Abstract
In this paper, considering an investment portfolio problem with stochastic returns, we present a novel approach to design a Stochastic Rule-Based Decision Support System (SRBDSS). The SRBDSS helps investors to infer reliable and near optimal investment weights without solving the conventional optimization model directly. The introduced rule-based inference system establishes a relationship between patterns of stock returns, optimal weights and objective function. Also, an implication method is proposed to infer the investment ratio/weights for a given realization of the portfolio returns. In the presented model, optimum knowledge, derived from a stochastic portfolio problem, is used to construct SRBDSS, instead of using a few number of experts’ opinion. A case study is presented to illustrate the model employing real data adopted from Tehran Stock Exchange. Provided results reveal that not only is the presented approach easy to use, but it also provides acceptable results without directly solving the investment portfolio problem.
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The data that support the findings of this study are openly available in the Tehran Stock Exchange website at www.tsetmc.com.
Code availability
EasyFit5.5 Professional software is employed to estimate probability density function of stochastic parameters and decision variables. Also, developed stochastic decision support system is coded by authors in MATLAB 2014 software. All codes and datasheets are available if needed.
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Yousefli, A., Heydari, M. & Norouzi, R. A data-driven stochastic decision support system to investment portfolio problem under uncertainty. Soft Comput 26, 5283–5296 (2022). https://doi.org/10.1007/s00500-022-06895-2
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DOI: https://doi.org/10.1007/s00500-022-06895-2