Market Prediction Research Articles

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.

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.

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.

MFB: A Generalized Multimodal Fusion Approach for Bitcoin Price Prediction Using Time-Lagged Sentiment and Indicator Features

Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.

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.

Avalúos inmobiliarios: un estudio de caso sobre el sistema Continental frente al Common Law

We use Colombia as a case study of a situation where emerging real estate valuation practices, mostly based upon the logic of Common Law legal systems, and oriented to market prediction and global standards, collides with a valuation tradition based upon the Continental legal tradition. In this tradition, the valuation process first and foremost must satisfy limiting regulatory checklists. These requirements minimize the value of focusing on market analyses to offer accurate predictions (appraisals). Our analysis builds upon the Laws 9 of 1989 and 388 of 1997, and their corresponding implementation legal enactments (resoluciones), showing that the valuation practice for public purposes determines a logic where appraisers operate as ‘temporary public workers’. Appraisers and their associations (Lonjas) have subsequently transferred such limiting procedural principles to valuation for business purposes, which should be oriented to market analysis and prediction.

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.

Forecasting migration movements using prediction markets

Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application.

Comparative Study on Stock Movement Prediction Using Hybrid Deep Learning Model

Applying machine learning techniques in stock market prediction has evolved significantly, with deep learning methodologies gaining prominence. Conventional algorithms such as Linear Regression and Neural Networks initially dominated but struggled to capture complex temporal dependencies in financial data. Recent research has explored deep learning architectures like LSTM and CNN and hybrids such as CNN-LSTM and LSTM-CNN, showcasing promising results. However, there's a gap in research comparing these models across different datasets, particularly in predicting stock movements. This study addresses this gap by conducting a comparative analysis of deep learning and hybrid models for stock movement prediction in the Indonesian banking sector. The evaluation based on RMSE and MAE reveals that the LSTM-CNN hybrid consistently outperforms other models, showcasing its versatility and accuracy across different data characteristics. Then, exploration through hyperparameter tuning demonstrates the criticality of parameter selection in optimizing model performance. These findings contribute to advancing predictive modeling in financial markets, offering valuable insights for investors, analysts, and policymakers. Further research in hyperparameter tuning and model optimization holds promise for enhancing accuracy and reliability in stock price prediction.

Can Expert Prediction Markets Forecast Climate-Related Risks?

Abstract Prediction markets use a betting-like mechanism to aggregate information from disparate sources to produce probability forecasts that represent the collective view of participants. They have similarities with financial markets but are designed specifically to discover information rather than transfer assets or risks. Contracts that pay out a fixed amount if a well-defined event occurs are traded in these markets and, under certain conditions, the price at which these event-contracts trade can be interpreted as a probability estimate that the event will occur. This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past 5 years. The predicted variables included the monthly temperature and rainfall for the United Kingdom, an index of El Niño–Southern Oscillation, Atlantic hurricane activity, and U.K. wheat yield. The markets had horizons of 2–12 months. Invitations to participate in the markets were extended to individuals and teams with relevant expertise. Participants did not have to pay to take part but did receive cash rewards based on their performance. Trades were made through an automated market maker overcoming the problems of low activity that have affected previous prediction markets for specialized topics. The predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size. Whether this level of reliability would persist for longer, multiyear, horizons relevant to climate change cannot be known without running markets on such time scales, something that we strongly advocate.

Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model

This paper utilizes a hybrid model to analyze the impression of information from the GECON indicator on the volatility prediction of the clean energy market. The model architecture is constructed by embedding a recurrent neural network (RNN) into the GARCH-MIDAS model. The results show that RNN-GARCH-MIDAS-GECON achieves optimal ranking in volatility prediction. This work confirms the advantages of embedded hybrid integrated models in capturing nonlinear information in financial markets and achieving significant progress in volatility forecasts. Notably, this research will help to promote the construction of clean energy development and energy transition pathways.

Stock Market Prediction Using Deep Attention Bi-directional Long Short-Term Memory

Stock Market Prediction Using Deep Attention Bi-directional Long Short-Term Memory

Revolutionizing Stock Market Prediction: Harnessing the Power of LSTM and CNN Hybrid Approach

Revolutionizing Stock Market Prediction: Harnessing the Power of LSTM and CNN Hybrid Approach

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Advancements in Stock Market Prediction Techniques: A Comprehensive Survey

The stock market, known for its unpredictable nature and complex workings, has been the focus of significant study. Conventional models frequently fail to fully capture the complex and diverse nature of stock market behavior. With the increasing complexity of financial markets, there is a rising demand for advanced strategies that can offer investors precise predictions and essential insight. There are several studies in this regard, from which we chose 24 modern studies from the year 2018 to the year 2023 in various ways to come out with results that are the focus of a new start in the prediction. This review collected different machine learning methods and deep learning techniques, including deep neural networks, which were then used to build powerful prediction models. Integrating behavioral finance theory with quantitative indicators and financial news provides a unique perspective on stock market dynamics. Also, The Efficient Market Hypothesis (EMH) highlights the significant impact of future information, such as news, on the valuation of stocks. Combining deep learning techniques in forecasting and examining the stock market represents an essential stage in financial research, it offers a more intricate comprehension of the intricacies of stock market dynamics. However, obstacles still need to be overcome, and it is essential to use prudence when applying reported accuracies to actual trading situations. The convergence of behavioral finance, efficient market hypothesis, and deep learning present opportunities for more investigation, offering a more comprehensive method of forecasting stock market trends