Stock Market Trends Research Articles

Application of deep learning and statistical methods in predicting Taiwanese stock trends

Since its inception, the stock market has been a topic of considerable interest. Its variation and the complexity of integrating technology into the stock market have made it difficult for stock market trends to be fully understood. Various metrics and analytical approaches have been proposed in response to such changes, ranging from purely technical metrics to hardware upgrades. The widespread application of deep learning in the stock market, from basic metrics (opening price, closing price, highest price, lowest price, trading volume) to machine learning in sentiment analysis, further increases the possibility of increasing profits. Some front-end techniques, such as noise reduction through mathematical models, enhance the accuracy of deep learning models. However, few studies have centered on predicting long-term stock price changes. The traditional moving average (MA) cannot rapidly reflect drastic changes on its curve even though it can display trends; therefore, this study proposes an MA-based approach that improves the 200-day MA such that its delayed response to actual prices in real-time can be overcome. This deep learning model training was performed by combining 200-day MA data with two other types of MA data, thereby creating a new approach to metric analysis. The sample consisted of stocks of 13 Taiwanese companies with a high market cap: Taiwan Semiconductor Manufacturing Co., Ltd., MediaTek Inc., Chunghwa Telecom Co., Ltd., Fubon Financial Holding Co., Ltd., Cathay Financial Holding Co., Ltd., Nan Ya Plastics Corp., United Microelectronics Corp., Delta Electronics, Inc., CTBC Financial Holding Co., Ltd., Mega Financial Holding Co., Ltd., Formosa Chemicals & Fibre Corp., Hon Hai Precision Industry Co., Ltd., and Formosa Plastics Corp. Through multiple evaluation metrics, the experimental results revealed that the proposed model performed better in general than the traditional MA model for all stocks.

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.

Working of Stock Price Prediction Using LSTM

Our proposal centers on the development of a robust tool designed to forecast stock prices by leveraging a sophisticated combination of advanced techniques. We're merging spatial features, which essentially encapsulate the distinctive attributes of individual stocks, with the powerful capabilities of long-and-short-term memory networks (LSTM). These networks excel at recognizing and interpreting patterns and trends over extended periods. By integrating these two elements, our model gains the ability to capture both short-lived fluctuations and enduring trends within stock prices.This fusion of spatial features and LSTM networks represents a significant advancement in stock price prediction methodologies. We're confident that this innovative approach will surpass existing methods in terms of accuracy and reliability. To validate our confidence, we're conducting rigorous testing and refinement of the system using diverse sets of data. This ensures that our model performs effectively across various market conditions and scenarios, thereby instilling trust among investors seeking dependable insights into stock market trends. Key Word: Long and Short-Term Memory, Time-Series Data, Yahoo Finance.

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.

Analysis of Stock Market Trends for Market Capitalization and Securities Management: An approach to Nepalese Stock Market

Attraction towards share market is increasing day by day. Relevance for guiding people towards purchase and sale of share is also increasing everywhere in the world. Nepal is a poor country having well developed share market, but some sort of hurdles and technical disparities are still creating problems in operation of this market. Increasing economic crisis for last two years has made this market critical. Decrease in price of share is caused by increasing losses in business and industry at present. The main objective of the study is to examine the growth trend of the Nepalese securities market. It helps to analyze the trend of Nepalese stock market in terms of market capitalization, number of transaction, number of securities traded and number of securities listed and to examine the space of primary market growth in Nepal. this work is motivated by the works of (see[6-31]).

Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment

Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations.

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.

Volatility Spillover Benefits of the Hang Seng Index and Shanghai Composite Index: A Vector Autoregressive (VAR) Model and Granger Causality Test Analysis

This study aims to explore the volatility spillover benefits between two major stock markets in Hong Kong and Shanghai, namely the Hang Seng Index and the Shanghai Composite Index. Empirical analysis is conducted using a Vector Autoregressive (VAR) model and Granger causality test based on daily return data from 2013 to 2023. The research finds a significant volatility interaction between the Hang Seng Index and the Shanghai Composite Index, which is more pronounced during periods of global economic instability. Impulse response analysis reveals that positive shocks in Hang Seng Index lead to increased volatility in the short term for the Shanghai Composite Index but gradually weaken to a weak negative effect in the long term. Conversely, positive shocks in the Shanghai Composite Index have a short-term inhibitory effect on the Hang Seng Index but transform into a weak promoting effect in the long term. These findings have important implications for investors and policymakers in risk management, forecasting future stock market trends, and capital allocation.

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.

Risk Premium and Volatility Analysis on Indonesia Stock Exchange

Market risk premium and market volatility are important in investment decisions. Volatility is an important variable in derivative securities which is a measure of changes in stock returns. The research focuses on stock return volatility, research that points to a risk premium in emerging markets. The purpose of this study is to explain the relationship between market equity premium and volatility using GARCH (1.1) on the Indonesia Stock Exchange. This research uses daily closing price data of the Indonesia Stock Exchange Composite Index (JCI). The result of this study, that there is a relationship between risk premium and volatility in the Indonesian stock market. The conclusion of this study is to try to test whether there is a relationship from the volatility of return to risk premium in the Indonesian stock market. Using the daily trend of the Indonesian stock market (IDX) from January 2010 to September 2023.

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.

Stock Data Analysis of Competing Companies in Competitive Market: The Case of NVIDIA Corporation

On May 24, 2023, NVIDIA released its impressive first-quarter results, and the stock soared 30% that day, making NVDA the sixth largest company in the world. NVDA has been a heavy player in almost most of the global technology fronts of recent years (cloud computing, cryptocurrency, metaverse, artificial intelligence, etc.). NVDA dominates the AI training space with over 95% market share. Thus, it is extremely important to keep an eye on the NVDA stock market trend because it is not only crucial for the stock market but also a key aspect of how human science and technology will progress in the future. This article mainly analyzes the correlation between the stock market data of NVDA and its competitors (from NVDA's data center, graphics cards, professional visualization, games, and other business aspects) to achieve the purpose of monitoring and predicting the future trend of NVDA's stock market. It is hoped that provide valuable insights for investors and market analysts, enabling them to make informed decisions and predict the trajectory of NVDA's stock market performance.

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.

CLUSTERING IN COMPILING INVESTMENT PORTFOLIOS

Clustering is an effective tool for diversifying investments, reducing risk and identifying new opportunities. Clustering plays a key role in data analysis, allowing you to group objects with similar characteristics. This study examines the application of clustering to the tasks of forming and optimizing investment portfolios. The paper presents two clustering methods: K-medoids and fuzzy clustering (C-means). K-medoids divides assets into clusters by correlation, and C-means allows assets to belong to several clusters with varying degrees. The article analyzes various clustering methods in the context of the stock market. Similarity measures for stock clustering are compared. The K-means algorithm for clustering companies by time series is presented. Chaotic cartographic clustering is used to analyze the dynamics of the stock market. The TreeGNG algorithm is used to identify stock market sectors. The HRK method has been developed to predict short-term changes in stock prices. The application of data mining methods to predict stock market trends is discussed. The K-means and C-means methods for clustering banking and energy companies are compared. The effectiveness of the SOM-SVR hybrid approach for predicting price dynamics and volatility is demonstrated. C-means combines with artificial neural networks to improve the accuracy of stock market forecasting. The study demonstrates the potential of various clustering methods in the context of compiling and optimizing investment portfolios.

Investigating the Role of Machine Leaning and Blockchain for Forecasting Stock Market Trends

There is a sudden deluge of evolutionary approaches in the domain of data processing and computation, which are significantly affecting several facets of applications globally. Artificial intelligence, machine learning and Blockchain happen to be at the forefront of evolutionary computation beating conventional approaches. Finance applications currently are heavily reliant of data driven models. Stock trend analysis happens to be one such approach, which lays the foundation for forecasting decisions to be made. The leeway in such applications is critically small as minimal inaccuracies in forecasting may lead to major losses. This paper presents the current perspective in terms of evolutionary algorithms such as artificial intelligence and Blockchain and how they are transforming software development, the application of machine learning algorithms to regression problems. Finally, the stock trend analysis based on in and out of sample datasets has been performed for standard S&P datasets. A comparative analysis with previous work clearly indicates the improved performance of the proposed work with respect to baseline approaches in the domain.

LQ45 Stock Portfolio Optimization by Considering Return Predictions Using the Support Vector Regression (SVR) Method

Investors require stock portfolio optimization to achieve desired outcomes. This process involves prediction to obtain optimal weights in the expected portfolio. Historically, portfolio optimization has primarily considered risk and expected value. However, a new approach has integrated return prediction into traditional time series models, enhancing the performance of portfolio optimization models. Machine learning has proven effective in predicting stock market trends, with many researchers applying these models in portfolio formation with satisfactory results. This study employs Support Vector Regression (SVR) method to integrate return prediction in portfolio formation, utilizing stock data from the LQ45 index. Test results indicate that return prediction using SVR method yields a Root Mean Square Error (RMSE) of 0.34973. Portfolios considering return prediction demonstrate superior performance compared to the LQ45 index, as evidenced by portfolio return averages, standard deviation, and Sharpe ratio.

Forecasting Stock Market Trends of S&P 500 based on Fractal Theory and Polynomial Regression Model

Forecasting Stock Market Trends of S&P 500 based on Fractal Theory and Polynomial Regression Model