Skip to main content

Mean Absolute Deviation Portfolio Frontiers with Interval-Valued Returns

  • Conference paper
  • First Online:
Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2019)

Abstract

This work discusses the frontiers of mean absolute deviation portfolios arising from the uncertainty of future rates of return. The risk of the overall portfolio is proposed as an objective function to attain a well-diversified portfolio with a predetermined target rate of return. The possible ranges of the target returns are suggested via the strong feasibility of the interval linear system of constraints. No short sales are allowed and a risk-averse investor is assumed to pursue the buy-and-hold strategy. The use of the method is illustrated with the historical returns of S&P 500 stocks, for which the negative correlation condition empirically holds, with a 6-month investment horizon from November 2018 to April 2019. The historical data is collected monthly over the past 4 years from November 2014 to October 2018.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Konno, H., Yamazaki, H.: Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Manag. Sci. 37(5), 519–531 (1991)

    Article  Google Scholar 

  2. Feinstein, C.D., Thapa, M.N.: A reformulation of a mean-absolute deviation portfolio optimization model. Manag. Sci. 39(12), 1552–1553 (1993)

    Article  Google Scholar 

  3. Embrechts, P., Frey, R., McNeil, A.: Quantitative Risk Management: Concepts. Techniques and Tools. Princeton University Press, New Jersey (2005)

    MATH  Google Scholar 

  4. Fiedler, M., Nedoma, J., Ramík, J., Rohn, J., Zimmermann, K.: Linear Optimization Problems with Inexact Data. Springer, New York (2006). https://doi.org/10.1007/0-387-32698-7

    Book  MATH  Google Scholar 

  5. Hladík, M.: Optimal value range in interval linear programming. Fuzzy Optim. Decis. Mak. 8(3), 283–294 (2009)

    Article  MathSciNet  Google Scholar 

  6. Bodie, Z., Kane, A., Marcus, A.J.: Investments. McGraw-Hill Education, New York (2014)

    Google Scholar 

  7. Ferris, M.C., Mangasarian, O.L., Wright, S.J.: Linear Programming with Matlab. SIAM, Philadelphia (2007)

    Book  Google Scholar 

  8. Kadan, O., Tang, X.: A Bound on Expected Stock Returns. https://doi.org/10.2139/ssrn.3108006. Accessed 22 May 2018

  9. Cochrane, J.H.: Asset Pricing. Princeton University Press, New Jersey (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Songkomkrit Chaiyakan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaiyakan, S., Thipwiwatpotjana, P. (2019). Mean Absolute Deviation Portfolio Frontiers with Interval-Valued Returns. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14815-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14814-0

  • Online ISBN: 978-3-030-14815-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics