Stock Market Trend Prediction Research Articles

AI-Enhanced Stock Market Prediction: Evaluating Machine Learning Models for Financial Forecasting in the USA

In the USA, one of the world's largest and most liquid financial markets, the ability to anticipate market trends has deep economic implications. Precise stock market forecasting is highly instrumental for investors and analysts in making better asset allocation decisions, managing risks, and setting investment strategies. This research aimed to analyze the efficiency of some machine learning models in stock market forecast evaluation. This research project concentrated on stock market data from the USA, exploring historical price patterns, trading volumes, and relevant economic indicators to assess the performance of various machine learning models. The dataset used for this research work about predicting stock market trends is an exhaustive collection of historical stock prices, some fundamental financial indicators, and relevant news about the market, gathered from various dependable sources. Historical stock prices are retrieved from financial market databases like Yahoo Finance and Google Finance. These sources have daily records of open, high, low, and close prices, and trading volumes for thousands of publicly traded companies for extended periods, normally running into several years. The analyst selected several strategic models, namely, Random Forest, Gradient Boosting, and Logistic regression. Logistic Regression outperformed the other two models with relatively higher accuracy, while the others are just a little below. The findings of this study have implications well beyond academic curiosity into investment strategies and financial analysis. By leveraging the strengths of the best models, investors can create better-informed trading strategies. These models can also include predictive insights that give a serious edge to the risk management strategy in an investment portfolio. By using the predictive power of models like Random Forest, investors can foresee market fluctuations and adjust their portfolios to reduce potential losses.

A novel heuristically adaptive dual attention-based long short-term memory for intelligent stock market trend prediction model

A novel heuristically adaptive dual attention-based long short-term memory for intelligent stock market trend prediction model

Machine Learning Solutions for Predicting Stock Trends in BRICS amid Global Economic Shifts and Decoding Market Dynamics

In this paper we examine the role of machine learning (ML) in predicting stock market trends within BRICS economies. Complex, interdependent global and regional economic factors are today and will in the future increasingly influence stock markets, which necessitates innovative techniques for trend analysis. Using this state of the art ML models Support Vector Machines (SVMs), Random Forests, and neural networks the study predicts market fluctuations based on historical stock data, economic indicators and geopolitical events. This research emphasizes the increasing role of deep learning, especially with models such as Transformers and LSTMs, to meet the demand for high accuracy predictive systems in the volatile market. Its analysis brings model performance into comparison of BRICS nations, taking into account the peculiar financial and economic behavior peculiar to each of them. These results illuminate how ML can provide actionable intelligence for investors and policymakers to better manage risk and better make strategic investments. Findings from the study underscore the requirement for adopting sophisticated data driven tools in order to negotiate the intricacies of globalized financial systems. This study also explains the basis in helping us understand how machine learning changes the perspective on stock market analysis. It is a great source to understand how different ML techniques such as Support Vector Machines which are good at classification problems and Random Forests, which are known to handle over fitting, can be used on a financial dataset. It shows cutting edge tools for market prediction such as deep learning models like LSTMs, which are able to handle sequential time series data, or Transformers that further improve the traditional architectures with attention mechanisms. The paper also discusses data preprocessing methods, such as feature engineering and normalization, and the importance of their inclusion in improving model performance. This research shows the value of ML literacy and provides future financial analysts and decision makers with tools for addressing market volatility in a data driven and strategic context.

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.

An explainable deep learning approach for stock market trend prediction

Given the intricate nature of stock forecasting as well as the inherent risks and uncertainties, analysis of market trends is necessary to capitalize on optimal investment opportunities for profit maximization and timely disinvestment for loss minimization. In this work, we propose a deep learning model for predicting five distinct stock market trends: upward, downward, double top, rounded bottom, and rounded top. The proposed model surpasses common benchmarks, including support vector machine, random forest, and logistic regression, achieving an average accuracy of 94.9%, compared to 85.7% for random forest, 60.07% for support vector machine, and 52.45% for logistic regression. Furthermore the proposed model excels in F1-score, with a 94.85% performance, compared to 77.95% for random forest, 21.02% for support vector machine and 46.23% for logistic regression, across four real world diverse datasets. Additionally, we employ explainable AI (XAI) techniques, SHAP and LIME, to enhance interpretability, enabling stakeholders to understand the key factors driving predictions. The SHAP analysis reveals the top 10 most important/influential features, enabling feature reduction while maintaining performance. Interestingly, while accuracy slightly decreases with top 10 features, precision, recall, and F1-score improve, suggesting a trade-off between comprehensiveness and performance. These results demonstrate the potential for practical application in financial decision-making, providing a balance between interpretability and predictive power that can support investors in risk management and strategic planning.

Artificial Intelligence (AI) in the Banking Industry: A Review of Service Areas and Customer Service Journeys in Emerging Economies

The banking industry's shift towards digitalization, particularly Web 2.0 banking, signifies a profound change in the bank-client relationship. This transformation integrates digital elements into consumers' lives, digitising everyday activities and highlighting the importance of online social platforms and digital business operations. AI's growing interest in academia and commerce underscores its significant role in customer service. The study examines AI strategies in the banking industry, focusing on service areas and adaptation to the evolving technological landscape, particularly in customer service in developing economies. By adopting an interpretivist philosophy emphasising practicality, a qualitative approach was employed through a systematic literature review of 90 articles using thematic analysis. The research highlights AI's growing use in process automation, customer experience, stock market trend prediction, credit risk management, online banking, marketing management, and auditing. It concludes AI's positive impact on banking operational efficiency and customer service by recommending ethical AI usage, stakeholder collaboration, trust in data handling, and robust internal control systems. Additionally, it suggests exploring the association between organisational culture, leadership, and AI integration in developing economies. The research contributes to the theoretical and empirical frameworks that explain how AI is optimally integrated into banking operations, guiding policymakers and practitioners in leveraging AI to foster financial inclusion and economic growth. Future researchers should include non-profit institutions and non-financial performance metrics to gain insights into AI’s sustainability implications in banks beyond financial metrics.

The role of big data in the secondary market

With the rapid development of information technology, Big Data has emerged as one of the important characteristics of the contemporary era. In the secondary market, the application of Big Data is increasingly widespread, which has a profound impact on market structure, trading patterns, and investment decisions. This paper aims to explore the utilization of Big Data in the secondary market, including data analysis techniques, enhancement of decision efficiency, augmentation of market transparency, and innovation in risk management practices while discussing the drawbacks of using Big Data. Then, the study focuses on how to improve and enhance the application of big data in the secondary market in the future. The findings underscore that Big Data plays an instrumental role in shaping the dynamics of the secondary market, this is predominantly manifested in the prediction of stock market trends, the assessment of investment risks, and so on. However, certain challenges persist. Continual development and improvement of Big Data can facilitate more informed investment decision-making.

Using Machine Learning to Predict the Stock Market Trend

For a long time, people have been trying to predict the direction of stocks, employing various methods. This paper explores the application of machine learning algorithms in predicting stock market trends, aiming to address the challenges of traditional methods and leverage the advantages of data-driven approaches. Highlights the limitations of conventional techniques and elucidates the potential of machine learning in capturing complex market dynamics. This paper discusses various machine learning algorithms and their characteristics, emphasizing their adaptability and scalability in analyzing vast amounts of data. Through empirical analysis and comparative evaluations, this paper demonstrates the efficacy of machine learning in enhancing prediction accuracy and informing investment decisions. This paper explores using machine learning for stock market prediction, overcoming traditional limitations and leveraging data-driven approaches. It discusses various algorithms, showing their adaptability and scalability, and demonstrates their efficacy in enhancing prediction accuracy. Our study contributes to the growing body of research on stock price prediction by leveraging advanced computational techniques to maneuver through the ever-changing financial markets.

Machine Learning Based Stock Market Trend Prediction and Analysis

Machine Learning Based Stock Market Trend Prediction and Analysis

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

Predicting Stock Market Trends using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data

This investigation aimed to utilize machine learning algorithms for predicting stock market movements in Iran. The study centered on three specific sectors - diversified finance, information technology (IT), and metals - within the Tehran Stock Exchange. Ten years of historical data were analyzed . Incorporating ten technical indicators. To achieve this goal, six machine learning models were deployed. Support Vector Regression (Linear)Support Vector Regression (RBF)Linear Regression, Random -Forests ,K-Nearest Neighbours (KNN) Decision Trees.

An AI Framework for Predicting Stock Market Trends

An AI Framework for Predicting Stock Market Trends

Utilizing the GARCH Model for Analysis and Prediction of Stock Market Trends

This study utilizes the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyze the Shanghai Stock Exchange Composite Index and the Shenzhen Composite Index. It computes the returns and logarithmic returns, fits the GARCH model with a student-t distribution, and calculates the standardized residuals of the stock. Tests for normality and heteroskedasticity of residuals are conducted, follows by the creation of auto-correlation and partial auto-correlation plots to aid in volatility forecasting. The findings indicate that both returns of stock distributions exhibit multiple peaks and significant fluctuations, revealing that there exists pronounced volatility clustering and strong abrupt changes. Historical fluctuations significantly affect current fluctuations, showing a clustering effect. Volatility in returns has been very persistent, reflecting strong speculation and elevated overall risk. Histograms and skewness assessments show a left-skewed distribution for both indices, with a kurtosis greater than three, indicating a "fat-tailed" distribution. The positive mean values imply that China's economy is growing constantly, while the low standard deviation implies the immaturity of China's stock market. These insights highlight the dynamic behaviors and risk profiles in Chinas major stock indices, offering a deeper understanding of their speculative nature and economic implications. The Advantages and disadvantages of GARCH models and limitations of a single model are acknowledged in the report.

Multivariate Bayesian Time-Series Model with Multi-temporal Convolution Network for Forecasting Stock Market During COVID-19 Pandemic

The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allows for the incorporation of potentially high-dimensional regression components, and it utilizes spike and slab priors to identify a parsimonious model. Second, the algorithm includes a post-model fitting diagnostic step where the residuals from the MBSTS step are processed through a multi-input/output temporal convolutional network (M-TCN) with multiple time scale feature learning. This step serves as an alternative to traditional subjective residual-based diagnostic procedures in time-series analysis, with the aim of improving forecasting accuracy. The key advantage of the M-TCN is its ability to capture sequential information efficiently. The M-TCN expands the field of convolution kernel without increasing the number of parameters, thus enhancing the capacity of model to capture complex sequential patterns. The paper presents two applications showcasing the effectiveness of the proposed hybrid algorithm. First, it utilizes pre-lockdown data from eleven Nifty stock sectoral indices to predict stock price movements, including the initial post-lockdown upturn. In the second application, it focuses on stock market data from pharmaceutical companies involved in manufacturing COVID-19 vaccines. In both cases, sentiment data sourced from newspapers and social media serve as the regression component. Through rigorous analysis, the paper demonstrates that the hybrid model outperforms various benchmark models, including LSTM, Bidirectional Encoder Representations from Transformers (BERT)-based LSTM, Deep Transformer Model, and GRU, among others, in terms of forecasting accuracy. This underscores the utility of the hybrid algorithm, particularly in predicting stock market trends during the COVID-19 pandemic period and its associated market dynamics.

Stock Market Prediction

The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets.

Analytical detection of “ Smart Stock Trading System” utilizing AI-model

In the realm of finance, stock trading stands out as one of the most significant activities. Predicting stock prices involves analyzing available market data to forecast the future value of a particular stock or share. In our research, we developed a machine learning model along with LSTM (Long Short-Term Memory) to predict stock prices. Our model follows the 70:30 principle, allowing the model to improve its accuracy over time. We collected a decade's worth of data from Yahoo Finance and employed customized feature engineering and deep learning techniques to analyze the stock market trends. Our approach has shown promising accuracy in predicting stock market trends. By carefully optimizing prediction term lengths, feature engineering methods, and data preprocessing techniques, our work contributes to advancing research in both financial analysis and technical modeling within the stock market domain. Keywords— LSTM, Machine learning, RNN, Data Prediction, finance, linear regression.

Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?

The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.

Financial market trend prediction model based on LSTM neural network algorithm

The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

Novel Deep Learning Approach for Predicting Stock Market Trends

Stock Market Prediction a Major Achievement in financial markets is precisely forecasting stock market trends, which remains a considerable hurdle. Deep learning, a subset of machine learning, has grown to be a potent tool for untangling complex relationships within data. What distinguishes deep learning is its capacity to handle large amounts of data, unwrinkle difficult characteristics, and adapt to dynamic structures, all while unearthing concealed truths. Deep learning, at its core, is predicated on artificial neural networks that simulate human brain learning and pattern recognition capabilities, making it ideal for analyzing financial data. Through novel profound neural systems, the strategy applies a varied collection of the distributed showcase information, including, but not limited to, authentic stock costs, trading volumes, and calculations extracted from news sources. Keywords— stock advertise patterns, back, profound learning, designs, authentic information, design acknowledgment, sentiments.