Investigating portfolio performance with higher moment considering entropy and rolling window in banking, insurance, and leasing industries
محورهای موضوعی : Financial EconometricsArash Amini 1 , Maryam Khalili Araghi 2 , Hashem Nikoomaram 3
1 - Department of Financial Management, Science and Research branch, Islamic Azad University, Tehran, Iran.
2 - Department of Business Management, Science and Research branch, Islamic Azad University, Tehran, Iran.
3 - Department of Accounting, Science and Research branch, Islamic Azad University, Tehran, Iran.
کلید واژه: Performance Evaluation, Entropy, Higher moments, Rolling window, Banking and insurance,
چکیده مقاله :
The optimal portfolio selection is vital for investment. The risk of portfolio Selection and return is the most critical concern of investment companies and private investors. According to modern portfolio theory, diversification should cover the risk. This theory is based on the normality of assets return. Experimental findings indicate that the assets return non-normality. Higher moments are sed to upgrade traditional models with the primary presumption of a normal distribution in recent years. This study uses a higher moment and the entropy for diversification and selects a portfolio given a non-normality assumption. It is essential to use up-to-date information to increase the model's efficiency, and accordingly, we used the rolling window for new price information. For the financial information method, we use the total index return in the last five working days and weigh the shares of the banking, insurance, and leasing industries on the next working day and evaluate this for three years. Python, math, and NumPy libraries were used to analyze the data. The results show that a much higher moment model can provide better portfolio selection results in most cases.
The optimal portfolio selection is vital for investment. The risk of portfolio Selection and return is the most critical concern of investment companies and private investors. According to modern portfolio theory, diversification should cover the risk. This theory is based on the normality of assets return. Experimental findings indicate that the assets return non-normality. Higher moments are sed to upgrade traditional models with the primary presumption of a normal distribution in recent years. This study uses a higher moment and the entropy for diversification and selects a portfolio given a non-normality assumption. It is essential to use up-to-date information to increase the model's efficiency, and accordingly, we used the rolling window for new price information. For the financial information method, we use the total index return in the last five working days and weigh the shares of the banking, insurance, and leasing industries on the next working day and evaluate this for three years. Python, math, and NumPy libraries were used to analyze the data. The results show that a much higher moment model can provide better portfolio selection results in most cases.
[1] Israelsen, C. A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 2005; 5(6): 423–427, doi: 10.1057/palgrave.jam.2240158
[2] Sumolson, E. F. Portfolio analysis in a stable Paretian market. Management science.1965; 11(3): 404-419.
[3] Arditti, F. D. Risk and the required return on equity. The Journal of Finance,1967: 22(1): 19- 36.
[4] Eslami Bidgoli, G., Talangi, A. This article is a revIew of the historical development of the Modern Portfolio Theory (MPT). Financial Research Journal, 1999; 4(1): 50-71 (in Persian).
[5] DeMiguel, V., Nogales F. J. Portfolio selection with robust estimation. Operations Research, 2009; 57(3): 560-577. doi: 10.1287/opre.1080.0566
[6] Bera, A. K., Park, S. Y. Optimal portfolio diversification using the maximum entropy principle. Econometric Reviews. 2008; 27(4-6): 484-512. doi: 10.1080/07474930801960394
[7] Seifolahi, N. Study of the impact of market orientation and managerial stability on the financial performance of companies. Quarterly Journal of Financial Economics, 2019; 13(48): 261-277 (in Persian).
[8] Dehghan, A., Kamyabi, M. How economic variables affect the returns of listed companies in boom and bust of the capital market. Quarterly Journal of Financial Economics, 2019: 13(48): 147-166 (in Persian).
[9] Markowitz, H. Portfolio Selection. Journal of Finance, 1952; 15: 77-91.
[10] Bekaert, G., Erb, C., Harvey, C. R., Viskanta, T. E. Distributional Characteristics of Emerging Market Returns & Asset Allocation. Journal of Portfolio Management, 1998; 24(2):102-116. doi: 10.3905/jpm.24.2.102
[11] Hoshmandnafabi, Z., Vakilifard, H., Talebnia, Gh. Comparative explanation of pricing models of classic capital and behavior assets in the Iranian capital market. Financial Economics Quarterly, 2017; 11(41): 85-122 (in Persian).
[12] Sortino, F., Price, L.N. Performance in a Downside Risk Framework. Journal of Investing, 1994; 3: 59-64. doi: 10.3905/joi.3.3.59
[13] Li, X., Qin, Z., Kar, S. Mean-variance-skewness model for portfolio selection with fuzzy parameter. European Journal of Operational Research, 2010; 202(1): 239-247. doi: 10.1016/j.ejor.2009.05.003
[14] Price, K., Price, B., Nantell. T. J. Variance and lower partial moment measures of systematic risk: some analytical and empirical results. The Journal of Finance, 1982; finance 37(3): 843-855.
[15] Rom, B. M., Ferguson, K. W. Post-modern portfolio theory comes of age. The Journal of Investing,1997; 3(3): 11-17.
[16] Fered, M. A., Westerfield, W. L. Diversification in a three-moment world. Journal of Financial and Quantitative Analysis, 1980; 13(5): 927-941.
[17] Prakash, A.J., Chang, C.H., Pactwa, T.E. Selecting a portfolio with skewness: Recent evidence from US, European and Latin American equity markets. J. Bank. Finance, 2003; 27: 1375–1390. doi: 10.2139/ssrn.2663177
[18] Christi-David, R., Chaudhry, M. Coskwness and Cokurtosis in futures markets. Journal of Epirical Finance, 2001; 8: 55-81. doi: 10.1016/S0927-5398(01)00020-2
[19] Chiao, C., Hung, K., Srirastava, S, Taiwan stock market and four-moment asset pricing model. Journal of international Financial markets, institutions & money, 2003; 3: 355-381. doi: 10.1016/S1042-4431(03)00013-1
[20] Chunhachinda, P., Dandapani, K., Hamid, S., Prakash, A.J. Portfolio selection and skewness: Evidence from international stock markets. J. Bank. Finance, 1997; 21: 143–167. doi: 10.1016/S0378-4266(96)00032-5
[21] Parkash, G. K., Leonard, P. A. Bank balance-sheet management: An alternative multiobjective model. Journal of the Operational Research Society, 1988; 39(4): 401-410. doi: 10.1057/jors.1988.68
[22] Usta, I., Mert Kantar, Y. Mean-Variance-Skewness-Entropy Measures: A Multi-Objective Approach for Portfolio Selection. Journal of Entropy, 2010; 13(1): 117-133. doi: 10.3390/e13010117
[23] Simkowitz, M.A., Beedles W.L. Diversification in a three-moment world. J. Financ. Quant. Anal. 1978; 13: 927–941.
[24] Hueng, C.J., Yau, R. Investor preferences and portfolio selection: Is diversification an appropriate strategy? Quant. Finance, 2006; 6: 255–271. doi: 10.1080/14697680600680134
[25] Canela, M. A., Collazo, E. P. Portfolio selection with skewness in emerging market industries. Emerging Markets Review, 2007; 8(3): 230-250. doi: 10.1016/j.ememar.2006.03.001
[26] Davies, R. J., Kat, H. M., Lu, S. Fund of hedge funds portfolio selection: A multiple-objective approach. Journal of Derivatives & Hedge Funds, 2009; 15(2): 91-115. doi: 10.1057/jdhf.2009.1
[27] Mhiri, M., Prigent, J. L. International portfolio optimization with higher moments. International Journal of Economics and Finance, 2010; 2(5): 157. doi: 10.5539/ijef.v2n5p157
[28] Škrinjarić, T. Portfolio Selection with Higher Moments and Application on Zagreb Stock Exchange. Zagreb International Review of Economics & Business, 2013; 16(1): 65-78. doi is not available.
[29] Proelss, J., Schweizer, D. Polynomial goal programming and the implicit higher moment preferences of US institutional investors in hedge funds. Financial Markets and Portfolio Management, 2014; 28(1): 1-28. doi: 10.1007/s11408-013-0221-x
[30] Zhao, N., Lin, W.T. A copula entropy approach to correlation measurement at the country level. Appl. Math. Comput. 2011; 218: 628–642. doi: 10.1016/j.amc.2011.05.115
[31] Liu, A., Chen, J., Yang, S.Y., Hawkes, A.G. The flow of information in trading: An entropy approach to market regimes. Entropy, 2020; 22: 1064. doi: 10.3390/e22091064
[32] Lu, S., Zhao, J., Wang, H. Trading imbalance in Chinese stock market—A high-frequency view. Entropy, 2020; 22: 897. doi: 10.3390/e22080897
[33] Otsa, F., Kantar, D. Predicting risk/return performance using upper partial moment/lower partial moment metrics. Journal of Mathematical Finance, 2011; 6(05): 900. doi: 10.4236/jmf.2016.65060
