Market Stock Price Prediction Using Machine Learning

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

Stock is a curve with a lot of unknowns. The stock market has a lot of intricacy and turbulence, which makes it difficult to predict what will happen. The primary goal of the topic's argument is to forecast future market stock stability. Many researchers have looked at the future market's evolution. Data is a vital source of efficiency because stock is made up of shifting data. The efficiency of the forecast has an impact on the same probability. Machine learning has been incorporated into the picture for the deployment and prediction of training sets and data models in the latest trend of Stock Market Prediction Technologies. Machine Learning uses a variety of predictive models and algorithms to forecast and automate tasks. The focus of the paper is on the application of regression and LSTM to forecast stock prices.

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

Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. 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. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market price prediction. This work contributes to the stock analysis research community both in the financial and technical domains.

IRJET, 2022

Stock Market Prediction is a challenging and trending topic for researchers in recent years. Although it contains significant risk, it is frequently utilized in investment schemes that promise big returns. The returns on stocks are quite erratic. They are influenced by a number of variables, including prior stock prices, current market trends, financial news, social media, etc. There are many methods used to forecast stock value, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, however none of these methods has been demonstrated to be a reliable forecasting method. In order to improve the accuracy of stock price prediction, a variety of machine learning approaches and algorithms are examined in this study.

International Journal for Research in Applied Science and Engineering Technology, 2018

Over the years stock price prediction has always attracted people investing in share market because of the direct financial benefits. Stock price prediction is a very challenging task because of the volatile nature of stock market. The stock prices are affected due to many reasons like company related news, political, social, economical conditions and natural disasters[1]. An intelligent trader predicts the stock price and buys a stock before the price rises, or sells it before its value declines. There are many models developed for predicting the future price of stock but each one has its drawback. In this paper we analyze the accuracy of Linear Regression, Support Vector Regression and Decision Tree Classifier by comparing the predicted values with the actual values over a period of time.

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.

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%.

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.

2020

1Assistant Professor, Dept. of Computer Science & Engineering, Datta Meghe Institute of Engineering Technology & Research, Wardha, Maharashtra, India 2,3,4,5,6 Student, Dept. of Computer Science & Engineering, Datta Meghe Institute of Engineering Technology & Research, Wardha, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Due to the unsettled, composite and time-to-time changing of prices, stock market prediction has attracted a lot of interest from investors and researchers for quite a time. It is very difficult to make dependable predictions. The stock market prices are predicted using the machine learning algorithms like k-Nearest Neighbors, in this system. The model that has been highly triumphant for classification and regression is the k-Nearest Neighbors model. KNN algorithm is a very popular classification algorithm demonstrating good performance characte...

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.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.