Abstract
In today’s highly integrated and globalized world, it’s been observed many times that a group of stocks move in tandem with each other, showing almost similar price movements. Stocks belonging to the same sector usually exhibit this phenomenon. But, the relation/similarity between stocks can be beyond just belonging to the same sector. Also, the relationship between stocks can vary over time. In this paper, an autoencoder is created to find similar stocks in a certain period of time using vector embeddings by using deep learning approach. The premise is that stocks with similar vector representation (bottleneck of autoencoder) will also be similar and would display similar behavior and characteristics. Stock data of Nifty 50 was considered, and performance is evaluated by using Euclidean, cosine, and a combination of Euclidean and cosine similarities, and it is proved that the Euclidean distance is outperforming other metrics.
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Pinapatruni, R., Mohammed, F. (2023). A Novel Deep Learning Approach to Find Similar Stocks Using Vector Embeddings. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_53
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DOI: https://doi.org/10.1007/978-981-99-6702-5_53
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