Stock Market Prediction Research Articles

Application of Artificial Intelligence in the Securities Market

Artificial Intelligence (AI) involves creating machines that replicate human cognitive functions, enabling them to think and act similarly to people. In the context of stock trading, AI refers to the use of technology designed to buy and sell shares with increased precision and efficiency compared to human capabilities. AI-driven systems are currently employed to predict stock market trends and analyze trading patterns among investors and brokers. Prominent financial institutions on Wall Street, such as Goldman Sachs and Morgan Stanley, are increasingly adopting specialized AI solutions, including data mining, natural language processing, and self-learning algorithms. These tools operate with greater speed than everyday applications. Additionally, AI aids wealth management firms in continuously monitoring market movements and adjusting portfolios to achieve target profits. While AI can significantly reduce workload and provide real-time recommendations, it does not entirely eliminate the need for human oversight. Keywords: artificial intelligence, securities, trading, forecasting.

Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market

Using neural networks in economics time series data is a new unexplored field. Lot of companies and economics research use mostly logistic regression or statistical approach when they try to predict the movement of stock market. Neural networks became a frequent tool for prediction in recent years and this approach has been confirmed to provide more reliable and better solutions when it comes to prediction and accuracy power. Within a wider context of current debate on neural networks employment in stock market predictions, we suggest an innovative methodology based on the combination of neural networks. In our analysis we use Wasserstein Generative Adversarial Network (WGAN) on Germany stock market as an example. We present how the trading strategy could be established on the prediction of the model and how it can be compared with other models in terms of returns. Overall, the WGAN monthly prediction outperformed Random Forest by 36%, benchmark by 32% and LSTM by 26% in the testing period. Our results also suggest that the WGAN model has on average higher returns than pure investment into index. Furthermore, WGAN is less volatile, which is always the preferred option for investors. Using neural networks for stock index prediction and confirming that WGAN investment strategy brings higher returns compared to generally used models is our main contribution to the current debate.

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.

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.

Comparative Analysis of Univariate and Deep Learning Models for Stock Market Prediction in Frontier Markets: A Case Study of Pakistan, Bangladesh, and Sri Lanka

Comparative Analysis of Univariate and Deep Learning Models for Stock Market Prediction in Frontier Markets: A Case Study of Pakistan, Bangladesh, and Sri Lanka

Stock Market Prediction Model Based on Deep Learning and Enhancement of Interpretabilit

Stock Market Prediction Model Based on Deep Learning and Enhancement of Interpretabilit

Harnessing Artificial Intelligence in Stock Market Investments: A Neomarxist Approach to Alternative Socioeconomic Funding Agendas for National State Revenue Accumulation

In the contemporary economic, sociological and political landscape, the successful integration of Artificial Intelligence (AI) computer programs into the predicted behaviour of stock market value in the financial markets represents a profound burgeoning frontier in economics, with profound implications for both wealth generation and national budget resource allocation. This paper explores the potential sociopolitical economic strategy of using AI-driven stock market prediction predictions to cumulatively accumulate capital funded for by taxpayer sourced representative income. As the baseline return accumulates in the form of investment potential then as a means of revenue accumulation for the state then the burden of the national budget upon its citizens decreases then ceases. This is the theorisation of the establishment of a Marxist economic social superstructure for the funding of social institutions through the powerful potential of artificial intelligence. By leveraging a portion of the national budget on advanced AI technologies, this strategy could dramatically facilitate the financial sustenance of political budgets aimed at social equity and economic justice. Through a critical review of sociological and economic theories, this theoretical research delineates exactly how such an approach could potentially reconfigure the mode of production and contribute to the reformation of a sustainable socialist economy.

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.

Predicting Market Share Patterns using LSTM

Forecasting stock prices is a significant challenge in the financial sector, marked by its complexity and the impact of numerous variables. In this paper, we undertake a comprehensive investigation into how machine learning algorithms can be effectively applied to address this challenge. Our main goal is to clarify the methodologies and strategies that can enhance the accuracy of stock market predictions. We explore the complex process of predicting stock values and scrutinize the various machine learning algorithms that have been suggested and employed for this purpose. By critically evaluating these algorithms, we aim to provide insights into their individual strengths and weaknesses, ultimately helping the reader make an informed decision about the most appropriate algorithm for their specific forecasting requirements. Beyond algorithm selection and attribute analysis, our review also considers external factors that can significantly influence stock prices. These factors include a broad range of variables, such as economic conditions, geopolitical events, corporate news, and market sentiment. Understanding the interaction between these external elements and stock market dynamics is essential for developing more robust and reliable prediction models.

Forecast Analysis of the Stock Market Based on Hidden Markov Model and Long Short-Term Memory Model- Taking the S and P500 Index as an Example

This paper proposes a hybrid method that combines the advantages of HMM and LSTM models to improve the accuracy of stock market predictions, using the S&P500 index as a case study. HMMs are used to identify and model potential market states, while LSTMs predict future stock prices based on these states. Through this integration, this paper hopes to take advantage of HMM’s ability to model market conditions and LSTM’s advantages in forecasting to provide a more robust forecasting framework. This article is mainly divided into five parts. The first part is an introduction to the research background and objectives. The second part is the review and arrangement of relevant literature. The third part is the elaboration of the theoretical basis and the collection and analysis of data. The fourth part is an explanation of the data analysis results. The fifth part is the conclusion and suggestions of this study.

Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions

Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market.GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.git

Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach

This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics.

Exploiting News Analytics for Volatility Forecasting

ABSTRACTThis study investigates the potential of news sentiment in predicting stock market volatility. We augment traditional time series models of realized volatility with the sentiment of macroeconomic and firm‐specific news. Our results demonstrate that incorporating the sentiment of domestic macroeconomic news significantly improves volatility predictions for individual stocks and the S&P 500 Index. Notably, we find substantial enhancements in long‐horizon volatility predictions when including the sentiment of macroeconomic news in the regression models. In contrast, firm‐specific news sentiment shows only modest predictive power in the general framework. However, expanding the set of predictors to include the news count of firm‐specific news occurring overnight between two consecutive trading periods significantly improves one‐period‐ahead volatility forecasts.JEL Classification: C53, C55, C58, G14, G17

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Stock Prediction using Deep Learning: A Comparison

Stock market prediction (SMP) is one of the most important areas in finance that greatly helps investors and financial institutions to anticipate market fluctuations and make wise decisions. This becomes very important in view of the potential challenge of obtaining optimum revenues and effective portfolio management strategies in view of the volatile market environments typically governed by underlying economic and socio-political influences. This paper proceeds to categorize stock prediction works into three principal categories: Convolutional Neural Network (CNN)-based models, Recurrent Neural Network (RNN)-based models, and hybrid models using more than one forecasting technique. We reviewed nine research papers and compared them in view of different datasets to show efficacy distinctions among these methodologies. Our experiments stated that the hybrid models that marry the spatial data processing ability of CNNs to the temporal sensitivity of RNNs outperform other approaches in accuracy and strength of predictions. This synthesis approach captures complex patterns more efficiently as well as relevant to the dynamic nature of financial markets. Results underscore the potential contribution of hybrid frameworks being a leading methodology for future developments in SMP. Further improving these models with the advancements of machine learning and data analytics may likely push the boundaries of what is feasible in financial forecasting.

Evaluation of the Multiple Regression Analysis Algorithm on Stock Market Prediction

Financial time series is one of the most challenging applications of modern time series forecasting. The financial time series is closely related to noise, non-stationary, and deterministic chaos. The characteristics suggest that no complete information can be obtained from the past behavior of financial markets to fully capture the dependency between future prices and that of the past. The data collection method was collected from the Stock Market Online Application "MetaTrader version 4" type "Daily" with a time range from "03/09/2001 to 25/07/2012", as many as 2052 data", with the attributes "Date, Open, High, Low, Close, Volume" with the main attribute "Close" using the Support vector machine algorithm, artificial neural network, and multiple linear regression. The conclusion of the value that is close to the series value is the value by testing on the support vector machine algorithm, with the parameter for the RMSE value that is close to the "0" value obtained from the measurement results on the SVM algorithm on the RBF kernel (radial base function) with a value of "gamma" γ = 100 with the value of RMSE = 0.000, and SE = 0.000. with prediction accuracy error = 0.976

Machine Learning Applications in Stock Price Prediction

With economic fluctuations, investors focus on the stock market where scientific investment strategies become increasingly crucial. This article utilizes linear regression to predict stock price trends, achieving an R value close to 1, indicating high accuracy in the model's predictions. Additionally, logistic regression and random forest classification models were employed to predict stock price movements, revealing a superior performance with the random forest model achieving an AUC value of 0.64. By employing a combination of regression and classification algorithms, this study offers a diverse perspective on stock price prediction, highlighting the importance of scientific forecasting in financial markets. The findings demonstrate that integrating multiple predictive models enhances the accuracy and reliability of stock price predictions, providing investors with precise market information to facilitate rational investment decisions. Furthermore, this research contributes valuable insights for future studies in stock market prediction, promoting continuous innovation and development in the theoretical and methodological aspects of this field. The results underscore the potential of advanced machine learning techniques in financial forecasting, offering a robust framework for understanding market dynamics and improving investment strategies. This study serves as a reference point for both academic research and practical applications, driving forward the evolution of predictive modeling in finance.

Embracing market dynamics in the post-COVID era: A data-driven analysis of investor sentiment and behavioral characteristics in stock index futures returns

This paper aims to enhance the understanding and prediction of stock market behavior during unexpected events like the COVID-19 pandemic, with a specific focus on the role of market attention, social media sentiment indicators, and the development and evolution of unexpected events. We highlight that the common trading and technical indicators used in forecasting the stock index futures prices often overlook investor sentiment and pandemic-related data, which can be instrumental in predicting stock market behavior during significant emergencies. In response, we propose a multi-faceted approach that incorporates these overlooked factors. First, we enhance the predictive index system by integrating investor sentiment, derived from stock message board commentary, and investor behavior influenced by the development and evolution of the pandemic. This innovative approach refines our model's predictive capabilities and is validated through comparative analysis. Second, we introduce a hybrid framework for predicting stock index futures closing prices. By decomposing the closing price series into long-term trends, cyclical variations, and random fluctuations, we create a more nuanced forecast. Each component is predicted separately using appropriate time-series algorithms, improving the overall predictive accuracy and offering generalizability and scalability. Third, we devise a dynamic trading strategy that recognizes pandemic-related data, evolving over time, as a pivotal factor. This strategy is adaptable to evolving market conditions, and our experimental evidence demonstrates its effectiveness in yielding higher returns and reducing associated risks. Our findings underline the importance of incorporating investor sentiment and pandemic-related data into stock market predictions, thus offering a more comprehensive and accurate approach to market forecasting and risk management.

A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting

Financial technology (FinTech) is a field that uses artificial intelligence to automate financial services. One area of FinTech is stock analysis, which aims to predict future stock prices to develop investment strategies that maximize profits. Traditional methods of stock market prediction, such as time series analysis and machine learning, struggle to handle the non-linear, chaotic, and sudden changes in stock data and may not consider the interdependence between stocks. Recently, graph neural networks (GNNs) have been used in stock market forecasting to improve prediction accuracy by incorporating the interconnectedness of the market. GNNs can process non-Euclidean data in the form of a knowledge graph. However, financial knowledge graphs can have dynamic and complex interactions, which can be challenging for graph modeling technologies. This work presents a systematic review of graph-based approaches for stock market forecasting. This review covers different types of stock analysis tasks (classification, regression, and stock recommendation), a generalized framework for solving these tasks, and a review of various features, datasets, graph models, and evaluation metrics used in the stock market. The results of various studies are analyzed, and future directions for research are highlighted.

Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.