sis 19(20): e1

Research Article

Stock Price Prediction using Artificial Neural Model: An Application of Big Data

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  • @ARTICLE{10.4108/eai.19-12-2018.156085,
        author={Malav Shastri and Sudipta Roy and Mamta Mittal},
        title={Stock Price Prediction using Artificial Neural Model: An Application of Big Data},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={6},
        number={20},
        publisher={EAI},
        journal_a={SIS},
        year={2019},
        month={1},
        keywords={News Headlines, Stock Market, Big Data, Artificial Intelligence, Artificial Neural Networks, Sentimental Analysis},
        doi={10.4108/eai.19-12-2018.156085}
    }
    
  • Malav Shastri
    Sudipta Roy
    Mamta Mittal
    Year: 2019
    Stock Price Prediction using Artificial Neural Model: An Application of Big Data
    SIS
    EAI
    DOI: 10.4108/eai.19-12-2018.156085
Malav Shastri1, Sudipta Roy2, Mamta Mittal3,*
  • 1: Department of Computer Science and Engineering, Ganpat University, Mehsana-Gozaria Highway, Mehsana-384012, Gujarat, India
  • 2: Washington University in Saint Louis, MIR department, 510 South Kings highway Blvd., MO 63110, USA
  • 3: Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, Okhla, New Delhi, India
*Contact email: mittalmamta79@gmail.com

Abstract

In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.