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Machine Learning and Deep Learning based Approaches to Predict Nifty Index


Affiliations
1 Associate Professor, Department of Management Studies, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
2 II MBA, Department of Management Studies, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
 

Stock price prediction is one of the most difficult machine learning issues to solve. The stock market, often known as the equity market, has a significant impact on today's economy. This study discusses about different machine learning and deep learning approaches to predict and evaluate stock prices. Time series data is used to depict stock values and algorithms are trained to learn patterns from trends. For the machine learning approaches, study used linear regression, logistic regression and decision tree and for deep learning approaches Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) are used to predict Nifty Index value. These variants are commonly used to forecast stock prices and movements. The algorithm is based on the concept of probability and it is used for predictive analysis. For continuous quantitative data, a regression tree is utilized. Linear Regression, Logistic Regression, Decision Tree, LSTM and RNN are the most noticeable techniques used in financial time series forecasting. The study observed from Python software, that the Linear and Logistic Regression model predicts accuracy of roughly 52% and provides an acceptable return ratio. As a result, study found that the Nifty-50 data set has been utilized to improve the precision of supervised learning and future prediction.

Keywords

Decision Tree, Deep Learning, Linear Regression, Logistic Regression and Nifty
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  • Machine Learning and Deep Learning based Approaches to Predict Nifty Index

Abstract Views: 330  |  PDF Views: 171

Authors

P. Karthikeyan
Associate Professor, Department of Management Studies, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India
N. Vigneshwaran
II MBA, Department of Management Studies, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India

Abstract


Stock price prediction is one of the most difficult machine learning issues to solve. The stock market, often known as the equity market, has a significant impact on today's economy. This study discusses about different machine learning and deep learning approaches to predict and evaluate stock prices. Time series data is used to depict stock values and algorithms are trained to learn patterns from trends. For the machine learning approaches, study used linear regression, logistic regression and decision tree and for deep learning approaches Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) are used to predict Nifty Index value. These variants are commonly used to forecast stock prices and movements. The algorithm is based on the concept of probability and it is used for predictive analysis. For continuous quantitative data, a regression tree is utilized. Linear Regression, Logistic Regression, Decision Tree, LSTM and RNN are the most noticeable techniques used in financial time series forecasting. The study observed from Python software, that the Linear and Logistic Regression model predicts accuracy of roughly 52% and provides an acceptable return ratio. As a result, study found that the Nifty-50 data set has been utilized to improve the precision of supervised learning and future prediction.

Keywords


Decision Tree, Deep Learning, Linear Regression, Logistic Regression and Nifty

References





DOI: https://doi.org/10.15613/hijrh%2F2022%2Fv9i2%2F218195