Main Reference PaperLeveraging social media news to predict stock index movement using RNN-boost, Data & Knowledge Engineering, 2018 [R]
  • By employing the hybrid prediction model referred to RNN-boost, the proposed method attempts to forecast the stock market volatility. Initially, It extracts the Latent Dirichlet allocation (LDA) features along with sentiment features from the social network news content, and then fed it as input to the hybrid model to predict the stock market.

Description
  • By employing the hybrid prediction model referred to RNN-boost, the proposed method attempts to forecast the stock market volatility. Initially, It extracts the Latent Dirichlet allocation (LDA) features along with sentiment features from the social network news content, and then fed it as input to the hybrid model to predict the stock market.

  • To accurately predict the stock market from the social network news content

  • To accomplish the excellent prediction performance

Aim & Objectives
  • To accurately predict the stock market from the social network news content

  • To accomplish the excellent prediction performance

  • The predictive performance improved by automatic selection of significant features through the superior feature engineering rules

Contribution
  • The predictive performance improved by automatic selection of significant features through the superior feature engineering rules

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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