Holding Period Return Based On The Following Information

1holding Period Return Based On The Following Information Calcul

1. Holding Period Return Based on the following information calculate the holding period return: P0 = $11.00 P1 = $11.40 D1 = $1

2. Risk and Return, Coefficient of Variation Based on the following information, calculate the coefficient of variation and select the best investment based on the risk/reward relationship. Std Dev. Exp. Return Company A 7.4 13.2 Company B 11.6 18.9

3. Holding Period Return Based on the following information calculate the holding period return: P0 = $10.00 P1 = $12.00 D1 = $1

4. Measures of Risk. Address each source of risk that is measured and relate it to two models addressed in this unit. Your response should be at least 250 words in length.

Paper For Above instruction

The assignment encompasses calculating holding period returns for specific investment periods, determining the coefficient of variation to evaluate risk-adjusted returns between different investments, and discussing sources of risk within the context of financial models. This comprehensive analysis provides a deeper understanding of investment performance measurement and risk assessment, essential for making informed investment decisions.

Calculation of Holding Period Return

The holding period return (HPR) quantifies the total return an investor earns over a specific period, considering both capital appreciation and income received during that period. It is calculated using the formula:

HPR = (P1 - P0 + D1) / P0

For the first scenario, where P0 is $11.00, P1 is $11.40, and D1 is $1.00, the HPR is:

HPR = ($11.40 - $11.00 + $1.00) / $11.00 = ($0.40 + $1.00) / $11.00 = $1.40 / $11.00 ≈ 0.1273 or 12.73%

Similarly, for the second scenario, P0 is $10.00, P1 is $12.00, and D1 is $1.00, the HPR is:

HPR = ($12.00 - $10.00 + $1.00) / $10.00 = ($2.00 + $1.00) / $10.00 = $3.00 / $10.00 = 0.30 or 30%

These calculations demonstrate how the total returns can be assessed over specific periods, combining capital gains with dividends.

Calculating Coefficient of Variation for Investment Comparison

The coefficient of variation (CV) is a statistical measure used to evaluate the risk per unit of return, providing a standardized way to compare investments with different volatility levels. It is calculated as:

CV = Standard Deviation / Expected Return

For Company A, with a standard deviation of 7.4 and an expected return of 13.2, the CV is:

CV_A = 7.4 / 13.2 ≈ 0.5606

For Company B, with a standard deviation of 11.6 and an expected return of 18.9, the CV is:

CV_B = 11.6 / 18.9 ≈ 0.6132

Lower CV indicates a more favorable risk-reward profile; hence, Company A appears to be a less risky investment relative to its return, making it the preferable choice based on this metric.

Discussion of Sources of Risk and Their Relation to Financial Models

In investment analysis, understanding the different sources of risk is crucial for comprehensive risk management and strategic decision-making. The primary types of risks include market risk, credit risk, liquidity risk, and operational risk. Market risk, also known as systematic risk, affects the entire market due to macroeconomic factors such as economic downturns, interest rate fluctuations, and geopolitical events. This type of risk is addressed in the Capital Asset Pricing Model (CAPM), which quantifies expected returns based on the beta coefficient measuring systematic risk exposure (Sharpe, 1964).

Credit risk pertains to the possibility of a borrower defaulting on financial obligations. Models such as the CreditMetrics model help quantify this risk by evaluating the probability of default and its potential losses (Litterman, 1998). Operational risk involves failures in internal processes, systems, or external events like fraud or natural disasters, which can be analyzed through Basel Accords' frameworks that emphasize stress testing and risk controls (Basel Committee, 2011).

Each risk source interacts with these models differently, enabling investors and risk managers to develop strategies that mitigate potential losses and optimize portfolio performance. For example, diversifying across assets reduces unsystematic or specific risks that are not captured by models like CAPM. Incorporating these diverse risk assessments enhances the robustness of investment decisions, ensuring better alignment with investors' risk tolerance (Elton & Gruber, 1995).

In conclusion, addressing the various sources of risk through appropriate models allows investors to anticipate potential downturns and strategically allocate resources. Models like CAPM and Basel frameworks are essential tools that facilitate understanding and managing risks within portfolios, ultimately supporting more resilient and sustainable investment strategies.

References

  • Basel Committee on Banking Supervision. (2011). Basel III: A global regulatory framework for more resilient banks and banking systems. Bank for International Settlements.
  • Elton, E. J., & Gruber, M. J. (1995). Modern Portfolio Theory and Investment Analysis. John Wiley & Sons.
  • Litterman, R. (1998). Modern Portfolio Theory. Goldman Sachs Global Economics Paper No. 99.
  • Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance, 19(3), 425–442.
  • Ross, S. A., & Westerfield, R., & Jaffe, J. (2010). Corporate Finance. McGraw-Hill Education.
  • Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments. McGraw-Hill Education.
  • Jorion, P. (2007). Financial Risk Manager Handbook. Wiley Finance.
  • Amenc, N., & Le Sourd, F. (2006). Theory and Practice of Fund Management. Cambridge University Press.
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.
  • Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), 427–465.

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