Manoj Kumar Saini
Anjali Bari
Aryan Pareek
Aman Gupta
- Assistant Professor, Department of Computer science, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
- Student, Department of Computer science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
Abstract
Stock market prediction using data science has become a popular area of research and application in recent years. This is because the stock market is a complex system with many variables and factors that affect its behavior, making it difficult to predict with certainty. The stock market has always been the aggression of buyers and sellers of stocks therefore in the global finance market stock trading is one of the most important activities. A stock market prediction is an act of trying to determine the future value of the company stock or other financial instruments which is done through different techniques. The stock market is renowned for its turbulence, changeability, and nonlinearity. Accurate stock price prediction is extremely challenging because of multiple (macro and micro) factors such as political, global, and economic conditions, unexpected events, the company’s financial performance, and so on. Due to its great learning capability for solving this nonlinear time this paper describes stock forecast using data science. Technical, fundamental, and time series analysis is used by most stockbrokers while making stock predictions. One of the key challenges in stock market prediction is dealing with the inherent uncertainty and volatility of the market. It is important to use robust statistical methods that can account for this uncertainty and provide accurate predictions and confidence intervals. Another important consideration in stock market prediction is the choice of features or variables used in the analysis. These features may include technical indicators such as moving averages and relative strength index, as well as fundamental data such as earnings reports and macroeconomic indicators. Overall, stock market prediction using data science is a complex and challenging task, but one that has the potential to provide valuable insights for investors and traders. By leveraging the power of data science techniques and tools, we can gain a deeper understanding of the market and make more informed investment decisions. The programming language used to predict the stock market using Data science are Python.
Keywords: LSTM, stock exchange Predictions, RNN, stock price prediction, data science, Long Short- Term Memory
[This article belongs to E-Commerce for Future & Trends(ecft)]
var fieldValue = “[user_role]”;
if (fieldValue == ‘indexingbodies’) {
document.write(‘Full Text PDF‘);
}
else if (fieldValue == ‘administrator’) { document.write(‘Full Text PDF‘); }
else if (fieldValue == ‘ecft’) { document.write(‘Full Text PDF‘); }
else { document.write(‘ ‘); }
References
1. Wang Z, Ho SB, Lin Z. Stock market prediction analysis by incorporating social and news opinion and sentiment. In2018 IEEE International Conference on Data Mining Workshops (ICDMW) 2018 Nov 17 (pp. 1375-1380). IEEE.
2. Shen J, Shafiq MO. Short-term stock market price trend prediction using a comprehensive deep learning system. Journal of big Data. 2020 Dec;7(1):1-33.
3. Reddy VK. Stock market prediction using machine learning. International Research Journal of Engineering and Technology (IRJET). 2018 Oct;5(10):1033-5.
4. Kipkemoi TH, Naibei I, Kirui JK. Relationship between exchange rate and securities’ market returns: Evidence from companies listed at Nairobi securities exchange.
5. Kumbure MM, Lohrmann C, Luukka P, Porras J. Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications. 2022 Jul 1;197:116659.
6. Srinivasan N, Lakshmi C. Stock prediction and analysis using intermittent training data with artificial neural networks. In2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) 2017 Mar 17 (pp. 1-4). IEEE.
7. Patel PV. Stock Market Prediction Using Machine Learning (Doctoral dissertation, California State University, Northridge). 2021.
8. Parmar I, Agarwal N, Saxena S, Arora R, Gupta S, Dhiman H, Chouhan L. Stock market prediction using machine learning. In2018 first international conference on secure cyber computing and communication (ICSCCC) 2018 Dec 15 (pp. 574-576). IEEE.
9. Smagulova K, James AP. Overview of long short-term memory neural networks. Deep Learning Classifiers with Memristive Networks: Theory and Applications. 2020:139-53.
10. Sonkavde G, Dharrao DS, Bongale AM, Deokate ST, Doreswamy D, Bhat SK. Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications. International Journal of Financial Studies. 2023 Jul 26;11(3):94.
E-Commerce for Future & Trends
| Volume | 11 |
| Issue | 01 |
| Received | February 21, 2023 |
| Accepted | December 15, 2023 |
| Published | January 15, 2024 |
function myFunction2() {
var x = document.getElementById(“browsefigure”);
if (x.style.display === “block”) {
x.style.display = “none”;
}
else { x.style.display = “Block”; }
}
document.querySelector(“.prevBtn”).addEventListener(“click”, () => {
changeSlides(-1);
});
document.querySelector(“.nextBtn”).addEventListener(“click”, () => {
changeSlides(1);
});
var slideIndex = 1;
showSlides(slideIndex);
function changeSlides(n) {
showSlides((slideIndex += n));
}
function currentSlide(n) {
showSlides((slideIndex = n));
}
function showSlides(n) {
var i;
var slides = document.getElementsByClassName(“Slide”);
var dots = document.getElementsByClassName(“Navdot”);
if (n > slides.length) { slideIndex = 1; }
if (n (item.style.display = “none”));
Array.from(dots).forEach(
item => (item.className = item.className.replace(” selected”, “”))
);
slides[slideIndex – 1].style.display = “block”;
dots[slideIndex – 1].className += ” selected”;
}