Machine Learning Models in Stock Market Prediction
Gurjeet Singh
Dr. Gurjeet Singh, Associate Professor & Dean, Department of Lords School of Computer Applications & IT, Lords University, Alwar, Rajasthan, India.
Manuscript received on January 15, 2022. | Revised Manuscript received on January 24, 2022. | Manuscript published on February 28, 2022. | PP: 18-28 | Volume-11, Issue-3, January 2022 | Retrieval Number: 100.1/ijitee.C97330111322 | DOI: 10.35940/ijitee.C9733.0111322
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. The techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM) and Decision Trees (DT). Experiments are based on historical data of Nifty 50 Index of Indian Stock Market from 22nd April, 1996 to 16th April, 2021, which is time series data of around 25 years. During the period there were 6220 trading days excluding all the non trading days. The entire trading dataset was divided into 4 subsets of different size-25% of entire data, 50% of entire data, 75% of entire data and entire data. Each subset was further divided into 2 parts-training data and testing data. After applying 3 tests- Test on Training Data, Test on Testing Data and Cross Validation Test on each subset, the prediction performance of the used models were compared and after comparison, very interesting results were found. The evaluation results indicate that Adaptive Boost, k- Nearest Neighbors, Random Forest and Decision Trees under performed with increase in the size of data set. Linear Regression and Artificial Neural Network shown almost similar prediction results among all the models but Artificial Neural Network took more time in training and validating the model. Thereafter Support Vector Machine performed better among rest of the models but with increase in the size of data set, Stochastic Gradient Descent performed better than Support Vector Machine.
Keywords: Artificial Neural Network, Stock, Market Prediction, Supervised Machine Learning Models, Time Series Data
Scope of the Article: Machine Learning.