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A Genetic Algorithm Approach for Portfolio Optimization

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Data Science and Applications (ICDSA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 818))

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Abstract

For many investors, investing in the capital market can be quite challenging. It involves the task of accurately selecting and allocating funds to different shares in order to create an optimal portfolio. The problem of determining the ideal combination and proportion of shares within the portfolio can be addressed through the utilization of a genetic algorithm method approach. Portfolio optimization is the process of strategically selecting the most advantageous combination of investments that offers the highest potential return for a given level of risk undertaken by investors an optimal portfolio is one that maximizes the Sharpe ratio, which measures the potential return an investment can yield in relation to the risk assumed by investors. This study explores the application of genetic algorithms, a class of optimization algorithms, to enhance portfolio optimization. Genetic algorithms offer a powerful approach to determining the most favorable investment portfolios based on various criteria, including profit maximization, risk reduction, and management of asset correlations. The research critically highlights the distinct advantages of utilizing genetic algorithms over conventional techniques for portfolio optimization. Moreover, it introduces a comprehensive method to assess the effectiveness of genetic algorithms in portfolio optimization.

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Author information

Authors and Affiliations

  1. CSE Department, SET, MUST, Lakshmangarh, India

    Ishwa Anadani & Anand Sharma

  2. CSE Department, SoE, JECRC University, Jaipur, India

    Akshita Sharma

  3. CSE Department, IIITV, Gandhinagar, India

    Dhruv Dave

Authors
  1. Ishwa Anadani
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  2. Akshita Sharma
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  3. Dhruv Dave
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  4. Anand Sharma
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Corresponding author

Correspondence to Akshita Sharma .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Satyasai Jagannath Nanda

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajendra Prasad Yadav

  3. Department of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  4. Department of Computer Science and Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Anadani, I., Sharma, A., Dave, D., Sharma, A. (2024). A Genetic Algorithm Approach for Portfolio Optimization. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 818. Springer, Singapore. https://doi.org/10.1007/978-981-99-7862-5_9

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