Historical Stock Prices Research Articles

An Exploratory Study on the Effect of Macroeconomic Indicators on the Stock Market Performance among 6-ASEAN Countries

The stock market's performance has been impacted by the global economy's recovery from the COVID-19 pandemic. This study investigates the impact of various factors on stock market performance, with a focus on the global economic recovery from the COVID-19 pandemic. The stock market has experienced positive growth, driven by low interest rates, ample liquidity, and sustained investor optimism, despite concerns over rising inflation and interest rates. This research aims to analyze stock market performance across six ASEAN countries, using a range of data sources, including historical stock prices, financial reports, and macroeconomic indicators. The findings reveal that the real exchange rate exerts the most significant influence on stock market performance. The insights from this study provide valuable information for investors, policymakers, and government bodies, aiding in strategic decision-making and investment planning in these markets.

IMPLEMENTASI MACHINE LEARNING ALGORITMA NEURAL NETWORK DALAM MEMPREDIKSI HARGA SAHAM MENGGUNAKAN RAPIDMINER

The research is aimed at implementing the Neural Network algorithm in predicting stock prices using RapidMiner. Historical stock price data is analyzed to train neural network models in identifying patterns and trends that can be used to predict future stock prices. The research phases include data collection, data preprocessing, Neural Network model development, model training, and model performance evaluation. Data used include daily closing prices, trade volumes, and other technical indicators. The data is processed through several stages of preprocesing, including normalization and filling of missing values, before being used to train Neural network models. The model was built with several layers and the number of neurons adjusted based on early experiments to find the optimal configuration. Models are trained using datasets that are divided into training sets and testing sets. The predicted result is compared to the actual value to evaluate the performance of the model. The results showed that the Neural Network model was able to predict stock prices with a fairly high accuracy rate, with an RMSE of 0.989 indicating a low prediction error rate. The research concludes that the Neural Network algorithm implemented in RapidMiner is effective in predicting stock prices and has significant potential to be used in investment decision-making. The use of RapidMiner as a data processing tool has proven to be efficient and facilitating the development and evaluation of predictive models. Keywords: Neural Network, Stock Price Prediction, RapidMiner, Machine Learning, Investment

ANALISIS DETERMINAN HARGA SAHAM BASIC MATERIALS TAHUN 2018-2022

The research intends to evaluate the impact of leverage ratios, market ratios, and dividend policies on stock prices. All basic materials companies on the Indonesian Stock Exchange until 2022 constitute the research population, then using a purposive sampling method the sample can finally be determined, namely 15 basic materials companies on the Indonesian Stock Exchange during the period 2018 to 2022. Data was collected through documentation techniques referring to annual financial data and historical stock price data and analyzed using multiple regression techniques using SPSS data processing tools. The results of the analysis show that the leverage ratio has no effect on stock prices, while the market ratio and dividend policy have a significant effect on stock prices.

Comparative analysis of machine learning and traditional models for financial option pricing

This study conducts a comparative analysis of traditional and machine learning models for financial option pricing, using historical stock prices and interest rates data. Traditional models such as the Black-Scholes, Heston, Merton Jump-Diffusion, and GARCH are evaluated against machine learning models including Multi-Layer Perceptrons (MLPs) and Long Short-Term Memory (LSTM) networks. The analysis employs performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R value. Results indicate that the GARCH model excels in predictive accuracy due to its ability to capture volatility clustering, while machine learning models, especially the Tuned Neural Network, demonstrate superior flexibility and adaptability in managing complex non-linear relationships in financial data. Traditional models, although theoretically robust, show limitations under varying market conditions. The study underscores the potential of hybrid approaches combining traditional and machine learning techniques to leverage their respective strengths for more accurate and reliable option pricing. Future research directions include exploring advanced machine learning architectures and improving model transparency through explainable AI.

Enhancing Stock Price Prediction in the Indonesian Market: a Concave LSTM Approach with RunReLU

This study addresses the pressing need for improved stock price prediction models in the financial markets, focusing on the Indonesian stock market. It introduces an innovative approach that utilizes the custom activation function RunReLU within a Concave Long Short Term Memory (LSTM) framework. The primary objective is to enhance prediction accuracy, ultimately assisting investors and market participants in making more informed decisions. The research methodology used historical stock price data from ten prominent companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. Evaluation metrics such as RMSE, MAE, MAPE, and R2 were employed to assess model performance. The results consistently favored the RunReLU-based model over the ReLU-based model, showcasing lower RMSE and MAE values, higher R2 values, and notably reduced MAPE values. These findings underscore the practical applicability of custom activation functions for financial time series data, providing valuable tools for enhancing prediction precision in the dynamic landscape of the Indonesian stock market.

Dung beetle optimization with deep learning approach for solving inverse problems in predicting financial futures

Leveraging deep learning (DL) to inverse problems has proven transformative in predicting financial futures, mainly in stock price prediction. In terms of financial markets, where predicting stock price is a challenging inverse problem, DL methods like long short-term memory networks (LSTM) and recurrent neural networks (RNN) demonstrate notable prowess. These techniques successfully capture complex patterns and dependencies in historical stock price information, learning to discern subtle correlations and fundamental market trends. DL facilitates the extraction of nuanced features by leveraging large datasets and sophisticated techniques, enabling accurate prediction of future stock prices. This intersection of advanced ML approaches and financial predicting signifies a paradigm shift in leveraging technology to navigate the complexity of the financial market and improves decision-making for traders and investors. This manuscript introduces a Dung Beetle Optimization with a Deep Learning Approach for Solving Inverse Problems in Predicting Financial Futures (DBODL-SIPPFF) technique. The DBODL-SIPPFF technique resolves the inverse problem and predicts the stock prices adequately. In addition, the DBODL-SIPPFF technique exploits data consistency (DC) optimizer to solve inverse problems. Primarily, linear scaling normalization (LSN) is applied for data normalization. The honey badger algorithm (HBA) is utilized for the feature selection. Moreover, convolutional long short-term memory (ConvLSTM) is used for stock price prediction. Lastly, the DBO technique is utilized for the optimum hyperparameter selection of the ConvLSTM model. The empirical assessment of the DBODL-SIPPFF technique takes place under diverse stock datasets. The obtained values stated that the DBODL-SIPPFF technique performs optimally over compared methods.

Stock market index prediction based on market trend using LSTM

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.

Online Portfolio Selection of Fuzzy Mean Regression Strategy Considering Investor Sentiment Based on Text Data

Investors are often affected by emotion, cognition, and other psychological factors in stock trading when making decisions. At present, people can use machine learning and other technologies to obtain a massive amount of text data from the Internet to mine information related to investor behavior and sentiment. Building intelligent online portfolio trading strategies that consider investor sentiment has become an important topic and key challenge in the financial field. Therefore, this paper explores how to use text data to depict investor sentiment, fuzzifies historical stock price data, designs a new weight transfer equation, and finally obtains a novel fuzzy mean regression strategy that considers investor sentiment based on text data. We conduct empirical tests on this strategy by using the stock price data selected from CSI300 constituent stocks, as well as the text data of investors’ opinions on the internet. The results show that the strategy proposed in this study has a higher Calmar ratio than other mean regression strategies previously studied.

Assessing the Predictive Power of Transformers, ARIMA, and LSTM in Forecasting Stock Prices of Moroccan Credit Companies

In this paper, we present a data-driven approach to forecasting stock prices in the Moroccan Stock Exchange. Our study tests three predictive models: ARIMA, LSTM, and transformers, applied to the historical stock price data of three prominent credit companies (EQD, LES, and SLF) listed on the Casablanca Stock Exchange. We carefully selected and optimized hyperparameters for each model to achieve optimal performance. Our results showed that the LSTM model achieved high accuracy, with R-squared values exceeding 0.99 for EQD and LES and surpassing 0.95 for SLF. These findings highlighted the effectiveness of LSTM in stock price forecasting. Our study offers practical insights for traders and investors in the Moroccan Stock Exchange, demonstrating how predictive modeling can aid in making informed decisions. This research contributes to advancing stock market forecasting in Morocco, providing valuable tools for navigating the Casablanca Stock Exchange.

Comparative Stock Performance Analysis of Leading Electric Vehicle Brands: Tesla, BYD, and NIO Using Python Programming Language

This research paper aims to perform a comparative stock performance analysis of three leading electric vehicle brands: Tesla, BYD, and NIO, utilizing the Python programming language. The primary objective is to examine the financial trajectories of these companies by analyzing their historical stock prices, volatility, and return on investment over a defined period. Python programming language is also a key part of data analysis and data visualization. Methodologically, the study employs various Python libraries for data collection, preprocessing, and analysis, ensuring a robust and efficient analytical process. The key findings reveal distinct performance patterns and market behaviors for each company. Tesla demonstrated high volatility but significant long-term returns, while BYD showed consistent growth with moderate volatility. NIO, as a newer entrant, exhibited rapid growth with higher short-term risks. The conclusions drawn from this study provide valuable insights into the financial health and market positioning of these EV giants. By leveraging Python's powerful data analysis capabilities, this research not only enhances understanding of stock performance in the EV sector but also offers a practical framework for investors and analysts.

Implementation of Diffusion Variational Autoencoder for Stock Price Prediction with the Integration of Historical and Market Sentiment Data

This study aims to predict stock prices using a Diffusion Variational Autoencoder (D-VAE) model that integrates technical data and market sentiment. Technical data is obtained from historical stock prices and trading volume, while sentiment data is derived from financial news analyzed using the IndoBERT model for sentiment classification. The research findings indicate that the integration of sentiment data in the D-VAE model enhances the accuracy of stock price predictions compared to a model that uses only technical data. Model evaluation is conducted using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²). The model with sentiment data integration has an MSE of 2753.204, MAE of 42.751, and R² of 0.94489, which are better than the model without sentiment data integration. This study demonstrates that the use of sentiment analysis can significantly contribute to improving stock price prediction performance using machine learning technology.

Stock Price Prediction Using Machine Learning: Evidence from Pakistan Stock Exchange

This research investigates the utilization of machine learning methodologies for the purpose of forecasting the fluctuations in stock values inside the financial market. The application of a Random Forest classifier is utilized on a dataset including historical stock prices (namely, the KSE-100 Index) to generate predictions regarding the future movement of stocks, specifically whether they would experience an increase or decrease. The model is trained via a sliding window methodology and is assessed through the utilization of precision, recall, and F1-score criteria. The study furthermore incorporates the utilization of back testing and hyper-parameter tweaking techniques in order to enhance the performance of the model. The findings indicate that the model demonstrates a precision score of 58%, representing an enhancement compared to the previous score of 48%. Nevertheless, the model's total accuracy stands at a mere 58%, underscoring the need for future enhancements. The report additionally proposes potential avenues for future research, such as exploring alternate data sources, employing sentiment analysis techniques, and developing more advanced algorithms. The findings of this study hold significant significance for investors and financial institutions, as they highlight the potential of machine learning in facilitating informed investment decisions and improving financial forecasts and analysis.

Integrating Multimodal Deep Learning for Enhanced News Sentiment Analysis and Market Movement Forecasting

This paper presents a novel multimodal deep learning framework for analyzing news sentiments and forecasting market movements by leveraging natural language processing, deep learning, and auxiliary data sources. Traditional methods often rely solely on textual news data, limiting their predictive power due to the complexity and ambiguity of language. Our approach incorporates additional modalities such as stock prices, social media sentiment, and economic indicators to capture a more comprehensive view of market dynamics. We employ a hybrid deep learning architecture that combines convolutional neural networks (CNNs) for text feature extraction, long short-term memory (LSTM) networks for capturing sequential dependencies, and attention mechanisms to selectively focus on the most relevant features. To address data scarcity, we introduce advanced data augmentation techniques, generating synthetic news headlines based on historical stock price movements and sentiment patterns. The proposed system is evaluated on a comprehensive dataset spanning multiple years, including news headlines, stock prices, social media data, and economic indicators. Our method achieves an accuracy of 77.51%, significantly outperforming traditional methods and demonstrating improved robustness and predictive power. This study highlights the potential of integrating diverse data sources and sophisticated deep learning techniques to enhance news sentiment analysis and market movement forecasting.

Amazon’s Stock Trends Prediction based on ARIMA Model

The rapid development of e-commerce in recent years has made stock market prediction a challenging yet important task for investors and traders. The ARIMA model has been extensively applied for forecasting in financial time series data. This study applied the ARIMA model to predict the Amazon stock price, utilizing historical stock price data from a period of a decade. The results of the analysis indicated that the ARIMA model can effectively predict short-term stock price movements with a certain level of accuracy. The model managed to encapsulate the predominant trend in the variations of Amazon’s stock prices, providing insights into potential future price movements. The predicted stock prices closely matched the actual prices, with a reasonable level of precision. Furthermore, the ARIMA model can be useful for investors and traders in making informed decisions. By utilizing the model’s predictions, investors can better assess the potential risks and returns associated with their investment strategies. It should be noted that the ARIMA model is not without its limitations. The model may not perform well during periods of structural changes or when there are significant events affecting the stock price. Besides, it can also be influenced by other factors such as policies, market sentiment and corporate fundamentals. In conclusion, the ARIMA model can assist in predicting short-term Amazon stock price movements, serving as a valuable tool for investors and traders. However, it is essential to integrate the model's forecasts with other pertinent information and analysis to form comprehensive investment decisions.

Real-Time Stock Forecasting: Leveraging Live Data and Advanced Algorithms for Accurate Predictions

This groundbreaking research introduces an innovative stock market prediction methodology that integrates financial modeling, machine learning, and real-time data analysis. Anchored in a deep understanding of stock market dynamics, including economic statistics, company performance, and market sentiment, our approach employs natural language processing (NLP) and predictive modeling to analyze live data for accurate stock price predictions. The method involves collecting and preprocessing a dynamic dataset enriched with financial indicators and historical stock prices. Utilizing Long Short-Term Memory (LSTM) algorithms, our model exhibits an impressive 96% accuracy in forecasting stock movements, showcasing adaptability to diverse market scenarios and responsiveness to economic factors and sentiment shifts. The incorporation of live data proves pivotal in providing timely insights for informed decision-making, establishing our model as a valuable tool for navigating the complexities of the modern financial landscape.

Prediction of Stock Price Volatility Using the Long Short Term Memory (LSTM) Model for Investment Portfolio Selection Strategy

Volatility is an important variable in financial data models. Predicting volatility in financial data is helpful for investors to make good decisions to reduce risk and to gain investment returns. In predicting volatility, many researchers have conducted research in building prediction models using data mining. This research uses a deep learning algorithm, namely Long Short Term Memory (LSTM) which has high accuracy compared to other models. The research aims to predict stock price volatility and for investment portfolio selection. The object of this study is the historical stock price of PT. Unilever Indonesia Tbk. (UNVR), PT. Fast Food Indonesia Tbk. (FAST) which manages KFC and PT. MAP Boga Adiperkasa Tbk. (MAPB) which manages Starbucks in the period 2023 to 2024, when there was a boycott caused by the war between countries that occurred in the Middle East. The data is analysed using the LSTM model where stock price volatility was determined by the variance of the return and log return on the next seven days, then using LSTM the stock price volatility data was predicted. The results show that the MSE and RMSE values are very small, which means that the volatility prediction results are almost the same as the actual data. And the average volatility prediction results in UNVR stock of 0.00841, MAPB stock of 0.01717, and FAST stock of 0.01323. From these results can be used as a reference for the selection of investment portfolios.

Machine Learning and Fama-French Three-Factor Model Applications for Analyzing Stock Price in Technology Enterprises

This study endeavors to forecast the stock prices of the leading U.S. technology entities - Google, Microsoft, Amazon, Meta, and Apple - through the application of diverse machine learning models, complemented by the traditional Fama-French three-factor model. The employed models encompass Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Support Vector Machines (SVM), and Decision Tree models. Initially, historical stock price data is utilized to train these machine learning models, enabling the identification of potential price trends. Subsequently, the integration of the Fama-French three-factor model enhances the analysis by scrutinizing the impacts of market risk, company size, and book-to-market value on stock prices. The outcomes illuminate both the effectiveness and limitations of various models in stock price prediction, highlighting the advantages of machine learning methodologies over traditional financial theories. This research provides financial market analysts and investors with a fresh perspective on the amalgamation of machine learning and traditional financial theories for enhanced stock price prediction.

Advancing Stock Market Predictions with Time Series Analysis including LSTM and ARIMA

Predicting stock market prices accurately is a major task for investors and traders seeking to optimize their decision-making processes. This research focuses on the comparative analysis of advanced machine learning (ML) techniques, particularly, the Long Short-Term Memory (LSTM) model and Autoregressive Integrated Moving Average (ARIMA) model for predicting stock market prices. The study enforces thorough data collection and preprocessing to ensure the quality and reliability of the historical stock price data, forming a robust foundation for the predictive models. The core contribution of this paper lies in its systematic and comparative analysis of these two models. A range of performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are employed to assess and contrast the predictive accuracy and efficiency of the LSTM and ARIMA models. The research findings indicate that the ARIMA model, contrary to expectations, outperforms the LSTM model in this study, achieving lower RMSE and MAE values. Specifically, the ARIMA model demonstrates a Test RMSE of 4.336 and a Test MAE of 3.45926, indicating its superior predictive accuracy compared to the LSTM model. Furthermore, the study sets its findings against the backdrop of existing literature by comparing the performance of its models with those reported in previous research. This comparison shows better results achieved by our stock market prediction models. By addressing limitations observed in prior studies and demonstrating practical applicability, this research contributes to advancing stock market prediction methodologies, offering valuable insights for investors and traders.

USER PERCEPTION TOWARDS STOCK PRICE PREDICTION USING LINEAR REGRESSION MODEL

Stock market prediction is a critical area of research with profound implications for investors, financial analysts, and policymakers. This study explores the efficacy of linear regression analysis within the domain of business analytics for forecasting stock market movements. Leveraging historical stock prices and relevant economic indicators, we employ a rigorous methodology to construct and evaluate predictive models using linear regression techniques. Technological advancement increases the study on stock and share market industry. Decision making is enhanced by various statistical and machine learning algorithms. Enormous research work has been concentrated on the feature prediction of stock prices based on historical prices and volume. Performance measures are analyzed in this work with S&P 500 Index using statistical methods in Python environment. Results obtained in this study are superior than the existing methods. The conventional methods for financial market analysis are based on linear regression. This paper focuses on the best independent variables to predict the closing value of the stock market. This study is used to determine specific factors which are providing the most impact on the prediction of the closing price.

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