Stock Market Prediction is used to predict and identify the future value of a company share or stock on exchange. Prediction of stock market performance accurately is a challenging task and the successful prediction can yield profit by predicting the future price. The proposed prediction model includes different attributes as input. One is numerical input and another one is textual input. Numerical inputs include technical indicators and historic prices. Textual inputs include financial news feeds and Tweets. Here we aim to improve the stock market prediction using machine learning technique .Prediction model consist of Recurrent Neural Network (RNN) to combine the three aspects for enhancing the predictability of the daily stock market trends.
Published In : IJCSN Journal Volume 8, Issue 3
Date of Publication : June 2019
Pages : 250-253
Tables : 01
Sreelekshmi S Nair :
received her B.Tech (CSE) degree from University of Kerala in 2017. She is currently pursuing her Masters in Computer Science & Engineering from KTU.
Dr. Radhakrishnan B :
is working as the Head of CSE department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, image mining.
Recurrent Neural Network, ANN, Technical Indicators
In this paper, an improved method uses Recurrent Neural Network (RNN) for stock market prediction using Numerical and textual inputs. Historical prices and technical indicators are numerical inputs and Financial News headlines and public sentiments or mood from a large collection of Twitter data are textual inputs.
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