Abstract:
Stock market prediction is one of the most popular
use cases for machine learning models. A general model that
can predict the rise and fall of stocks is an arduous task as
there maybe multifarious factors that can affect stock prices.
This paper attempts to create a model by emulating the
approach traders, investors and analysts take to evaluate stock
investment strategy. A conjunction of both technical analyses
using available numerical data about stocks and fundamental
analysis using news headlines are attempted to understand and
predict market behavior for the Google stock. For this
purpose, sentiment analysis is used to understand news data
regarding the stock along with existing time series data as
input for an LSTM neural network. It is observed that such an
approach yields a more intuitive and accurate yet generalized
model that can be used for prediction of the stock market.
