Strategic Portfolio Allocation: An Analytical Approach to Balancing Market and Style 23 Investments: A Case Study
DOI:
https://doi.org/10.54097/nczb5584Keywords:
Portfolio Optimization; Risk-Return Trade-off; Sharpe Ratio; Value at Risk; Asset Allocation; Transaction Costs.Abstract
This study explores optimal portfolio design by evaluating the risk-return dynamics of the Style 23 and Market portfolios, employing advanced statistical and analytical tools such as Sharpe ratio, regression analysis, cumulative returns, and Value at Risk (VaR). By examining both the entire and half-sample datasets, we developed a robust investment strategy that combines a 0.343 weight for Style 23 and a full weight for the Market Portfolio, demonstrating the benefits of diversification. The findings suggest that this strategy offers superior risk-adjusted returns compared to investing in a single portfolio, as losses in one can be mitigated by gains in the other. However, the analysis also identifies key challenges, including the impact of transaction costs and external factors like economic conditions, which can affect overall performance. These insights underscore the need for continuous strategy evaluation and adjustment. The study concludes that while the combined strategy provides a sound framework for portfolio management, ongoing refinements and adaptability are essential to navigate evolving market landscapes effectively.
Downloads
References
[1] Can, Y. Junjie, Z. and Helong, L. (2022). ‘The upper bound of cumulative return of a trading series’, PLOS ONE, Vol. 17, No. 4.
[2] Duffie, D. and Pan, J. (1997). ‘An overview of value at risk’, Journal of derivatives, Vol. 4, No. 3, pp. 7-49.
[3] Dimitrios, S. and Athanasios, A. (2020). ‘Determinants of hedge fund performance during ‘good’ and ‘bad’ economic periods’, Research in International Business and Finance, Vol. 52.
[4] Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment, fully revised and updated. Simon and Schuster.
[5] Dreman, D. (2008). Contrarian investment strategies: The next generation. Simon and Schuster.
[6] Barberis, N., & Shleifer, A. (2003). Style investing. Journal of financial Economics, 68(2), 161-199.
[7] Hsieh, H. H. (2013). A review of performance evaluation measures for actively-managed portfolios. Journal of Economics and Behavioral Studies, 5(12), 815-824.
[8] Detemple, J. B., Garcia, R., & Rindisbacher, M. (2003). A Monte Carlo method for optimal portfolios. The journal of Finance, 58(1), 401-446
[9] Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A., & Focardi, S. M. (2007). Robust portfolio optimization and management. John Wiley & Sons..
[10] Connor, G., Goldberg, L. R., & Korajczyk, R. A. (2010). Portfolio risk analysis. Princeton University Press.
[11] Martin, O. (2018). Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. Packt Publishing Ltd.
[12] Feibel, B. J. (2003). Investment performance measurement. John Wiley & Sons.
[13] Bacon, C., & Chairman, S. (2009). How sharp is the Sharpe ratio? Risk-adjusted Performance Measures. Statpro, nd.
[14] Vancas, I. (2010). Due Diligence and Risk Assessment of an Alternative Investment Fund. Diplomica Verlag.
[15] Jorion, P. (1996). Risk2: Measuring the risk in value at risk. Financial analysts journal, 52(6), 47-56.
[16] Haimes, Y. Y. (2011). Risk modeling, assessment, and management. John Wiley & Sons.
[17] Pedersen, C. S., & Rudholm-Alfvin, T. (2003). Selecting a risk-adjusted shareholder performance measure. Journal of asset management, 4(3), 152-172.
[18] Lobo, M. S., Fazel, M., & Boyd, S. (2007). Portfolio optimization with linear and fixed transaction costs. Annals of Operations Research, 152, 341-365.
[19] Schachermayer, W. (2004). The fundamental theorem of asset pricing under proportional transaction costs in finite discrete time. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 14(1), 19-48.
[20] Musawa, N., Kapena, S., & Shikaputo, C. (2018). A comparative analysis of Fama-French Five and Three-Factor model in explaining stock returns variation. International Journal of Economics, 3(1), 30-48.
[21] Taneja, Y. P. (2010). Revisiting Fama French three-factor model in Indian stock market. Vision, 14(4), 267-274.
[22] Loos, N. (2007). Value creation in leveraged buyouts: analysis of factors driving private equity investment performance. Springer Science & Business Media.
[23] Siegel, J. J. (2021). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. McGraw-Hill Education.
[24] Lynch, A. W. (2001). Portfolio choice and equity characteristics: Characterizing the hedging demands induced by return predictability. Journal of Financial Economics, 62(1), 67-130.
[25] Grewal, R., & Tansuhaj, P. (2001). Building organizational capabilities for managing economic crisis: The role of market orientation and strategic flexibility. Journal of marketing, 65(2), 67-80.
[26] Cavusgil, S. T., & Zou, S. (1994). Marketing strategy-performance relationship: an investigation of the empirical link in export market ventures. Journal of marketing, 58(1), 1-21.
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2024 Journal of Education, Humanities and Social Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.