Historical Stock Data Research Articles

UCB and Thompson Sampling Algorithms in Long-Term Investing

In the rapidly evolving financial markets, traditional investment strategies are increasingly being replaced by advanced algorithms. Among these, the Upper Confidence Bound (UCB) and TS algorithms, which originate from the multi-armed bandit framework, have demonstrated significant potential across various sectors. This study utilizes historical stock data from companies during the period from 2019 to 2024 to evaluate the performance of these algorithms in long-term investment strategies. The research aims to identify how these algorithms manage investment risks and maximize returns under varying market conditions. The results reveal distinct behaviors of UCB and TS in the financial markets: UCB performs better in markets with lower volatility, while TS exhibits stronger adaptability in high-volatility environments, thereby enhancing potential returns. Through a detailed comparison of UCB and TS in long-term investment scenarios, this study provides valuable insights into the strategic application of these algorithms. It offers investment strategy guidance for investors and financial strategists and supplies effective information for economic decision-making in relevant fields.

Integrating Fintech-Driven Technical Analysis into Value Investing: A Study of Moving Averages in Cryptocurrency Market Dynamics

This research explores the application of fintech-driven technical analysis, specifically focusing on moving averages within value investing. Using Coinbase as a case study, the study examines the effectiveness of moving averages in volatile market conditions where traditional metrics are less applicable. Historical stock data was exported from Yahoo Finance and analyzed using Excel, emphasizing the 30 vs. 10 moving average combination. This combination was chosen to balance short-term and long-term market perspectives. The analysis revealed that while moving averages effectively highlight trends and momentum shifts, they are lagging indicators and require supplementary analysis to account for external factors such as news and regulatory changes. Despite not being a precise predictive tool, moving averages provided valuable insights into market behavior, supporting the argument that fintech tools can complement traditional value investing strategies by offering real-time analysis and dynamic approaches to market assessment.

Measuring Impact of Market Sentiment on Stock Prices

This study considers the impact of investor sentiment on stock market prices, it is when something beyond traditional economic factors, such as emotions or perceptions that play dominating role in driving psychological and real pricing. This behaviour in investor sentiment often leads to irrational decision making, always influenced by news, social media and the movements of markets. This will make stocks price more volatile. Through sentiment analysis, historical stock data and behavioural finance models the study examines times when there were significant movements in the market. These trends suggest that happy days can cause major prices to rise (along with the sun) and bad news can punch holes in a report even if business hasn't really looked any worse. The regulators and the investors should understand that sentiment can improve the efficiency of the stock price-prediction algorithms as well as bring anomalies to light in the market. The study of market sentiment and its effect on the stock price. In the given paper of research, it is easy to notice the impact of interaction between investor sentiment and stock price.

A Comprehensive Methodology for Analysing Options Trading: Utilizing Historical Data and the Black-Scholes Model

This paper presents a systematic methodology for analysing options trading, integrating user inputs with historical stock data to facilitate informed decision-making. The process begins with the collection of user-specified stock tickers and expiration dates, followed by the retrieval of historical price data to assess past stock performance. Key components of the analysis include calculating historical volatility, fetching the option chain, and filtering options into put and call categories. The methodology employs the Black-Scholes model to estimate fair option prices, which are further evaluated by calculating expected returns. The analysis culminates in the ranking of the top three put and call options based on their expected returns, providing users with actionable insights. Additionally, the methodology incorporates visual representations of stock prices and option data, alongside calculations of option Greeks to assess risk sensitivity. This comprehensive approach empowers traders to navigate the complexities of options trading with a clear understanding of potential outcomes and associated risks.

The Impact of Stochastic Volatility on Option Pricing Using the Black-Scholes Model: Empirical Evidence from NVIDIA

This paper investigates the impact of volatility on option pricing using the Black-Scholes model. Utilizing historical stock and option data from NVIDIA, this study demonstrates the limitations of the Black-Scholes model in capturing dynamic market conditions. The empirical analysis reveals significant discrepancies between model predictions and actual market prices, highlighting the importance of accurate volatility estimation. The findings suggest that incorporating better volatility measures can improve the accuracy of option pricing models. Additionally, the implications of these findings for traders and risk managers are discussed, emphasizing the need for more sophisticated approaches to volatility estimation in financial markets. This study adds to the body of knowledge by presenting actual data regarding the efficiency of different volatility metrics in option pricing. Moreover, it underscores the necessity of continuous improvement in financial modeling techniques to adapt to changing market dynamics. The study's results align with previous research that has shown the limitations of the Black-Scholes model and the benefits of alternative volatility estimation methods. This paper also discusses the potential application of these findings to improving trading strategies and risk management practices. The practical implications are significant, suggesting that traders and risk managers should consider incorporating advanced volatility measures into their models to enhance decision-making processes and financial outcomes. The conclusions drawn from this study highlight the critical role of accurate volatility estimation in achieving more reliable option pricing and underscore the need for ongoing research and innovation in this field.

Enhancing stock market prediction accuracy with recurrent deep learning models: A case study on the CAC40 index

This paper explores the application of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for stock price prediction over a 10-day horizon. The study aims to compare the predictive performance of these two deep learning architectures within the context of financial forecasting. Utilizing historical stock data from the CAC40 dataset, which represents a capitalization-weighted measure of the 40 most significant stocks on the Euronext Paris, we train and evaluate RNN and LSTM models to forecast future stock prices. Our results demonstrate the superior performance of LSTM networks in capturing the intricate temporal dependencies inherent in stock price data. Compared to standard RNNs, LSTM models exhibit higher accuracy and provide more reliable forecasts over the 10-day prediction period. The specialized memory cells and gating mechanisms in LSTM networks enable them to effectively identify both short-term changes and long-term patterns in stock prices, thus outperforming traditional RNN architectures. This enhanced ability to model the complex dynamics of stock market data underscores the potential of LSTM networks to improve investment decision-making, risk management, and the overall efficiency of financial markets. The insights gained from this study contribute to the growing body of knowledge on the application of deep learning in finance and investment, offering valuable guidance for practitioners and researchers seeking to harness the power of advanced algorithms for stock market prediction and optimization.

Stock Market Prediction Analysis using Deep Learning

The abstract provides an overview of a proposed approach for stock market value prediction using deep learning techniques, specifically focusing on artificial neural networks (ANN) and long-short-term memory (LSTM) algorithms. The stock market is a dynamic environment influenced by various factors, such as economic conditions and market sentiment, making accurate prediction challenging yet crucial for investors.In this study, the objective is to leverage deep learning methodologies to enhance the accuracy and robustness of stock market predictions compared to traditional methods. By harnessingthe Python programming language, the research aims to develop a model capable of analyzing historical stock data and generating forecasts of future stock prices

Predictive Analysis of Indonesian Stock Market Prices Using Deep Learning: An Application of Diffusion Variational Autoencoders

This study introduces the application of the Diffusion Variational Autoencoder (D-VAE), a deep learning technique, for predicting stock prices in the Indonesian stock market. With the challenges presented by market volatility and complex data distributions, D-VAE is explored for its capability to encapsulate uncertainty and model complex distributions. This study is significant as it explores the potential of D-VAE in the context of the Indonesian stock market, which has not been widely studied before. Historical stock data from Yahoo Finance was collected over one year and preprocessed for training and validation of the model. The model is trained with an architecture designed to allow tuning of the latent space, utilizing ReLU and linear activation functions for the encoder and decoder. The model's performance is evaluated using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared metrics, yielding results that highlight the model's capability to enhance the accuracy of stock price predictions. By leveraging machine learning techniques in stock price prediction models, this study underscores the significant contribution such approaches can make to informed and successful investment decisions underpinned by robust data.

Stock Price Prediction Using Machine Learning

Abstract: The "Stock Price Prediction Using Machine Learning" project aims to develop an advanced predictive model for forecasting stock prices in financial markets. The volatility and complexity of stock markets make accurate predictions challenging, and the utilization of machine learning techniques offers a promising approach to address this challenge. This project leverages historical stock data, technical indicators, and sentiment analysis to create a robust predictive model. The methodology involves collecting and preprocessing a vast dataset of historical stock prices and relevant financial indicators. Various machine learning algorithms, including but not limited to linear regression, decision trees, support vector machines, and neural networks, are employed to analyze patterns and relationships within the data. The project focuses on model evaluation and comparison to identify the most accurate and reliable prediction model. Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy are utilized to assess the effectiveness of the models. Hyperparameter tuning and cross- validation are employed to enhance the models' generalization capabilities

Stock Market Prediction Using Machine Learning Techniques

The prediction of stock market prices holds significant importance within the contemporary economic landscape. Consequently, there has been a notable surge in scholarly interest directed towards exploring novel avenues for enhancing stock market prediction capabilities. Recent research endeavors have illuminated the potential predictive value inherent in various data streams, including historical stock data and user-generated content sourced from platforms such as Twitter and web news. These investigations have revealed a discernible relationship between social mood, as reflected in online discourse, and future stock price movements. However, prior studies have often overlooked the incorporation of such sentiment-derived information, thus presenting an information gap. In the present study, we address this gap by proposing an effective methodology for the integration and analysis of multiple information sources to facilitate more accurate stock price predictions. Leveraging Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models, we conduct a comprehensive analysis of disparate data sources. Furthermore, we employ an ensemble approach, incorporating Weighted Average and Differential Evolution techniques, to refine the predictive accuracy of our models. Our findings demonstrate the efficacy of the proposed methodology in generating highly accurate stock price predictions across varying future time horizons, including one-day, seven-day, 15-day, and 30-day intervals. These predictions offer valuable insights for investors seeking to make informed decisions regarding their investment strategies and enable companies to gauge their anticipated performance within the stock market landscape.

Web Trade Analytics

This research introduces an innovative web application developed using the MERN (MongoDB, Express.js, React, Node.js) stack, enhanced with fundamental machine learning algorithms, designed to address the complexities of stock market analysis. The central focus is on creating a user-customizable dashboard, allowing investors to select specific stocks for real-time analysis, sentiment tracking, and future price prediction. The methodology integrates historical stock data with sentiment analysis sourced from news and social media. The machine learning algorithms leverage this data to generate buy or sell recommendations and forecast future price trends. Our findings underscore the practicality of this approach, enhancing investors' abilities to navigate the unpredictable stock market landscape and make informed decisions with an eye on the future. Keywords— Stock Market, MERN Stack, Predictive Analysis, Future Forecasting

Algoritma Gated Recurrent Unit untuk Prediksi Harga Indeks Penutupan Saham LQ45

The Indonesia Stock Exchange (IDX) states that stocks, including LQ45 stocks, which constitute the stock market index for the IDX, have become one of the preferred investment options for the public. Investors need to have accurate analysis and information to gain significant profits as stock prices fluctuate due to company performance, industry factors, changes in interest rates, liquidity, global market conditions, market sentiment, and investor psychology. The Gated Recurrent Unit algorithm is suitable for application on historical stock data sets because they are time series data, can be computed and compared on a numerical scale. This algorithm is a variant of the Long Short-Term Memory algorithm or other types of processing modules for Recurrent Neural Networks. The data set used consists of closing price data or close features, comprising a training data set of 4,406 data and a test data set of 1,889 data that have undergone data preparation using various techniques, including data cleansing, data scrubbing, data splitting, data normalization, and data reshaping. The results showed that the Gated Recurrent Unit algorithm is the right strategy because it obtains a good evaluation of model performance, namely MSE of 0.0009; RMSE of 0.17325 and MAE of 0.0207.

Stock price forecasting using machine-learning methods

The stock market is essential in the economic growth of the nations in which it operates, and stock price prediction is of great significance to investors and government departments, as stocks provide both high reward and high risk. Nowadays, stock price prediction makes extensive use of machine learning algorithms. A large number of machine-learning models are available for predicting stock prices in the existing literature. In this article, the K-Nearest Neighbor (KNN), Random Forest (RF), Long Short-Term Memory (LSTM), and Gate Recurrent Unit (GRU) methods are applied to construct models to make stock forecasting based on Airbnb's historical stock data. The stock data are collected from 10th December 2020 to 19th August 2022. In addition, the accuracy of these four different models is analyzed and compared through Mean Square Error (MSE), Mean Absolute Error (MAE), and Resolvable coefficient (R^2) score metrics. The result shows that the LSTM and GRU models perform better than KNN and RF, with GRU showing the best results.

Studying Stock Prediction in the Context of Machine Learning Exemplified by Analyzing SVM and LSTM Models

The impact of stocks on economic development is significant, with most companies employing quantitative finance methods to analyze and trade stocks. In the present day, there is a growing plethora of stock prediction methods, requiring individuals to carefully select the appropriate means of prediction. This study aims to present the highly regarded machine learning algorithms of recent years. Machine learning enables the extraction of trading strategies by learning patterns from historical stock data. Deep learning, a subset of machine learning, eliminates the need for researchers to manually extract features, instead utilizing neural network models that have proven effective for stock analysis. This paper also provides an extensive analysis of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), elucidating their algorithmic principles and assessing their strengths and weaknesses based on the findings of other researchers. It is observed that SVM is effective for predicting small quantities, but lacks suitability for non-stationary data prediction due to the non-informative nature of financial time series data, which significantly diminishes the accuracy of SVM predictions. In contrast, LSTM emerges as the most comprehensive single algorithm, exhibiting high accuracy and efficiency across all aspects. The paper also explores advanced hybrid models, which demonstrate that combining the advantages of multiple models can lead to higher accuracy and efficiency. The future of stock prediction algorithms holds promise for development, with the potential to construct an intelligent system that automatically selects data for analysis.

ANALISIS ABNORMAL RETURN DAN TRADING VOLUME ACTIVITY UNTUK MENGUKUR TERJADINYA JANUARI EFFECT PADA KELOMPOK SAHAM INDEKS LQ 45 DI BEI

ANALISIS ABNORMAL RETURN DAN TRADING VOLUME ACTIVITY UNTUK MENGUKUR TERJADINYA JANUARI EFFECT PADA KELOMPOK SAHAM INDEKS LQ 45 DI BEI

Survey on Stock Price Forecasting

This research project delves into the challenging domain of stock price prediction by harnessing the capabilities of neural networks. In an ever-evolving financial landscape, the project seeks to develop a robust model that can analyze historical stock data, identifying intricate patterns and trends to inform future price movements. Through the convergence of data science and financial analysis, this endeavor aims to provide investors with valuable insights, aiding them in navigating the complexities of the stock market. Employing machine learning algorithms, particularly neural networks, the model scrutinizes extensive sets of historical stock data, considering factors such as past price trends, trading volumes, and relevant financial indicators. Techniques such as regression, time-series analysis, and neural networks are leveraged to unveil complex relationships within the data, enhancing the model's predictive capabilities. The model undergoes meticulous training on historical data to glean insights from past market behaviors. Subsequently, it undergoes rigorous testing on unseen data to evaluate its predictive accuracy. Continuous refinement and optimization strategies are implemented to ensure the model's adaptability to the ever-changing dynamics of the market. Ultimately, this project aspires to furnish investors with a powerful tool for making informed decisions in the face of the unpredictable nature of financial markets. By integrating the precision of neural networks with the intricacies of stock market analysis, the research aims to contribute to the advancement of stock price forecasting methodologies.

Comparative Analysis Of Stock Performance Before And After Determining Asymmetric Auto Rejection In Companies Listed On The Idx

ABSTRACTThe asymmetric auto rejection policy has an impact on stock performance, namely influencing investors' perceptions of the security and quality of order execution. Comparing stock performance before and after establishing asymmetric auto rejection can provide benefits for market players, namely evaluating effectiveness in improving market integrity and protecting investors. The aim of this research is to analyze the comparison of stock performance before and after the establishment of asymmetric auto rejection in companies listed on the IDX. The method used in this research is quantitative by collecting data in the form of historical stock data before and after the asymmetric auto rejection policy. The data analysis in this research used statistical tests with the help of SPSS version 26, namely paired t-test samples. The results of this research show that there is no significant difference between stock performance before the asymmetric auto rejection policy was implemented and stock performance after the asymmetric auto rejection policy was implemented. This is because the average value is only different by 0.033. Apart from that, the significance value is 0.975. This means that if it is greater than 0.05, H0 is accepted and H1 is rejected.

Stock Price Prediction Using Machine Learning Models: A Study of NSE Stocks

Abstract: This paper presents a study on stock price prediction utilizing the ARIMA and Linear Regression algorithms, focusing on companies listed on the National Stock Exchange (NSE) of India. The aim is to compare the predictive accuracies of these models while considering the lifetime data of stocks obtained through the Yahoo Finance API. Through comprehensive analysis, historical stock data spanning the entire lifespan of the stocks is utilized, enabling a thorough exploration of long-term trends and patterns. It was inferred that for NSE (Indian Company) stocks and Linear Regression prove to be more efficient than ARIMA. The research methodology involves data retrieval, preprocessing, and model training, with Python being the primary programming language for implementation. Findings indicate the effectiveness of ARIMA and Linear Regression models in forecasting NSE stock prices, with implications for financial decision-making and investment strategies. This study contributes to the understanding of machine learning applications in the stock market domain, emphasizing the importance of leveraging comprehensive historical data for enhanced predictive performance.

Stock Forecasting Based on Linear Regression Model and Nonlinear Machine Learning Regression Model

To enhance the accuracy of stock price prediction for Netflix and provide individuals with a comprehensive understanding of stock trading prices, this study constructs a predictive model by employing three distinct approaches: a linear regression model, a Long Short-term Memory (LSTM) artificial neural network, and a Gated Recursive Unit (GRU) which serves as a component of the LSTM architecture. A prediction scheme is devised, utilizing historical stock data spanning from 2002 to 2022 for Netflix. The primary objective is to forecast the stock price of Netflix for the subsequent 20-day period. To evaluate the efficacy of the three models, a rigorous assessment is conducted employing robust evaluation indices. The outcomes of this analysis will enable a determination of the fitting adequacy of each model, thereby facilitating the identification of the most suitable approach for accurate stock price prediction in the context of Netflix. This research endeavors to contribute to the field of stock market analysis by leveraging advanced predictive modeling techniques for enhanced forecasting accuracy and insightful decision-making.

An approach to optimization of Gated Recurrent Unit with Greedy Algorithm

This study focuses on enhancing the performance of Stacked Gated Recurrent Unit (GRU) model in time series data processing, specifically in stock price prediction. The most significant innovation occurs in the integration of a Greedy Algorithm for optimizing hyperparameters such as look-back period, number of epochs, batch size, and units in each GRU layer. Historical stock data from Apple Inc. is utilized for the model's application, emphasizing the model's effectiveness in predicting stock prices. The study methodology involves a sequence of steps, such as data loading, preprocessing, dataset splitting, model construction, and evaluation. The role of the Greedy Algorithm is to iteratively adjust hyperparameters to minimize the Root Mean Squared Error (RMSE) metric, resulting in refining the model's predictive accuracy. The outcomes reveal that the integrated Greedy Algorithm significantly enhances the model's accuracy in predicting stock prices, indicating its potential application in various scenarios requiring precise time series forecasting.