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Journal articles on the topic 'Absolute Stock Price'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Jin, Ying. "Absolute price attention and low-priced stock volatility: empirical evidence from China's A-share market." Journal of Applied Economics and Policy Studies 18, no. 4 (2025): 70–75. https://doi.org/10.54254/2977-5701/2025.23450.

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This paper focuses on China's A-share market and empirically examines the relationship between investors' attention to the absolute prices of stocks and the volatility of low-priced stocks. Based on daily trading data of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2021 to June 2024, we employ a fixed-effects panel regression model to investigate this issue. The findings reveal that the lower the stock price, the greater its volatility, indicating the existence of a "low-price, high-volatility" phenomenon. Investors' "nominal price illusion" leads to excessive att

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Prasetyo, Dian Angga, and Rofikoh Rokhim. "Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis." International Journal of Business, Economics, and Social Development 3, no. 2 (2022): 64–70. http://dx.doi.org/10.46336/ijbesd.v3i2.273.

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This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bi

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3

Yu, Menghan, Panji Wang, and Tong Wang. "Application of Hidden Markov Models in Stock Forecasting." Proceedings of Business and Economic Studies 5, no. 6 (2022): 14–21. http://dx.doi.org/10.26689/pbes.v5i6.4453.

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In this paper, we tested our methodology on the stocks of four representative companies: Apple, Comcast Corporation (CMCST), Google, and Qualcomm. We compared their performance to several stocks using the hidden Markov model (HMM) and forecasts using mean absolute percentage error (MAPE). For simplicity, we considered four main features in these stocks: open, close, high, and low prices. When using the HMM for forecasting, the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST, respectively. By calculating the MAPE for the four data sets of

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Islam, Noman, Misbah Afzal, Muhammad Arsal Wali, and Hamza Shakeel. "Data Analysis, Visualization and Prediction of Stock Market Prices of K-Electric." Pakistan Journal of Engineering and Technology 5, no. 2 (2022): 226–33. http://dx.doi.org/10.51846/vol5iss2pp226-233.

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Predicting stock price is a trend yet very challenging task. It is because the stock prices depend upon several internal and external factors. Stock price prediction can be very useful for financial sectors and the government and help in informed decision-making. This paper analyzes the stock market prices of K-Electric Karachi. It is found that the stock prices of K-electric depend on the stock prices of the refinery sector. The paper analyzes the stock price data of the two sectors. Also, the paper compares the stock price prediction based on moving average, auto-regressive integrated moving

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Febiyanti, Dewi, Nonong Amalita, Dony Permana, and Tessy Octavia Mukhti. "Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk." UNP Journal of Statistics and Data Science 1, no. 5 (2023): 441–48. http://dx.doi.org/10.24036/ujsds/vol1-iss5/113.

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Investors often make mistakes when making stock transactions even though having chosen good company stocks. The thing that needs to be considered in making stock transactions is to see the movement of stock prices. The movement of the stock price in PT Bank Rakyat Indonesia Tbk has changed in the form of a decrease or increase. An increasing stock price will provide benefits for investors by selling stocks. But, investors actually decide to make stock purchases. The existence of stock purchase transactions causes investors to take a high risk because stock prices fluctuate. To anticipate the o

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Azizan, Farah Liyana, Nur Fazliana Rahim, and Nur'azra Alia Nisa Zulpakar. "Predicting Market Trends: A Stock Prices Forecasting with Artificial Neural Network." Applied Mathematics and Computational Intelligence (AMCI) 14, no. 1 (2025): 96–119. https://doi.org/10.58915/amci.v14i1.1151.

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Machine learning plays a crucial role in predicting stock prices, as it aids investors in making well-informed decisions amidst the vast array of stocks traded on the stock exchange. The unpredictability of stock price behaviour, influenced by numerous factors, adds complexity to this process. Consequently, numerous studies have explored the use of machine learning for stock price forecasting. However, it is also difficult to predict the behaviour of stock prices due to the uncertainty associated with them. Hence, this study focuses on employing an Artificial Neural Network model as a machine

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Oprasianti, Risky, Dadan Kusnandar, and Wirda Andani. "STOCK PRICE FORECASTING USING THE HYBRID ARIMA-GARCH MODEL." Parameter: Journal of Statistics 4, no. 2 (2024): 110–19. https://doi.org/10.22487/27765660.2024.v4.i2.17162.

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In the current era, many people have made investments, namely capital investment activities within a certain period to seek and get profits. One of the most popular investment instruments in the capital market is stocks, which consist of conventional stocks and Islamic stocks. Conventional stocks are shares traded on the stock market without adhering to Sharia principles. In contrast, Sharia-compliant stocks meet Islamic principles and are traded in the sharia capital market. One form of development of the Islamic capital market in Indonesia is the existence of the Indonesian Sharia Stock Inde

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Bagalkot, Sneha S., Dinesha H. A., and Nagaraj Naik. "A novel technique for selecting financial parameters and technical indicators to predict stock prices." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2192. https://doi.org/10.11591/ijece.v15i2.pp2192-2201.

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Stock price predictions are crucial in financial markets due to their inherent volatility. Investors aim to forecast stock prices to maximize returns, but accurate predictions are challenging due to frequent price fluctuations. Most literature focuses on technical indicators, which rely on historical data. This study integrates both financial parameters and technical indicators to predict stock prices. It involves three main steps: identifying essential financial parameters using recursive feature elimination (RFE), selecting quality stocks with a decision tree (DT), and forecasting stock pric

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Bagalkot, Sneha S., A. Dinesha H., and Nagaraj Naik. "A novel technique for selecting financial parameters and technical indicators to predict stock prices." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2192–201. https://doi.org/10.11591/ijece.v15i2.pp2192-2201.

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Stock price predictions are crucial in financial markets due to their inherent volatility. Investors aim to forecast stock prices to maximize returns, but accu- rate predictions are challenging due to frequent price fluctuations. Most litera- ture focuses on technical indicators, which rely on historical data. This study integrates both financial parameters and technical indicators to predict stock prices. It involves three main steps: identifying essential financial parameters us- ing recursive feature elimination (RFE), selecting quality stocks with a decision tree (DT), and forecasting stoc

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10

Huang, Mingxuan. "Effectiveness Validation of LSTM for Stock Prices Prediction on Four Stocks." Applied and Computational Engineering 8, no. 1 (2023): 596–601. http://dx.doi.org/10.54254/2755-2721/8/20230279.

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Due to the significant volatility and complexity of financial data, stock price prediction is a difficult undertaking. Researchers have also begun using various models to predict stock prices. LSTM (Long short-term memory) are used in this study to predict the stock prices of four companies between 2013 and 2019. The author compares the performance of two loss functions, MSE (Mean squared error) and MAE (Mean absolute error), and evaluate the effectiveness of the proposed approach. The experiments show that LSTM is a promising model for stock price prediction, and the data's long-term dependen

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Ningsih, Rahma Dwi, Sarwido Sarwido, and Gentur Wahyu Nyipto Wibowo. "Comparative Analysis of Linear Regression, Decision Tree and Gradient Boosting for Predicting Stock Price of Bank Rakyat Indonesia." Journal of Dinda : Data Science, Information Technology, and Data Analytics 4, no. 2 (2024): 98–104. http://dx.doi.org/10.20895/dinda.v4i2.1566.

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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 retur

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Marjuni, Aris. "Peramalan Harga Saham Serentak Menggunakan Model Multivariate Singular Spectrum Analysis." JURNAL SISTEM INFORMASI BISNIS 12, no. 1 (2022): 17–25. http://dx.doi.org/10.21456/vol12iss1pp17-25.

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Stock price fluctuations in the stock market are widely influenced by financial environment changes in both micro and macro that are usually unpredictable and can not be controlled by stock players. On the other side, stock price information is very essential and much needed for both buyers and traders. Stock price forecasting is one of the analytical techniques to obtain stock price prediction based on the previous historical stock prices. The open and close prices are important information in stock trading. The opening price can influence the movement towards the closing price, and the closi

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13

Ma, Xiao. "The research on the effect on stock price of executives' selling behavior in Chinese GEM." Advances in Economics, Management and Political Sciences 1, no. 1 (2021): 1–8. http://dx.doi.org/10.54254/aemps.2021001.

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This paper empirically analyzes the impact of executives' reduction on stock price by taking the listed companies on Chinese GEM as research samples. The results show that the behavior of senior executives reducing their holdings will significantly reduce the stock price, and the effect changes in an inverted U-shape with the proportion of stocks reduced. Further research shows that for the companies with higher earnings per share and absolute price, the behavior of executives reducing their stock holdings will do more harm to the stock price.

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Ma, Xiao. "The research on the effect on stock price of executives' selling behavior in Chinese GEM." Advances in Economics, Management and Political Sciences 1, no. 1 (2021): 8–15. https://doi.org/10.54254/2754-1169/1/aemps_001.

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This paper empirically analyzes the impact of executives' reduction on stock price by taking the listed companies on Chinese GEM as research samples. The results show that the behavior of senior executives reducing their holdings will significantly reduce the stock price, and the effect changes in an inverted U-shape with the proportion of stocks reduced. Further research shows that for the companies with higher earnings per share and absolute price, the behavior of executives reducing their stock holdings will do more harm to the stock price.

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15

Nugraha, Edwin Setiawan, and Celine Alvina. "THE APPLICATION OF STANDARD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (SGARCH) MODEL IN FORECASTING THE STOCK PRICE OF BARITO PACIFIC." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 0849–62. http://dx.doi.org/10.30598/barekengvol18iss2pp0849-0862.

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Stock potentially yields higher returns than other investments, but is riskier due to volatile prices. To minimize the risk of loss, investors can forecast the stock price to help in deciding whether to buy, sell, or hold the stock. Several methods are available for forecasting the stock price such as ARIMA, ARCH, and SGARCH. ARIMA model works best for series with a constant variance of error. However, almost all stock price series have a non-constant variance of error, known as heteroscedasticity, as such ARIMA isn’t suited for modeling the stock price. In contrast, the SGARCH model can handl

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16

Wang, Yuchen. "Stock Price Prediction for Technology Company." Advances in Economics, Management and Political Sciences 56, no. 1 (2023): 284–90. http://dx.doi.org/10.54254/2754-1169/56/20231103.

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Individuals aim to develop accurate models for stock prices to make informed decisions as investors, so they can determine opportune moments to purchase and sell stocks for maximizing profits. This paper select Apple stock from yahoo finance range from Aug 1st 2013 to Aug 1st 2023, and then forecasting its future 30 days stock price. This study contain four models, which are XGboost, linear regression, K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM). Those models are all fit the train and test data and then draw a visualization plot. For selecting the best model, this paper use roo

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17

Khan, Usama Waheed, Muhammad Bilal Saeed, and Aleena Nadeem. "Stock Price Prediction Model: Assessing the Performance of a Hybrid Deep Learning Model Employing Multi-Stream Data." NICE Research Journal 17, no. 1 (2024): 40–63. http://dx.doi.org/10.51239/nrjss.v17i1.459.

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Purpose - The study investigates the effectiveness of the ConvLSTM model in the next-day closing price prediction for stocks using a novel combination of input features. These features include past prices, prices of related stocks, technical indicators of the target stock, mutation point impact on closing price, stock market sentiment, stock market index, interest rate, and dollar exchange rate Study Design/Methodology/Approach - Sentiment analysis of financial news related to the Pakistan Stock Exchange (PSX) was performed using the pre-trained FinBERT model. Relevant stocks were identified t

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18

Indrayono, Yohanes. "Improper Uses of Stock Price Variables in Empirical Research: A Review Article." Journal of Business and Management Studies 4, no. 3 (2022): 91–103. http://dx.doi.org/10.32996/jbms.2022.4.3.9.

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This review article shows how empirical studies are often inappropriate in using stock price data to be related to firm financial performance and other relevant variables. The analyses of the articles about stock price as a sample show that there is improper use of data on the stock price. Most of them use prices which are closing prices of annual financial statements, when financial statements information is not known to investors because the financial statements have not been published as of that date. All of the article samples used stock prices in absolute terms that are not relative to th

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19

Chen, Qinqing. "Stock price forecasting using machine-learning methods." Applied and Computational Engineering 52, no. 1 (2024): 208–14. http://dx.doi.org/10.54254/2755-2721/52/20241570.

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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

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Agung, Ignatius Wiseto Prasetyo. "Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (2022): 82–90. http://dx.doi.org/10.31289/jite.v6i1.7158.

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The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network s

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21

Sadewa, Bima, Safwandi Safwandi, and Fajriana Fajriana. "Implementation of Simple Exponential Smoothing and Weighted Moving Average in Predicting Netflix Stock Prices." International Journal of Engineering, Science and Information Technology 5, no. 1 (2025): 264–71. https://doi.org/10.52088/ijesty.v5i1.708.

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This study aims to develop a stock price prediction system for Netflix using the Simple Exponential Smoothing and Weighted Moving Average methods and evaluate the accuracy of both methods. The system provides future stock price estimates based on historical data and includes evaluation metrics such as Mean Absolute Error and Mean Absolute Percentage Error. The implementation results show that SES achieved an MAE of 4.40 and a MAPE of 1.08%, while WMA resulted in an MAE of 8.65 and a MAPE of 2.11%. These findings indicate that SES is more effective in predicting stock prices with lower error ra

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22

Karim, Abdul, and Abdul Rasheed. "Forecasting Modeling of Day of the Week Calendar Anomalies in Pakistan Stock Exchange: An Artificial Intelligence Perspective." Bulletin of Business and Economics (BBE) 13, no. 2 (2024): 436–47. http://dx.doi.org/10.61506/01.00351.

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Stock price forecasting provide valuable insight to the investor to facilitate well-informed investment decision making. The aim of this study is to examine the calendar anomalies i.e. DOW in Pakistan stock exchange though Artificial intelligence techniques. For this purpose, Support vector machine (SVM), Decision Tree (DT) and Artificial Neural Network is used to forecast the daily stock prices. The daily stock prices data of KSE100 index ranges from May,1994 to August 2023 is used as out variable while stock open, close, high and low prices are used as features/input variables. The training

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Renda Sandi Saputra, Moch Panji Agung Saputra, and Muhammad Bintang Eighista Dwiputra. "Indofood CBP Sukses Makmur Tbk Stock Price Prediction Using Long Short-Term Memory (LSTM)." International Journal of Global Operations Research 6, no. 1 (2025): 1–5. https://doi.org/10.47194/ijgor.v6i1.363.

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Fluctuating stock price movements are a challenge in the investment world, so an accurate prediction model is needed to assist decision making. This study aims to evaluate the ability of the LSTM model to predict ICBP stock prices based on historical data and will compare the results of the LSTM model predictions with actual stock price movements to determine the extent to which this model is able to capture trends and patterns of ICBP stock prices. The results show a comparison of the original price and the predicted price indicating that the model can follow market trends, although there are

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Michael, Michael, and Amrizal Amrizal. "ANALISIS PREDIKSI HARGA SAHAM PT GUDANG GARAM TBK INDONESIA." Computer and Science Industrial Engineering (COMASIE) 10, no. 1 (2024): 123–34. https://doi.org/10.33884/comasiejournal.v10i1.8356.

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This research aims to predict PT Gudang Garam Tbk's stock prices using the ARIMA method. Historical daily stock price data were analyzed to detect trends and seasonality. The ARIMA model was developed and tested, demonstrating significant predictive accuracy. Evaluation metrics such as Mean Absolute Error and Root Mean Squared Error were employed for performance assessment. The research contributes to stock price prediction methodologies, offering practical implications for investors and financial analysts. In conclusion, the study establishes a robust foundation for utilizing the ARIMA method

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Li, Zongze. "Comparison of Decision Tree Regression with Linear Regression Based on Prediction of Apple Stock Price." Advances in Economics, Management and Political Sciences 45, no. 1 (2023): 62–69. http://dx.doi.org/10.54254/2754-1169/45/20230259.

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Machine learning has been increasingly used in stock price prediction with outstanding success. Decision tree regression models and linear regression models are both important models for predicting stock prices. The paper use decision tree regression and linear regression models to predict the opening price, closing price, high price and low price of Apple's stock price data respectively. The prediction effects of the two models are evaluated by the indicators of goodness of fit, mean square error, root mean square error and mean absolute error, and the prediction effects of the two models are

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Mian, Tariq Saeed. "Evaluation of Stock Closing Prices using Transformer Learning." Engineering, Technology & Applied Science Research 13, no. 5 (2023): 11635–42. http://dx.doi.org/10.48084/etasr.6017.

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Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine

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Tian, Zhuoqun. "An Optimized Deep Learning Model for Stock Price Prediction Using Bi-Directional LSTM with Multi-Inputs and Multi-Steps." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1079–86. http://dx.doi.org/10.54097/fyg9kh89.

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Predicting stock prices accurately is an inherently challenging task due to the dynamic and fluctuating nature of various influencing factors. However, with the advent and implementation of deep learning, achieving precise stock predictions has become feasible. This study employs Bi-Directional Long Short-Term Memory (LSTM) models to forecast the closing stock price of Tesla for the following day. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are selected as indicators to show methods' performance. A new variable has been created through open and close stock price. Th

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Raissa, Zabrina. "A COMPARATIVE ANALYSIS OF FINANCIAL PERFORMANCE FORECASTING MODELS: ARIMA, ARIMA-GARCH & LSTM IN INDONESIAN BANKING STOCKS." JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi). 12, no. 1 (2025): 328–40. https://doi.org/10.35794/jmbi.v12i1.61515.

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The banking sector is a crucial generator of economic activity and financial stability in Indonesia's stock market, so precise forecasting of bank stock prices is critical for making informed investment decisions. This study evaluates the forecasting performance of ARIMA, ARIMA-GARCH, and Long Short-Term Memory (LSTM) models for predicting daily closing prices of five key Indonesian banking stocks: BBCA.JK, BBNI.JK, BBRI.JK, BMRI.JK, and BNGA.JK. Using historical data from January 2022 to December 2024, the models are evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), a

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Abucay, Guy Alexander, Karl Cristian Almonia, Ruel Dean Buray, and Earl Peter Gangoso. "Stock Price Predictor: Implementing Stocks Predictive Model Using Deep Learning." International Journal of Computing Sciences Research 8 (January 1, 2024): 3147–56. https://doi.org/10.25147/ijcsr.2017.001.1.209.

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Purpose–This paper proposes a novel deep neural network model, specifically long short-term memory (LSTM) networks,for predicting stock prices using historical data and financial indicators. Method–LTSM can handle long sequenceswhile capturing temporal dependencies, making it an excellent choice for NLP or timeseries. The model is trained and tested on the Ayala Corporation (AYALY)stock dataset from 2016 to 2019, using four financial indicators: earnings per share (EPS), EPS growth, price/earnings ratio, and price/earnings-to-growth ratio. Results–The results show that the model achieves high

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Maliha, Fatiya, and Diah Anggeraini Hasri. "Proyeksi Harga Saham Perbankan BUMN Dengan Metode Trend Analysis." Jurnal Ilmiah Raflesia Akuntansi 11, no. 1 (2025): 93–103. https://doi.org/10.53494/jira.v11i1.833.

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The capital market plays a crucial role in economic growth by serving as a platform for long-term financial instruments, including stocks, bonds, and mutual funds. Stock price movements are influenced by various factors, both internal and external, making accurate forecasting essential for investors to minimize risks and optimize returns. This study aims to forecast the stock prices of state-owned banks (BUMN) in Indonesia using the Trend Analysis method, specifically employing linear, quadratic, exponential growth, and S-curve models. The research utilizes historical closing price data from S

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Priyatno, Arif Mudi, Lailatul Syifa Tanjung, Wahyu Febri Ramadhan, Putri Cholidhazia, Putri Zulia Jati, and Fahmi Iqbal Firmananda. "Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price." Jurnal Teknik Industri Terintegrasi 6, no. 3 (2023): 718–32. http://dx.doi.org/10.31004/jutin.v6i3.16933.

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Stock trading is a popular financial instrument worldwide. In Indonesia, the stock market is known as the Indonesia Stock Exchange (BEI), and one actively traded stock is PT Bank Central Asia (BBCA). However, predicting stock price movements is challenging due to various influencing factors. Investors use fundamental and technical analyses for decision-making, but results often vary. Machine learning, particularly random forest regression and linear regression algorithms, can be used for stock price forecasting. In this paper, we compares these two machine learning methods to forecast BBCA sto

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Silfiani, Mega, Farida Nur Hayati, and Muhammad Azka. "Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting." Jurnal Statistika dan Komputasi 2, no. 1 (2023): 12–19. http://dx.doi.org/10.32665/statkom.v2i1.1594.

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Background: Stock price forecasting assists investors to anticipate risks and opportunities in making prudent investments and maximizing returns. Objective: This study aims to identify the most accurate model for stock forecasting. Methods: This paper utilized the daily closing stock price of Unilever Indonesia, Tbk (UNVR) from January 1, 2018 to July 31, 202. Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), was utilized in this study. Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) are used to compare forecasting accuracy. Results: Following c

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Nurdyah, Himda Anataya, Betty Subartini, and Sukono Sukono. "Investment Portfolio Optimization Using the Mean-Variance Model Based on Holt-Winters Stock Price Forecasting of Food Sector in Indonesia." International Journal of Quantitative Research and Modeling 6, no. 2 (2025): 264–74. https://doi.org/10.46336/ijqrm.v6i2.1017.

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The importance of the food sector to Indonesia's economy makes it one of the most attractive sectors to consider in an investment portfolio. An optimal portfolio is the best choice for investors among various efficient portfolios, aiming to maximize returns while minimizing risk. Moreover, since investment is inherently associated with fluctuating stock prices, accurate forecasting is necessary to anticipate future stock movements. This study aims to accurately predict stock prices and construct an optimal portfolio consisting of five food sector stocks listed on the Indonesia Stock Exchange,

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Br. Sinulingga, Wita Oktaviana, Ronsen Purba, and Muhammad Fermi Pasha. "Combination of Regression and ARIMA Methods ( Reg – ARIMA ) Stock Price Prediction Model." Journal of Computer Networks, Architecture and High Performance Computing 7, no. 1 (2025): 329–40. https://doi.org/10.47709/cnahpc.v7i1.5474.

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This research is motivated by the limitations of the ARIMA method, which is only suitable for short-term forecasting and specific periods. Therefore, a combination of Regression and ARIMA methods (Reg- ARIMA) is introduced to predict stock prices over a longer period. The purpose of this study is to implement a combination of Regression and ARIMA methods to build a stock price prediction model. The research methodology involves using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to measure the accuracy of the generated prediction model. The study results indicate sign

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Desman, Hansen Sagala, and Sumirat Erman. "Financial Performance and Stock Valuation of Tobacco Company in Indonesia Stock Exchange (IDX) Amidst the Hike of Excise Tax Rate Period 2017-2021." International Journal of Current Science Research and Review 05, no. 09 (2022): 3581–95. https://doi.org/10.5281/zenodo.7088970.

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<strong>ABSTRACT: </strong>In the tobacco sector, there was a consistent decline in the price of shares outstanding during 2017 - 2021. The consistent decline in stock prices in the market in the tobacco sector initiated the author to analyze the financial performance condition of all companies in the tobacco sector and the stock valuation of companies with the best financial performance compared to other companies. So, it can be concluded that the market price position is now undervalued or still in an overvalued position to be the basis for making investment decisions. In this study, the fin

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Maulana, Dimas Avian, A'yunin Sofro, Danang Ariyanto, Riska Wahyu Romadhonia, Affiati Oktaviarina, and Mohammad Dian Purnama. "STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 1 (2025): 97–106. https://doi.org/10.30598/barekengvol19iss1pp97-106.

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Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between G

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Pengestika, Tasya Natalia, and Ari Christianti. "Valuasi Saham dan Pengambilan Keputusan Investasi: Perbandingan Metode Absolute dan Metode Relative." Jurnal Bisnis dan Manajemen 8, no. 2 (2021): 291–99. http://dx.doi.org/10.26905/jbm.v8i2.6127.

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One of the benefits of stock valuation is that it helps investors in making investment decisions. This study aims to compare the intrinsic value of shares using absolute and relative methods. The absolute method used in this study consists of the Dividend Discounted Model (DDM), Discounted Cash Flow (DCF), and Free Cash Flow to Equity (FCFE) while, the relative method consists of Price Earning Ratio (PER), Price to Book Value ( PBV), and Price to Sales (P/S). This study uses four samples of cement sub-sector companies listed on the Indonesia Stock Exchange in 2015-2019. The results based on th

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Ashar, Kholik Zaenudin, Muhammad Raffi Muttaqin, and Yudhi Raymond Ramadhan. "Linear Regression Method Predicting BMRI Stock Price Using Machine Learning." Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi 12, no. 3 (2023): 1128. https://doi.org/10.35889/jutisi.v12i3.1427.

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<p><em>Investing in stocks carries considerable risk as stock prices fluctuate depending on market conditions and company performance. Therefore, it is necessary to analyze stock price movements so that investors can use the analysis results to make investment decisions. In this study</em>, using the linear regression method, machine learning is used to predict the closing price of Bank Mandiri Tbk (BMRI) shares<em>. The attributes used in this research are Open, High, and Low as inputs and Close labels to determine the prediction value. The data obtained is processed u

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Putri, Vivin Mahat, M. Syafrizal Zain, and Satria Agus Darma. "Prediksi Harga Saham Malindo Feedmill Tbk. (MAIN) Menggunakan Jaringan Saraf Tiruan Long Short-Term Memory (LSTM)." Jurnal Pengembangan Sistem Informasi dan Informatika 6, no. 3 (2025): 79–90. https://doi.org/10.47747/jpsii.v6i3.2789.

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Stock price prediction presents a significant yet intricate challenge in financial forecasting, primarily due to volatile market dynamics and the nonlinear nature of data. This study investigates the efficacy of the Long Short-Term Memory (LSTM) model, a specialized Recurrent Neural Network (RNN), for forecasting the stock price of PT. Malindo Feedmill, Tbk., a publicly listed agribusiness firm on the Indonesia Stock Exchange. A five-year historical dataset of daily stock prices (open, high, low, close, volume) was utilized. Pre-processing involved data normalization, the application of a slid

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Yan, Chenguang. "Stock Price Prediction of Walmart Based on Combination of SVM and LS-SVM Models." BCP Business & Management 38 (March 2, 2023): 363–71. http://dx.doi.org/10.54691/bcpbm.v38i.3716.

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One of the most significant operations in the finance sector is stock trading. The stock market is an essential part in the economy of a country and serves as the indicators of the situation of a country’s economy as the stock prices go up or down. Therefore, stock price prediction, the behavior of attempting to predict the potential worth of a corporation or any financial instruments successfully, will maximize investor’s gain, enhance market’s confidence, and help government policymakers to make economic decisions. In order to forecast the price of a stock, a machine learning approach is con

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Andy Hermawan, Angga Sukma Budi Darmawan, Muhammad Iqbal, Mochammad Rivan Akhsa, Nila Rusiardi Jayanti, and Zidan Amukti Rajendra. "TOWR Stock Forecasting From 2021-2025 Using Machine Learning." Jurnal Penelitian Teknologi Informasi dan Sains 3, no. 1 (2025): 92–101. https://doi.org/10.54066/jptis.v3i1.3136.

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Accurate stock price forecasting is a crucial yet challenging task due to the complex and dynamic nature of financial markets. This study employs the Prophet model to predict the stock prices of PT Sarana Menara Nusantara Tbk (TOWR) from 2021 to 2025. The research leverages historical stock data, incorporating dividend distribution dates and Annual General Meeting (AGM) events as external regressors to enhance predictive accuracy. The model was developed using machine learning-based time series forecasting, with hyperparameter tuning applied to optimize performance. The evaluation metrics indi

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Mahtab, Md Tanvir, A. G. M. Zaman, Montasir Rahman Mahin, Mohammad Nazim Mia, and Md. Tanjirul Islam. "Stock Price Prediction: An Incremental Learning Approach Model of Multiple Regression." AIUB Journal of Science and Engineering (AJSE) 21, no. 3 (2022): 159–66. http://dx.doi.org/10.53799/ajse.v21i3.490.

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The endeavour of predicting stock prices using different mathematical and technological methods and tools is not new. But the recent advancements and curiosity regarding big data and machine learning have added a new dimension to it. In this research study, we investigated the feasibility and performance of the multiple regression method in the prediction of stock prices. Here, multiple regression was used on the basis of the incremental machine learning setting. The study conducted an experiment to predict the closing price of stocks of six different organizations enlisted in the Dhaka Stock

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Zhou, Yuyang. "Analysis of Stock Prediction M+odel Based on LSTM." Advances in Economics, Management and Political Sciences 152, no. 1 (2025): 42–47. https://doi.org/10.54254/2754-1169/2024.19430.

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As the complexity and volatility of the stock market continue to increase, investors are placing greater emphasis on the need for accurate stock price predictions. Traditional statistical models often face limitations in time series forecasting, particularly when it comes to capturing the intricate and dynamic changes in stock prices. This paper explores the application of the LSTM model for stock price prediction by developing a forecasting model based on LSTM. First, relevant stock data is collected through a web crawler, and then an LSTM model is trained using this data to predict the price

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Hamdani, Aldan Maulana, Fery Widhiatmoko, and Sa'adatul Fitri. "Perbandingan Akurasi Metode Autoregressive Integrated Moving Average dan Geometric Brownian Motion untuk Peramalan Harga Saham Indonesia." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 13, no. 1 (2025): 14–20. https://doi.org/10.37905/euler.v13i1.30760.

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Investment is an activity of managing sources of funds with the goal of increasing profits within a certain period of time. The number of investors in the capital market, especially stock investments continue to increase. Stock movements result in returns that investors can obtain. Randomly fluctuating share prices make it difficult for investors to forecast share prices. This research helps investors in forecasting stock price movements based on PT. Gudang Garam Tbk. (GGRM) for the period 2022. This research aims to determine the level accuracy of the Geometric Brownian Motion (GBM) and Autor

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Nie, Junrong. "Research on Stock Price Prediction Model Based on LSTM." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 486–92. http://dx.doi.org/10.54097/56k4q130.

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Stock prices are a non-linear, long-term series of data. The research topic of this paper is to use the Long Short-Term Memory Network (LSTM) model to analyze the historical stock price data of BYD (BYD) to predict the future stock price trend, and to provide intelligent reference for investors by evaluating the model performance, aiming to improve the accuracy of stock price prediction. The accuracy and consistency of stock price prediction can be increased by using this method, which successfully takes advantage of the time characteristics in historical data. After explaining the idea and ar

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Queen, Umbaran, Noveria Ana, and Maya Damayanti Sylviana. "Stock Valuation Analysis of Technology Sector Companies Listed in Indonesia Stock Exchange in the Year 2018-2022." International Journal of Current Science Research and Review 07, no. 08 (2024): 6227–41. https://doi.org/10.5281/zenodo.13285946.

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Abstract : In the year 2018, Indonesia’s equity market experienced an IPO boom where there is a significant increase in the number of companies who went through initial public offering (IPO). In the next years, Indonesia IPO market continue to grow with no less than 50 companies going public each year, rendering Indonesia as the biggest IPO market in Southeast Asia since 2018. Indonesian IPO within the year 2018-2022 is dominated by four sectors, consumer, properties & real estate, basic materials, and technology. Though only the fourth largest IPO sector, the sector grew significant

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Seru, Feby, Miftachul Jannah, and Tiku Tandiangnga. "Implementation of Jump Diffusion to Predict Stock Prices and Risk Analysis Using Value At Risk and Expected Shortfall (Case Study: PT. Indofood Sukses Makmur Tbk)." Jurnal Matematika, Statistika dan Komputasi 20, no. 3 (2024): 680–92. http://dx.doi.org/10.20956/j.v20i3.33261.

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Stock prices often fluctuate; therefore, a model is needed to predict the stock price. One of the models that can be used to predict stock prices when experiencing a jump is Jump Diffusion. In addition to predicting, investment is inseparable from the risks that may be borne, so it is also necessary to measure risk. This study aims to implement the Jump Diffusion Model in predicting the stock price of PT Indofood Sukses Makmur Tbk and conduct a risk analysis of the prediction results using Value at Risk (VaR) and Expected Shortfall (ES). In this study, a model was obtained that was used to pre

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Mukhanov, Samat, Saltanat Amirgaliyeva, Daryn Amrin, Syrym Zhakypbekov, and Beibut Amirgaliyev. "COMPARISON OF SINGLE AND HYBRID NEURAL NETWORK MODELS FOR STOCK PRICE FORECASTING." Вестник КазАТК 136, no. 1 (2025): 402–18. https://doi.org/10.52167/1609-1817-2025-136-1-402-418.

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Forecasting stock prices has always been a complex and challenging task due to its high volatility, noise in data, and abrupt price fluctuations caused by different factors. Traditional approaches, such as quantitative, fundamental, and technical analysis, often struggle to accurately predict stock price movements due to their difficulty in processing non-linear data. The evolution of machine learning, particularly neural network models, opened new opportunities for improving prediction accuracy by handling non-linear and multidimensional data with high effectiveness. This study focuses on eva

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Priyatno, Arif Mudi, Wahyu Febri Ramadhan Sudirman, and Raja Joko Musridho. "Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1906. http://dx.doi.org/10.11591/ijece.v14i2.pp1906-1915.

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination

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Priyatno, Arif Mudi, Wahyu Febri Ramadhan Sudirman, and Raja Joko Musridho. "Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1906–15. https://doi.org/10.11591/ijece.v14i2.pp1906-1915.

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination

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