Open AccessStock Prediction Using Technical And Sentimental Analysis
Author(s) -
Prof. N.P. Kadale,
Gavali Prajwal,
Pratik Jadhav,
Sachin Landge,
Pratiksha Bhoite
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1072
Subject(s) - sentiment analysis , stock (firearms) , technical analysis , stock market , public opinion , regression analysis , social media , computer science , econometrics , financial economics , economics , marketing , artificial intelligence , business , machine learning , political science , world wide web , history , law , context (archaeology) , archaeology , politics
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.