Stock Market Trend Prediction Research Articles

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.

ML Model for Stock Classification

Abstract: This study examines how effectively volatility indices can forecast stock market movements by applying different machine learning techniques. It uses a dataset from Kaggle for the TCS Company, including variables such as 'Date,' 'Symbol,' 'Series,' 'Prev Close,' 'Open,' 'High,' 'Low,' 'Last,' 'Close,' 'VWAP,' 'Volume,' 'Turnover,' 'Deliverable Volume,' and '%Deliverable.' Various models were tested and evaluated based on metrics like the AUC-ROC Curve, Accuracy, Precision, Recall, F1-Score, Cross-Validation Accuracy, and the Confusion Matrix. The findings reveal that the Linear Regression (Classification model) surpasses other models in forecasting stock market directions with the highest levels of accuracy across all evaluation metrics. This highlights the potential of machine learning methods in leveraging volatility indices to accurately predict stock market trends

Stock Market Analysis Using Machine Learning

The stock market is a dynamic and complex financial system influenced by numerous factors, making accurate forecasting a challenging task. Traditional methods often fall short in capturing the intricate patterns within financial data. This research paper explores the application of machine learning techniques for stock market forecasting, aiming to provide a comprehensive overview of the current state of the field. The paper reviews relevant literature, discusses popular machine learning algorithms, and assesses their effectiveness in predicting stock market trends. Additionally, real-world applications and challenges in implementing machine learning for stock market forecasting are explored.

Machine Learning Strategies for Understanding Stock Market Behaviour

The stock market is a dynamic and complex financial system influenced by numerous factors, making accurate forecasting a challenging task. Traditional methods often fall short in capturing the intricate patterns within financial data. This research paper explores the application of machine learning techniques for stock market forecasting, aiming to provide a comprehensive overview of the current state of the field. The paper reviews relevant literature, discusses popular machine learning algorithms, and assesses their effectiveness in predicting stock market trends. Additionally, real-world applications and challenges in implementing machine learning for stock market forecasting are explored.

Predicting Stock Market Trends Employing Machine Learning: A Survey

The stock prices depend both on time as well as associated variables and finding patterns in among the variables aid forecasting future stock prices which is often termed as stock market forecasting. Stock market prediction extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. For real life applications utilizing stock market data, it is necessary to predict stock market data with low errors and high accuracy. This needs design of appropriate artificial intelligence (AI) and machine learning (ML) based techniques which can analyze large and complex data sets pertaining to stock markets and forecast future prices and trends in stock prices with relatively high accuracy. This paper presents a comprehensive review on the various techniques used in recent contemporary papers for stock market forecasting. Keywords: Time Series Models, Stock Market Forecasting, Artificial Intelligence, Artificial Neural Networks, Forecasting accuracy.

Cluster Based Classification Model for Trend Prediction in Time Series Data

Abstract: Real-time stock market trend prediction plays a essential role in the technical analysis for trend prediction process. Traditional historical technical indicators are difficult to trace and predict the trend due to noise or uncertainty in the training dataset. Since, most of the traditional homogeneous or heterogeneous stocks are used to predict the overall market sentiment for the institutions or retailors. Also, traditional approaches are difficult to find the outliers in the technical indicators data for the clustering process. These models only consider the static metrics or static outlier meaures due to variation in data distribution according to time basis. In this work, a hybrid knn classifier is used to predict the trend of the stock.

An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques

: Anticipating stock market trends is a challenging endeavor that requires a lot of attention because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, loud, and chaotic data, stock market prediction is challenging. Investors need help to forecast where they should spend their money to make a profit. Investment methods in the stock market are intricate and based on the analysis of large datasets. Expert analysts and investors have placed a high value on developments in stock price prediction. Due to intrinsically noisy settings and increased volatility concerning market trends, the stock market forecast for assessing trends is tricky. The intricacies of stock prices are influenced by several elements, including quarterly earnings releases, market news, and other altering habits. Traders use a number of technical indicators based on stocks that are collected on a daily basis to make decisions. Even though these indicators are used to analyze stock returns, predicting daily, and weekly market patterns are difficult. Machine learning techniques have been extensively studied in recent years to see if they might boost market predictions compared to legacy or conventional methods. The existing methodologies have devised several strategies for predicting stock market trends. Various machine learning and deep learning algorithms, such as SVM, DT, LR, NN, kNN, ANN, and CNN, can boost performance in predicting the stock market. Based on a survey of current literature, this work aims to identify future directions for machine learning stock market prediction research. This research aims to provide a systematic literature review process to discover relevant peer-reviewed journal papers from the last two decades and classify studies with similar methods and situations into the machine learning approach and deep learning. In the current article, the methods and the performance of those adopted methods will be identified for measuring the effectiveness of those techniques.

Optimisation-Enabled Transfer Learning Framework for Stock Market Prediction

Stock market prediction is a vital task with high attention for gaining attractive profits with proper decisions to invest. Predicting the stock market is becoming a major challenge nowadays due to chaotic data, non-stationary data, and blaring data. Hence, it’s challenging for investors to invest money to make profits. Many techniques are developed to predict stock market trends, but each differs based on time and year. In this paper, hybridised optimisation algorithm, namely the proposed Gannet Ladybug Beetle Optimisation (GLBO), is used for training Transfer Learning (TL), which considers Convolutional Neural Network-enabled Long Short-Term Memory (CNN-enabled LSTM). This TL is responsible for predicting the stock market from augmented data. Here, the stock market dataset of two companies is used as input time series data. Moreover, many features are extracted from those input data and then from those features, necessary features are selected based on Motyka similarity. The bootstrap method is used in this paper for data augmentation. Also, GLBO is hybridised with Gannet Optimisation Algorithm (GOA) along Ladybug Beetle Optimisation (LBO) algorithm. Furthermore, the proposed model is verified for its performance capability based on three metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), as well as Mean Absolute Percentage Error (MAPE) with values of 5.8%, 24.1%, and 78.8%.

Stock market and securities index prediction using artificial intelligence: A systematic review

The recognition of the value and importance of recognizing patterns in the stock market is widely accepted. As a result, using innovative decision-making strategies is expected to lead to significant returns in stock prices. Predicting the stock market proves challenging because of the limited volatility and inherent disorderliness observed within the data. Hence, investors need help optimizing their profits through informed predictions in the stock market. Stock market projections are made by employing mathematical approaches and machine learning methods. The stock market is inherently vulnerable to unforeseen changes due to its intricate nature and absence of a linear progression. Since the advent of personal computers and the proliferation of technological advancements in the 1990s, scholars have investigated the application of artificial intelligence in the investment sector. Many solutions have been created to tackle the matter of volatility in stock market prices. This study examined a total of 146 research articles that were published in academic journals over twelve years (2011-2023). These papers focused on applying artificial intelligence in predicting stock market trends. The listed works encompass several methodologies, including technical analysis, fundamental analysis, sentiment analysis, and time series analysis. Every academic field is comprehensively explored, encompassing its initial findings and most recent advancements. Moreover, the existing body of literature suggests a growing focus on this particular sector, with a heightened level of specialization and an expanded range of topics being explored.

Toward an Enhanced Stock Market Forecasting with Machine Learning and Deep Learning Models

The use of both machine learning and deep learning in forecasting the stock market has attracted the interest of finance, as it holds the promise of better and more efficient prediction of stock market trends. With the markets becoming more intricate and fluid with every passing moment, relying on sophisticated predictive models becomes indispensable. This work is intended to investigate how good machine learning versus deep learning can be for stock market forecasts — based on their ability to predict stock market trends — and analyze techniques' effectiveness inside each methodological field. We compared the exploration of advances in long short-term memory with the traditional methods that demonstrate improved accuracies in predicting stock markets. Therefore, this study attempts to place itself within the framework of the discussion about using artificial intelligence for financial decisions by contributing new knowledge. Indeed, the complexity and unpredictability of stock market dynamics necessitate a diverse range of data sources for accurate prediction, and this research aims to shed light on the potential of integrating advanced technologies to enhance forecasting capabilities within the finance sector.

Analyzing the performance of machine learning in predicting stock market trends

Analyzing the performance of machine learning in predicting stock market trends

EFFECTIVE MACHINE LEARNING TECHNIQUES FOR STOCK PRICE FORECASTING

The paper explores the dynamic intersection of financial markets and advanced data analytics. In a world where markets evolve swiftly, informed decision-making is imperative for investors, traders, and financial analysts. The paper addresses this need by developing a machine learning model employing a random forest classifier to forecast the direction of the S&P 500 index. It unfolds through a systematic process, commencing with data retrieval from Yahoo Finance, data preprocessing to ensure data quality, attribute selection, and model training. We rigorously evaluate the model using precision as the primary metric, which measures its accuracy in predicting stock market trends. We integrate data visualization too ls to enhance interpretability and user -friendliness, allowing users to intuitively grasp the model's performance and outcomes. Beyond its predictive capabilities, the project offers an educational tool for learners interested in machine learning and its applications in finance. The system's architecture prioritizes modularity and scalability, establishing the foundation for potential future improvements.

Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data

Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data