Stock Price Prediction Research Articles

Fundamental Analysis of Stock Price Changes in Pharmaceutical Subsector Companies Listed on the Indonesia Stock Exchange for the Period 2016 – 2020

The aim of this research is to analyse the impact of Return On Assets (ROA), Return On Equity (ROE), and Earnings Per Share (EPS) on the stock prices of pharmaceutical companies listed on the Indonesia Stock Exchange. This study employs a quantitative approach. The data used are secondary data in the form of company financial reports obtained from the Indonesia Stock Exchange (IDX). The sample selection in this study used purposive sampling, which involves selecting samples based on specific criteria. The data analysis method applied is multiple linear regression analysis. The results of this study indicate that the independent variable ROE positively correlates with stock price prediction, as evidenced by a calculated t-value of 5.721, which is greater than the critical t-value of 2.02619. EPS also positively correlates with stock price prediction, with a calculated t-value of 2.004, which is slightly less than the critical t-value of 2.02619. However, the ROA variable negatively correlates with stock price prediction, with a calculated t-value of -4.273, which is less than the critical t-value of 2.02619. Return On Assets (ROA), Return On Equity (ROE), and Earnings Per Share (EPS) collectively have a significant impact on stock prices. From the regression coefficient test, it is concluded that 56% of the stock price can be explained by the independent variables. This study illustrates that the pharmaceutical subsector companies listed on the Indonesia Stock Exchange rely more on a strong capital structure to improve financial performance, with a focus on increasing stock prices.

Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review

Stocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.

Stock Price Prediction: A Comparative Study of Random Forest and LSTM Models

Stock Price Prediction: A Comparative Study of Random Forest and LSTM Models

Stock price prediction using LSTM neural networks: Techniques and applications

The prediction of stock prices has garnered significant attention due to the potential financial gains and complex questions involved. This paper elaborates a comparison between the Long Short-Term Memory (LSTM) model, optimised using the early-stopping method, and the conventional mathematical method Autoregressive Integrated Moving Average Model(ARIMA), which is conducted using the S&P500 from 2022, May 01 to 2024, May 01. The results indicate that the LSTM surpasses ARIMA. To be more specific, LSTM achieves a 92% reduction in error rates compared to ARIMA. In addition, when the optimised LSTM is implemented in 6 different stocks, the results indicate a negative correlation between the volatility and accuracy of the stocks. This study demonstrates the advantages of optimised LSTM for predicting stock prices, and the importance of market volatility as a crucial aspect that significantly impacts the accuracy of stock price forecast.

Philippines Stock Exchange Prediction Using Hybrid Neural Network

The stock market is dynamic and highly chaotic due to its complicated nature. The prediction of future stock prices has gained the interest of investors and researchers. Numerous conventional and hybrid approaches were put forth. The projections performed poorly and strangely did not get any better. This study aimed to predict Philippine Stock Exchange closing prices using a proposed hybrid method. This proposed study aimed to improve the Artificial Neural Network (ANN) performance using the Kepler Optimization Algorithm, a metaheuristic algorithm, in conjunction with the Artificial Neural Network (ANN-KOA). ANN is a widely accepted technique in predicting. Still, the Kepler Optimization Algorithm can provide random initial inputs for the method through effective feature selection. It can identify superior subsets of input variables to integrate into ANN, enabling more accurate prediction. In this study, the ups and downs were grouped, and 12 technical indicators with varying lengths of days 3, 5, 10, 15, and 20 were used to decompose the original historical data, which are regular and smoother than the original data. The historical data was normalized into [-1, 1], so the predicted result would be 0 (decrease) or 1 (increase) otherwise. Finally, to obtain the train and test data, it was fused into five groups and tested using the ANN and ANN-KOA. The experiment's outcome of 0 (a declining stock price) indicates two alternative courses of action based on the two scenarios: holding if investors have already purchased it or purchasing if they haven't made a decision. In this proposed study, ANN-KOA resulted in higher accuracy than ANN. However, regarding the number of elapsed times, KOA-ANN provided a slower time than ANN. On the other hand, in terms of variations of lengths of the days, 3-5-10-KOA-ANN outperformed the variations of lengths of days: 3-5-10-ANN, 3-5-10-15-KOA-ANN, 3-5-10-15-20-KOA, and 3-5-10-15-20-KOA-ANN. In conclusion, this study suggested that it be utilized for other problems, such as predicting academic performance, diseases, floods, etc.

Stock Price Prediction Using Deep Learning Algorithms Based on Technical Indicators

Stock Price Prediction Using Deep Learning Algorithms Based on Technical Indicators

Using Machine Learning to Predict the Stock Market Trend

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

Effectiveness of AI-based Machine Learning Algorithms in Predicting Global Market Movements

The challenging yet traditional field of stock market forecasting has garnered significant interest from data scientists and economists. Given the high risks associated with trading, where investors can lose part or all of their initial capital, there is a growing need for advanced methods to support investment decisions. By leveraging the capability to predict future stock performance patterns by machine learning or AI, the return on investment for short-term trading can potentially be increased. This research reviews the structures of various AI-based machine learning techniques with a case study of Apple stock data spanning from 2010 to 2021 and the effectiveness of the algorithms in terms of various real-time factors. Among several machine learning algorithms, the SVM algorithm is shown to be the most accurate, with an accuracy rate of 76% for the case study. However, the effectiveness of machine learning systems in predicting stock prices is influenced by several factors. The inherent volatility and unpredictability of financial markets make it challenging for models to anticipate sudden, unforeseen events. Overfitting is a common issue where models that perform well on historical data fail to generalize to new, unseen data because they capture noise instead of genuine market trends. Data quality issues, such as noisy or incomplete data, further complicate predictions. Additionally, the complexity of machine learning models can hinder interpretability, making it easier for analysts to trust and utilize their outcomes. This research underscores the necessity for careful and ongoing refinement of ML techniques in stock price forecasting.

Gaussian process regression model based on stacking algorithm and application to stock price prediction

Gaussian process regression model based on stacking algorithm and application to stock price prediction

Prediction of stock prices of Yuexiu REITs based on GARCH models

Prediction of stock prices of Yuexiu REITs based on GARCH models

PMANet: a time series forecasting model for Chinese stock price prediction

Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model’s efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.

Comparative Analysis of Linear Regression, Decision Tree and Gradient Boosting for Predicting Stock Price of Bank Rakyat Indonesia

An investment is the placement of a current amount of funds in the hope of generating a profit in the future. There are several types of investments, including stocks, which are attractive options as they can bring a huge return to investors. However, rapidly fluctuating stock prices are influenced by various factors, such as company performance, interest rates, economic conditions, and government policies. In Indonesia, PT Bank Rakyat Indonesia Tbk (BBRI) had the largest profit among the 10 largest banks by the end of March 2024, with a profit of IDR 13.8 trillion. The higher the bank's return, the greater the investor's interest in purchasing the stock, influencing the stock price. The goal of stock price prediction is to forecast the stock's future price in order to increase investors' potential profits. Various methods, such as Linear Regression, Decision Tree, and Gradient Boosting, have been developed for stock price prediction. Comparative analysis is needed to determine the most accurate method in predicting the stock price of People's Bank of Indonesia, using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared metrics (R2). The analysis showed that Linear Regression achieved the highest accuracy with R2 of 0.96, MAE of 65.72 and RMSE of 86.74 compared to the Decision Tree and Gradient Boosting models.

Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia

Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the temporal sequences of stock data. The data used in this research are the closing stock prices of Bank Syariah Indonesia (BSI), Bank Tabungan Pensiunan Negara Syariah (BTPN Syariah), and Bank Panin Dubai Syariah (PDSB) from January 2, 2020, to July 4, 2024. Testing these three stocks yielded MAPE values of 2.376%, 2.092%, and 0.629%, respectively. The study results show that the CNN-BiLSTM prediction model produced has very good accuracy in predicting stock prices.

STSB Model Based on STL Decomposition Algorithm and Its Application in Stock Price Prediction Studies

STSB Model Based on STL Decomposition Algorithm and Its Application in Stock Price Prediction Studies

Predicting Stock Prices Using LSTM Networks: A Comprehensive Approach

Abstract: Prediction of stock prices calls for strong algorithmic foundations for predictions of greater magnitude in share prices because the stock market epitomizes volatility. There exist several models used for the prediction of stock prices. The Long ShortTerm Memory algorithm is one model that seems well-suited for such time series problems. The key objective is to best predict current trends in the market and stock prices, which can be done through point prediction, scenario prediction, anomaly prediction, interval prediction, and volatility prediction. The objective of the study is to provide insight to investors and analysts to understand and predict the behavior of the stock market

Prediction of Stock Price Using XG Boost: A Machine Learning Technique

Prediction of Stock Price Using XG Boost: A Machine Learning Technique

Sentiment to Stocks: Rule-Based and Deep Learning Sentiment Analysis for LSTM-Driven Stock Prediction

How to accurately predict stock prices is a persistent problem in the financial realm. According to behavioral economics, human actions in the stock market tend to be irrational, emotional, and easily misled. In such context, this papers approach involves adopting various sentiment analysis models in subsequent sections of the paper, which aims to synthesize a precise methodology for forecasting stock prices aligned with the available data, namely structured technical analysis, while considering and quantifying investor sentiment, namely unstructured fundamental analysis. This work points out that by adopting sentiment analysis using the VADER and FinBERT models separately, there are accuracy improvements in both integration of VADER or FinBERT and transactional data compared with merely doing stock price prediction by forecasting using historical data alone. This outcome resemble an valuable proposition of modeling and prediction on irrational human behavior. Nevertheless, this paper provides insights on future possible enhancements in this area, which analyzing additional emotional dimensions in textual data and recognizing the multifaceted nature of human emotions is needed.

Application of Attention-Based LSTM Hybrid Models for Stock Price Prediction

The stock market plays a pivotal role in the national economy, while the application of artificial intelligence (AI) in stock price prediction has gained traction. This paper evalu-ates the performance of five advanced deep learning (DL) models: Long Short-Term Memory (LSTM), Self-attention, Convolutional Neural Network-LSTM with attention (CNN-LSTM-attention), Gated Recurrent Unit-LSTM with attention (GRU-LSTM-attention), and CNN-Bidirectional LSTM-GRU with attention (CNN-BiLSTM-GRU-attention), utilizing a decade of data on Amazons closing prices. Our results show that the CNN-BiLSTM-GRU-attention model exhibits superior performance, achieving a root mean square error (RMSE) of 1.054589 and a coefficient of determination (R2) of 0.970123, indicative of its proficiency in handling intricate financial data. This papers significance lies in its validation of the effectiveness of attention-based ensemble models in stock market prediction, as well as the introduction of the innovative application of the CNN-BiLSTM-GRU-attention model in financial forecast-ing, which holds potential for wide-ranging applicability.

Stock prediction using Bi-LSTM and RoBERTa sentiment analysis

Stock price forecasting is a widespread research issue in the field of finance. Stock price prediction can give investors more accurate advice and yield higher returns. With the popularity of social media, more and more people choose to express their opinions, and more and more media outlets published news on social media. This paper mainly conducts stock analysis and prediction by presenting the RoBERTa-BiLSTM model with the Bahdanau attention mechanism. It consists of two existing models: the RoBERTa and bidirectional LSTM models. The experimental results show that this model is more accurate in predicting stock prices than the Multi-Layer Perception (MLP) and LSTM models. Inputs to the model include historical AAPL stock price data and sentiment data analysed by news and Twitter tweet sentiment. In this paper, the high-precision RoBERTa-BiLSTM model for stock prediction was proposed, which provides a solution for stock prediction. The practical applications and the typical problem of most studies about stock prediction called the time-shifted problem, are provided in this paper and also have research value.

Predicting Edwards Lifesciences Stock Prices Using Machine Learning Model

Predicting stock prices in the stock market is difficult work because the price can fluctuate up and down throughout the trading day, influenced by so many factors. However, the researchers can also use some advanced algorithms to capture the trajectory of the stock market as much as possible. This paper chooses the adjusted closing price as the stock price and aims to predict it for the next trading day based on opening prices, highest prices, lowest prices, volume, and date. Exploratory data analysis is conducted to explore hidden relationships in our stock data. Then, four machine-based learning models: linear regression, K-nearest, support vector machine, and random forest are applied to make predictions for our outcome: adjusted closing price. By comparing the root mean squared error, the model with the best performance is selected and treated as one of the candidates for future stock price prediction. Although these models are relatively fundamental and may not fully capture the complexities of stock prices, they provide a solid foundation for future work.