Stock Management Using Artificial Intelligence

V. Kumar, S. Hemalatha

2023

Abstract

Investing in the stock market is a complex and difficult undertaking that necessitates a high level of competence and understanding. Portfolio optimisation is a well-known approach for maximizing returns while minimizing risks. With the increased availability of data and advancements in machine learning and artificial intelligence, there is a growing interest in designing intelligent systems for portfolio optimisation. In this study, we propose an artificial intelligence-based approach for stock portfolio optimization. The proposed approach utilizes machine learning algorithms to identify the best performing stocks and to predict their future behavior. The algorithm also considers various risk factors and constraints, such as transaction costs, liquidity, and diversification. We compare the performance of the proposed methodology to traditional portfolio optimisation methods on a dataset of stock market data. Our technique surpasses existing methods in terms of risk-adjusted returns and provides a more robust and effective means to optimize stock portfolios, according to the data. The proposed method has the potential to help financial institutions and individual investors make better investment decisions and earn higher returns. The process of picking a set of stocks that maximizes profits while minimizing risk is known as stock portfolio optimisation. This process involves evaluating a large number of stocks and determining the optimal weights for each stock in the portfolio. Traditional methods of portfolio optimization rely on mathematical models, such as Markowitz’s mean-variance optimization, which assumes that asset returns follow a normal distribution and that investors are risk-averse. However, these assumptions may not always hold in real-world scenarios, leading to suboptimal investment decisions. With the increased availability of data and advancements in machine learning and artificial intelligence, there is a growing interest in designing intelligent systems for portfolio optimisation. This study’s recommended approach uses machine learning algorithms to identify the top performing stocks and predict their future behavior. These algorithms are capable of analyzing vast volumes of data, such as financial statements, news stories, and market trends, in order to detect patterns and trends that may influence stock values. The algorithm also considers various risk factors and constraints, such as transaction costs, liquidity, and diversification, which are important factors in portfolio optimization.

Paper Citation

in Harvard Style

Kumar V. and Hemalatha S. (2023). Stock Management Using Artificial Intelligence. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 640-645. DOI: 10.5220/0012614000003739

in Bibtex Style

@conference{ai4iot23,
author={V. Kumar and S. Hemalatha},
title={Stock Management Using Artificial Intelligence},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={640-645},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012614000003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Stock Management Using Artificial Intelligence
SN - 978-989-758-661-3
AU - Kumar V.
AU - Hemalatha S.
PY - 2023
SP - 640
EP - 645
DO - 10.5220/0012614000003739
PB - SciTePress