Stock Market Prediction using Machine Learning Techniques: A Systematic Review

Gupta, Aditi; Akansha,; Joshi, Khushboo; Patel, Madhu; Pratap, Vibha

doi:10.1109/picc57976.2023.10142862

Cited by 2 publications

(1 citation statement)

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“…With the help of Artificial Intelligence & Machine Learning (AIML), humans can make many kinds of predictions such as Rain predictions, Astrological predictions, GDP growth, and winning predictions in games and sports [1]. Even sometimes AIML can predict the important decisions of the Governing body's policies, the Company's growth, and the future, it can also predict some public-related needs like House prices and Car prices.…”

Section: Introductionmentioning

confidence: 99%

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

Sisodia,

Nayak,

Boghey

2024

J. Appl. Artif. Intell.

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As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.

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Section: Introductionmentioning

confidence: 99%