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<p>Investors are interested in the growth of their portfolios. The investment strategy focuses primarily on investing in low-risk and low-volatility companies. Nevertheless, it is important to acknowledge that every investment portfolio carries an inherent danger of experiencing financial losses due to negative progression and market crashes. This paper proposes an innovative methodology for forecasting rare events in financial time series based on the analysis of “images”. In addition, this paper deals with an enhancement to the current image formulation techniques, with the objective of addressing specific limitations that hinder their practical application in the financial analysis. In contrast to prior studies, which impose limitations on the “image-based-examination” of multiple securities, the approach presented in this paper enables the evaluation of various US Large Cap Securities, hence expanding its breadth and significance. Furthermore, the increased scope of applicability enhances the underlying algorithm’s adaptability and pertinence. One notable advancement is the incorporation of CVaR as a key measure for distinguishing between rare-event and non-rare-event scenarios. Expanding upon the limitations identified in the preceding studies, this paper presents a comprehensive enhancement in the study of financial data based on image analysis techniques.</p>

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From pixels to profits: a novel approach to identify rare events for a group of US equities

  • 期刊论文

  • 发表日期：

    2025年6月29日

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  • Lubdhak Mondal,
  • Kapil Chandak,
  • Goirik Chakrabarty,
  • Indranil SenGupta

摘要

Investors are interested in the growth of their portfolios. The investment strategy focuses primarily on investing in low-risk and low-volatility companies. Nevertheless, it is important to acknowledge that every investment portfolio carries an inherent danger of experiencing financial losses due to negative progression and market crashes. This paper proposes an innovative methodology for forecasting rare events in financial time series based on the analysis of “images”. In addition, this paper deals with an enhancement to the current image formulation techniques, with the objective of addressing specific limitations that hinder their practical application in the financial analysis. In contrast to prior studies, which impose limitations on the “image-based-examination” of multiple securities, the approach presented in this paper enables the evaluation of various US Large Cap Securities, hence expanding its breadth and significance. Furthermore, the increased scope of applicability enhances the underlying algorithm’s adaptability and pertinence. One notable advancement is the incorporation of CVaR as a key measure for distinguishing between rare-event and non-rare-event scenarios. Expanding upon the limitations identified in the preceding studies, this paper presents a comprehensive enhancement in the study of financial data based on image analysis techniques.