higher than the rate at which wage rate increases. Increase of loans and mortgages has also resulted into increase in the house prices. With increase in the development and improvement of the financial system, there derivatives has resulted into improvement of the performance for financial markets Figure 2: Movement of Average house prices Figure 3: Movement of inflation From the excel calculation, the graphs are presented in figure 1 and 2. It is clear from the graphs that inflation and the house prices were almost moving in the same direction indicating that inflation was the major force behind the increase in house prices. After the second quarter of 2012 that is immediately after the financial meltdown, the behaviour of the prices and inflation rate moves in different direction an indication of other factors influencing house prices. 1.3 The Monte Carlo simulation for retail business model The Monte Carlo simulation is one of the common method which can be used in studying the volatility of the instruments traded in the stock market. Very few analysis used it in retail lending due to the inherent nonlinearities in the performance of loans. It is a problem solving technique which is used to approximate the number of probability of outcome by running several trials which are called simulations (Muslimah & Simatupang, 2014). In this case, we take a retail business model and we are going to relate the unit price, the unit cost sales and find out the relationship between the four. We can have the equation Profit = Sales *(unit price –unit cost). We further assume that all of them are random variables The calculation was majorly done in excel with the net income tax before, the frequency and the probability being calculated. The probability distribution graphs, frequency graphs and tables are produced to help in establishing the simulation The result of simulation are shown in the figure below Net income before tax Frequency Probability [4,664] 7 0.23333333 [665,1324] 5 0.16666667 [1325,1984] 12 0.4 [1985,2644] 5 0.16666667 [2645,3300] 1 0.03333333 Figure 4: Probability Figure 5: Frequency Monte Carlo simulation are used not only in risk and portfolio management, but also in science and engineering works. It helps in improving forecasting and in most cases, the simulation is being done either in excel or Matlab. In our case here we have used excel in estimating the probability hence making decision in the retail business model as to the profit forecasting is simple and easy (Muslimah & Simatupang, 2014). 2.0 Part II 2.1 The investment strategies of hedge funds. Hedge funds can be defines as investments that uses pooled funds to invest in alternative asset or strategies. These alternatives main include but not limited to derivative markets and other leverage investments both in local and international markets. It is projected that hedge fund will overtake traditional methods of investment. It uses a more complex strategies in investment compared to traditional managed funds (Grinblatt et al., 2016). The main aim of hedge find is for positive and less volatile returns both rising and falling markets. Due to complexity of the derivative markets, the hedge fund strategies of a given specific, large span, variety and many other factors, different institutions have different methods of classification. Hedge fund have been classified into eleven strategies and they include:- i. Market neutral ii. Arbitrage iii. Long and short term strategy iv. Global macro v. Fundamental growth vi. Fundamental value vii. Multi strategy viii. Activist strategy ix. Event driven x. Convertible bonds xi. Warrant bond among others. The leverage for instance occurs when a fund increases its level of exposure to certain assets or strategies which are usually done through borrowing. It is important to note that even though leverages have high chances of increasing returns, it also has high chances of increases losses. The derivatives are used by hedge fund to help in gaining or reducing the risk exposure in the market uncertainties. Derivatives are securities with the value depending on the underlying assets, markets and events (Yin, 2016). The derivative are used by hedge fund manager to help in managing investment risk and also to speculate on the future value of the asset s and market value. In this case we use the EFT funds which are downloaded from the yahoo finance to help in calculation of weight index. In excel, we first calculate the daily returns of the daily adjusted close prices of every index with equal weight of the prices. Then the figures are plotted in the graph. The calculation is shown in excel. Figure 6: Equal weighted index Figure 7: Further weighted index The first exchange traded fund VI first trust ZNH, UAL, LUV, CEA and DAL. The equal weighted index is calculated and the tables are shown above. Figure 8: LUV-Index From the figures above, the trend of each and every portfolio can be well fitted and the level of risk calculated in order to choose portfolio with less risk following similar tendency. The decision maker must be able to choose the best combination of the portfolio. 2.2 Quantitative Aspect of hedging The primary objective of hedging in quantitative is to help in understanding the shock and the options which have the negative correlation in the statistical then quantified the proportion of the coefficient factors between the stocks and the options. Hedge funds have less regulation compared to the mutual funds. The quantitative hedge fund can be described as any hedge fund which relies on the algorithmic or systematic strategies for their implementation on trading decisions (Yin, 2016). The trading strategies may mainly focus on any asset class with trades that are based on the systematic strategies instead of discretionary decision. They employ automatic trading rules rather than that of employees at the fund identifying and evaluate. There are several quantitative techniques that can be used in establishing the optimal decision in investment process (Grinblatt et al., 2016). The quantitative trading model and predictions include complex mathematical models which are capable of predicting investment opportunities. Example include:- Rit = α + βiλ + βiFt + σiεit Where Rit, i = 1, …, N and t = 1, … , T, is the excess (over the risk-free rate) return of asset i at time t, and Ft is the factor under consideration with zero mean and variance σ2F. 2.3 Role of risk analysis in portfolio protection and how such analysis should be implemented in practice The concept of risk assessment and risk analysis is an old concept. It is a scientific field which is used by the current portfolio managers. The risk functions in portfolio management gives the quantification of the risk, it helps investors and portfolio managers to be able to estimate as to how much risk the financial markets, the investment portfolio and a given assets exposes for its investment. The effective information help the managers to be able to evaluate the losses from the portfolio (Aven, 2015). Normally financial investment outcome are quite uncertain and they can be relatively safe, unsure and very risky. The managers are able to caution these through risk analysis. It help in establishing the future certainty in the investment and give the investor surety of its investment. One way of managing these risk is through the use of Value at Risk (VaR). An investment may have a higher VaR individually but when negatively correlated to other portfolio, it may result into a lower VaR of the individual VaR. Therefore, analyst using VaR are in the position of informing the investor that a given investment is ideal even if the risk is very high simply because, portfolio diversification can help in cautioning such loss. VaR model help in determining the potential loss which a given portfolio possess. It is measured by assessing the amount of potential loss using the probability concept (Fiksel, 2015). The analysis should be implemented using the relevant procedures and requirements. The relevant portfolios are taken and investigated using the relevant software like MATLAB to investigate the relationship. In this manner, the portfolio manager is able to come up with relevant probability to establish the extent of the risk (Aven, 2015).. 2.4 The coherence definition for risk functions The definition of the coherence that the risk functions satisfy all the following condition is called the coherence. They include:- a. Monotonicity: In case one of the portfolio produces a bad results compared to another portfolio for a given probability, its risk measurement should be more and should be given more capital (Ziegel, 2016). b. The translation invariance: In situations when the amount of cash C to a given portfolio provides buffer against losses and the risk of the portfolio should be low by C. c. Sub-additivity: - The risk measures for tow portfolios after they have been combined together should not be more than the sum of their risk measures before they were combined (Ziegel, 2016). d. Homogeneity: If the size of the portfolio is double, then the requirement of capital also be twice as much. Some of the example of coherent risk functions include:- (i) Normalize = ρ (∅) =0 (ii) Sub-additivity = ρ (A1+A2) ≤ ρ (A1) + ρ (A2) (iii) Positive homogeneity = ρ (λA) = λρ (A) (iv) Translation invariance = ρ (A+x) =ρ (A) −x The incoherent risk function is the VaR. The ES function is normally better than VaR in assessing risk function. However, the VaR function has more strength compared to ES function in applicability. In summary, the coherent risk function can be used to assess the level of portfolio risk while the incoherent risk as VaR can still be used in evaluating the multidimensional risk from different markets (Ziegel, 2016). 3.0 Conclusion Risk management is very crucial part of investment and cannot be overlooked when investors would like to get returns for their money. The quantification approach of risk management is widely used by many players and it plays important when it comes to risk management in the corporate sector. The methods are very much realistic and may help in portfolio allocation. It can also help in postponing a given investment when there are stability in the market. Analyst and investors alike should be well acquainted with the various risk investment analysis techniques and also the software’s for performing risk analysis. Reference Aven, T. (2015). Risk analysis. John Wiley & Sons. Fiksel, J. (2015). From Risk to Resilience. In Resilient by Design (pp. 19-34). Island Press/Center for Resource Economics. Grinblatt, M., Jostova, G., Petrasek, L., & Philipov, A. (2016). Style and Skill: Hedge Funds, Mutual Funds, and Momentum. Lai, H. C., & Wang, K. M. (2014). Relationship between the trading behavior of three institutional investors and Taiwan Stock Index futures returns. Economic Modelling, 41, 156-165. Muslimah, M. D., & Simatupang, T. M. (2014). Supply chain collaboration for ensuring retail product availability at Glaxosmithkline. In 6th International Conference on Operations and Supply Chain Management. Shiller, R. J. (2014). Speculative asset prices. The American Economic Review, 104(6), 1486. Syamala, S. R., Reddy, V. N., & Goyal, A. (2014). Commonality in liquidity: An empirical examination of emerging order-driven equity and derivatives market. Journal of International Financial Markets, Institutions and Money, 33, 317-334. Yin, C. (2016). The optimal size of hedge funds: conflict between investors and fund managers. The Journal of Finance, 71(4), 1857-1894. Ziegel, J. F. (2016). Coherence and elicitability. Mathematical Finance, 26(4), 901-918. Read More