Stock trading is one of the most important practices in the financial
world. The act of attempting to forecast the future value of a stock or other
financial instrument traded on a financial exchange is known as stock market
prediction. The majority of stockbrokers use technical and fundamental
analysis, as well as time series analysis, when making stock predictions.
Python is the programming language used to use machine learning to forecast the
stock market. In this paper, we propose a Machine Learning (ML) method that
will be trained using publicly accessible stock data to obtain intelligence,
and then use that intelligence to make an accurate prediction. The paper
focuses on the use of Regression and LSTM based Machine learning to predict
stock values. Factors considered are open; close, low, high and volume. The
dataset should be as precise as possible, because even minor changes in the
data might result in large changes in the results. In this paper, supervised
machine learning is employed on a data set obtained from Alpha vantage api.
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ad hoc announcements, Transfer learning with RNN and LSTM, Word embedding
(em),Polarity score, Direction accuracy of nominal return, Result- RNN
0.552LSTM 0.576 LSTM-em0.578.
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investors’ forum posts, methods used-LSTM, NB, selection of textual data manually
labelled sentiment by experts, measure used -Direction accuracy, result -
87.86%.
