Author(s) :

Kanchan Singh.

Volume/Issue :

Abstract :

This paper tries to address the problem of stock request vaticination using artificial intelligence (AI) strategies. The stock request vaticination can be modeled grounded on two top analyses called specialized and abecedarian. In the specialized analysis approach, the retrogression machine literacy (ML) algorithms are employed to prognosticate the stock price trend at the end of a business day grounded on the literal price data. In discrepancy, in the abecedarian analysis, the bracket ML algorithms are applied to classify the public sentiment grounded on news and social media. In the specialized analysis, the literal price data is exploited from Yahoo Finance, and in abecedarian analysis, public tweets on Twitter associated with the stock request are delved to assess the impact of sentiments on the stock request’s cast. The results show a median performance, inferring that with the current technology of AI, it's too soon to claim AI can beat the stock requests.

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