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Portfolio Optimization, a Decision-Support Methodology for Small Budgets

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Book cover Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

Several machine learning paradigms have been applied to financial forecasting, attempting to predict the market’s behavior, with the final objective of profiting from trading shares. While anticipating the performance of such a complex system is far from trivial, this issue becomes even harder when the investors do not have large amounts of money available. In this paper, we present an evolutionary portfolio optimizer for the management of small budgets. The expected returns are modeled resorting to Multi-layer Perceptrons, trained on past market data, and the portfolio composition is chosen by approximating the solution to a multi-objective constrained problem. An investment simulator is then used to measure the portfolio performance. The proposed approach is tested on real-world data from Milan stock exchange, exploiting information from January 2000 to June 2010 to train the framework, and data from July 2010 to August 2011 to validate it. The presented tool is finally proven able to obtain a more than satisfying profit for the considered time frame.

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Notes

  1. 1.

    https://finance.yahoo.com/.

  2. 2.

    Obtaining the desired quantity at the wished price might not always be possible.

  3. 3.

    In the real world, sometimes it is impossible to sell a stock in time, and as a result an investor might not have money available to buy another desired one.

  4. 4.

    In a real market, a stock that has a bid-ask spread too wide could be suspended from the negotiation, and goes to auction, depending on market regulations.

  5. 5.

    FTSE-all share, 03/06/2010-02/08/2011. During this time frame, the index had considerable fluctuations, ranging from a maximum of 21600 reached before October 2010 to a fall to 19105 in November, up to a quote of 23167 in February 2011, and a final decrease to a minimum of 17270.

  6. 6.

    http://www.itcup.it/, known as Top Trader Cup in 2011.

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Authors and Affiliations

  1. Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Torino, Italy

    Igor Deplano

  2. Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Torino, Italy

    Giovanni Squillero

  3. INRA, UMR 782 GMPA, 1 Avenue Lucien Brétignières, 78850, Thiverval-grignon, France

    Alberto Tonda

Authors
  1. Igor Deplano
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  2. Giovanni Squillero
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  3. Alberto Tonda
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Correspondence to Alberto Tonda .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Deplano, I., Squillero, G., Tonda, A. (2016). Portfolio Optimization, a Decision-Support Methodology for Small Budgets. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_5

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