An AI-assisted method artefact for investing decision: automatic identification of winning stocks within a value portfolio

Purpose: This paper combines the key aspects of artefact-based systems. DSS and artificial intelligence in a unified approach to solve a typical financial analysis problem within the design science paradigm. Design/methodology/approach: Our proposition is based on the seven design science guidelines. Our proposed “method” artefact is a financial application catering to the investment domain in particular. We present a detailed analysis of stock data from the Indian market for an extended period of 17 years by deploying state-of-the-art algorithms. The use of computational intelligence involving machine and deep learning helps in automatically identifying winning stocks within a value portfolio, that too, on a forward-looking basis. Findings: Our “method” artefact depicts superior results by identifying outperforming stocks, differentiated from the weak ones, within the value portfolio. Originality/value: This has significant implications for the investing community, particularly the Indian investors. Traditional research in value stocks has shown underwhelming performance in differentiating lucrative stocks from the rest.