Automated Stock Trading Using Machine Learning
Predicting stock market movements is a well-known problem of interest. Now-a- days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. The approach through sentimental analysis is to observe how well the changes in stock prices i.e. the rise and fall are correlated to the opinion of people that are expressed by them on Twitter. Sentimental analysis helps in analyzing the public sentiments on Twitter, this approach is our approach through using make of sentimental analysis. Another approach in the same topic of our project is using technical analysis. We model the stock price movement as a function of these input features and solve it as a regression problem in a multiple kernel learning regression framework. The machine learning coupled with fundamental and/ or technical analysis also yields satisfactory results for stock market prediction. We also evaluated the model for taking buy-sell decision at the end of day which is also known as intraday trading
Year of publication: |
[2021]
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Authors: | Munde, Anup ; Jadhav, Ashwin ; Kaute, Harshal ; Gosavi, Ajay |
Publisher: |
[S.l.] : SSRN |
Subject: | Künstliche Intelligenz | Artificial intelligence |
Saved in:
freely available
Extent: | 1 Online-Ressource (4 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 01, 2021 erstellt |
Other identifiers: | 10.2139/ssrn.3772584 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013239905
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