Stock Prediction Research Articles

Optimizing Stock Predictions With Bi-Directional LSTM and Levy Flight Fuzzy Social Spider Optimization (LFFSSO)

Stock Market Prediction (SMP) has developed into a significant area of research, especially in recent decades. Major novelty of the work is to develop an Evolutionary Bidirectional Long Short-Term Memory (EBi-LSTM) framework depends on investors' sentiment in tweets to Stock Market (SM). In addition, three feature selectors: the Chi-Square Test (CST), Analysis Of VAriance (ANOVA) technique and Mutual Information (MI) method are introduced for the selecting most important features. Levy Flight Fuzzy Social Spider Optimization (LFFSSO) algorithm is used for optimal tuning of parameters in the Bi-LSTM classifier. EBi-LSTM algorithm has been worked on datasets like Twitter, Stock, Weather, and Coronavirus disease (COVID-19). The proposed model extends the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, and robustly optimized Bidirectional Encoder Representations from Transformers Retraining Approach (RoBERTa) for sentiment analysis. Highest results of 88.26%, 90.43%, 89.33% and 92.63% for precision, recall, F1-score and accuracy has been attained by proposed system.

Comparative Analysis of Stock Price Prediction Using Deep Learning with Data Scaling Method

The dynamic and unpredictable nature of stock prices makes accurate forecasting an important challenge in financial analysis. This study aims to compare the performance of three deep learning models, namely, Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) in predicting stock prices on historical daily banking data from Yahoo Finance. The main objective is to determine the model that is best able to capture sequential patterns and temporal dependencies in stock price movements. Each model was trained and op-timized through data scaling, namely MinMax Scaler and Standard Scaler, with performance evaluated using Root Mean Square Error (RMSE) as the primary metric. Results show that while the RNN provides a basic approach, the GRU and LSTM models produce higher prediction accuracy, with GRU achieving the lowest RMSE thanks to its better ability to maintain long-term depend-encies. The RMSE achieved by RNN, GRU, and LSTM were 211.47, 158.89, and 197.45, respectively. The lowest error results were achieved when using MinMax Scaler. The use of MinMax Scaler here shows a better performance improvement with an average improvement of 22.57% compared to using Standard Scaler. This comparative analysis contributes to providing empirical insight into the relative effectiveness of the tested architectures. The findings suggest that the combination of GRU and MinMax Scaler can be a more reliable tool for financial forecasting, with the potential to develop more robust stock prediction applications under fluctuating market conditions.

A hybrid stock prediction method based on periodic/non-periodic features analyses

Stock investment is an economic activity characterized by high risks and high returns. Therefore, the prediction of stock prices or fluctuations is of great importance to investors. Stock price prediction is a challenging task due to the nonlinearity and high volatility of stock time series. Existing deep learning models may not capture the periodic and non-periodic features of stock data effectively. In this paper, we propose a novel model that leverages Complete Ensemble Empirical Mode Decomposition (CEEMD), Time2Vec, and Transformer to better capture and utilize various patterns in stock data for enhanced prediction performance, and we call it ETT. CEEMD decomposes the stock data into different frequency components based on their intrinsic scales. Time2Vec provides a time vector representation that captures both periodic and non-periodic patterns while being invariant to time scaling. Transformer learns the long-term dependencies and global information from the data. We apply ETT to predict stock prices in the Chinese A-share market and compare it with several baseline models. The results show that ETT reduces the mean squared error (MSE) by an average of 4% and increases the average cumulative return by 58% on the CSI 100 and Hushen 300 datasets.

Seasonal meat stock demand used comparison of performance smoothing-average forecasting

Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.

Forecasting Stock Prices with Artificial Intelligence

The purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). To conduct the study, Wal-Mart stock data is utilized to accurately predict future stock prices. The results show that the MSE for the SimpleRNN test is higher, indicating weaker generalization. The MSE of the basic LSTM test is lower than that of the RNN, indicating stronger generalization. The validated and tested MSEs of LSTM with peephole connectivity are higher than the basic LSTM and GRU. GRU performs as well as the basic LSTM but has the lowest LSTM training MSE. This stock prediction task requires the GRU model, which is the most suitable choice based on the training time.

Price prediction of dual-listed stocks with RF and LSTM algorithms: NYSE and BIST comparison

Companies are looking for ways to access capital from developed markets instead of local markets to find financing. While some companies use debt instruments for this purpose, others use equity financing methods. One of the techniques used in equity financing is the simultaneous registration of shares on national and foreign stock exchanges, also known as the dual-registration method. Investors entering international markets by investing in dual-registered shares is important for companies to gain capital. However, another important issue for those investing in stocks is the ability to gain capital through accurate prediction of price movements. The aim of this study is to predict the prices of Turkcell stocks traded on Borsa Istanbul and the New York Stock Exchange (NYSE) using machine learning and deep learning methodologies. The results of the analyses conducted with the Random Forest Regressor and Long Short-Term Memory algorithms, which are machine learning and deep learning algorithms, respectively, showed that both algorithms exhibited a lower error rate in predicting the closing prices of Turkcell stocks on the NYSE.

Exploring the Performance of the CNN-LSTM Model in Stock Prediction

Stock prediction in the financial market helps investors formulate investment strategies. However, the stock prediction has the characteristics of high dimension and high noise, and traditional linear models have difficulty meeting this demand. Therefore, it is necessary to use deep learning algorithms with stronger processing power for prediction. This paper combines the advantages of convolutional neural network (CNN) in feature extraction and the advantages of long short-term memory network (LSTM) in large-time scale relationship prediction to construct a CNN-LSTM hybrid network. In order to evaluate the performance of the model, this paper selects the closing price of the Shanghai Stock Exchange (SSE) 50 index in the past five years for experimental prediction. This hybrid model was compared with the ARIMA model and a single neural network, and it was found that the prediction results were more accurate, only lower than the LSTM model, but the interpretability of the prediction results was lower, only higher than the CNN model. This paper can provide ideas for the improvement of CNN-LSTM in the field of prediction. It also provides a reference for investors to decide on investment strategies and create a healthier stock market.

Stock Prediction Of Single-Use Medicine Using Autoregressive Integrated Moving Average

Stock Prediction of Single-Use Medicine Using Autoregressive Integrated Moving Average the(3, 1, 3) model, derived from the (p,q,d) model where p is the AR level, d is the process level that makes the data stationary, and q is the MA level. The (3, 1, 3) model used provides quite good results the ARIMA (3, 1, 3) model can be a good tool to predict the need for consumable drug stocks show that the ARIMA (3, 1, 3) model gives good results,log likelihood values and information criteria indicating that the model is reliable. Predictions for the demand for consumable drugs in 2025 show a downward trend, Requires further attention to understand the causes. health centres can plan drug procurement more precisely and efficiently meet patient needs without experiencing overstocks or shortages.

A survey of recent machine learning techniques for stock prediction methodologies

A survey of recent machine learning techniques for stock prediction methodologies

A High-Performance Stock Prediction System Leveraging LSTM Neural Networks

A High-Performance Stock Prediction System Leveraging LSTM Neural Networks

Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50

This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the simple moving average (SMA), exponential moving average (EMA), moving average convergence/divergence (MACD), stochastic oscillator, relative strength index (RSI) and accumulation/distribution (A/D), were employed to improve the model’s responsiveness to market trends and momentum shifts. The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility. ANN models provide reliable benchmark predictions. Among the models, CNN with RSI obtained the best results, with an RMSE of 0.0263, an MAE of 0.0186 and an R2 of 0.9825, demonstrating high accuracy in price prediction. The integration of indicators such as SMA and EMA improves trend detection, while MACD and RSI increase the sensitivity to momentum, which is essential for identifying buy and sell signals. This research demonstrates the potential of machine learning models for refined stock prediction and informs data-driven investment strategies, with CNN and LSTM models being particularly well suited for dynamic price prediction.

Exploration of the Application of Multimodal Model in Psychological Analysis

Multimodal sentiment analysis is one of the important research areas in the field of artificial intelligence today. Multimodal sentiment analysis is to extract features from various human modalities such as facial expressions, body movements, and voice information, perform modal fusion, and finally classify and predict emotions. This technology can be used in multiple scenarios such as stock prediction, product analysis, movie box office prediction, etc., especially psychological state analysis, and has important research significance. This paper introduces two important datasets in multimodal sentiment analysis, namely CMU-MOSEI and IEMOCAP. It also introduces the feature-level fusion, model-level fusion, decision-level fusion and other fusion methods in multimodal fusion methods, and also introduces the semantic feature fusion neural network and sentiment word perception fusion network in multimodal sentiment analysis related models. Finally, the application of multimodal sentiment analysis models in depression and other related mental illnesses and the challenges of multimodal sentiment analysis models in the future are introduced. This paper hopes that the above research will be helpful for multimodal sentiment analysis.

CNN-CBAM-LSTM: Enhancing Stock Return Prediction Through Long and Short Information Mining in Stock Prediction

Deep learning, a foundational technology in artificial intelligence, facilitates the identification of complex associations between stock prices and various influential factors through comprehensive data analysis. Stock price data exhibits unique time-series characteristics; models emphasizing long-term data may miss short-term fluctuations, while those focusing solely on short-term data may not capture cyclical trends. Existing models that integrate long short-term memory (LSTM) and convolutional neural networks (CNNs) face limitations in capturing both long- and short-term dependencies due to LSTM’s gated transmission mechanism and CNNs’ limited receptive field. This study introduces an innovative deep learning model, CNN-CBAM-LSTM, which integrates the convolutional block attention module (CBAM) to enhance the extraction of both long- and short-term features. The model’s performance is assessed using the Australian Standard & Poor’s 200 Index (AS51), showing improvement over traditional models across metrics such as RMSE, MAE, R2, and RETURN. To further confirm its robustness and generalizability, Diebold–Mariano (DM) tests and model confidence set experiments are conducted, with results indicating the consistently high performance of the CNN-CBAM-LSTM model. Additional tests on six globally recognized stock indices reinforce the model’s predictive strength and adaptability, establishing it as a reliable tool for forecasting in the stock market.

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.

Learning A-Share Stock Recommendation from Stock Graph and Historical Price Simultaneously

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market.

Research on stock prediction based on CED-PSO-StockNet time series model

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R2 metric. To further assess the model’s generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction.

Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle-Light Detection and Ranging and Machine Learning.

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle-Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R2 = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R2 = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment.

Integrating Twitter Sentiment with ML Models for Enhanced Stock Forecasting

This research investigates the integration of Twitter sentiment analysis with machine learning models, specifically Long Short-Term Memory (LSTM), Facebook Prophet, and Neural Prophet, to enhance the accuracy of stock market predictions. While these models are accurate in capturing long-term trends and seasonality, they often fail when exploring the short-term volatility in stock prices. By using real-time social media sentiments, we aim to improve short-term prediction accuracy. Sentiment data from Twitter, extracted and classified using NLP techniques, was integrated with the models to predict stock prices for Apple Inc. (AAPL), Paramount Global (PARA), and BP plc (BP) over the period 2016-2024. The study compares the performance of the models using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that Neural Prophet stood the best in terms of its accuracy and efficiency in stock predictions when compared to LSTM and Facebook Prophet, reducing RMSE by 25% and MAE by 20%. While the integration of sentiment analysis improved prediction accuracy, limitations remain in predicting the scale of sudden market changes. The findings highlight the potential of sentiment analysis in stock prediction but also call for further model enhancements to fully address short-term market volatility.

Above-ground bole biomass and carbon stock of recreational trees at Agodi Gardens, Oyo State, Nigeria

This study used allometric equations to evaluate the biomass and carbon stock of recreational trees at Agodi Gardens in Ibadan Metropolis. Stratified sampling technique was used to delineate 15 plots (25m x 25m). Six allometric equations were selected to estimate Aboveground Biomass (AB) of the tree species. Models selected were assessed based on highest adjusted coefficient of determination (AdjR2), significant F-ratio (P<0.05) and least Root Means Square Error (RSME). A total of 220 trees belonging to 18 species were encountered. The dominant Diameter at Breast Height (DBH) and Height (Ht) were 62.8cm and 39.1m, respectively for Alstonia bonnei and Elaeis guineensis. The mean AB/ha was 2.8006Kgha-1 and the highest carbon stock of 3.332147 x 10-4 was recorded for Khaya ivorensis. Equation C= 2.6 x 10-7+ln (AB) + lnDBH²+lnHt (AdjR²=99%), gave the best fit to estimate carbon stock in the study area. The study found a significant relationship between AB, DBH and Ht, with a high correlation between DBH and Ht (P-value=0.00). Hence, as the DBH increases, the Ht increases with an increase in AB. Most trees had low slenderness coefficients (<70). The developed allometric model improved the prediction of aboveground stem biomass and carbon stock, making it useful for future research.