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Journal articles on the topic 'Stock Prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

TOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network." Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, no. 2 (2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.

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One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep lea

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Li, Yung-Chen, Hsiao-Yun Huang, Nan-Ping Yang, and Yi-Hung Kung. "Stock Market Forecasting Based on Spatiotemporal Deep Learning." Entropy 25, no. 9 (2023): 1326. http://dx.doi.org/10.3390/e25091326.

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This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Short Term Memory (LSTM) and recent Transformer models lack the ability to directly incorporate spatial information, making the Spacetimeformer model a valuable addition to stock price prediction. This article uses the ten minute stock prices of the constituent stocks of the Taiwan 50 Index and the intraday data of individual stock on the Taiwan Stock Ex

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alan, Gun, Kavin Kumar, Su rya, and Kalai Chelvi. "Stock Market Prediction." International Academic Journal of Science and Engineering 9, no. 1 (2022): 18–22. http://dx.doi.org/10.9756/iajse/v9i2/iajse0909.

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Researchers have been investigating various approaches to accurately forecast stock market prices. Trading professionals can gain better insights regarding data, such as potential trends, by using useful prediction tools. Additionally, since the study predicts future market conditions, investors stand to gain significantly. Using machine learning algorithms for predicting is one such approach. The goal of this study is to increase the accuracy of stock market predictions made using stock valuation. Many academics have developed various approaches to address this issue, primarily using conventi

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Yang, Jiapeng. "Goldman Sachs’s Price Forecast Based on ARIMA and LSTM." Highlights in Business, Economics and Management 24 (January 22, 2024): 2194–201. http://dx.doi.org/10.54097/zk7c4c90.

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The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the

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Santhoshi, Mrs, Rianna Kristin M, Manojj D, and Rajeshwar v. "Stock Market Prediction." International Journal of Research Publication and Reviews 6, no. 5 (2025): 4091–95. https://doi.org/10.55248/gengpi.6.0525.1730.

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Jaiswal, Gourav. "Stock Prediction Model Using TensorFlow." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 99–103. http://dx.doi.org/10.22214/ijraset.2021.39207.

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Abstract: In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend available in market prediction technologies is that the use of machine learning that makes predictions on the basis of values of current stock exchange indices by training on their previous values. Machine learning itself employs completely different models to create prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Considering the factors are open, close, low, high and volume. Keyw

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Jagtap, Ajitkumar, Yash Patil, and Darshan Oswal. "Visualizing and Forecasting Stocks Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2562–66. http://dx.doi.org/10.22214/ijraset.2022.41846.

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Abstract: India's stock market is exceedingly changing and reductionism, which has a countless number of features that control the directions and trends of the stock price; therefore, prediction of uptrend and downtrend is a complex process. This paper point of view to demonstrate the use of recurrent neural network in finance to prediction of the closing price of a selected stock and analyse opinions around it in real-time. By combining both techniques, the submitted model can give buy or sell recommendation. In Stock Market Prediction, the aim is to predict the upcoming future value of the f

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BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU, and M. BHARGAV SAI. "STOCK PRICE PREDICTION." YMER Digital 21, no. 05 (2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.

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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of r

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Dong, Yuxin, and Yongtao Hao. "A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion." Electronics 13, no. 20 (2024): 4111. http://dx.doi.org/10.3390/electronics13204111.

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Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional d

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Singh, Aditya Kumar, Anurag Gupta, Faraz Rabbani, Abhijeet Yadav, and Mr Atma Prakash Singh. "Stock Prediction using LSTM Technique." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1352–55. http://dx.doi.org/10.22214/ijraset.2024.61815.

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Abstract: We attempt to use a machine learning approach to anticipate stock prices in this project. Whenit comes to stock price predictions, machine learning works well. The goal is to forecast future stock prices. make more accurate and better investment decisions We propose incorporating mathematical functions into stock prices. To arrive at an acceptable timescale, examine the prediction system, machine learning, and other external factors. delivers accurate stock predictions and lucrative trades There are some There are two types of stocks. Day trading is another name forintraday trading.

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Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clus

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Sahroni, Muhammad, Mochammad Firman Arif, and Muhammad Misdram. "STOCK PRICE PREDICTION USING THE LONG SHORT-TERM MEMORY METHOD." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1769–77. https://doi.org/10.52436/1.jutif.2024.5.6.2615.

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Stocks are a highly risky investment instrument if not handled correctly. Therefore, accurately predicting stock prices is crucial to supporting better investment decisions. Today, more young people in the current generation know the importance of investing in stocks. Hence, understanding prediction methods early on is essential to reduce potential losses for prospective investors. With accurate prediction methods, the results will be more reliable. The data used consists of daily stock prices of Bank Syariah Indonesia from May 2019 to May 2024, totaling 1,215 data points. The research method

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Chen, Haoxin. "Comparison of Different Machine Learning Scenarios for Stock Price Prediction of Netflix." BCP Business & Management 38 (March 2, 2023): 868–74. http://dx.doi.org/10.54691/bcpbm.v38i.3789.

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Buying stocks based on relatively accurate predictions to make a profit is what investors have been yearning for. However, due to the volatility and stochastic intrinsic of the stock, the price is also full of uncertainty, which is difficult to predict. With the improvement of computer performance and the popularization of machine learning methods nowadays, effective stock prediction methods emerge one after another. In this paper, random forest, XGBoost, and LSTM techniques are utilized for predicting the closing price of Netflix, which is one of the major stocks in Nasdaq and has been fluctu

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Kadam, Mr Yash, Mr Sujay Kulkarni, Mr Suyog Lonsane, and Prof Anjali S. Khandagale. "A Survey on Stock Market Price Prediction System using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 322–30. http://dx.doi.org/10.22214/ijraset.2022.40635.

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Abstract: 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

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Ankan, Ghosh, Mallick Kohinoor, Khamaru Saikat, Shee Rudrasom, Das Aritra, and Anindita Mukherjee Dr. "Stock Market Prediction Using Linear Regression." Advanced Innovations in Computer Programming Languages 6, no. 2 (2024): 33–37. https://doi.org/10.5281/zenodo.11195196.

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<em>Stock prediction analysis is crucial for investors to make informed decisions in financial markets. This project explores the application of linear regression in predicting stock prices using Python. Leveraging Python's strong libraries such as NumPy, Pandas, and scikit-learn, we preprocess historical stock data, select relevant features, and train linear regression models. Through thorough evaluation and prediction, we aim to identify trends and patterns in stock data, enabling more accurate forecasting of future stock prices. By conducting stock prediction analysis using linear regressio

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Patil, Dr S. T. "Machine Learning Model for Stock Market Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4057–62. http://dx.doi.org/10.22214/ijraset.2021.35822.

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: In recent time’s stock market predictions is gaining more attention, maybe due to the fact that if the trend of the market is successfully predicted, the investors may be better guided. A stock exchange is a system where you can buy and sell stocks. By stock we mean the share in the ownership of the company. Companies buy stocks to get the money they need to grow. Whereas people buy the stocks, also called as securities as investment or ways of possibly earning money. A stock Market Prediction model will help people to predict particular company’s stock price before they want to invest. This

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Rahman, Ansari Abdur. "Machine Learning Project in Python to Predict Stock Price." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29645.

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The Stock Price Prediction System is an innovative application of machine learning and data analysis techniques aimed at forecasting the future prices of publicly traded stocks. Stock markets are dynamic and influenced by a multitude of factors, making stock price prediction a complex and challenging task. This system leverages historical stock price data, along with relevant financial and macroeconomic indicators, to provide accurate and valuable predictions for investors, traders, and financial analysts. Index Terms Data Collection and Preprocessing, Machine Learning Models based, Prediction

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Shivraj, R., S. Vikas, Abhishek MN Naga, Kumar GN Naveen, Deepak NR Dr., and B. Ompraksash. "Prediction of Stock Market Performance Analysis by Using Machine Learning Regressor Techniques." Recent Trends in Computer Graphics and Multimedia Technology 7, no. 2 (2025): 11–21. https://doi.org/10.5281/zenodo.15331553.

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<em>Stock market prediction is a widely researched and crucial topic for investors, traders, and financial analysts. Precisely predicting stock price fluctuations can aid in making informed decisions regarding the buying or selling of stocks. One approach to achieving this is through sentimental analysis that has emerged as a popular approach for predicting stock prices. The research employs machine learning methods to enhance the accuracy of stock market predictions. It focuses on analyzing the efficiency of five advanced machine learning regression model.</em> <em>Bagging Regressor, XGB Regr

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Mushliha, Mushliha. "Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia." Jambura Journal of Mathematics 6, no. 2 (2024): 195–203. http://dx.doi.org/10.37905/jjom.v6i2.26509.

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Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the tem

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Josey, Aaron, and Amrutha N. "Stock Market Prediction." Indian Journal of Data Mining 4, no. 1 (2024): 34–37. http://dx.doi.org/10.54105/ijdm.a1641.04010524.

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The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional m

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Aaron, Josey. "Stock Market Prediction." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 34–37. https://doi.org/10.54105/ijdm.A1641.04010524.

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<strong>Abstract:</strong> The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its perfor

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Liu, Keqian, Ang Li, Xinran Lin, Zhuobin Mao, and Weiyang Zhang. "Empirical study on the performance of various machine learning models in predicting stock price movements as a binary classification task." Applied and Computational Engineering 55, no. 1 (2024): 129–44. http://dx.doi.org/10.54254/2755-2721/55/20241403.

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This paper examines the accuracy of stock price rise-or-fall predictions of seven different machine learning algorithms, including support vector machines and random forests, for three industry types: securities, banks, and Internet companies. The purpose of the research is to explore the effects of different models in the stock market, so as to help people choose the optimal machine learning model in predicting different types of stocks. The study produced nine features based on the study by Patel et al for prediction. By collecting 9 types of stock data from companies in different industries

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Adithya, Vishnu. "Stock Market Analysis Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47888.

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Abstract— The stock market price prediction and classification is Stock market price prediction is a common problem in finance that involves using historical data to forecast future prices of stocks or other financial instruments. The goal of stock market price prediction is to identify profitable trading opportunities and make informed investment decisions, to resolve this issues different machine learning algorithm is implemented for predict the model accuracy of stock market price level The problem statement for stock market price prediction involves identifying the factors that influence s

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Dange, Tejas. "T49 Style Stock Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30341.

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Stock market prediction has long been a pursuit of investors seeking to gain insights into the future trajectory of stock prices. Traditionally, analysts relied on fundamental analysis, technical indicators, and market sentiment to forecast stock movements. However, with the advent of machine learning (ML) and artificial intelligence (AI), predictive analytics in the stock market has seen a significant shift. ML algorithms offer the advantage of processing vast amounts of data and identifying complex patterns that might elude human analysts. In the context of stock market prediction, these alg

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Mo, Hanlin. "Comparative Analysis of Linear Regression, Polynomial Regression, and ARIMA Model for Short-term Stock Price Forecasting." Advances in Economics, Management and Political Sciences 49, no. 1 (2023): 166–75. http://dx.doi.org/10.54254/2754-1169/49/20230509.

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This research investigates the effectiveness of three prominent stock price prediction methodologies: Linear Regression, Polynomial Regression, and AutoRegressive Integrated Moving Average (ARIMA) model. The study leverages one and a half years of historical data from Apple, Tesla, Amazon, and Nike stocks to predict average prices over the ensuing 14 days. The predictive efficacy of each model is tested against actual data, revealing their respective strengths and limitations. Linear Regression offers an overview of stock trends with limited intricacy, while Polynomial Regression delivers a mo

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Singh, Arpita, Basar Imam Mazhari, and Karan Gupta. "Stock Market Prediction and Visualisation." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4460–67. http://dx.doi.org/10.22214/ijraset.2024.61019.

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Abstract: Stock price forecasting is a widely discussed and significant topic in both financial and academic circles. The stock market is inherently unpredictable, lacking clear rules for estimating or predicting share prices. Various methods, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, have been employed to forecast stock prices. However, none of these methods consistently serve as reliable prediction tools. In this paper, we explore the implementation, prediction, and analysis of stock market prices. Artificial Neural Networks and Machi

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Kumar, Mr V. Manoj. "Stock Prediction using Long Short-Term Memory, Support Vector Regression and Linear Regression Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3632–38. http://dx.doi.org/10.22214/ijraset.2021.37183.

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Prediction is most important for stock market not only for traders but also for computer engineers who analyses stock data. We can perform this prediction by two ways one is using historical stock data and other by analyzing by information gathered from social media. It is based on model/pattern used to predict stock dataset, there are so many models are available for predicting stocks, simply model is algorithm that’s from machine learning and deep learning. In the data set the two main parameters open and close value are used for stock prediction mostly but we can also predict by its volume

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Zhao, Ruiqi. "Stock Price Prediction Based on BP Neural Network and ARIMA Model." Frontiers in Business, Economics and Management 17, no. 1 (2024): 230–35. http://dx.doi.org/10.54097/rdksmg60.

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With the progress and development of society, stock investment has become an important part of people's daily lives. High risk and high return are the characteristics of stock investment. Individual investors and institutional investors always pay attention to stock market trends, analyze financial data, and try to predict the development trend of stocks. Studying the future trend of stock prices of listed companies and predicting the stock price of a certain company not only has extremely attractive application value, but also has significant theoretical significance, attracting the attention

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Liao, Xinyu. "Stock Price Prediction Based on ARIMA and Neural Network." Advances in Economics, Management and Political Sciences 56, no. 1 (2023): 163–71. http://dx.doi.org/10.54254/2754-1169/56/20231102.

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The stock price is affected by many factors and is a very complex nonlinear and non-stationary system. Predicting stock prices is a classic problem. People hope to predict stock prices more accurately, so as to make profits through stocks. This article selects five stocks in the Nasdaq stock market from 2020 to 2023, and tries to use 3 AI models (ARIMA, CNN, LTSM) to predict and analyze their next days closing prices and use the RMSE as the index to analyze the prediction performance. This paper finds that the three models can predict the stock price next day well, among which the ARIMA model

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Li, Shengnan, and Lei Xue. "The application of the propensity score matching method in stock prediction among stocks within the same industry." PeerJ Computer Science 10 (January 30, 2024): e1819. http://dx.doi.org/10.7717/peerj-cs.1819.

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Stock price prediction is crucial in stock market research, yet existing models often overlook interdependencies among stocks in the same industry, treating them as independent entities. Recognizing and accounting for these interdependencies is essential for precise predictions. Propensity score matching (PSM), a statistical method for balancing individuals between groups and improving causal inferences, has not been extensively applied in stock interdependence investigations. Our study addresses this gap by introducing PSM to examine interdependence among pharmaceutical industry stocks for st

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Liu, Liujun. "A Comparative Examination of Stock Market Prediction: Evaluating Traditional Time Series Analysis Against Deep Learning Approaches." Advances in Economics, Management and Political Sciences 55, no. 1 (2023): 196–204. http://dx.doi.org/10.54254/2754-1169/55/20231008.

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The contemporary financial landscape is characterized by dynamic market behavior. Accurate predictions of stock price movements are not only of paramount importance for financial decision-makers but also pose a significant challenge due to the inherent complexities of financial markets. This research study delves into the realm of stock market prediction by employing a comprehensive approach that combines time series analysis and machine learning techniques. The main goal is to assess different models in predicting price trends, potentially reshaping stock market forecasts and emphasizing the

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Yuvaraj, K., Dr J. Sreerambabu, and S. Kalidasan. "Trading View API and Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 978–81. http://dx.doi.org/10.22214/ijraset.2022.46313.

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Abstract: Stock market may be a market that permits seamless exchange of shopping for and commercialism of company stocks. each stock market has their own index price. Index is that the average price that's calculated by combining many stocks. Everyday billions of bucks ar listed on the exchange, ANd behind every greenback is an capitalist hoping to profit in a method or another. Entire corporations rise and fall daily supported the behaviour of the market. ought to AN capitalist be able to accurately predict market movements, it offers a tantalizing guarantees of wealth and influence.This hel

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Gaur, Varun, Sharad Bhardwaj, Utsav Gaur, and Sushant Gupta. "Stock Market Prediction & Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4404–8. http://dx.doi.org/10.22214/ijraset.2022.43403.

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Abstract: 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 W

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Venikar, Isha, Jaai Joshi, Harsh Jalnekar, and Shital Raut. "Stock Market Prediction Using LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 920–24. http://dx.doi.org/10.22214/ijraset.2022.47967.

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Abstract: This study proposes a model which will make use of an LSTM model for predicting stock prices. The stock prices will be predicted on the basis of past information. Stacked LSTM will be employed for the prediction because it utilizes the historic data, therefore, making the predictions more accurate since it is able to learn long term dependencies in data, which makes LSTM an ideal technique for stock market prediction due to its dynamic as well as complex nature. After training the model its accuracy will be checked by using the test data and then using the model the stock prices for

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Zuo, Xiaonan. "Prediction of Facebook and GOOG Prices based on Linear Regression and LSTM Regression." BCP Business & Management 44 (April 27, 2023): 688–95. http://dx.doi.org/10.54691/bcpbm.v44i.4919.

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Stock market analysis is a very difficult task, and stock markets are very complex and constantly changing environments. More and more stock investors are now becoming aware of the prominence of machine learning in the field of stocks and finance, and over the last decade or so machine learning has driven advances in the stock market, such as the ability to use different machine learning methods to predict stock movements in order to arrive at the best decisions and algorithmic trades. The problem that this project wants to investigate is the use of machine learning methods for stock predictio

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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns." Advances in Artificial Neural Systems 2014 (September 7, 2014): 1–7. http://dx.doi.org/10.1155/2014/246487.

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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of

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Guan, Jialin. "The Application of Artificial Intelligence to Stock Forecasting: A Literature Review." SHS Web of Conferences 218 (2025): 02028. https://doi.org/10.1051/shsconf/202521802028.

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Due to the non-linearity, high volatility and noise characteristics of stock prices, the prediction of stocks has become a challenging issue. The results of stock prediction algorithms rely on the selected indicators, including financial indicators and market sentiment indicators, and the algorithm model. A large number of scholars have conducted studies and innovations from different perspectives respectively to optimize the prediction results. This paper reviews the development of artificial intelligence in stock application from two perspectives of index and algorithm model. Among them, the

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Peng, Luna. "Stock Price Prediction of “Google” based on Machine Learning." BCP Business & Management 34 (December 14, 2022): 912–18. http://dx.doi.org/10.54691/bcpbm.v34i.3111.

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By 2022, many countries have declared the epidemic's end, both an opportunity and a challenge for many investors. More and more investors are manipulating prices to influence the stock market. So investors want to predict the price of stocks to make suitable investments. The author wants to start with the platform YouTube to study the price trend of this stock and make predictions to analyze whether there are traces of the factors affecting the stock price based on linear regression and random forest regression models. The author first backtested the price of this stock and analyzed the data a

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Deshmukh, Tejas, Suraj Hume, Ritesh Rana, Yash Chahande, Harshal Kubde, and Charan Pote. "Stock Market Price Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4531–34. http://dx.doi.org/10.22214/ijraset.2023.51234.

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Abstract: In stock request valuation, the end is to prognosticate the unborn value of the fiscal stocks of a company. Machine learning itself employs different models to make vaccinating easier and more authentic. The paper focuses on the use of retrogression and LSTM-grounded machine literacy to prognosticate stock values. stock request valuation, the end is to prognosticate the unborn value of the fiscal stocks of a company. Machine learning itself employs different models to make vaticination easier and further authentic. The paper focuses on the use of retrogression and LSTM-grounded machi

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L, Dushyanth. "A SURVEY ON STOCK PRICE PREDICTION USING DEEP LEARNING." International Research Journal of Computer Science 9, no. 2 (2022): 5–8. http://dx.doi.org/10.26562/irjcs.2022.v0902.002.

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Stock is a curve with a lot of unknowns. Stock market forecasting is fraught with complications and unpredictability. One of the most challenging and sophisticated methods of doing business is investing in the stock market. Stock forecasting is a difficult and time-consuming activity since the stock market is extremely volatile with stock prices fluctuating due to a variety of variables. Investors nowadays want quick and precise information to make informed decisions, thanks to the rapid growth of technology in stock price prediction. Understanding a company's stock price pattern and estimatin

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Chen, Xiaozhou, Wenping Hu, and Lei Xue. "Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm." Electronics 13, no. 4 (2024): 771. http://dx.doi.org/10.3390/electronics13040771.

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Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPB

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Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price

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Wu, Zhuojin. "Prediction for Some Tech Stock Prices of U.S. Stock Market based on ARIMA Model." Highlights in Business, Economics and Management 24 (January 22, 2024): 1220–26. http://dx.doi.org/10.54097/1kn3cb98.

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Predicting the future price of stocks is a common practice in the financial industry. Individuals and organizations engage in stock price forecasting for investment decision-making, risk management, portfolio optimization and asset allocation. However, the predicting stock prices is challenging due to the complexity of financial markets, the influence of numerous variables, and the presence of random and unpredictable events, which all make it harder to predict. This paper uses predicted future stock price trends for technology companies in The United States based on ARIMA and breaks it down u

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Chen, Zihao. "Stock Price Forecasts Based on KNN and LSTM." Advances in Economics, Management and Political Sciences 56, no. 1 (2023): 70–77. http://dx.doi.org/10.54254/2754-1169/56/20231064.

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Every stock trader wants to successfully predict the price or trend of a stock in order to make a profit because stock price forecasts provide investors, traders, and financial professionals with signals about potential price movements, which can help them make more informed decisions about buying, selling, or holding stocks. This article selects the four largest stocks in the U.S. stock market by market capitalization: Google, Apple, Microsoft, and Amazon, and predicts their closing prices from 2013 to 2023. First, K-Nearest Neighbors (KNN) model is established for the closing price sequence

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Li, Jianyao. "A Comparative Study of LSTM Variants in Prediction for Tesla’s Stock Price." BCP Business & Management 34 (December 14, 2022): 30–38. http://dx.doi.org/10.54691/bcpbm.v34i.2861.

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Long short-term memory (LSTM) is widely used in the stock market to train the prediction model and forecast future stock prices. Applying the LSTM method to research may incur some problems and facilitate the improvement of the method. Therefore, many LSTM variants are put forward under different circumstances. This paper surveys four LSTM variants, including Vanilla, Stacked, Bi-directional, and CNN LSTM on two different data sets regarding Tesla's stock price. Two data sets mentioned in this paper represent different stock types. To be more specific, data set 1 refers to stocks with a single

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Huang, Renjun. "Research on the selection of stock prediction models." Theoretical and Natural Science 30, no. 1 (2024): 141–46. http://dx.doi.org/10.54254/2753-8818/30/20241086.

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Against the backdrop of increasing attention to the integration of machine learning and stock analysis, stock prediction models are a hot topic. The question this paper is studying in this study is which stock prediction model is more accurate in predicting stocks. The method of this study is based on the stock prices of new energy vehicle leader Tesla Motors in the past three years as a data set, using a random forest model and an SVR model to predict the stock prices over the next 10 days. Based on the parameter MSE values of the training models of two stock prediction models, compare their

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Li, Jingyao. "Comparison of Different Machine Learning Approaches for Forecasting Stock Prices." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 17–23. http://dx.doi.org/10.54097/2re5n809.

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Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a f

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Bhavanagarwala, Mustafa Shabbir, Nagarjun K N, Tanzim Abbas Charolia, Vishal M, and Ashwini M. "STOCK AND CRYPTOCURRENCY PREDICTION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (2022): 182–86. http://dx.doi.org/10.26562/ijirae.2022.v0908.06.

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In our project, the point is to anticipate long term esteem of the money related stocks of a company and crypto coins individually with fine precision. The future prices of stock and cryptocurrency are predicted by using the past available values. “Buy low, sell high" is a good saying but it is not a good choice for making speculations. Investment is best stock or crypto currency in awful time can have bad results, while investment in best stock or cryptocurrency at right time can have best benefits. Prediction for long term values is easy as compared to day-to-day basis as prices fluctuate a

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Liang, Luocheng. "ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction in the US stock market." SHS Web of Conferences 196 (2024): 02001. http://dx.doi.org/10.1051/shsconf/202419602001.

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Absteact: Stock price forecasting is considered one of the most difficult tasks in financial forecasting. Combining ARIMA with neural networks helps to enhance the model’s predictive capabilities when dealing with complex, nonlinear time series data. Attention-based CNN-LSTM and XGBoost hybrid model achieves the accuracy of stock prediction results. However, the predictive effect of this hybrid model has only been confirmed by Chinese stock market data. Therefore, this paper proposes to use ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price of five differen

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Yin, Shuo, Youwei Gao, Shuai Nie, and Junbao Li. "SSTP: Stock Sector Trend Prediction with Temporal-Spatial Network." Information Technology and Control 52, no. 3 (2023): 653–64. http://dx.doi.org/10.5755/j01.itc.52.3.33360.

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In financial big data field, most existing work of stock prediction has focused on the prediction of a single stock trend. However, it is challenging to predict a stock price series due to its drastic volatility. While the stock sector is a group of stocks belonging to the same sector, and the stock sector index is the weighted sum of the prices of all the stocks in the sector. Therefore the trend of stock sector is more stable and more feasible to predict than that of a single stock. In this paper, we propose a new method named Stock Sector Trend Prediction (SSTP) to solve the problem of pred

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