Design and Development of Artificial Intelligence Framework to Forecast the Security Index Direction and Value in Fusion with Sentiment Analysis of Financial News

The domain of stock price prediction is extensively researched owing to its complex data structure and numerous influential factors. In the current epoch, many modern financial applications demonstrate non-linear and uncertain characteristics that exhibit temporal variability. As a result, there has been a notable rise in the need for resolutions to highly non-linear and time-variant issues. External factors, including public sentiment and political events, can influence stock market trends. The primary aim of this research is to propose a novel framework for predicting the blue-chip stock price in the future by combining historical stock data corresponding to the financial news published regarding stock in the newspaper. The initial step involves integrating sentiment and situational features into a machine-learning model. This integration aims to evaluate public sentiments’ influence on algorithms’ predictive precision thirty days ahead. Furthermore, regression models are utilized to analyze the inter-dependencies that exist among companies. To facilitate an experimental analysis, the researchers acquired historical data from the stock market through reputable sources such as the National Stock Exchange-India and collected news data about stock from the financial newspaper “EconomicTimes”. A series of machine learning models were tested to facilitate the classification of news sentiments of the stocks, followed by the fusion of resultant polarities with the stock’s historical data on the standard column “DATE” and then tested series of deep learning models to observe the impact of news on stock price and by taking reference of this, the authors successfully predicted the stock price on a random day. The findings unequivocally indicate that the proposed research design produces better results than other state-of-the-art models.

The data that support the findings of this research are available from the corresponding author upon reasonable request.

CNN:

Convolutional neural network

GRU:

Gated recurrent unit

RF:

Random forest

SVM:

Support vector machine

EMD:

Empirical mode decomposition

LSTM:

Long-short term memory

CNN:

Convolutional neural network

ALSTM:

Attention-based long-short term memory

MAE:

Mean absolute error

RMSE:

Root mean squared error

SVR:

Support vector regression

BOW:

Bag of words

CPU:

Central processing unit

LDA:

Linear discriminant analysis

TLBO:

Teaching and learning based optimization

GAN:

Generative adversarial networks

PCA:

Principal component analysis

LASSO:

Least absolute shrinkage and selection operator

VADER:

Valence aware dictionary for sentiment reasoning

BERT:

Bidirectional encoder representations from transformers

NSE:

National stock exchange

NB:

Naive Bayes

SGD:

Stochastic gradient descent

KNN:

K-nearest neighbor

GPC:

Gaussian process classification

RFC:

Random forest classifier

MLP:

Multi-layer perceptron

CISI:

Chartered Institute for Securities and Investment

The authors did not receive support from any organization for the submitted work.

Authors and Affiliations

1. University School of Computing, Rayat Bahra University, SAS Nagar, 140104, Punjab, India

   Harmanjeet Singh

2. University Institute of Computing, Chandigarh University, SAS Nagar, 140413, Punjab, India

   Manisha Malhotra

3. School of Computer Science, UPES, Dehradun, 248007, Uttarakhand, India

   Supreet Singh

4. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

   Preeti Sharma & Chander Prabha

Contributions

Harmanjeet Singh: conceptualization, methodology, writing—original draft. Manisha Malhotra: investigation, writing—review and editing, supervision. Supreet Singh: Writing—review and editing, software. Preeti Sharma: methodology, writing—review & editing. Chander Prabha: validation, writing—review & editing.

Corresponding author

Correspondence to Preeti Sharma.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics approval

This material is the authors’ own original work, which has not been previously published elsewhere.

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