Abstract
The ability to generate wealth from stocks requires two key decisions: buying and selling at the right time. When it comes to investing, deciding when to buy and sell a stock is often challenging. Executing both of these decisions correctly, investors consider various factors. Much of the current research focuses on the historical stock price that influences future stock prices. Although individual historical characteristic is important, other factors also affect stock prices. To capture such internal relations and influence, we used multiple parameters as input. This research paper proposes a knowledge graph (KG) approach for generating stock trade recommendations. We use a combination of historical data, technical data, fundamental data, and market sentiments to build a KG that captures the relationships between stocks for recommendations. To evaluate the effectiveness of our KG approach, we conduct experiments on a real-time stock market dataset. We compare the performance of our approach to a baseline system that uses only traditional machine learning techniques. Our results show that our KG approach outperforms the baseline system in terms of accuracy, and profitability. We found that our approach generates more profitable trades than the baseline system, indicating its potential for practical use in stock trading applications. Overall, our research demonstrates the potential of KG techniques for generating accurate and profitable stock trade recommendations. We believe that our approach can contribute to the development of more effective and reliable stock trading systems that can help investors make informed decisions.
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Sonar, R., Menaria, S., Shukla, S. (2023). Stock Trade Recommendations Using Knowledge Graph. In: Chandran K R, S., N, S., A, B., Hamead H, S. (eds) Computational Intelligence in Data Science. ICCIDS 2023. IFIP Advances in Information and Communication Technology, vol 673. Springer, Cham. https://doi.org/10.1007/978-3-031-38296-3_13
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DOI: https://doi.org/10.1007/978-3-031-38296-3_13
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