A Comparative Study on Financial Stock Market Prediction Models

International Journal of Creative Research Thoughts (IJCRT), 2023

Stock market prediction and analysis are some of the most difficult jobs to complete. There are numerous causes for this, including market volatility and a variety of other dependent and independent variables that influence the value of a certain stock in the market. These variables make it extremely difficult for any stock market expert to anticipate the rise and fall of the market with great precision. However, with the introduction of Machine Learning and its strong algorithms, the most recent market research and Stock Market Prediction advancements have begun to include such approaches in analysing stock market data. The Opening Value of the stock, the Highest and Lowest values of that stock on the same days, as well as the Closing Value at the end of the day, are all indicated for each date. Furthermore, the total volume of the stocks in the market is provided, with this information, it is up to the job of a Machine Learning Data Scientist to look at the data and develop different algorithms that may help in finding appropriate stocks values.

Stock market prediction is a typical task to forecast the upcoming stock values. It is very difficult to forecast because of unbalanced nature of stock. Stock market prices are changing continuously. Many Stock holders invest more money on this stock market. Without having a clear idea on stock market, many people are losing a lot of money. More analyzing stock market may results in high profits. In this paper explore the use of artificial neural network is a very popular technique to forecast the stock market price and support vector machines. Other traditional methods are Hybrid markov model, Support vector machine and ARIMA models. Using these models to list the advantages and disadvantages of all these models and compare the performance of stock market.

Successful predictions of future stock will maximize the profit of the investors. The Prediction of the stock market is the task to determine the upcoming value of instrument traded on a company stock or financial exchange. The Report proposes a Simple AI model to foresee the stock value esteem. The proposed calculation incorporates molecule swarm advancement (PSO) and least help vector (LS-SVM). PSO calculation has been utilized to streamline LS-SVM to foresee costs of every day stock. The proposed model depends on the investigation of verifiable information and specialized pointers. The PSO calculations have been utilized to streamline LS-SVM to foresee of every day costs of day by day stock. The proposed model depends on the investigation of verifiable information and specialized markers. The PSO calculation chooses a blend of sans best parameters for LS-SVM so that over-fittings and nearby minima issues can be kept away from and improve forecast exactness. The proposed model was assessed utilizing thirteen benchmark money related datasets and it was contrasted and the Artificial Neural Network with Leavenberg-Marquard (LM) calculation. The outcomes got uncovered that the proposed model can have better expectation exactness and PSO calculation in improving LS-SVM. Several financial institutions use the powerful ML for predicting time-series data with unmatched accuracy level. The present-day research is focused on improving this model day by day. In a simple words one can say that Machine-learning is a process in which computer algorithms are used to make the machine learn from the available data and information to improve the result. This study with deals with several machine learning methods that can be applied to predict the market stock price with the help of previous data set provided by organization or company.

International Journal of Creative Research Thoughts (IJCRT), 2023

The stock market is a complex and dynamic environment where investors and traders strive to make informed decisions to maximize profits or minimize losses. Predicting stock prices accurately has long been a challenging task due to the multitude of factors influencing the market. With the advent of machine learning techniques, researchers have attempted to leverage these methods to forecast stock prices more effectively. This thesis aims to explore the application of machine learning algorithms for stock price prediction, comparing various models and features, and assessing their performance on historical data. The research intends to contribute valuable insights into the viability and effectiveness of machine learning in the financial domain.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Stock trading is one of the most essential activities in the financial sector. The act of attempting to anticipate the future value of a stock or other financial instrument is known as stock market prediction. A financial exchange-traded instrument. This document illustrates how Machine Learning is used to predict a stock. The time series analysis or technical and fundamental analysis is used most stockbrokers use when deciding on a stock predictions. To forecast the outcome, the computer language is employed. Python is a stock market that uses machine learning. This paper is about We suggest a Machine Learning (ML) strategy that will be cost-effective. taught from publicly available stock data and intelligence and then applies what they've learned to make an accurate prediction. This work use machine learning in this setting. Support Vector Machine (SVM) is a technology for predicting Stock prices for large and small cap companies, as well as in the three different markets, using daily and weekly pricing Frequencies that are up to date.

Bulletin of Electrical Engineering and Informatics, 2025

Stock market price prediction is a challenging task that plays a crucial role in investment decision-making and financial risk management. Traditional approaches often rely on a single machine learning (ML) algorithm for predictive modeling. In this contribution, an innovative framework that integrates logistic regression (LR) with support vector machine (SVM) to improve the accuracy and reliability of stock market price prediction. Combining the strengths of both algorithms, the proposed model harnesses the interpretability of LR and the robustness of SVM to capture complex relationships in stock market data. Experiments conducted on publicly available Yahoo Finance stock dataset and the Dhaka dataset, the results show that the proposed model yielded accuracies of 97.15% and 98.86% respectively. In comparison with other models, the proposed method outperformed the other models in terms of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and accuracy. The contribution and importance of leveraging hybrid modelling techniques to enhance stock market price prediction and facilitate informed investment decision-making.

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In this context this study uses a machine learning technique called Support Vector Machine (SVM) to predict stock prices for the large and small capitalizations and in the three different markets, employing prices with both daily and up-to-the-minute frequencies.

Journal of King Saud University - Science, 2022

The main perfect of this composition is to discover the stylish version to prognosticate the cost of the inventory request. During the procedure of analyzing the colorful ways and variables to remember, we plant that approaches similar as Random woodland, machine help Vector were not absolutely exploited. On this composition, we will introduce and assessment a in addition practicable gadget to prognosticate the motion of shares with lesser delicacy. The first issue we looked at turned into the previous time’s stock price dataset. The dataset has been preprocessed and refined for actual analysis. For this reason, our composition can even focus on preprocessing the raw data of the dataset. 2nd, after preprocessing the facts, we are able to observe the use of the arbitrary wood, we can aid the vector machine on the dataset and the results it generates. Similarly, the proposed composition examines the use of the soothsaying device in actual surrounds and the problems related to the delicacy of the overall values handed. The composition additionally provides a system literacy version for prognosticating the lifestyles of shares in a aggressive request. Predicting the success of shares might be a main asset for stock request institutions and could give actual effects to the troubles facing equity investors. By Using Stock Prediction algorithm overall accuracy is 80.3%.

International Journal of Recent Technology and Engineering, 2020

 Abstract: Stock market price prediction is an inspiring problem domain of financial sector as it involves several factors and drives people towards a complex problem solving approach. Financial markets are one of the key points of any country economy. Researchers have been working over several years on stock market prediction which is an interesting problem in itself since a number of variables are involved for prediction. In this study, an extensive review of existing techniques dedicated to stock market forecasting is carried out. A comparative analysis of the existing techniques, mentioning methodologies along with the future direction also provided.

Annals of Data Science, 2019

The stock market is a popular investment option for investors because of its expected high returns. Stock market prediction is a complex task to achieve with the help of artificial intelligence. Because stock prices depend on many factors, including trends and news in the market. However, in recent years, many creative techniques and models have been proposed and applied to efficiently and accurately forecast the behaviour of the stock market. This paper presents a comparative study of fundamental and technical analysis based on different parameters. We also discuss a comparative Analysis of various prediction techniques used to predict stock price. These strategies include technical analysis like time series analysis and machine learning algorithms such as the artificial neural network (ANN). Along with them, few researchers focused on the textual analysis of stock prices by continuous analysing the public sentiments from social media and other news sources. Various approaches are compared based on methodologies, datasets, and efficiency with the help of visualisation.