Skip to main content
SpringerLink
Log in

Tactical Asset Allocation Through Random Walk on Stock Network

  • Conference paper
  • First Online:
Intelligent Systems (BRACIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13073))

Included in the following conference series:

Abstract

Tactical asset allocation is an essential method for defining a profitable portfolio for a given period. An analyst usually creates a tactical asset portfolio through technical analysis, a process subjective to the analyst’s knowledge and interpretations. Another aspect that can directly influence the quality of a portfolio is the number of assets considered for analysis. Human analysts tend to focus on a pre-defined group of assets, limiting choices, and, consequently, the possibility of better results. This work proposes the Stock Network Portfolio Allocation (SNPA) algorithm for the automatic recommendation of a stock portfolio, aiming to maximize profit and minimize risk. The proposed method considers a possibly large set of assets represented as a complex network. In which the nodes represent assets, and the edges stand for the correlation between their returns. Portfolio allocation is done through a random walk on the stock network, selecting, in the end, the most visited nodes (stocks). We conducted investment simulations on Brazilian stocks from the IBrX100 index, for 24 month periods, from Jan. 2018 to Dec. 2019. We compare the results with portfolio strategies: Ibovespa index (IBOV), classic Markowitz’s mean-variance portfolio (MV), Mean Absolute Deviation (MAD) portfolio, Conditional Value at Risk (CVaR), and Hierarchical Risk Parity (HRP). The Shape Ratio (SR), Maximum Drawdown (MDD), and Cumulated Wealth (CW) were used as performance metrics. The SNPA algorithm demonstrated its effectiveness, presenting a CW of 236.3%, being 203.5% above MV portfolio; 181.7% above CVaR portfolio; 175.6% above MAD portfolio, 184.6% above IBOV index, and 165.1% above HRP. SNPA also surpassed the benchmarks considering the performance metrics SR and MDD, with values 0.67 and −1.37 respectively, the best results among the benchmarks were observed by the HRP strategy, with 0.48 in SR index, and MV with −1.39 in MDD index.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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. Amenc, N., Le Sourd, V.: Portfolio Theory and Performance Analysis. Wiley, Chichester (2003)

    Google Scholar 

  2. Baser, P., Saini, J.R.: Agent based stock clustering for efficient portfolio management. Int. J. Comput. Appl. 116, 35–41 (2015)

    Google Scholar 

  3. Brandt, M.: Portfolio choice problems. In: Handbook of Financial Econometrics vol. 1 (2010)

    Google Scholar 

  4. Brugière, P.: Quantitative Portfolio Management. STBE, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37740-3

    Book  MATH  Google Scholar 

  5. Chen, W., Jiang, M., Jiang, C.: Constructing a multilayer network for stock market. Soft Comput. 1–17 (2019). https://doi.org/10.1007/s00500-019-04026-y

  6. DeMiguel, V., Garlappi, L., Uppal, R.: Optimal versus naive diversification: how inefficient is the 1/n portfolio strategy? Rev. Financ. Stud. 22, 1915–1953 (2009)

    Article  Google Scholar 

  7. George, S., Changat, M.: Network approach for stock market data mining and portfolio analysis. In: 2017 International Conference on Networks and Advances in Computational Technologies (NetACT), pp. 251–256 (2017)

    Google Scholar 

  8. Kim, D.H., Jeong, H.: Systematic analysis of group identification in stock markets. Phys. Rev. E72, 046133 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  10. Lee, K., Lee, J., Hong, H.: Complex networks in a stock market. Comput. Phys. Commun. 177, 186 (2007)

    Article  Google Scholar 

  11. Lee, Y.J., Cho, H.G., Woo, G.: Analysis on stock market volatility with collective human behaviors in online message board. In: Proceedings of the IEEE International Conference on Computer and Information Technology, pp. 482–489 (2014)

    Google Scholar 

  12. Magdon-Ismail, M., Atiya, A.F., Pratap, A., Abu-Mostafa, Y.S.: On the maximum drawdown of a Brownian motion. J. Appl. Probab. 41, 147–161 (2004)

    Article  MathSciNet  Google Scholar 

  13. Makowitz, H.M.: Portfolio Selection: Efficient Diversification of Investments. Wiley, New York (1951)

    Google Scholar 

  14. Makowitz, H.M.: Porfolio selection. J. Finance 7(1), 77–91 (1952)

    Google Scholar 

  15. Mantegna, R.: Hierarchical structure in financial markets. Eur. Phys. J. B Condens. Matter Complex Syst. 11, 193–197 (1999). https://doi.org/10.1007/s100510050929

    Article  Google Scholar 

  16. Pareek, M.K., Thakkar, P.: Surveying stock market portfolio optimization techniques. In: 2015 5th Nirma University International Conference on Engineering (NUiCONE), pp. 1–5 (2015)

    Google Scholar 

  17. Building diversified portfolios that outperform out of sample: Lopez de Prado, M. J. Portfolio Manag. 42, 59–69 (2016)

    Article  Google Scholar 

  18. Raudys, S.: Portfolio of automated trading systems: complexity and learning set size issues. IEEE Trans. Neural Netw. Learn. Syst. 24, 448–459 (2013)

    Article  Google Scholar 

  19. Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–41 (2000)

    Article  Google Scholar 

  20. Shapira, Y., Kenett, D., Ben-Jacob, E.: The index cohesive effect on stock market correlations. Eur. Phys. J. B72, 657 (2009). https://doi.org/10.1140/epjb/e2009-00384-y

    Article  MATH  Google Scholar 

  21. Sharpe, W.: Mutual fund performance. J. Bus. 39, 657–669 (1965)

    Google Scholar 

  22. Shen, W., Wang, B., Pu, J., Wang, J.: The Kelly growth optimal portfolio with ensemble learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1134–1141 (2019)

    Google Scholar 

  23. Shen, W., Wang, J.: Portfolio blending via Thompson sampling. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) (2016)

    Google Scholar 

  24. Shen, W., Wang, J., Jiang, Y.G., Zha, H.: Portfolio choices with orthogonal bandit learning. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-15) (2015)

    Google Scholar 

  25. Uryasev, S., Krokhmal, P., Palmquist, J.: Portfolio optimization with conditional value-at-risk objective and constraints. J. Risk 7, 43–68 (2002)

    Google Scholar 

  26. Zhang, D., Hu, M., Ji, Q.: Financial markets under the global pandemic of COVID-19. Finance Res. Lett. 36, 101528 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Technology, University of Campinas, Limeira, SP, Brazil

    Washington Burkart Freitas & João Roberto Bertini Junior

Authors
  1. Washington Burkart Freitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Roberto Bertini Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Washington Burkart Freitas .

Editor information

Editors and Affiliations

  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Freitas, W.B., Bertini Junior, J.R. (2021). Tactical Asset Allocation Through Random Walk on Stock Network. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91702-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91701-2

  • Online ISBN: 978-3-030-91702-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation