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As an important indicator of the state of the economy and a productive arena for wealth generation and risk management, the financial market is essential to the global economy. This financial market research explores the complex dynamics of the market, concentrating especially on fixed income, foreign exchange, and equities. The analysis of market and financial trend behavior forms the basis of this work. First, historical data on SBI stock is used in this study to recognize and comprehend the trends that have influenced the market. After the data was cleansed, it underwent preprocessing to uncover some valuable insights using data exploration (EDA). In summary, this research explores the intricate dynamics of the financial market, with a focus on fixed income, foreign exchange, and equities. The combination of historical data analysis, data cleansing, exploratory data analysis, and machine learning techniques contributes to a comprehensive understanding of market trends and behaviors.

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Machine Learning in Finance: Transformative Insights and Strategies for the Financial Markets

  • 会议论文

  • 发表日期：

    2025年

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  • Sudhanshu Prakash Tiwari,
  • Gurbakash Phonsa,
  • Vipin Kumar,
  • Navneet Malik,
  • Amanpreet Kaur

摘要

As an important indicator of the state of the economy and a productive arena for wealth generation and risk management, the financial market is essential to the global economy. This financial market research explores the complex dynamics of the market, concentrating especially on fixed income, foreign exchange, and equities. The analysis of market and financial trend behavior forms the basis of this work. First, historical data on SBI stock is used in this study to recognize and comprehend the trends that have influenced the market. After the data was cleansed, it underwent preprocessing to uncover some valuable insights using data exploration (EDA). In summary, this research explores the intricate dynamics of the financial market, with a focus on fixed income, foreign exchange, and equities. The combination of historical data analysis, data cleansing, exploratory data analysis, and machine learning techniques contributes to a comprehensive understanding of market trends and behaviors.