HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model. (arXiv:2203.08143v1 [q-fin.ST])

One of the pillars to build a country’s economy is the stock market. Over the
years, people are investing in stock markets to earn as much profit as possible
from the amount of money that they possess. Hence, it is vital to have a
prediction model which can accurately predict future stock prices. With the
help of machine learning, it is not an impossible task as the various machine
learning techniques if modeled properly may be able to provide the best
prediction values. This would enable the investors to decide whether to buy,
sell or hold the share. The aim of this paper is to predict the future of the
financial stocks of a company with improved accuracy. In this paper, we have
proposed the use of historical as well as sentiment data to efficiently predict
stock prices by applying LSTM. It has been found by analyzing the existing
research in the area of sentiment analysis that there is a strong correlation
between the movement of stock prices and the publication of news articles.
Therefore, in this paper, we have integrated these factors to predict the stock
prices more accurately.

Source: https://arxiv.org/abs/2203.08143


Related post