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Zeitschriftenartikel zum Thema „Stock Forecasting“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 26. Juli 2025

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1

Phatangare, Sheetal, Abbas Taherbhai Madhvaswala, Kashish Rahate, Mohammed Nogamawala, and Mohamed Maged Mohamed Ahmed. "Stock Market Forecasting." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 556–60. http://dx.doi.org/10.22214/ijraset.2023.51550.

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Abstract: Stock market forecasting seeks to determine the worth of a firm’s financial stocks in the future. Machine learning is being used in recent developments in stock market forecasting technology to produce forecasts based on the values of current stock market indices by training on their previous values. Future stock price projections can be difficult to make when trying to anticipate the stock market. It is incredibly challenging to forecast the stock market since shares fluctuate so frequently. Every day and frequently, stock. Foreseeing trends in the stock market is often correct usin

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Vaghela, Vimalkumar B., Tushar A. Champaneria, and Hitesh D. Rajput. "Stock Price Forecasting using the Machine Learning Based on the Historical Stock Prices." International Journal of Membrane Science and Technology 10, no. 1 (2023): 1902–10. http://dx.doi.org/10.15379/ijmst.v10i1.3670.

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Forecasting stock price is an imperative financial subject matter that has involved researchers’ attention for numerous years. The successful stock price forecasting helps to profit the company or individual otherwise it makes the problem of loss. Stock forecasting involves an assumption that basic information publicly accessible in the past has some predictive relationships to the future stock returns. This proposed research work tries to help the company and investors in the stock market to come to a decision for better timing for buying or selling stocks based on the information extracted f

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Reddy, Yajanth Rami. "A Tensorized Hierarchical Graph Attention Network for Stock Market Forecasting." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 590–94. http://dx.doi.org/10.22214/ijraset.2023.56043.

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Abstract: Stock market forecasting is a tough mission because of its complex and dynamic nature. Deep learning models have recently been proven to be successful at stock market predictions. Traditional deep learning models, however, frequently disregard the hierarchical structure and temporal relationships of the stock market. Here, we introduce the StockTensor, a brand-new tensorized hierarchical graph attention network for stock market forecasting. StockTensor models the hierarchical structure of the stock market data by constructing a hierarchical graph of stocks. At each level of the hiera

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Yang, Zhenhao, and Zhiyang Wang. "The Research of NVIDIA Stock Price Prediction Based on LSTM And ARIMA Model." Highlights in Business, Economics and Management 24 (January 22, 2024): 896–902. http://dx.doi.org/10.54097/dndygw34.

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The paper explores stock forecasting methods using NVIDIA as the research object. It contrasts how well LSTM and ARIMA models forecast NVIDIA's stock return. According to the study, LSTM surpasses ARIMA in terms of prediction accuracy. However, both models capture the overall trend of the stock. The results suggest that LSTM is better suited for forecasting stock movements due to its ability to handle time series data. The non-stationary nature of the stock market adds complexity to predictions. The significance of stock forecasting is that informed investment decisions can be made to maximise

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Aeni, Kurnia. "Penerapan Model Indeks Tunggal dalam Menganalisis Portofolio Saham Optimal dan Peramalan Harga Saham Optimal Menggunakan Long Short-Term Memory." Journal of Comprehensive Science (JCS) 3, no. 11 (2024): 4989–5010. https://doi.org/10.59188/jcs.v3i11.2706.

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Investors in choosing stocks are not only on one type of stock, but can diversify. In this study, Blue Chip stocks were used which was carried out in two stages. The first stage is the Single Index Model to obtain an optimal stock portfolio. The second stage, the optimal share price results from the Single Index Model, namely Bank Mandiri (Persero) Tbk (BMRI), is followed by a forecasting process using Long Short-Term Memory (LSTM). The purpose of this study is to apply the Single Index Model to analyze the portfolio on Blue Chip stocks, forecast the selected optimal stock price in the next da

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Rawlin, Rajveer S., and Satya Surya Narayana Raju Pakalapati. "Forecasting Stock Prices of Select Indian Private Sector Banks – A Time Series Approach." SDMIMD Journal of Management 13, no. 1 (2022): 35. http://dx.doi.org/10.18311/sdmimd/2022/29270.

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<p>Forecasting stock markets and individual stocks has been a well-researched area in the world of finance. Fundamental and technical analysis is widely used by investors in analysing stock prices. Researchers have used various methods to predict stock prices such as Hidden Markov models, genetic algorithms and neural networks (Enke, Grauer, and Mehdiyev, 2011; Hassan, Nath, and Kirley 2007). Time series analysis is used in forecasting asset prices (Long et al, 2021; Eita, 2012). Indian private sector banks are among the best-performing stocks on the Indian stock exchanges over the last

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RMCDK, Rajasinghe, Weerapperuma WDNM, Wijesinghe WUNN, Rathnayake KKKP, and Seneviratne L. "Forecasting Stock Prices Using a Hybrid Approach." European Journal of Advances in Engineering and Technology 5, no. 3 (2018): 162–69. https://doi.org/10.5281/zenodo.10702405.

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<strong>ABSTRACT </strong> Stock Market provides the basis for transactions between large business organizations and individual investors. Companies issue stocks to general public to raise funds while investors buy the stocks to gain profits. The Random Walk Hypothesis governs the Stock Prices as it changes constantly due to various factors. The research, Forecasting Stock Prices Using a Hybrid Methodology is carried out to implement a decision support system that provides insight for selecting profitable stocks using multiple forecasting mechanisms.

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Susanti, Dwi, Kirana Fara Labitta, and Sukono Sukono. "Forecasting Indonesian Stock Index Using ARMA-GARCH Model." Operations Research: International Conference Series 5, no. 3 (2024): 99–104. http://dx.doi.org/10.47194/orics.v5i3.328.

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The stock market is an institution that provides a facility for buying and selling stocks. Covid-19 is an issue that has affected the stock markets of many countries, including Indonesia. Due to the pandemic, the condition of stocks before and during Covid-19 is certainly different. Stocks can be measured using stock indices. To predict future stock conditions, it is necessary to forecast the stock index. Therefore, this research aims to forecast the Indonesian stock index before and during Covid-19 using the ARMA-GARCH time series model. The results show that the best forecasting model for be

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Harel, Arie, and Giora Harpaz. "Forecasting stock prices." International Review of Economics & Finance 73 (May 2021): 249–56. http://dx.doi.org/10.1016/j.iref.2020.12.033.

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Jones, Charles P., and Leonard L. Lundstrum. "Forecasting Stock Returns." Journal of Wealth Management 9, no. 1 (2006): 31–36. http://dx.doi.org/10.3905/jwm.2006.628678.

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Jagtap, Ajitkumar, Yash Patil, and Darshan Oswal. "Visualizing and Forecasting Stocks Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2562–66. http://dx.doi.org/10.22214/ijraset.2022.41846.

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Abstract: India's stock market is exceedingly changing and reductionism, which has a countless number of features that control the directions and trends of the stock price; therefore, prediction of uptrend and downtrend is a complex process. This paper point of view to demonstrate the use of recurrent neural network in finance to prediction of the closing price of a selected stock and analyse opinions around it in real-time. By combining both techniques, the submitted model can give buy or sell recommendation. In Stock Market Prediction, the aim is to predict the upcoming future value of the f

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Sukartini, Mery, and Abdul Moin. "The Implementation of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model on the Index Forecasting of Sharia Stocks in Asian Countries." International Journal of Economics, Business and Management Research 06, no. 06 (2022): 138–56. http://dx.doi.org/10.51505/ijebmr.2022.6611.

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The study of forecasting volatility of stocks has been discussed and investigated among scholars. Volatility plays important role in determining stock value as well as portfolio in stock market. This study investigates the use of GARCH model (generalized autoregressive conditional heteroskedasticity) in forecasting Islamic index stock in Asian countries. This study employs data from yahoo. finance including six countries namely India, Singapore, Japan, China, Malaysia, and Indonesia. There are 1304 data observation of daily closing price for the period between January 2016 and December 2020. T

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Macharia, Kevin. "Enhancing Stock Market Forecasting with ARIMA and Artificial Neural Networks." Africa Journal of Technical and Vocational Education and Training 10, no. 1 (2025): 129–37. https://doi.org/10.69641/afritvet.2025.101186.

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Forecasting the stock market has been a topic of great interest with researchers exploring different methods to predict the future price action of stock prices. There are well-established statistical methods of forecasting that can be applied to the stock prices to try and predict the future prices of listed stocks with the highest accuracy. This study sought to predict the price action in the stock market using two forecasting methods and compare the results to determine which of the two has the highest forecasting accuracy. Specifically, the study investigated the forecasting accuracy of Art

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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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Chih-Ming, Hsu. "Forecasting Stock Prices by Applying the Whale Optimization Algorithm and Genetic Programming." Journal of Scientific and Engineering Research 9, no. 5 (2022): 33–47. https://doi.org/10.5281/zenodo.10519717.

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<strong>Abstract </strong>It is a critical task to accurately forecast stock prices in the future for an investor to make more money in the dynamic stock market. However, there are various factors, such as politics, business trade cycle, government financial policy, variation of an exchange rate, inflation, as well as business operation of a corporation, etc., that can affect the stock prices issued by a corporation, thus making the problems of forecasting the stock prices very complicated and difficult to resolve. Hence, the problems regarding forecasting stock prices always can attract the g

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Dwi Miranda, Anggel, Siska Yosmar, and Septri Damayanti. "APPLICATION OF FUZZY TIME SERIES WITH FIBONACCI RETRACEMENT FOR FORECASTING STOCK PRICE PT. BANK RAKYAT INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 2 (2023): 0787–96. http://dx.doi.org/10.30598/barekengvol17iss2pp0787-0796.

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Stock can be defined as securities that indicate the ownership of a person or legal entity to the company issuing the shares. Good stocks for long-term investment are stocks that have good fundamentals and large market capitalization. The purpose of investing is to make a profit. In investing in stocks, investors need to know the risk management that can affect the ups and downs of a stock. Forecasting or forecasting is an analysis to predict everything related to the production, supply, demand, and use of technology in an industry or business. One of the forecasting methods is using fuzzy tim

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Gurav, U. P., and S. Kotrappa. "Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model." Engineering, Technology & Applied Science Research 10, no. 5 (2020): 6356–61. http://dx.doi.org/10.48084/etasr.3805.

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Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by differe

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Tu, Shiying, Jiehu Huang, Huailong Mu, Juan Lu, and Ying Li. "Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast." Mathematics 12, no. 8 (2024): 1187. http://dx.doi.org/10.3390/math12081187.

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Stock market performance is one key indicator of the economic condition of a country, and stock price forecasting is important for investments and financial risk management. However, the inherent nonlinearity and complexity in stock price movements imply that simple conventional modeling techniques are not adequate for stock price forecasting. In this paper, we present a hybrid model (ARIMA + GPRC) which combines the autoregressive integrated moving average (ARIMA) model and Gaussian process regression (GPR) with a combined covariance function (GPRC). The proposed hybrid model can account for

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Gurav, U. P., and S. Kotrappa. "Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model." Engineering, Technology & Applied Science Research 10, no. 5 (2020): 6356–161. https://doi.org/10.48084/etasr.3805.

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Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by differe

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Qur'ani, Anggun Yuliarum, and Chandra Sari Widyaningrum. "The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action." Mikailalsys Journal of Mathematics and Statistics 2, no. 1 (2024): 8–26. http://dx.doi.org/10.58578/mjms.v2i1.2673.

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The non-seasonal Holt-Winters method is one of the methods of smoothing theory. This method can be implemented on time series data that does not have a seasonal component. In this study, this method is used to forecast the stock price returns of companies affected by the Boycott, Divestment, and Sanctions (BDS) action. Forecasting gets very good results that can be seen from the MAPE value of modeling the six stocks affiliated with Israel that continue to carry out Zionism against Palestine is not more than 10%. This method can also accommodate the limitations of existing data while still obta

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.Vipinkumar M, Dr. "TITLE: FORECASTING OF STOCK RETURN WITH RESPECT TO THE INDICES OF NSE, BANK AND IT." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03273.

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ABSTRACT: This research paper focuses on the forecasting of stock returns with respect to the indices of NSE (National Stock Exchange), BANK, and IT sectors. The study aims to employ forecasting techniques, specifically using Autoregressive Integrated Moving Average (ARIMA) models, to predict the future returns of stocks listed in these sectors. By analyzing historical data over a specific period, which spans One year (01.04.2022 to 31.03.2023) in this study, the research seeks to provide insights into the potential movements and trends in stock returns for NSE, BANK, and IT sectors. The findi

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Li, Yung-Chen, Hsiao-Yun Huang, Nan-Ping Yang, and Yi-Hung Kung. "Stock Market Forecasting Based on Spatiotemporal Deep Learning." Entropy 25, no. 9 (2023): 1326. http://dx.doi.org/10.3390/e25091326.

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This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Short Term Memory (LSTM) and recent Transformer models lack the ability to directly incorporate spatial information, making the Spacetimeformer model a valuable addition to stock price prediction. This article uses the ten minute stock prices of the constituent stocks of the Taiwan 50 Index and the intraday data of individual stock on the Taiwan Stock Ex

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Du, Ninghui. "Research on Apple’s Stock Price Trend Forecasting." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 46–55. http://dx.doi.org/10.54097/nt9m0k49.

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As the world's largest company by market capitalization, Apple has attracted the attention of many investors. Many investors have developed a strong interest in Apple stocks. However, it is not easy to study the trend of Apple stocks. Because there are many factors affecting Apple's stock price changes, it is very complicated and difficult to review the details of all these factors. Predicting and analyzing stock prices can provide investors with practical tools to raise funds and reduce investment risks. This paper starts with the time series research method of stock prices, which has proved

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Dongmei, Lee, Liu Weiqi, and Tian Yuxin. "Volatility, Firm Size and Economic Growth: Evidence from Chinese Stock Market." International Journal of Business Management and Technology 2, no. 3 (2023): 64–73. https://doi.org/10.5281/zenodo.7648166.

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Forecasting real economic growth by using the information contents of financial asset prices is one of the main themes in financial studies in recent years. Based on the micro-level stock data from Shenzhen Stock Exchange Market, the paper constructs a cross-section volatility measure using sample stocks, investigates the impact of stock price volatility on economic growth, and forecasts economic growth with stock prices volatility of different firm size. The empirical results indicate that stock price volatility is a good indicator for forecasting economic growth. The results also show that v

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Dong, Can. "Stock Trend Forecasting Using the ARIMA Model." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 56–62. http://dx.doi.org/10.54097/hset.v16i.2239.

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Stocks have always been a very important tool in the investment market. Nowadays, the stock market attracts a large number of investors as more and more people are exposed to investing their money. One of the most attractive features of equities for investors is the high returns, however, the high risks are also affecting investors’ confidence. Therefore, predicting the long-term performance of the stock market can lower the risk and give investors more ideas on how to invest and help them understand the future trend of their preferred stocks, thus reducing their risk rate. In this paper, the

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Lin, Ying-Lei, Chi-Ju Lai, and Ping-Feng Pai. "Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis." Electronics 11, no. 21 (2022): 3513. http://dx.doi.org/10.3390/electronics11213513.

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Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual sentiment analysis by translating non-native English-speaking countries’ texts into English. This study used unstructured data from social media and structured data, including trading data and technical indicators, to forecast stock markets. Deep learning techniques and machine lea

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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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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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Hartanto, Anggit Dwi, Yanuar Nur Kholik, and Yoga Pristyanto. "Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2270. http://dx.doi.org/10.62527/joiv.7.4.1740.

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In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM)

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Hartanto, Anggit Dwi, Yanuar Nur Kholik, and Yoga Pristyanto. "Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model." JOIV : International Journal on Informatics Visualization 7, no. 4 (2023): 2270. http://dx.doi.org/10.30630/joiv.7.4.01740.

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In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM)

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Patidar, Jaydeep. "Web Trade Analytics." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01704.

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This research introduces an innovative web application developed using the MERN (MongoDB, Express.js, React, Node.js) stack, enhanced with fundamental machine learning algorithms, designed to address the complexities of stock market analysis. The central focus is on creating a user-customizable dashboard, allowing investors to select specific stocks for real-time analysis, sentiment tracking, and future price prediction. The methodology integrates historical stock data with sentiment analysis sourced from news and social media. The machine learning algorithms leverage this data to generate buy

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Zhao, Danxuan. "Forecasting Stock Prices with Artificial Intelligence." ITM Web of Conferences 70 (2025): 02023. https://doi.org/10.1051/itmconf/20257002023.

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The purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). To conduct the study, Wal-Mart stock data is utilized to accurately predict future stock prices. The results show that the MSE for the SimpleRNN test is higher, indicating weaker generalization. The MSE of th

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Jung, Chulho, and Roy Boyd. "Forecasting UK stock prices." Applied Financial Economics 6, no. 3 (1996): 279–86. http://dx.doi.org/10.1080/096031096334303.

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McMillan, David G. "Forecasting U.S. stock returns." European Journal of Finance 27, no. 1-2 (2020): 86–109. http://dx.doi.org/10.1080/1351847x.2020.1719175.

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Bramante, Riccardo, and Santamaria Luigi. "Forecasting stock index volatility." Applied Stochastic Models in Business and Industry 17, no. 1 (2001): 19–26. http://dx.doi.org/10.1002/asmb.423.

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Kulkarni, Aseema, and Ajit More. "Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors." Oriental journal of computer science and technology 9, no. 3 (2016): 212–25. http://dx.doi.org/10.13005/ojcst/09.03.07.

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Prediction of stock prices using various computer programs is on rise. Popularly known in the field of finance as algorithmic trading, a radical transformation has taken place in the field of stock markets for decision making through automated decision making agents. Machine learning techniques can be applied for predicting stock prices. This paper attempts to study the various stock market forecasting processes available in the forecasting plugin of the WEKA tool. Twenty experiments have been conducted on twenty different stocks to analyse the prediction capacity of the tool.

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Zhang, Fengyi, Zhigao Liao, and Hongping Hu. "Application of Multi-Input Hamacher-ANFIS Ensemble Model on Stock Price Forecast." Advances in Data Science and Adaptive Analysis 11, no. 01n02 (2019): 1950004. http://dx.doi.org/10.1142/s2424922x19500049.

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The stock market is a complex, evolving, and nonlinear dynamic system. Forecasting stock prices has been regarded as one of the most challenging applications of modern time series forecasting. This paper proposes a novel multi-input Hamacher-ANFIS (adaptive network-based fuzzy inference system based on Hamacher operator) ensemble model to forecast stock prices in China’s stock market and achieve good prediction performance. We selected five stocks with the largest total market capitalization from the Shanghai and Shenzhen Stock Exchanges, measured their historical volatility over the same time

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Rathnayaka, R. M. Kapila Tharanga, D. M. K. N. Seneviratna, and Wei Jianguo. "Grey system based novel approach for stock market forecasting." Grey Systems: Theory and Application 5, no. 2 (2015): 178–93. http://dx.doi.org/10.1108/gs-04-2015-0014.

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Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been foc

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Ugomma, Chukwudi Anderson. "Evaluation of Stock Price Volatility of MTN and Airtel in Nigeria Stock Markets." European Journal of Theoretical and Applied Sciences 2, no. 2 (2024): 692–701. http://dx.doi.org/10.59324/ejtas.2024.2(2).60.

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This study seeks to model and forecast the stock price market volatility in Nigeria Stock Market. The study covered ten years (2012-2022) of two selected telecommunication companies from enlisted in the Nigeria Stock Exchange and the data was obtained from www.ng.investing.com. From the time series plot it was evidenced that none of the series showed stationarity and differencing was therefore employed as to achieve stationarity. Various ARCH and GARCH models were fitted to the two series and GARCH (1,1) was selected to fit the two series since it has minimum unconditional variance for the two

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G, Kaveri, L. Tejashwini, and Manjunath K. "Role of Sentiment in Stock Forecasting." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1649–53. https://doi.org/10.22214/ijraset.2025.66656.

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Abstract: The Stocks uses the sentiment analysis in predicting stock market movements by analyzing data from sources like news, social media, and financial reports. Employing a Random Forest machine learning model, the system aggregates sentiment indicators, such as positive and negative emotions, to forecast stock trends and market fluctuations. The results show that sentiment analysis improves prediction accuracy, reduces investment risks, and supports data-driven decision-making. By integrating real-time data, the model adapts to changing market conditions and provides timely forecasts. Thi

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Li, Ying, Xiaosha Xue, Zhipeng Liu, Peibo Duan, and Bin Zhang. "Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction." Information 15, no. 12 (2024): 743. http://dx.doi.org/10.3390/info15120743.

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Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships

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Abraham, Rebecca, Mahmoud El Samad, Amer M. Bakhach, et al. "Forecasting a Stock Trend Using Genetic Algorithm and Random Forest." Journal of Risk and Financial Management 15, no. 5 (2022): 188. http://dx.doi.org/10.3390/jrfm15050188.

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This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Fore

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Hou, Junhao. "Stock Index Prediction Based on ARIMA Model." Advances in Economics, Management and Political Sciences 14, no. 1 (2023): 319–28. http://dx.doi.org/10.54254/2754-1169/14/20230846.

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Stock index price forecasting is a very important thing for financial markets. Stock indices select the most representative stocks in the stock market, which are the most favorable representatives of the industry, sector or market. Successful prediction of them can guide investors to a good payoff and allow researchers to understand the workings of the market economy. Time series models are widely used in stock price forecasting precisely because they have the advantage of being able to predict in complex environments such as large shocks. Therefore, this paper introduces the process of using

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Gultom, Edra Arkananta, Kartika Dewi Sri Susilowati, and Anik Kusmintarti. "Design of a Stock Forecasting Dashboard using Python-Streamlit and FB Prophet with AI." Formosa Journal of Science and Technology 3, no. 11 (2024): 2445–64. https://doi.org/10.55927/fjst.v3i11.12216.

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This research aims to develop a stock price forecasting application using time series analysis with the Prophet model. The application retrieves historical stock data from Yahoo Finance (2015–present) for Indonesian stocks, which is then processed and analyzed to predict future prices. The study integrates yfinance for data collection, Prophet for forecasting, and Plotly for visualizing the results. The application allows users to select stocks and customize prediction periods (1–4 years). The findings indicate that while the model provides useful short-term predictions, its accuracy is limite

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Jiang, Aiping, Junjun Gao, Qiuguo Chi, and Sixian Zheng. "The Effects of the Correlation of Electric Materials on Forecasting and Stock Control." European Scientific Journal, ESJ 12, no. 22 (2016): 107. http://dx.doi.org/10.19044/esj.2016.v12n22p107.

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Forecasting and stock control play an important role in the electric companies because outstanding forecasting and stock control increase service level obviously and decrease stock cost effectively. However, the majority of the electric materials are intermittent demand, resulting in poor forecasting and stock control performance. Therefore, exploring the reasons that affect forecasting performance and stock control is necessary. This paper explores the effects of the correlation of intermittent electric materials on forecasting and stock control. First, we divide the correlation into three ca

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Sekreter, Ahmet, and Giorgi Bagaturia. "Forecasting Stock Index Movement Direction by Using Individual Forecasting Methods and Combining Forecasting Method." Journal of Business 2, no. 2 (2014): 19–22. http://dx.doi.org/10.31578/job.v2i2.58.

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A better forecasting accuracy helps the investors and managers to provide relevant and reliable information about present andfuture events in stock market. Important decisions can be given confidently by managers if forecasting provides informationabout the potential future events and their consequences for possible stock market movements. A better forecasting methodreduces the level of uncertainty for the investors and managers. The index movement direction is forecasted by using eighttime series forecasting and naïve model and combination of these models which are more than five hundred. The

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Chukwudi, Anderson Ugomma. "Evaluation of Stock Price Volatility of MTN and Airtel in Nigeria Stock Markets." European Journal of Theoretical and Applied Sciences 2, no. 2 (2024): 692–701. https://doi.org/10.59324/ejtas.2024.2(2).60.

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Annotation:

This study seeks to model and forecast the stock price market volatility in Nigeria Stock Market. The study covered ten years (2012-2022) of two selected telecommunication companies from enlisted in the Nigeria Stock Exchange and the data was obtained from www.ng.investing.com. From the time series plot it was evidenced that none of the series showed stationarity and differencing was therefore employed as to achieve stationarity. Various ARCH and GARCH models were fitted to the two series and GARCH (1,1) was selected to fit the two series since it has minimum unconditional variance for th

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Tian, Weiyi. "OMX20 and Shanghai Composite Index Forecasts Based on ARIMA and ETS Models." Advances in Economics, Management and Political Sciences 144, no. 1 (2025): 158–66. https://doi.org/10.54254/2754-1169/2024.ga19103.

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The stock index is a comprehensive reflection of the overall performance of a range of company stocks within a country. These companies are often an important part of the national economy, so changes in the index can directly reflect the rise and fall of the national economy. Things like increased consumption and investment, increased corporate profits, etc. will increase stock prices, and the index will rise eventually. Falling demand and rising costs will lead to a decline in corporate profits, resulting in a decline in stock prices and weak index performance. Stock index forecasting is a ve

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FAUZI, AHMAD. "FORECASTING SAHAM SYARIAH DENGAN MENGGUNAKAN LSTM." Al-Masraf : Jurnal Lembaga Keuangan dan Perbankan 4, no. 1 (2019): 65. http://dx.doi.org/10.15548/al-masraf.v4i1.235.

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Islamic stocks as one of the many stocks listed on the JCI are a barometer of the Islamic market in Indonesia. One approach in predicting stock prices is by using machine learning. The purpose of this study is to make a model that is used to predict JII shares using the LSTM approach. The data used amounted to 1402 records related to the Jakarta Islamic Index (JII) stock from March 4, 2014 - January 2, 2019. Model making uses 3 Epochs (1, 10 and 20). The results showed the best model was to use 20 Epochs. The increase in Epoch affects the value of MSE and RMSE which are getting smaller. For Ep

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Hanan Albarr and Rosita Kusumawati. "Hybrid Autoregressive Integrated Moving Average-Support Vector Regression for Stock Price Forecasting." Jurnal Matematika Sains dan Teknologi 24, no. 2 (2023): 1–17. http://dx.doi.org/10.33830/jmst.v24i2.4983.2023.

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Stock investment provides high-profit opportunities but also has a high risk of loss. Investors use various decision-making methods to minimize this risk, such as stock price forecasting. This research aims to predict daily closing stock prices using a hybrid Autoregressive Integrated Moving Average (ARIMA)-Support Vector Regression (SVR) model and compare it with the single model of ARIMA and SVR, as well as compiling the R-shiny web for the hybrid ARIMA-SVR model which makes it easier for investors to use the model to support investment decision making. The hybrid ARIMA-SVR model is composed

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