Stock Price Prediction Research Articles

Comparative Analysis of Machine Learning Algorithms in Stock Price Prediction

Stock is part of a company's principal. A person who buys stock of a company shares the profit or loss of this company. Large volume transactions are made on stock exchanges where stocks are traded. Stock prices are difficult to predict because they are affected by many variables, but when they can be predicted, great benefits are provided. Prediction of stock prices is possible with today's computers using machine learning algorithms. Machine learning provides more successful results than fundamental and technical analysis in stock price prediction. In our study, daily closing price predictions were made by collecting approximately 5-years data of the top 5 stocks with the highest market value traded in BIST 100 between 2016 and 2020. Multiple linear regression, Bayesian regression, random forest, decision trees, support vector machines, artificial neural networks algorithms were applied to include maximum 22 features and the results were compared. The most successful result was obtained in the artificial neural networks algorithm. To achieve the highest success, data pre-processing, normalization, cross-validation, parameter optimization and feature selection were applied. It has been observed that using these methods together increases the success.

ANALISIS PREDIKSI HARGA SAHAM PT GUDANG GARAM TBK INDONESIA

This research aims to predict PT Gudang Garam Tbk's stock prices using the ARIMA method. Historical daily stock price data were analyzed to detect trends and seasonality. The ARIMA model was developed and tested, demonstrating significant predictive accuracy. Evaluation metrics such as Mean Absolute Error and Root Mean Squared Error were employed for performance assessment. The research contributes to stock price prediction methodologies, offering practical implications for investors and financial analysts. In conclusion, the study establishes a robust foundation for utilizing the ARIMA method in forecasting PT Gudang Garam Tbk's future stock prices.

Comparative Analysis of LSTM Variants for Stock Price Forecasting on NSE India: GRU's Dominance and Enhancements

The intricate realm of time series prediction using stock market datasets from the NSE India is delved into by this research. The supremacy of LSTM architecture for forecasting in time series is initially affirmed, only for a paradigm shift to be encountered when exploring various LSTM variants across distinct sectors on the NSE (National Stock Exchange) of India. Prices of various stocks in five different sectors have been predicted using multiple LSTM model variants. Contrary to the assumption that a specific variant would excel in a particular sector, the Gated Recurrent Unit (GRU) emerged as the top performer, prompting a closer examination of its limitations and subsequent enhancement using technical indicators. The ultimate objective is to unveil the most effective model for predicting stock prices in the dynamic landscape of NSE India.

Stock Price Prediction System LSTM Based on Deep Learning

Stock price prediction is a highly complex task due to the inherent volatility and non-linear patterns of financial markets. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have yielded promising results in time series forecasting. This paper provides an in-depth analysis of LSTM models applied to the stock price prediction of major technology companies, including Apple (AAPL), Tesla (TSLA), Microsoft (MSFT), and Nvidia (NVDA). The study utilizes historical stock data to train and evaluate LSTM models, comparing their performance against various other regression models such as Linear Regression, Support Vector Regression (SVR), Decision Tree Regression, and Gradient Boosting Decision Trees (GBDT).The results demonstrate that the LSTM model outperforms traditional models, particularly in its ability to capture long-term dependencies and manage complex, volatile stock data. Among the companies analyzed, the LSTM model achieved the most accurate predictions for Apple, as indicated by the lowest Mean Squared Error (MSE). Tesla's predictions, while less accurate due to high volatility, still showed improvement compared to other models. In conclusion, the LSTM network proved to be the most effective method for stock price prediction in this study, particularly excelling in forecasting long-term trends and reducing prediction errors for volatile stocks.

Hybrid Preprocessing for Neural Network-Based Stock Price Prediction

In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis.

Efficient Integration of CNN and LSTM for Data Mining Observation Algorithms: A Systematic Review

An extensive analysis of the combination of Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) is presented in this research. within data mining applications. CNNs, optimized for spatial data processing, and LSTMs, made to manage data in a sequential fashion, are combined to leverage their respective strengths in feature extraction and temporal pattern recognition. By analyzing over 60 published studies, we evaluate the effectiveness of CNN-LSTM models across diverse domains such as medical image analysis, financial time series forecasting, stock price prediction, and sentiment analysis. The review reveals that CNN-LSTM hybrids significantly outperform traditional methods in activities requiring both temporally and spatially understanding. However, challenges such as model interpretability, hyperparameter optimization, and the need for diverse datasets remain. Future research should focus on optimizing these models for specific applications, improving scalability, and addressing limitations like data scarcity. This review underscores the significant potential of CNNLSTM integration in advancing data mining techniques and solving complex problems in various fields. We present the related work of some of the most important publication in the field and classify over 60 published papers from different perspective. This review intends to classify the major techniques, methods, algorithms in the domain of medical images, financial time series analysis, stock price and social media.

Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets

In the Artificial Intelligence development market in recent years, the atmosphere felt by the financial field will be relatively strong, and some, especially in the deep learning model, widely exist in the performance of complex financial data, which has certain advantages. Therefore, as one of the most prominent models of deep learning, LSTM neural network models are just good at processing some complex financial data of the rest of the time series, such as stock price prediction and trading strategy, optimization, and so on. However, in the actual application process, the stock price prediction of such models still has certain data quality, historical data market fluctuations, complex, non-linear data and other related factors, so there are certain challenges and development space in the process of processing.Nevertheless, by properly addressing these issues and combining them with best practices, LSTM algorithms are a powerful tool to help uncover underlying patterns in financial markets and optimize trading decisions.

Integration of Technical Analysis and Machine Learning to Improve Stock Price Prediction Accuracy

Integration of Technical Analysis and Machine Learning to Improve Stock Price Prediction Accuracy

Nepal Stock Exchange Index Movement

Due to the features of the stock market and NEPSE index, predicting stock price movements is a challenging task. The nature of NEPSE index volatility makes it challenging for investors to booked profit from stocks. The main issues whether the trends and pattern can identify the movement of NEPSE Index or not? To address this problem, investors need to know accurate patterns of past index and make predictions about future price changes. In this study, The research design based on quantitative methods data has collected for 10 years NEPSE Index, used from January 2014 to December 2023 and technical trading tools such as Diagram and trend chart were used to determine the movement of the NEPSE index. As a result, Bullish market start after completion of long pattern of sideways market and exist 13 to 14 month only and falling towards bearish trend the NEPSE index pattern provides buy signal in March and selling signal in July. while the trader can benefit from it in the very short term, but Past performance is no guarantee of future results.

The Approaches to Predict Stock Prices by Random Forest

Abstract. People are always curious about how to predict the stock prices, various type of prediction tools are introduced to help people explore the trend of the stock price movement. With the development of the machine learning, there are more and more models and methods that we can use to predict the stock prices, while Random Forest, a typical machine learning algorithm, will be discussed in this article to examine its performance in the stock market. The data utilized in this study is sourced from Yahoo Finance, with several additional indicators computed and incorporated into feature set of the Random Forest model. To evaluate its performance, a comparison between the Random Forest model and the simple linear regression model is going to be conducted, which is a considerably basic model and serves as a benchmark for the minimum returns individuals can expect. The RMSE and MAPE will be compared firstly, which can help us assess which model demonstrates superior predictive accuracy, while the ROI derived from the strategy based on these models predictions will serve as a measure of their practical performance in the stock market. Collectively, this data will substantiate that Random Forest is indeed an effective model that is capable of assisting individuals in achieving greater profits.

Informer-Based Method for Stock Intraday Price Prediction

As a component of the capital market, the stock market plays a very important role in economic development. How to predict the stock price more accurately is the key concern of researchers and investors. To solve the above problems, this paper proposes an Informer based method for stock intraday price prediction. Informer based on attention mechanism can efficiently capture precise long-range dependencies between input and output to increase the prediction capacity. In this research, historical K-line data of traditional securities in China’s financial market are collected. Based on this data set, the intraday stock price prediction performance of Informer and Long Short-Term Memory neural network (LSTM) is compared. The experiment also uses the three-day K-line model to perform pattern recognition on the K-line data of candidate stocks. The results show that the prediction performance of Informer is significantly higher than that of LSTM, and the data information after three-day K-line pattern recognition can improve the prediction accuracy of Informer and reduce time and space overhead of Informer.

Enhancing stock market predictions via hybrid external trend and internal components analysis and long short term memory model

When it comes to financial decision-making, stock market predictability is extremely important since it offers valuable information that may guide investment strategies, risk management, and portfolio allocation overall. Traditional methods often fail to accurately predict stock prices due to their complexity and inability to handle non-linear and non-stationary patterns in market data. To address these issues, this study introduces an innovative model that combines the External Trend and Internal Components Analysis decomposition method (ETICA) with the Long Short-Term Memory (LSTM) model, aiming to enhance stock market predictions for S&P 500, NASDAQ, Dow Jones, SSE and SZSE indices. Through rigorous testing across various training data proportions and epoch settings, our findings reveal that the proposed hybrid model outperforms the single LSTM model, delivering significantly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This enhanced precision reduces prediction errors, underscoring the model’s robustness and reliability. The superior performance of the ETICA-LSTM model highlights its potential as a powerful financial forecasting tool, promising to transform investment strategies, optimize risk management, and enhance portfolio performance.

Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.

Feature Expansion Effect Approach for Improving Stock Price Prediction Performance

Feature Expansion Effect Approach for Improving Stock Price Prediction Performance

An Empirical Study on Implementation of AI & ML in Stock Market Prediction

The introduction of Artificial Intelligence (AI) and Machine Learning (ML) has transformed numerous fields, including agriculture, industry, economy, and medicine, with significant advancements in automation and decision-making processes. Today, AI and ML have also made notable strides in financial markets, particularly in stock and foreign exchange (Forex) forecasting, where complex algorithms are used to predict market movements and assist in decision-making. This paper examines such applications, particularly on using AI and ML techniques in the stock trading and market prediction. Specifically for this paper, the general approach to the application of AI and more concretely the ML in the trading of stocks is examined in terms of learning processes and the algorithms which are used to make predictions. The paper examines data extraction techniques, which are crucial for identifying such patterns as historical stock prices and volumes of trading. These patterns are utilized in the determination of the future market tendencies hence of more utility in exploring the elusive tendencies of the financial markets. It will also be seen that the usage of deep learning models, as well as neural networks, is very helpful in discovering as well as addressing these patterns. A considerable part of the study is devoted to the presentation of AI-based models implemented in R programming for stock price prediction. Two primary models are examined: the Artificial Neural Network (ANN) and the time series model by employing Auto-Regressive Integrated Moving Average (ARIMA). Another kind of deep learning model is known as the ANN which is a kind of artificial neural network or a computer model of the brain derived after researching the way the human brain processes information, which efficiently learns and identifies patterns in large data presumed to make future predictions on these patterns. On the other hand, the ARIMA is a model developed to handle time series data, because through the exploration of the data used in the analysis when developing a model for the estimation of the future stock price. Thus, the use of ANN and ARIMA models presents a complete solution for forecasting the stock market. Indeed, the use of the ANN model to analyze data demonstrates its strength in finding patterns that could not be easily discernable using standard approaches, in contrast, the ARIMA model is the most effective for short-term forecasting utilizing trends that have already been observed and set. Taken together, this study seeks to improve the reliability of the models used in predicting stock price fluctuations and to contribute to effective investment decision-making. Last but not least, this study intends to raise awareness about how AI and ML perform better in stock market business to negotiate the challenges that are related to market volatility and the unpredictability of data. The realizations produced through these models not only equip the investors with a strategic guide on where to invest but also offer a more technical and rational means of decision-making compared to applying the 'gut feel' in the financial markets. Whereas R programming makes it easier to apply both the ANN and the ARIMA models, the research shows how AI and ML can be employed to control for risks and maximize returns and thus raise the efficiency of the trading models. This research adds to the current knowledge on the trends of applying AI and ML to financial markets since the technology has massive potential to provide additional and advanced tools for traders and investors.

Artificial Intelligence and Deep Learning in Stock Prediction: A Bibliometric Review

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

A Review of Prediction Techniques used in the Stock Market

The prediction of stock market movements is a critical task for investors, financial analysts, and researchers. In recent years, significant advancements have been made in the field of stock prediction, driven by the integration of machine learning and data analysis techniques. Though stock market predictions are highly desired, there are many factors contributing towards volatility of the market. There is a need for extensive study and concentration on various predictive techniques to investigate different scenarios triggering such volatility. This paper reviews the latest methodologies employed for predicting stock prices, with a particular focus on the Australian stock market. Key techniques such as time series analysis like ARIMA & GARCH, machine learning models like SVM, LSTM & Neural Network, and sentiment analysis are discussed, highlighting their applications, key strengths, and some limitations.

COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO

This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. Four regression algorithms – linear regression, support vector regression (SVR), polynomial regression and LASSO regression – were applied to Apple Inc’s historical price data for two-years ending on October 1st, 2024, to predict the next day’s closing stock price. Technical indicators such as moving average, relative strength index (RSI) and lag features were included in the regressions to improve prediction performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) were employed to assess the models’ performance. Results show that linear regression and LASSO regression were the most performant models, reaching R² values of more than 0.95, with minimal error values. While SVR yielded the poorest results when accounting for all metrics. Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.

GEOPOLITICAL INSTABILITY AND ITS IMPACT ON FINANCIAL MARKET DYNAMICS: AN ARTIFICIAL INTELLIGENCE APPROACH AND SENTIMENT ANALYSIS

This paper investigates the impact of geopolitical instability, specifically the Russia-Ukraine conflict, on the dynamics of the Ukrainian financial market. The study focuses on key indices and companies, including the PFTS Index, Kernel Holding SA (KER), MHP SA DRC (MHPC), and Ukrnafta (UNAF). Utilizing advanced machine learning models—decision trees, Random Forest, and Long Short-Term Memory (LSTM) networks—the research predicts stock price changes in response to market volatility induced by geopolitical events. The analysis reveals a significant correlation between the onset of conflict and stock price fluctuations, particularly in the agricultural and energy sectors, with notable resilience differences across industries. The findings underscore the importance of incorporating predictive analytics for decision-making in turbulent market environments, offering valuable insights for investors and policymakers navigating uncertainty.

Stock price prediction based on hybrid CNN-LSTM model

Abstract. Stock price prediction is of great significance but faces numerous challenges. In this study, a hybrid CNN-LSTM model was utilized. The stock data were processed and feature engineering was conducted. The model architecture and training strategies were expounded. Experimental results demonstrated that this model outperformed traditional methods and benchmark models on the test set, featuring strong capabilities in feature extraction and handling long-term dependencies. Key roles of data preprocessing, hyperparameter adjustment, and model fusion were also summarized, providing valuable references for stock price prediction.