Open AccessA Study of Different Algorithms used to Predict the Stock Price
Author(s) -
Aditya Singh Rajpurohit,
Shravani Prakash Ahirrao,
Pradnya Sangitbabu Gaikwad,
Nutan Bhairu Dhamale
Publication year - 2021
Publication title -
international journal of engineering and management research
Language(s) - English
Resource type - Journals
eISSN - 2394-6962
pISSN - 2250-0758
DOI - 10.31033/ijemr.11.5.11
Subject(s) - stock (firearms) , autoregressive integrated moving average , stock market , computer science , stock price , econometrics , algorithm , financial economics , time series , machine learning , economics , engineering , series (stratigraphy) , mechanical engineering , paleontology , biology , horse
A stock market is a place where we can purchase the stocks of various companies(part of the company), which makes it volatile, and predicting it becomes a tedious task. So we need various algorithms and methodologies to predict the stock prices. We cannot depend on one type of algorithm because each algorithm has its own pros and cons and also it depends on the style of the trader on how he trades stocks. This paper will deal with different aspects like quantitative aspect- LSTM, RNN, ARIMA, and qualitative with sentiment analysis for predicting the stock prices, in an efficient manner.