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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

Stock Recommendations Using Machine Learning and Natural Language Processing

verfasst von : Akruti Sinha, Mahin Anup, Deepak Sinwar, Ashish Kumar

Erschienen in: ICT: Applications and Social Interfaces

Verlag: Springer Nature Singapore

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Abstract

Due to the increasing number of investors in the past decade, the financial markets are now accountable for any country’s economic stability. Stock markets have grown increasingly unpredictable in recent years, yet they continue to be the most crucial for both investors and industries. There are several stock trading advice systems on the market that claim to be able to accurately predict future trends. Recommendation systems for stock trading are of great significance to a layperson who wants to benefit from stock trading despite not having a seasoned trader’s capacity or experience. The present study proposed a three-fold function. First, it provides a comparative analysis of the existing recommendation systems. It then applies and compares the results of the four Machine Learning models for price prediction on a real dataset. Finally, six sentiment analysis models are compared for the analysis of stock-based tweets. The best of the two are lastly integrated to arrive at a stock buy or sell recommendation.

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Metadaten
Titel
Stock Recommendations Using Machine Learning and Natural Language Processing
verfasst von
Akruti Sinha
Mahin Anup
Deepak Sinwar
Ashish Kumar
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
ICT: Applications and Social Interfaces

Print ISBN: 978-981-9702-09-1

Electronic ISBN: 978-981-9702-10-7

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-0210-7_38

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