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@article{206663,
        author = {Dr.Aanal Rushabh Desai},
        title = {Integrated Trading Rule Discovery and Portfolio Optimization for Stock Market Forecasting Using Applied Statistics and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2665-2668},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206663},
        abstract = {This study proposes an integrated framework that combines machine learning (ML) models for trading rule discovery with quantitative portfolio optimization. Traditional stock trading and investment methods often treat stock prediction and asset allocation as two separate tasks. This separation can lead to poor performance because prediction errors are not accounted for during the portfolio construction process. To address this issue, we present a two-stage approach. In the first stage, machine learning classifiers use historical market technical indicators to discover reliable trading rules and forecast price trend directions. In the second stage, applied statistical methods and a mean-risk portfolio optimization framework are combined to dynamically adjust asset weights. Specifically, we adapt the investor's risk-aversion level based on the probability of market trends predicted by the machine learning model.},
        keywords = {Machine learning, portfolio optimization, risk-aversion coefficient, stock market forecasting, trading rules.},
        month = {July},
        }