One of the main objectives of fund managers in financial service industry is to select superior stocks by analyzing financial ratios. This paper proposes a novel methodology for stock selection by integrating optimistic and pessimistic ordered weighted averaging (OWA) and data envelopment analysis (DEA) methods. The paper first reveals the drawback of using the standard DEA models for stocks evaluation and then proposes a new method by using the OWA operator. Unlike the classical DEA, the proposed method in this paper does not involve the specification of inputs and outputs. The paper incorporates optimistic and pessimistic scenarios and generates interval OWA scores for all stocks. This is followed by using appropriate interval DEA models for selecting superior stocks. The proposed method in this paper is applied to identify high financial performance stocks in the Tehran stock market.
The main objective of stock selection is to select a set of assets in the stock market with high‐expected returns. There are many financial variables that affect the performance of stock firms. This paper proposes a novel linear programming model based on the ordered weighted averaging (OWA) operator for identifying superior stocks without requiring the re‐ordering process. The paper first converts a stock selection problem into a preference voting system by considering two different perspectives: an investor perspective in which the goal is to select stocks with the highest return, and a creditor perspective in which the goal is to maximize the repayment ability. The OWA operator is then used to formulate a linear programming model for identifying superior stocks. The usefulness of the proposed method in this paper is shown through an application in the Tehran stock market.
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