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In this article, we propose an automatic trading system for portfolio selection that incorporates an investor's trading strategy (aggressive, conservative, or neutral). The system employs technical indicators to forecast assets' future price behavior. In particular, it clusters assets into three groups: 1) the promising assets are clustered in the “Buy” group, 2) the assets in danger of imminent losses are clustered in the “Sell” group, and 3) the remaining assets are clustered in the “Hold” group. We develop a gradient-based fuzzy rule system that can identify the three groups based on the technical indicator values of the cluster centers. We also develop a labeling algorithm as a corrective measure in case the fuzzy rule-based system identifies more than one group as buy, sell, or hold. Subsequently, we input the clusters to a credibilistic portfolio optimization model that models asset returns using coherent fuzzy numbers. We employ a genetic algorithm to solve the optimization model that exploits the problem's special structure. The proposed methodology is illustrated with a case study of the components of the NASDAQ-100 index.

In this paper, the COPRAS (Complex Proportional Assessment) method is extended for interval-valued q-rung orthopair fuzzy numbers (IVq-ROFNs) to solve multi-attribute group decision-making (MAGDM) problems. A novel distance measure for IVq-ROFNs is proposed, and its properties are also probed. This distance measure is used in an improved weights determination method for decision-makers. A weighted projection optimization model is developed to evaluate the completely unknown attributes’ weights. The projection of assessment values is defined by the positive and negative ideal solutions, which determine the resemblance between two objects by considering their directional angle. An Indian cities’ ranking problem for a better solid waste management infrastructure is solved using the proposed approach based on composite indicators, like recycling waste, greenhouse gas emissions, waste generation, landfilling waste, recycling rate, waste-to-energy rate, and composting waste. Numerical comparisons, sensitivity analysis, and other relevant analyses are performed for validation.