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Journal articles on the topic 'Closing Stock Index'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Amrul Hinung Prihamayu. "Prediction Of Closing Price Combined Stock Index (Ihsg) Using The Fuzzy Mamdani Method." SOUTHEAST ASIA JOURNAL oF GRADUATE OF ISLAMIC BUSINESS AND ECONOMICS 1, no. 2 (2022): 74–79. http://dx.doi.org/10.37567/sajgibe.v1i2.1862.

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This study proposes a method for predicting the closing IHSG stock price using the Mamdani fuzzy approach. This model uses historical closing stock price data as input, and generates closing stock price predictions using the Mamdani fuzzy rule. However, experimental results show that this model may not be suitable for predicting stock prices accurately and reliably. Therefore, this study does not recommend the use of the Mamdani fuzzy method for the purpose of predicting closing stock prices.

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2

Denie, Jo, Surachman, Nur Khusniyah Indrawati, and Mintarti Rahayu. "Nexus Between Oil, Gold Price and Dxy Index on Indonesian Stock Market During Geopolitical Events (2022 – 2024)." Revista de Gestão Social e Ambiental 18, no. 6 (2024): e06634. http://dx.doi.org/10.24857/rgsa.v18n6-142.

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Objective: The aim of this study is to observe the impact of oil, gold, and the DXY index on the Indonesian stock market during geopolitical events in 2022-2024. Theoretical Framework: Rising political tensions also have a major impact on global currencies, financial market and commodity market. This event lead to uncertainty which increasing the investment risk. Hence, geopolitical events could affect stock return in capital market. Method: The data used consists of daily Jakarta Composite Index (JCI) closing data, WTI daily closing prices, gold daily closing prices, and DXY closing data from

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3

Todorov, Ivan Borisov, and Fernando Sánchez Lasheras. "Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries." Energies 16, no. 9 (2023): 3856. http://dx.doi.org/10.3390/en16093856.

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In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include exponential smoothing with multiplicative errors and additive trend (MAN), exponential smoothing with multiplicative errors (MNN), and simple exponential smoothing with additive errors (ANN) for the forecasting of the stock prices of six different companies in the petroleum, electricity, and gas industries that are listed in the IBEX35 index. The datab

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4

Zhao, Pengyu. "Prediction of the Fluctuation of the Shanghai Composite Index Based on the ARIMA Model." Advances in Economics, Management and Political Sciences 193, no. 1 (2025): 199–206. https://doi.org/10.54254/2754-1169/2025.lh24921.

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As one of the most influential emerging countries in the world, China's international influence and economic status are gradually strengthening globally. The Shanghai Composite Index is the core stock index of the Shanghai Stock Exchange, reflecting the overall performance of A-share and B-share stocks in the Shanghai market. This index serves as a key benchmark for China's stock market. In order to better analyze the stock market situation and explore the practicality and limitations of the ARIMA model, this paper selected the closing prices of the Shanghai Composite Index from January 2, 202

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Zhang, Hongyu. "Vietnam V30 Closing Price Forecast Based on ARIMA and ETS." Advances in Economics, Management and Political Sciences 147, no. 1 (2025): 29–34. https://doi.org/10.54254/2754-1169/2024.ga19104.

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Stock price forecasting is a key subject in the financial field. Accurate stock price simulations are crucial for investors to make decisions about when to buy or sell stocks for profit. Under the current environment of global economic fluctuations, it is particularly urgent to develop and implement an effective stock price forecasting model. In this study, the VN30 composite index of Vietnam from 2016 to 2023 was selected as the research object, and the ARIMA model and ETS model were used to predict the index. The results show that the ARIMA model outperforms the ETS model in forecasting accu

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Yao, Hongxing, and Yunxia Lu. "Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method." Journal of Systems Science and Information 5, no. 5 (2017): 446–61. http://dx.doi.org/10.21078/jssi-2017-446-16.

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Abstract In this paper, we analyze the 180 stocks which have the potential influence on the Shanghai Stock Exchange (SSE). First, we use the stock closing prices from January 1, 2005 to June 19, 2015 to calculate logarithmic the correlation coefficient and then build the stock market model by threshold method. Secondly, according to different networks under different thresholds, we find out the potential influence stocks on the basis of local structural centrality. Finally, by comparing the accuracy of similarity index of the local information and path in the link prediction method, we demonst

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7

Wisnu Daru Setiawan, Mariati Tamba, and Wardojo. "Analysis Of Investor Rationality Towards Stock Price Index And Optimal Portfolio In Go Public Company Shares On The Indonesian Stock Exchange." Journal of Entrepreneur and Business 2, no. 1 (2023): 77–82. http://dx.doi.org/10.52643/joeb.v2i1.44.

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This study aims to determine the existence of investor rationality in choosing optimal stocks and portfolios by using a single index model on company shares listed on the IDX in 2018. The type of research used is analytic research. This analytic study aims to draw general conclusions and prove hypotheses about the average difference of two independent samples. The data used are secondary data obtained from information released by the Indonesia Stock Exchange including the daily closing stock price and the daily closing stock price index of listed companies. The sample selection method used is

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Wisnu Daru Setiawan, Mariati Tamba, and Wardojo. "Analysis of Investor Rationality Towards Stock Price Index and Optimal Portfolio in Go Public Company Shares on The Indonesian Stock Exchange." Journal of Entrepreneur and Business 2, no. 1 (2023): 35–42. http://dx.doi.org/10.52643/joeb.v2i1.55.

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This study aims to determine the existence of investor rationality in choosing optimal stocks and portfolios by using a single index model on company shares listed on the IDX in 2018. The type of research used is analytic research. This analytic study aims to draw general conclusions and prove hypotheses about the average difference of two independent samples. The data used are secondary data obtained from information released by the Indonesia Stock Exchange including the daily closing stock price and the daily closing stock price index of listed companies. The sample selection method used is

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9

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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Catherine, Happy, and Robiyanto Robiyanto. "PERFORMANCE EVALUATION OF LQ45 STOCKS IN THE INDONESIA STOCK EXCHANGE DURING PERIOD OF 2016-2018." Journal of Management and Entrepreneurship Research 1, no. 1 (2020): 37–44. http://dx.doi.org/10.34001/jmer.2020.6.01.1-4.

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Objective: This study investigates the performance evaluation of each LQ45 stock in the Indonesia Stock Exchange conducted by using the Sharpe Index, Treynor Ratio, Jensen Alpha, Sortino Ratio, and Information Ratio. Stocks evaluated are those that consistently listed in the LQ45 index during 2016-2018. Research Design & Methods: The number of samples used in this study was 32 stocks taken using a purposive sampling technique. The data used in this study are the monthly closing price of stocks, the composite stock price index, and the BI 7-day Repo Rate interest rate data. Findings: The re

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Wang, Yilin. "Machine Learning Based Stock Market Trend Prediction and Analysis." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 219–26. http://dx.doi.org/10.62051/9tqz2p11.

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Stock price prediction can help investors to create initial pre-scenarios. The topic of this research is to predict the stock market scenario through machine learning methods. By successfully predicting the stock market situation, the movement of different stocks, etc., the possibility of buying the wrong stocks can be greatly reduced, making it possible to make huge profits by buying and selling stocks. The purpose of the research is threefold. This paper uses a market capitalization weighted index consisting of the most important 40 stocks out of the top 100 stocks with the largest market ca

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Poornima, S. "Forecasting the Stock Market Closing Price of Nifty Commodity index Using Arima." Journal of Research and Review in Purchasing and Supply Management 2, no. 2 (2024): 28–37. https://doi.org/10.5281/zenodo.14504772.

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<em>The study which had made in this research paper is to forecast the stock market prices of Nifty commodity index which is one of the stock markets indices under NSE (National Stock Exchange) of India. This work is made for the time period of 5 months closing stock market price of Nifty commodities in that 4 month as training data and 1 month as Validation data using statistical analysis of ARIMA and Exploratory data analysis for better analysis of stock prices in the visual formation. The work concluded that the ARIMA time series analysis is not a better analysis of stock prices which comes

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Yenireddy, Ankireddy, Marimganti Srinivasa Narayana, Kalla Venkata Bangaru Ganesh, Guvvaladinne Prasanna Kumar, and Madduri Venkateswarlu. "Stock market index prediction based on market trend using LSTM." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1601. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1601-1609.

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The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learni

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14

Alim, Khairul, Bayun Matsaany, and Anisa Rahmawati. "Diversification of Jakarta Islamic Index (JII) Stock Optimal Portfolio for the Period 2018-2023." J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika 16, no. 2 (2023): 585–93. http://dx.doi.org/10.36456/jstat.vol16.no2.a8339.

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Investment is placing funds into an asset to gain profits in the future through changes in asset prices or capital gains. In Indonesia, stock investment, primarily through the Indonesia Stock Exchange (BEI), is popular among the public. BEI offers various indices, including the Jakarta Islamic Index (JII), which has garnered significant attention. JII comprises stocks of companies that adhere to Sharia principles. In investment, careful analysis is crucial to avoid errors in stock selection. Diversification is a strategy that involves spreading investments across several stocks, aiming to maxi

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15

Anwar. "Use of the Single Index Model in Determining Investment Decisions During the Covid-19 Pandemic." Economics and Business Journal (ECBIS) 1, no. 2 (2023): 93–102. http://dx.doi.org/10.47353/ecbis.v1i2.16.

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This study aims to determine stock investment decisions during the COVID-19 pandemic using a single index model. The population in this study is all the shares of companies that are included in the IDX30 market index on the Indonesia Stock Exchange for the 2020-2021 period, namely 40 stocks, while the sample is 39 stocks selected based on purposive sampling technique. Data collection using documentation techniques. Data analysis was carried out using a single index model, starting with collecting the closing stock prices of each to get the optimal portfolio. The results of this study indicate

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16

Sun, Wenjie. "H7N9 not only endanger human health but also hit stock marketing." Advances in Disease Control and Prevention 2, no. 1 (2017): 1. http://dx.doi.org/10.25196/adcp201711.

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Objective: This study aims to discuss the correlation between daily reported H7N9 cases and stock price indices in China. Methods: Information on daily reported H7N9 cases and stock market sectors indices between February 19, 2013 and March 31, 2014 were collected. A distributed lag non-linear model was used to describe the variation trend for the stock indices Results: The daily reported number of H7N9 cases was associated with the closing price of the Avian Influenza Sector Index (P < 0.05) and the opening price of the Shanghai Composite Index (P = 0.029). The Avian Influenza

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17

Manik, Efron. "Relationship between opening and closing of stock prices for IHSG and issuers: A case study in the Indonesia Stock Exchange." Bulletin of Applied Mathematics and Mathematics Education 5, no. 1 (2025): 71–80. https://doi.org/10.12928/bamme.v5i1.12975.

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Identifying the most influential variables in stock price movements is a crucial aspect of developing an accurate mathematical model for predicting market trends. This study analyzes two main variables: the composite stock price index (IHSG) and the closing price of company shares, to determine the extent of their influence on stock prices on the observation day. The findings indicate that the IHSG from one day prior to the observation day does not have a significant impact on the closing price of a particular stock. This means that changes in the IHSG on the previous day cannot be used as the

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18

Jarrah, Mutasem, and Morched Derbali. "Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning." Applied Sciences 13, no. 14 (2023): 8356. http://dx.doi.org/10.3390/app13148356.

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Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achi

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19

Hu, Yueni. "Shanghai Stock Composite Index Forecasts: Evidence from ARIMA and LSTM." Advances in Economics, Management and Political Sciences 57, no. 1 (2024): 303–8. http://dx.doi.org/10.54254/2754-1169/57/20230775.

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Prediction of stock prices is a classic problem. People are always trying to predict stock prices as accurately as possible, and also trying to build stock price model. With the development of technology, machine learning has been applied more and more to stock prediction problems, and good results have been obtained. This paper selects the Shanghai Stock Composite Index from 1990 to 2016 and forecasts its closing price. First, the ARIMA (1,1,1) model is established for the closing price sequence after first-order difference, and then the two-layer LSTM model is constructed to visualize the pr

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20

Ali, Muhammad, Dost Muhammad Khan, Muhammad Aamir, Amjad Ali, and Zubair Ahmad. "Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine." Complexity 2021 (December 2, 2021): 1–13. http://dx.doi.org/10.1155/2021/2906463.

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Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock e

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Tian, Feng, Dan Wang, Qin Wu, and Daijun Wei. "An empirical study on network conversion of stock time series based on STL method." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 10 (2022): 103111. http://dx.doi.org/10.1063/5.0089059.

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A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ([Formula: see text]) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time

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Viona, Elsa, Fitri Santi, Berto Usman, and Dewi Rahmayanti. "Is there Herding Behavior in the Indonesia Stock Market during the COVID-19 Pandemic?" Journal of Madani Society 2, no. 1 (2023): 1–8. http://dx.doi.org/10.56225/jmsc.v2i1.172.

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This study investigates the signs of herding behavior during the COVID-19 pandemic in the Indonesian Stock Exchange. Various studies found no herding in Indonesian stock markets during the COVID-19 pandemic, but we believe those studies have a limited methodology to capture the herding behavior. We believe that herding appears in a short time during the pandemic period, so we have to reexamine the existence of herding behavior using sectoral stock indexes rather than the stock market-wide index (IHSG) and using the rolling regression technique to capture the possibilities of herding that might

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Bashir, U., K. Singh, and V. Mansotra. "Examining Daily Closing Price Prediction of the NSE Index using an Optimized Artificial Neural Network: A Study of Stock Market." Journal of Scientific Research 17, no. 1 (2025): 195–209. https://doi.org/10.3329/jsr.v17i1.74640.

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Prediction of the stock market is considered a challenging task because of non-linear and speculative nature of data. Stock prediction means an attempt to forecast the future of a stock or any other financial instrument listed on any stock exchange. The success of stock prediction returns a significant profit for investors and daily traders. Deep learning models have proven to be a reliable option for developing successful prediction systems. In recent years, the use of hyperparameter optimization techniques for the creation of precise models has grown significantly. In this study, an attempt

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Ankireddy, Yenireddy Marimganti Srinivasa Narayana Kalla Venkata Bangaru Ganesh Guvvaladinne Prasanna Kumar Madduri Venkateswarlu. "Stock market index prediction based on market trend using LSTM." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1601–9. https://doi.org/10.11591/ijeecs.v35.i3.pp1601-1609.

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The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learni

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Pandin, Maria Yovita R. "COMPARATIVE ANALYSIS OF STOCK PORTFOLIO RISK & RETURN”WITH SINGLE INDEX METHOD." JEA17: Jurnal Ekonomi Akuntansi 8, no. 2 (2023): 26–38. http://dx.doi.org/10.30996/jea17.v8i2.9799.

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This study aims to compare the return and risk of investment in blue chip stocks (first liner) and second liner stocks of manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the 2022 period. The method used is qualitative with single index theory, and closing price data is used for analysis. The results show that a portfolio of second liner stocks provides higher expected returns and lower risk compared to first liner stocks. Nonetheless. This research provides insights for investors and the public about the investment alternatives of first liner and second liner stocks

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Bimantoro, Surya Ageng, and Ach Yasin. "Volatility Analysis of the Indonesia Sharia Stock Index (ISSI) Technology Sector Period 2019 – 2023." Formosa Journal of Applied Sciences 4, no. 1 (2025): 191–206. https://doi.org/10.55927/fjas.v4i1.13202.

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The Indonesian capital market faced significant challenges during the COVID-19 pandemic, which caused stock price volatility across various sectors, both in the conventional and sharia capital markets, particularly in the technology sector (ISSI). This study uses a descriptive quantitative approach, employing purposive sampling to analyze secondary data obtained from Yahoo Finance to explore technology stocks consistently listed from 2019 to 2023 on the ISSI, focusing on daily closing prices, volatility, and utilizing ARCH/GARCH modeling in EViews 10. Descriptive analysis reveals significant s

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Nurhayati, Immas, Endri Endri, Renea Shinta Aminda, and Leny Muniroh. "Impact of COVID-19 on Performance Evaluation Large Market Capitalization Stocks and Open Innovation." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1 (2021): 56. http://dx.doi.org/10.3390/joitmc7010056.

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This research is an event study that evaluates the performance of large market capitalization shares using a performance model that is adjusted to risks due to the COVID-19 outbreak. The study measured the performance of large market capitalization stocks which represented each tick size on the Indonesian Stock Exchange during the COVID-19 pandemic using the Sharpe Index, the Treynor Ratio, and Jensen’s Alpha. The sample selection used a purposive sampling technique and 24 stocks were selected as samples in the study. We used the daily closing price of stocks, the Indonesia composite index, an

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Nagy, László, and Mihály Ormos. "Friendship of Stock Market Indices: A Cluster-Based Investigation of Stock Markets." Journal of Risk and Financial Management 11, no. 4 (2018): 88. http://dx.doi.org/10.3390/jrfm11040088.

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This paper introduces a spectral clustering-based method to show that stock prices contain not only firm but also network-level information. We cluster different stock indices and reconstruct the equity index graph from historical daily closing prices. We show that tail events have a minor effect on the equity index structure. Moreover, covariance and Shannon entropy do not provide enough information about the network. However, Gaussian clusters can explain a substantial part of the total variance. In addition, cluster-wise regressions provide significant and stationer results.

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Mishra, Shambhavi, Tanveer Ahmed, Vipul Mishra, et al. "Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering." Computational Intelligence and Neuroscience 2021 (December 17, 2021): 1–14. http://dx.doi.org/10.1155/2021/6400045.

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This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the cu

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Khodke, Yash, and Sonali Deshpande. "Stock Price Prediction Using LSTM and GRU." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1163–67. http://dx.doi.org/10.22214/ijraset.2023.53826.

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Abstract: Because the stock market is a very complicated nonlinear movement system whose fluctuation law is influenced by a wide range of factors, forecasting the stock price index is challenging. Numerous examples show how neural network algorithms can accurately forecast time series and frequently produce results that are adequate. The two stocks' short-term closing values were predicted using a Regularised GRULSTM neural network model that we created in this paper based on existing models. Experiments show that our suggested model predicts stock time series better than the existing GRU and

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Subanti, Sri, and Asti Rahmaningrum. "Forecasting on Closing Stock Price Data Using Fuzzy Time Series." Indonesian Journal of Applied Statistics 7, no. 1 (2024): 41. https://doi.org/10.13057/ijas.v7i1.54309.

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The stock prices move up and down during trading time which is obtained from time series data. Investors need to estimate the fluctuation of stock prices in the future day to make the best investment decision. Fuzzy time series can be used as an alternative by investors in making stock price predictions. The advantage of this forecasting method compared to others is that it can formulate a problem based on expert knowledge or empirical data. This research aims to apply fuzzy time series in estimating the future value of closing stock price on the LQ45 Index. Three different methods will be app

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Safitri, Yunita Dewi, and Robiyanto Robiyanto. "KORELASI DINAMIS ANTARA PERGERAKAN HARGA MINYAK DUNIA DAN INDEKS HARGA SAHAM SEKTORAL DI BURSA EFEK INDONESIA." Jurnal Ekonomi Bisnis dan Kewirausahaan 9, no. 3 (2020): 188. http://dx.doi.org/10.26418/jebik.v9i3.42949.

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Changes in the situation that move very quickly on the commodity market have an impact on financial markets, one of which is the stock market in Indonesia. Therefore this study aims to examine the dynamic correlation between the movement of world oil prices and the Sectoral Stock Price Index listed on the Indonesia Stock Exchange (IDX). The data used is obtained from secondary data in the form of daily closing price data for world oil prices and Sectoral Stock Price Index from January 2017 to June 2020. The analysis technique used is Dynamic Conditional Correlation-Generalized Autoregressive C

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Ruzgar, Nursel Selver, and Clare Chua-Chow. "Behavior of Banks’ Stock Market Prices during Long-Term Crises." International Journal of Financial Studies 11, no. 1 (2023): 31. http://dx.doi.org/10.3390/ijfs11010031.

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Countries are drastically impacted by financial and fiscal crises. Financial crises have the worst impact on not only society, but also the economy. The Canadian economy underwent financial crises and recessions several times during the last century. In this paper, daily closing stock prices of five large Canadian banks were studied during the last five crisis periods. It is aimed to determine the most effective or dominant index prices on the daily closing stock price of the banks during the crisis periods. The five periods were selected from secondary data from January 1975 to December 2020

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Gyamfi, Emmanuel N., Frederick A. A. Sarpong, and Anokye M. Adam. "Drivers of Stock Prices in Ghana: An Empirical Mode Decomposition Approach." Mathematical Problems in Engineering 2021 (September 25, 2021): 1–7. http://dx.doi.org/10.1155/2021/2321042.

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This study utilized the empirical mode decomposition (EMD) technique and examined which group of investors based on their trading frequencies influence stock prices in Ghana. We applied this technique to a dataset of daily closing prices of GSE Financial Stock Index for the period 04/01/2011 to 28/08/2015. The daily closing prices were decomposed into six intrinsic mode functions (IMFs) and a residue. We used the hierarchical clustering method to reconstruct the IMFs into high frequency, low frequency, and trend components. Using statistical measures such as Pearson product moment correlation

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Jaber, Abobaker M., Mohd Tahir Ismail, and Alsaidi M. Altaher. "Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/708918.

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This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

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Auliannisa, Khairina, Evy Sulistianingsih, and Neva Satyahadewi. "OPTIMASI MULTI OBJEKTIF DAN ANALISIS PEMBENTUKAN PORTOFOLIO SAHAM JAKARTA ISLAMIC INDEX (JII) MENGGUNAKAN METODE NADIR COMPROMISE PROGRAMMING (NCP)." EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN (EPSILON: JOURNAL OF PURE AND APPLIED MATHEMATICS) 19, no. 1 (2025): 27. https://doi.org/10.20527/epsilon.v19i1.14198.

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Multi-objective problems involve multiple objective functions to solve complex problems, and the Nadir Compromise Programming (NCP) method is one way to solve these problems. These problems. Compared to other multi-objective methods, the NCP method has several advantages. Firstly, the weighting in the NCP method can utilize specific parameters to produce an effective optimal portfolio. Additionally, the optimum value of the risk coefficient can be achieved, thereby minimizing large losses. Achieved so as not to cause significant losses. When investing, several essential things need to be consi

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Tfaily, Fatima, and Mohamad M. Fouad. "Multi-level stacking of LSTM recurrent models for predicting stock-market indices." Data Science in Finance and Economics 2, no. 2 (2022): 147–62. http://dx.doi.org/10.3934/dsfe.2022007.

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<abstract> <p>The ability to predict stock-market indices is important to investors and financial decision-makers. However, the uncertainty of available information makes accurate prediction extremely challenging. In this work, we propose and validate a multi-level stacking model of long short-term memory (LSTM) units for the short-term prediction of stock-index closing prices. The proposed machine-learning model is trained using historical data to predict next-day closing prices. The first layer of the multi-level stacked structure contains an ensemble of recurrent LSTM models tha

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LIN, AIJING, PENGJIAN SHANG, GUOCHEN FENG, and BO ZHONG. "APPLICATION OF EMPIRICAL MODE DECOMPOSITION COMBINED WITH k-NEAREST NEIGHBORS APPROACH IN FINANCIAL TIME SERIES FORECASTING." Fluctuation and Noise Letters 11, no. 02 (2012): 1250018. http://dx.doi.org/10.1142/s0219477512500186.

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The purpose of this paper is to forecast the daily closing prices of stock markets based on the past sequences. In this paper, keeping in mind the recent trends and the limitations of previous researches, we proposed a new technique, called empirical mode decomposition combined with k-nearest neighbors (EMD–KNN) method, in forecasting the stock index. EMD–KNN takes the advantages of the KNN and EMD. To demonstrate that our EMD–KNN method is robust, we used the new technique to forecast four stock index time series at a specific time. Detailed experiments are implemented for both of the propose

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Bing, Yang, Jian Kun Hao, and Si Chang Zhang. "Stock Market Prediction Using Artificial Neural Networks." Advanced Engineering Forum 6-7 (September 2012): 1055–60. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1055.

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In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite I

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Arsy, Izza Dinikal, and Dedi Rosadi. "MEASUREMENT OF SUPPORT VECTOR REGRESSION PERFORMANCE WITH CLUSTER ANALYSIS FOR STOCK PRICE MODELING." MEDIA STATISTIKA 15, no. 2 (2023): 163–74. http://dx.doi.org/10.14710/medstat.15.2.163-174.

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Risk-averse investors will seek out stock investments with the minimum risk. One step that can be taken is to develop a model of stock prices and predict their fluctuations in the coming months. Significant studies on the modeling of stock movements have used the ARCH/GARCH method, but this method requires some assumptions. This paper will discuss the performance of stock modeling using Support Vector Regression. The performance is measured using the root mean square error value in two stock clusters based on its volatility value, e.g., stocks with large volatility and stocks with small volati

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Iqbal, Javed, Aboubakar Mirza, Abid Mehmood, and Fariha Ashraf. "Stock Selection through Hidden Markov Model: A Case of Pakistan Stock Exchange." Review of Education, Administration & Law 5, no. 4 (2022): 695–714. http://dx.doi.org/10.47067/real.v5i4.292.

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To foresee hidden regimes inside the data, Hidden Markov Model is widely used. Many researchers had used various data mining methods to predict stock market prices. This research will describe usage of Hidden Markov Model (HMM) via MATLAB to forecast stock prices for stock’ selection and portfolios development. In this study listed companies of PSX (Pakistan Stock Exchange) are explored specifically KSE-100 Index companies. From January 2012 to June 2022 monthly closing stock prices are used in this study. Many studies based on the historical data have been conducted in developed markets, but

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Kaya, Ferhat. "Effects of Financial Development and Financial Globalization on Stock Market Prices: The Case of Türkiye." Journal of Eurasian Economies 4, no. 1 (2025): 38–43. https://doi.org/10.36880/j04.1.0135.

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A multitude of factors exert influence on stock prices. In this era of globalized capital markets, characterized by the absence of borders and the interpenetration of economies, development and globalization have emerged as pivotal elements in shaping stock prices and financial returns. Noteworthy is the decision made in 1980 in Turkey, wherein policies aimed at import substitution were relinquished, and efforts toward liberalization and globalization were prioritized. In this context, a crucial undertaking entails the examination of capital market returns within Turkey, particularly in the co

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Yuliana, Ashalia Fitri, and Robiyanto Robiyanto. "PERAN EMAS SEBAGAI SAFE HAVEN BAGI SAHAM PERTAMBANGAN DI INDONESIA PADA PERIODE PANDEMI COVID-19." Jurnal Ilmiah Bisnis dan Ekonomi Asia 15, no. 1 (2021): 1–11. http://dx.doi.org/10.32815/jibeka.v15i1.217.

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The purpose of this study is to analyze the role of gold as safe haven or hedge for mining stocks in Indonesia during the COVID-19 pandemic period. The data used in this study are mining stock index data (JASICA) daily closing on the Indonesia Stock Exchange and daily closing gold price data on the international market during the period January 2020 - May 2020. Data analysis was performed using QREG to see the potential of gold as a safe haven and GARCH 1.1 to see the potential of gold as a hedge. The results of this study are gold can serve as a robust safe haven for Indonesian mining stocks

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Rafiana A. S., Andi Besse, M. Ikhwan Maulana, Anwar, Burhanuddin, and Nurman. "OPTIMAL PORTFOLIO FORMATION ANALYSIS USING THE SINGLE INDEX MODEL DURING THE COVID-19 PANDEMIC: A Study on The LQ 45 Index on the Indonesian Stock Exchange (IDX)." JOURNAL OF HUMANITIES SOCIAL SCIENCES AND BUSINESS (JHSSB) 2, no. 3 (2023): 468–81. http://dx.doi.org/10.55047/jhssb.v2i3.625.

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This study aims to determine the optimal portfolio formation using the single index model method on LQ 45 Index stocks for the period from March 2020 to December 2021. The population in this study consists of all company shares included in the LQ 45 Index during the specified period, totaling 45 stocks. A sample of 36 companies was selected using a nonprobability sampling technique with a purposive sampling method. Data collection was carried out using documentation techniques, primarily focusing on collecting closing stock price data. The data analysis process involved collecting the closing

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Aljohani, Hassan M., and Azhari A. Elhag. "Using Statistical Model to Study the Daily Closing Price Index in the Kingdom of Saudi Arabia (KSA)." Complexity 2021 (March 22, 2021): 1–5. http://dx.doi.org/10.1155/2021/5593273.

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Classification in statistics is usually used to solve the problems of identifying to which set of categories, such as subpopulations, new observation belongs, based on a training set of data containing information (or instances) whose category membership is known. The article aims to use the Gaussian Mixture Model to model the daily closing price index over the period of 1/1/2013 to 16/8/2020 in the Kingdom of Saudi Arabia. The daily closing price index over the period declined, which might be the effect of corona virus, and the mean of the study period is about 7866.965. The closing price is

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S., Murugan *1. "PERFORMANCE AND COMPARATIVE ANALYSIS OF INDIAN STOCK MARKET DATA USING MULTI LAYER FEED FORWARD NEURAL NETWORK AND FUZZY TIME SERIES MULTI LAYER FEED FORWARD NEURAL NETWORK MODEL WITH TRACKING SIGNAL APPROACH." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 3 (2019): 61–69. https://doi.org/10.5281/zenodo.2595802.

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This study, proposes a novel neural network and fuzzy-neural network approach for predicting the closing index of the stock market. It strives to adapt the number of hidden neurons of a Multi Layer Feed Forward Neural Network (MLFFNN) and Fuzzy Time Series Multi Layer Feed Forward Neural Network (FTS-MLFFNN) model. It uses the Tracking Signal (TS) and rejects all models which result in values outside the interval of [-4, +4]. The effectiveness of the proposed approach is verified with one step ahead of Bombay Stock Exchange (BSE100) closing stock index of Indian stock market. This novel approa

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Sumiyana, Sumiyana. "The Behavior of Opening and Closing Prices Noise and Overreaction." Gadjah Mada International Journal of Business 11, no. 1 (2009): 73. http://dx.doi.org/10.22146/gamaijb.5542.

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This study extends several previous studies that conclude that noise and overreaction on intraday data occur. Those studies have yet to be clear about the kind of price that explains for this noise and overreaction. This study examines the opening price and closing price behavior, and tries to explain the noise and overreaction on the Indonesia Stock Exchange using intraday data in every 30-minute interval. Sample is firms listed in LQ45 index. Sequentially, this research sample is filtered to stocks that are the most actively traded on the Indonesia Stock Exchange based on trading frequency i

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Megawati, Resmawan, Boby Rantow Payu, and Amanda Adityaningrum. "Prediksi Pergerakan Saham Menggunakan Metode Simulasi Monte Carlo untuk Pembentukan Portofolio Optimal dengan Pendekatan Model Markowitz." Jurnal Statistika dan Aplikasinya 6, no. 1 (2022): 86–95. http://dx.doi.org/10.21009/jsa.06108.

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Stock movements that follow a stochastic process move randomly at certain times, have led stock prices challenging to predict. For this reason, the monte carlo simulation method is used to get the possibilities of stock prices in the future. This case study focused on the shares listed on the Jakarta Islamic Index 70 in 2018, by simulating 10 times the daily closing price data, thus, the possible stock prices in 2019 were obtained. Portfolio optimization was then carried out using the markowitz model approach from the predicted data. Based on the prediction data, there are 20 stock have a posi

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Xie, Zizheng, and Yi Wang. "Exploration of Stock Portfolio Investment Construction Using Deep Learning Neural Network." Computational Intelligence and Neuroscience 2022 (May 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/7957097.

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To study the intelligent and efficient stock portfolio in China’s financial market, based on the relevant theories such as deep learning (DL) neural network (NN) and stock portfolio, this study selects 111 stable stocks from the constituent stocks of the China Security Index (CSI) 300 from January 1, 2018, to December 31, 2021, as the research samples. Then, it analyzes these research samples and imports the relevant data of 111 stocks into the DL NN model. The corresponding prediction results of stock prices are obtained. Finally, the stock portfolio model based on DL NN is compared with the

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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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