The modified mean absolute deviation integer linear programming model for global portfolio asset allocation

Authors

  • Nadia Edmaz Abdul Hadi Pelaburan Hartanah Berhad, Universiti Utara Malaysia, Malaysia
  • Sahubar Ali Mohamed Nadhar Khan Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jcia2024.3.2.1

Keywords:

asset allocation, ETFs, global portfolio, integer programming, linear programming, optimization

Abstract

This study develops a practical yet robust Mean Absolute Deviation (MAD) Mixed-Integer Linear Programming (MILP) model for a global investment portfolio tailored to the constraints faced by Malaysian retail investors, such as transactional fees and foreign currency exchange spreads, to provide more accurate estimations of portfolio returns. The MAD ILP model is modified to address these factors, aiming to enhance utility and efficiency. The study utilizes a Maybank 12-month fixed deposit cash account and ten selected Exchange-Traded Funds (ETFs) from iShares, Vanguard, and State Street Global Advisors for global portfolio construction. Forecasting techniques were utilized to generate expected returns for the ETFs and foreign currency exchange spreads. Based on the MAD model proposed by Konno and Wijayanayake (2001), the study introduces three modifications: incorporating transaction cost elements specific to Malaysian investors, including foreign currency exchange spreads in return calculations, and transforming the model from mixed-integer to pure integer model to accommodate minimum transaction lot constraint. Verification and validation exercises demonstrate the model’s ability to replicate real-world portfolio construction and reliable simulations. The model offers a practical tool for self-ascribed investors aiming to optimize global investment portfolios while considering unique constraints and transaction costs.

References

Agrrawal, P., & Clark, J. M. (2009). Determinants of ETF liquidity in the secondary market: A five-factor ranking algorithm. ETFs and Indexing, 2009(1), 59–66. https://guides.pm-research.com/content/iijetfind/2009/1/59

Akhigbe, A., Balasubramnian, B., & Newman, M. (2020). Exchange Traded Funds and the likelihood of closure. American Journal of Business, 35(3/4), 105–127. https://doi.org/10.1108/ajb-07-2019-0054

Bacon, C. R. (2019). Performance Attribution: History and Progress. CFA Institute Research Foundation. https://shorturl.at/Dg2VK

Baumann, P., & Trautmann, N. (2013). Portfolio-optimization models for small investors. Mathematical Methods of Operations Research, 77(3), 345–356. https://doi.org/10.1007/s00186-012-0408-3

Bebchuk, L. A., Hirst, S., & Harvard, L. A. B. (2019). The specter of the giant three (No. w25914). National Bureau of Economic Research. https://www.nber.org/papers/w25914.

Beraldi, P., Violi, A., Ferrara, M., Ciancio, C., & Pansera, B. A. (2021). Dealing with complex transaction costs in portfolio management. Annals of Operations Research, 299(1–2), 7–22. https://doi.org/10.1007/s10479-019-03210-5

Brinson, G. P., Singer, B. D., & Beebower, G. L. (1991). Determinants of portfolio performance II: An update. Financial Analysts Journal, 47(3), 40–43. https://doi.org/https://doi.org/10.2469/faj.v47.n3.40

Brown, K. C., Garlappi, L., & Tiu, C. (2010). Asset allocation and portfolio performance: Evidence from university endowment funds. Journal of Financial Markets, 13(2), 268–294. https://doi.org/10.1016/j.finmar.2009.12.001

Chiodi, L., & Mansini, R. (2003). Semi-absolute deviation rule for mutual funds portfolio selection. Annals of Operations Research, 124, 245–265. https://doi.org/https://doi.org/10.1023/B:ANOR.0000004772.15447.5a

Chow, T. M., Li, F., & Shim, Y. (2018). Smart beta multifactor construction methodology: Mixing versus integrating. Journal of Index Investing, 8(4), 47–60. https://doi.org/10.3905/jii.2018.8.4.047

Daul, S., Jaisson, T., & Nagy, A. (2022). Performance attribution of machine learning methods for stock returns prediction. The Journal of Finance and Data Science, 8, 86–104. https://doi.org/10.1016/J.JFDS.2022.04.002

Department of Statistics Malaysia. (2020). Household Income & Basic Amenities Survey Report 2019. Department of Statistics Malaysia. https://shorturl.at/dbttZ

Feiring, B. R., Wong, W., Poon, M., & Chan, Y. C. (1994). Portfolio selection in downside risk optimization approach: Application to the Hong Kong stock market. International Journal of Systems Science, 25(11), 1921–1929. https://doi.org/10.1080/00207729408949322

Froot, K., & Teo, M. (2008). Style investing and institutional investors. Journal of Financial and Quantitative Analysis, 43(4), 883-906. https://doi.org/10.1017/S0022109000014381

Graham, B. (1973). The intelligent investor (4th Edition). Harper and Row. https://ebookdisc.com/wp-content/uploads/2023/12/the-intelligent-investor-pdf.pdf

Hensel, C. R., Ezra, D. D., & Ilkiw, J. H. (1991). The Importance of the Asset Allocation Decision. Financial Analysts Journal, 47(4), 65–72. https://doi.org/10.2469/faj.v47.n4.65

Hsiao, Y. J., & Tsai, W. C. (2018). Financial literacy and participation in the derivatives markets. Journal of Banking and Finance, 88, 15–29. https://doi.org/10.1016/J.JBANKFIN.2017.11.006

Ibbotson, R. G. (2010). The importance of asset allocation. Financial Analysts Journal, 66(2), 18–20. https://doi.org/https://doi.org/10.2469/faj.v66.n2.4

Ibbotson, R. G., & Kaplan, P. D. (2000). Does asset allocation policy explain 40, 90, or 100 percent of performance? Financial Analyst Journal, 56, 26–33. https://doi.org/https://doi.org/10.2469/faj.v56.n1.2327

Jankova, Z. (2019). Comparison of Portfolios Using Markowitz and Downside Risk Theories on the Czech Stock Market. Innovation Management, Entrepreneurship and Sustainability, 291–303. https://www.ceeol.com/search/chapter-detail?id=784356

Kadoya, Y., Khan, M., & Rabbani, N. (2017). Does financial literacy affect stock market participation? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3056562

Kalayci, C. B., Ertenlice, O., & Akbay, M. A. (2019). A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications, 125, 345-368. https://doi.org/10.1016/j.eswa.2019.02.011

Kellerer, H., Mansini, R., & Speranza, M. G. (2000). Selecting portfolios with fixed costs and minimum transaction lots. Annals of Operations Research, 99, 287-304. https://doi.org/10.1023/A:1019279918596

Konno, H., & Wijayanayake, A. (2001). Portfolio optimization problem under concave transaction costs and minimal transaction unit constraints. Mathematical Programming, Ser. B, 89, 233–250. https://doi.org/10.1007/s101070000205

Konno, H., & Yamamoto, R. (2005). Integer programming approaches in mean-risk models. Computational Management Science, 2(4), 339–351. https://doi.org/10.1007/s10287-005-0038-9

Konno, H., & Yamazaki, H. (1991). Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Management Science, 37(5), 519–531. https://doi.org/10.1287/mnsc.37.5.519

Le Thi, H. A., Moeini, M., & Dinh, T. P. (2009). DC programming approach for portfolio optimization under step increasing transaction costs. Optimization, 58(3), 267-289. https://doi.org/10.1080/02331930902741721

Leung, M. F., & Wang, J. (2022). Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization. Neural Networks, 145, 68–79. https://doi.org/10.1016/J.NEUNET.2021.10.007

Li, Y., Simon, Z., & Turkington, D. (2022). Investable and interpretable machine learning for equities. The Journal of Financial Data Science, 4(1), 54-74. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3747203

Mansini, R., Ogryczak, W., Speranza, M. G., & EURO: The Association of European Operational Research Society. (2015). EURO Advanced Tutorials on Operational Research Linear and Mixed Integer Programming for Portfolio Optimization (Vol. 21). http://www.springer.com/series/13840

Market Chameleon. (n.d.). Search. Market Chameleon. https://marketchameleon.com/search/pages

Markowitz, H. (1952). Portfolio Selection. Source: The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

Morningstar. (n.d.). Mutual Fund Prices, Data, ESG, Research and News. Morningstar. https://www.morningstar.ca/ca/funds/default.aspx

Neely, C. J., Weller, P. A., & Ulrich, J. M. (2009). The adaptive markets hypothesis: Evidence from the foreign exchange market. Journal of Financial and Quantitative Analysis, 44(2), 467–488. https://doi.org/10.1017/S0022109009090103

New York Stock Exchange. (2019). NYSE Guide, Regulation, Rule 0., New York Stock Exchange, Regulation of the Exchange and its Member Organizations. http://researchhelp.cch.com/License_Agreement.htm

Panos, G. A., Karkkainen, T., & Atkinson, A. (2020). Financial literacy and attitudes to cryptocurrencies. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3482083

Perold, A. F. (1984). Large-scale portfolio optimization. Management Science, 30(10), 1143–1160. https://doi.org/10.1287/mnsc.30.10.1143

Rakuten Trade. (2022). Fees. Rakuten Trade. https://www.rakutentrade.my/fees

Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of risk, 2, 21-42. https://t.ly/m18Xd

Securities Commission Malaysia. (2016). Prospectus Guidelines for Collective Investment Scheme. https://www.sc.com.my/api/documentms/download.ashx?id=fda59971-0bdb-4da4-8d60-b61a20d01fe1

Sharpe, W. F. (1963). A simplified model for portfolio analysis. Management Science, 9(2), 277–293. https://doi.org/https://doi.org/10.1287/mnsc.9.2.277

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x

Sharpe, W. F. (1992). Asset allocation: Management style and performance measurement. Journal of portfolio Management, 18(2), 7-19.. http://www.stanford.edu/~wfsharpe/art/sa/sa.htm

Sherrill, D. E., & Stark, J. R. (2018). ETF liquidation determinants. Journal of Empirical Finance, 48, 357–373. https://doi.org/10.1016/j.jempfin.2018.07.007

Stein, M. (2007). Mean-Variance portfolio selection with complex constraints [Unpublished doctoral dissertation]. Universit¨at Karlsruhe.

Awaludin, D. T., & Rahman, F. D. (2021). Analysis of the effect of asset allocation on portfolio performance with diversification as an intervening variable. International Journal of Science and Society, 3(3), 30-39. http://ijsoc.goacademica.com

Wei, J., Yang, Y., Jiang, M., & Liu, J. (2021). Dynamic multi-period sparse portfolio selection model with asymmetric investors’ sentiments. Expert Systems with Applications, 177, 1-18. https://doi.org/10.1016/j.eswa.2021.114945

Williams, J. B. (1938). The Theory of Investment Value: Vol. 1997 Reprint. Fraser Publishing.

Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25–34. https://doi.org/10.1109/MCI.2018.2866727

Young, M. R. (1998). A minimax portfolio selection rule with linear programming solution. Management Science, 44(5), 673–683. https://doi.org/10.1287/mnsc.44.5.673

Zarei, A., Ariff, M., & Bhatti, M. I. (2019). The impact of exchange rates on stock market returns: new evidence from seven free-

floating currencies. European Journal of Finance, 25(14), 1277–1288. https://doi.org/10.1080/1351847X.2019.1589550

Zhang, Y., Li, X., & Guo, S. (2018). Portfolio Selection Problems with Markowitz’s Mean–variance Framework: A Review of Literature. Fuzzy Optimization and Decision Making, 17(2), 125–158. https://doi.org/10.1007/s10700-017-9266-z

Downloads

Published

31-07-2024

How to Cite

Hadi, N. E. A., & Khan, S. A. M. N. (2024). The modified mean absolute deviation integer linear programming model for global portfolio asset allocation. Journal of Computational Innovation and Analytics (JCIA), 3(2), 1-25. https://doi.org/10.32890/jcia2024.3.2.1

Research impact

Harvested 2026-09-26
0 citations recorded so far

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jcia2024.3.2.1 OpenAlex W4402115299