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Relevant bibliographies by topics / Closing Stock Index

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Academic literature on the topic 'Closing Stock Index'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Closing Stock Index"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Closing Stock Index"

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Karanfil, Salih. "Obtaining the membership function by using the neural network in Istanbul stock exchange to find the relation between the low and closing prices." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/96007.

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chang, gwo ruey, and 張國瑞. "Taiwan Stock Exchange Capitalization Weighted Stock Index closing rise and drop discussion." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/89679955852951165169.

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碩士<br>國立高雄應用科技大學<br>金融資訊研究所<br>96<br>This paper mainly is the discussion by the stock weighting stock price index date in the material, pushes estimates the same day to close rises or falls, research period from 2004 to 2007 as a base period, respectively take the opening price law computation rise and drop scope, unfolds by the masculine and feminine elements K line as the spatial strength, (counter potential operating system) distinguishes strong weak by CDP. the conformity three kind of targets altogether 81 parameters, again the affiliation gradually returns the way to discover the foreca

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Li, Jhe-Ruei, and 李哲睿. "Predicting Taiwan Stock Index Futures Closing Price By Three Institutional Investors With LSTM Artificial Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y3gqny.

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碩士<br>輔仁大學<br>金融與國際企業學系金融碩士班<br>107<br>The data period is from July 3, 2007 to April 26, 2019. This study uses information provided by the Taiwan Stock Exchange and the Taiwan Futures Exchange to provide information of three institutional investors, including changes in futures open positions and changes in options open positions, and net purchases or net sales, and then predict closings price with LSTM Model. From the network structure of foreign investors and investment trust and the minimum RMSE network structure of the three institutional investors and the best trend hit rate, and the netw

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Book chapters on the topic "Closing Stock Index"

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Chen, Tao. "Prediction Calculation of Stock Composite Index Closing Price Based on Grey Correlation Analysis Method." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1428-9_20.

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Cantzos, Constantine, Petros Kalantonis, Aristidis Papagrigoriou, and Stefanos Theotokas. "The Impact of Economic Sentiment, Consumer, Producer and Investor's Confidence Indices on Stock Returns of the Listed Companies in FTSE-20 in Greece." In Advances in Finance, Accounting, and Economics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-6114-9.ch007.

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This chapter examines the relationship between stock returns of companies listed in the FTSE-20 on the Athens Exchange and behavioral indicators. The research is based on the behavioral APT model, which examines stock returns' risk factors through the involvement of macroeconomic variables and behavioral indicators. The data is the closing price of 17 shares listed in the FTSE-20 index, a number of macroeconomic variables, and a series of behavioral indicators for the period of January 2001-December 2014. Regressions were conducted with dependent variable stock returns of a portfolio invested

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Özdemir, Enes, and Burhan Uluyol. "Determining the Best Machine Learning Model by Predicting the Participation Index of the Borsa Istanbul Stock Exchange With Artificial Intelligence." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-9586-8.ch006.

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Forcasting the future direction of stock indinces has been received significant attention by researchers and investors. Due to the complexcity of information, it is very difficult to predict future stock market price behavior. In this paper, we determine the best machine learning model by forecasting the Borsa Istanbul Stock Exchange participation index with Artificial Intelligence (AI). Six different machine learning algorithms are used to predict the prices of a participation index such as Linear Regression, LSTM, KNN, Auto-ARIMA, Gradient Boosting and Random Forest. Models were built by usi

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Orta, Kenan. "The Effect of Foreign Portfolio Investments on Istanbul Stock Exchange BIST-30 Analysis." In Advances in Finance, Accounting, and Economics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5303-5.ch012.

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This chapter presents the findings of a study which aimed to investigate the effect of foreign portfolio investments in companies traded in the BIST-30 index after the 2008 crisis on the market value, stock closing price and stock return of companies. Another aim of this study was to examine the effects of macroeconomic variables such as real effective exchange rate, CPI index and current account deficit. For these purposes, daily stock changes, market value and foreign share data of Borsa İstanbul BIST-30 companies for the years 2010-2014 were collected. In the study, six different models wer

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Wu, Jianing. "Data Analytics and Data Mining Techniques in Financial Investment Risk Management." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241100.

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In order to effectively manage financial risks, the application of data analysis and data mining technology in financial investment risk management has been proposed. Taking the daily closing price data of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index as the research object, the marginal distribution is obtained by using the nonparametric kernel distribution estimation function method, the parameters of the commonly used Copula function are estimated by Matlab software, and the Euclidean distance is used as the evaluation index of the Copula model. Then, based on binary norma

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Serin, Selim, and Gülder Kemalbay. "LSTM-Based Multivariate Deep Neural Networks for Stock Price Forecasting." In Güncel Ekonometrik ve İstatistiksel Uygulamalar ile Akademik Çalışmalar. Özgür Yayınları, 2024. http://dx.doi.org/10.58830/ozgur.pub518.c2135.

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The inherent complexity and dynamic nature of financial markets present substantial challenges for accurately forecasting price movements. Traditional forecasting models often struggle to capture these intricacies, leading to suboptimal predictive performance. In this context, deep learning offers a promising alternative due to its ability to process and analyze large volumes of financial data. This chapter aims to forecast the one-day-ahead price of a Turkish stock over the period from July 30, 2007, to October 11, 2023, using multivariate inputs, including the closing price of the XU030 inde

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Ozili, Peterson K., and Thankom Arun. "Spillover of COVID-19." In Managing Inflation and Supply Chain Disruptions in the Global Economy. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5876-1.ch004.

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How did a health crisis translate to an economic crisis? Why did the spread of the coronavirus bring the global economy to its knees? The answer lies in two methods by which coronavirus stifled economic activities. First, the spread of the virus encouraged social distancing which led to the shutdown of financial markets, corporate offices, businesses and events. Second, the exponential rate at which the virus was spreading, and the heightened uncertainty about how bad the situation could get, led to flight to safety in consumption and investment among consumers, investors and international tra

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Conference papers on the topic "Closing Stock Index"

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Jayaraman, Gopu, Mahalakshmi Venkatachalam, and Thirunarayanasamy M. "Predicting Daily Closing Value of Indian Stock Index Through Artificial Neural Network." In 2021 International Conference on Decision Aid Sciences and Application (DASA). IEEE, 2021. http://dx.doi.org/10.1109/dasa53625.2021.9681931.

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Fu, Yifeng, and He Xiao. "Stock Price Prediction Model based on Dual Attention and TCN." In 8th International Conference on Signal, Image Processing and Embedded Systems (SIGEM 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122007.

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The stock market is affected by many variables and factors, and the current forecasting models for time series are often difficult to capture the complex laws among multiple factors. Aiming at this problem, a stock price prediction model based on dual attention mechanism and temporal convolutional network is proposed. First, a convolution network more suitable for time series is used as the feature extraction layer. Feature attention is introduced to dynamically mine the potential correlation between the input factor features and closing prices. Second, based on Gated Recurrent Unit, on the ot

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Kendirli, Selçuk, and Muhammet Çankaya. "Effects of USD Exchange Rate over the Istanbul Stock Market 30 Index and Investigation of the Relationship between Them." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01278.

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It is known that financial markets have important place in today's economy. Individuals could be evaluated their saving with their own research or they could be evaluated their savings with financial experts recommendations. A large portion of those funds of individual or institutional investors managed are directed to the stock market of the country. When considered in terms of Turkey, Istanbul Stock Exchange is examples for this topic.  The changes in economic data, is influenced to many variables especially the stock market. It is perceived in the market as bad data that the rising in

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Mukherjee, Chiradeep, Arindam Chakraborty, Subhalaxmi Chakraborty, Sagnik Chakraborty, Pallabi Roy, and Arijit Bhattacharjee. "Recurrent Neural Network-based Closing Index Prediction of Indian Software Industry Stocks." In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2024. http://dx.doi.org/10.1109/iatmsi60426.2024.10503019.

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