Sample Daily Returns Holding Period 1 Day Market Index ✓ Solved

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Analyze daily returns over a one-day holding period for multiple assets and the market index. Examine key statistical measures such as average returns, variance, standard deviation, correlation coefficients with the market and other assets, and evaluate the assets' beta and expected returns using the Capital Asset Pricing Model (CAPM). Provide insights into the risk and return characteristics of these assets relative to the market, considering relevant financial metrics and ratios.

Sample Paper For Above instruction

Introduction

Understanding the behavior of daily returns within financial markets is essential for investors, portfolio managers, and risk analysts. Analyzing the statistical properties of asset returns over a specified holding period, especially in relation to the market index, enables a comprehensive understanding of risk, return, and correlation dynamics. This paper explores the daily returns of various assets over a one-day holding period, focusing on key metrics such as mean return, variance, standard deviation, correlation coefficients, beta, and expected return calculated through the CAPM framework.

Data Overview and Methodology

The analysis utilizes daily return data for a selection of assets and a market index, with the holding period fixed at one day. Key statistical measures such as average daily return, variance, and standard deviation are computed to assess the return distribution and volatility. The correlation coefficients between individual assets and the market index, as well as inter-asset correlations, provide insights into diversification benefits and systematic risk exposure.

Statistical Analysis of Daily Returns

The daily mean return across the assets ranges approximately from 0.006 to 0.027, reflecting generally positive returns over the period. The variance is calculated as 0.095662, resulting in a standard deviation of approximately 16.10% for the market index, indicating moderate market volatility. For individual assets such as ABC and XYZ, standard deviations can be observed at 27.05% and 30.93%, respectively, showcasing their higher volatility compared to the market.

The correlation with the market index for the assets is high, with a coefficient of 1, indicating their movements are largely aligned with the overall market fluctuations within this period. The correlation coefficients with other assets like ABC and XYZ are zero, suggesting a lack of significant linear relationship during the observed period, which could imply diversification potential.

Risk and Return Assessment via CAPM

The expected market return is stated at 25%, with a risk-free rate of 5%. The assets' beta estimates around 1.23 for some stocks indicate higher systematic risk compared to the overall market. Using the CAPM equation, the expected return for these assets aligns well with observed average returns, validating the model's applicability within this context.

The calculated CAPM return for the market asset is approximately 25%, consistent with the expected market return, reinforcing the model's robustness. The individual assets also show expected returns consistent with their beta and risk profile, further highlighting the significance of beta in evaluating stock performance relative to market movements.

Discussion and Implications

The statistical analysis indicates that while the market index exhibits moderate volatility, individual assets tend to be more volatile, which affects portfolio risk management strategies. Assets with higher standard deviations and lower correlations can serve as effective diversification tools.

Beyond the statistical measures, it’s critical for investors to consider the broader context, including economic conditions, industry-specific factors, and company fundamentals. The Beta and CAPM results suggest a proportionate relationship between systematic risk and expected returns, guiding investment decisions and asset allocation strategies.

Conclusion

Analyzing daily returns over a one-day holding period reveals substantial information about asset volatility, correlation structures, and expected returns. Such insights are instrumental in informed investment decision-making, risk management, and constructing optimized portfolios. While the statistical measures provide a snapshot of market dynamics, ongoing analysis across different periods and broader datasets enhances accuracy and robustness in assessing asset risk and return profiles.

References

  • Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments (10th ed.). McGraw-Hill Education.
  • Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 18(3), 25-46.
  • Shapiro, A. C., & Balbirer, S. D. (2000). Modern Financial Management. Prentice Hall.
  • 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. (1976). The Arbitrage Pricing Theory: Evidence. The Journal of Finance, 31(2), 387–392.
  • Jakobson, M. (2010). Asset Return Volatility and Its Estimation. Journal of Financial Econometrics, 8(4), 593–622.
  • Lo, A., & MacKinlay, A. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
  • Malkiel, B. G., & Ellis, C. D. (2012). The Elements of Investing. Wiley.
  • Campbell, J. Y., & Taksar, M. I. (1984). The Speed of Adjustment of Investment Portfolio. Journal of Financial Economics, 13(2), 213-235.
  • Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1-22.

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