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Relevant bibliographies by topics / Stock Forecasting

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Academic literature on the topic 'Stock Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Stock Forecasting"

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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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Dissertations / Theses on the topic "Stock Forecasting"

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Kwan, Wai-ching Josephine. "Trend models for price movements in financial markets /." [Hong Kong] : University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13841397.

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Rahou, Amar A. M. "A generalised framework for modelling & forecasting share prices : a field study on modelling and forecasting the share prices from the banking sector." Thesis, University of South Wales, 2009. https://pure.southwales.ac.uk/en/studentthesis/a-generalised-framework-for-modelling--forecasting-share-prices(10fcca19-ff9a-4497-a0be-55f3e980cbed).html.

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Modelling and forecasting the stock market remains a challenge because of the high volatilities in individual stock prices and the market itself. Hence, this topic has received much attention in the literature since forecast errors represent the systematic risk faced by investors. Therefore, the ability to reliably forecast the future values of the shares would provide essential help in reducing that risk to those investors. The main aim of this research is to develop and calibrate a framework that can be used to model the daily share prices of the companies from the banking sector and hence p

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Gower, Craig Paul. "Modelling and forecasting stock and stock market volatility." Thesis, Swansea University, 2001. https://cronfa.swan.ac.uk/Record/cronfa42339.

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The examination of stock price volatility has come under increased scrutiny due to the large swings in stock price movements that have occurred with greater frequency than the historical average. Additionally, the substantial increases in the volume of options trading has increased the importance of accurate volatility forecasts due to the volatility forecast being the most important parameter affecting the pricing of options. Consequently, the aim of the thesis is to analyse the volatility of forty-five FTSE 100 stocks, the FTSE 100 index together with other major and emerging market stock in

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Shan, Yaowen School of Banking &amp finance UNSW. "Analysts' forecasts and future stock return volatility: a firm-level analysis for NYSE Firms." Awarded by:University of New South Wales. School of Banking & finance, 2006. http://handle.unsw.edu.au/1959.4/26963.

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This study demonstrates that financial analysts significantly affect short-term stock prices, by examining how non-accounting information particularly contained in analysts' forecasts contributes to the fluctuation of future stock returns. If current non-accounting information of future earnings is more unfavourable or more volatile, we could observe a larger shift in the current stock return. The empirical evidence strongly supports these theoretical predictions that stem from the combination of the accounting version of Campbell-Shiller model (Campbell and Shiller (1988) and Vuolteenaho (200

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Chen, Gary. "Behavioural heterogeneity in ASX 200 a dissertation submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Master of Business (MBus), 2009 /." Click here to access this resource online, 2009. http://hdl.handle.net/10292/758.

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Rangel, Jose Gonzalo. "Stock market volatility and price discovery three essays on the effect of macroeconomic information /." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3220417.

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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed September 7, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 125-130).

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Millevik, Daniel, and Michael Wang. "Stock Forecasting Using Artificial Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166455.

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This paper studies the potential of artificial neural networks (ANNs) in stock forecasting. It also investigates how the number of neurons in the network, as well as the distribution of the training data into training, validation and testing sets, affect the accuracy of the network. By using MATLAB and its Neural Network Toolbox tests were carried out with a two-layer feedforward neural network (FFNN). These are carried out by collecting five years of historical data from the Dow Jones Industrial Average (DJIA) stock index, which is then used for training the network. Finally, retraining the n

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NEVES, GUILHERME DE SOUSA. "STOCK FORECASTING FOR ELETRONICS SPARE PARTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=11330@1.

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Existe consenso entre os pesquisadores de que o modelo de séries temporais não é adequado para previsão de peças de reposição. Entretanto, a maioria das ferramentas de previsão existentes no mercado emprega o modelo de séries temporais. Este trabalho apresenta a distribuição de Poisson como alternativa para a previsão de estoque de peças eletrônicas de reposição. A partir de noções básicas de gestão de estoques utilizando séries temporais e dos conceitos de confiabilidade, disponibilidade e do Processo de Poisson é proposto um modelo alternativo. Com o uso de exemplos reais são apresen

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Skagerström, William, and Daniel Skantz. "Stock forecasting using ensemble neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229798.

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This paper explores the viability of creating an artificial neural network for stock forecasting using an ensemble method, where each network is differentiated with a different set of input parameters. The inputs were chosen based on previous research and by using a stepwise addition parameter search method. The problem was approached as both a regression and a classification problem, where we evaluated the networks performance for the purpose of stock forecasting using relevant measurements. For the regression part, the result was negative: the neural network was not able to beat a naive pred

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Zhang, Yuzhao. "Essays on return predictability and volatility estimation." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1666139151&sid=3&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Books on the topic "Stock Forecasting"

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Zheng, Xiaolian, and Ben M. Chen. Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5.

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Plummer, Tony. Forecasting Financial Markets. Kogan Page Publishers, 2010.

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Chua, Jess H. Gains from stock-market timing. Salomon Brothers Center for the Study of Financial Institutions, Graduate School of Business Administration, New York University, 1986.

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Chua, Jess H. Gains from stock market timing. Salomon Brothers Center for the Study of Financial Institutions, 1986.

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Chua, Jess H. Gains from stock-market timing. Salomon Brothers Center for the Study of Financial Institutions, Graduate School of Business Administration, New York University, 1986.

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S, Woodward Richard, and New York University, eds. Gains from stock market timing. Salomon Brothers Center for the Study of Financial Institutions, Graduate School of Business Administration, New York University, 1986.

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Krag, Joachim. Sind Aktienkurse prognostizierbar? Nomos, 1995.

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Ang, Andrew. Stock return predictability: Is it there? National Bureau of Economic Research, 2001.

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Campbell, John Y. Efficient tests of stock return predictability. National Bureau of Economic Research, 2003.

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Mohan, Neeraj. Artificial neural network models for forecasting stock price index in Bombay Stock Exchange. Indian Institute of Management, 2005.

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Book chapters on the topic "Stock Forecasting"

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Thomopoulos, Nick T. "Safety Stock." In Demand Forecasting for Inventory Control. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11976-2_11.

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Saaty, Thomas L., and Luis G. Vargas. "The Stock Market." In Prediction, Projection and Forecasting. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7952-0_4.

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Jadon, Raja, Shivam Yadav, and Abdul Aleem. "Stock market price forecasting." In Artificial Intelligence, Blockchain, Computing and Security Volume 1. CRC Press, 2023. http://dx.doi.org/10.1201/9781003393580-76.

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Rapp, Birger. "Forecasting and Stock Control." In Computer-Aided Production Management. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73318-5_19.

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Zheng, Xiaolian, and Ben M. Chen. "Market Turning Period Forecasting." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_6.

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Zheng, Xiaolian, and Ben M. Chen. "Introduction." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_1.

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Zheng, Xiaolian, and Ben M. Chen. "A System Adaptation Framework." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_2.

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Zheng, Xiaolian, and Ben M. Chen. "Market Input Analysis." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_3.

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Zheng, Xiaolian, and Ben M. Chen. "Analysis of Dow Jones Industrial Average." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_4.

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Zheng, Xiaolian, and Ben M. Chen. "Selected Asian Markets." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_5.

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Conference papers on the topic "Stock Forecasting"

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Aswini, J., Dinesh S, C. Lakshmipriya, Lokesh Krishnaa M, and Siva Subramanian R. "Stock Market Forecasting Using LSTM." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692057.

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Pimpalkar, Aarti P., Rohan Urmude, Adarsh Ghule, Pankaj Jayappa, Jayashree Rajesh Prasad, and Mayuresh Gulame. "Machine Learning-Based Stock Forecasting." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10988318.

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Liashenko, Olena, Tetyana Kravets, and Vladyslav Plushchov. "Stock Price Forecasting using Sentiment Analysis of Stock Tweets." In 2024 14th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2024. http://dx.doi.org/10.1109/acit62333.2024.10712521.

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Saiyyad, Arshiya, Snehlata Wankhade, Apeksha Sakhare, Purva Kale, Grishma Yenchilwar, and Pranali Sharma. "Stock Price Prediction for Stock Market Forecasting using Machine Learning." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070798.

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Jain, Kashika, and Panav Jain. "Stock Price Forecasting Using LSTM Model." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986696.

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Surekha, Reshma, Divya Gupta, Adarsh Kumar Jha, Maridu Bhargavi, and Poorna Sai. "Stock Price Forecasting by Time Series Analysis." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910437.

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K, Pavithra, Varshini P, P. Anu, D. Mohanapriya, and Soundharya S. "Machine Learning Based Indian Stock Market Forecasting." In 2024 First International Conference on Data, Computation and Communication (ICDCC). IEEE, 2024. https://doi.org/10.1109/icdcc62744.2024.10961860.

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Altooq, Kawthar Abdulla Ali, Layla Jawad Hasan, Sumathi Kumaraswamy, Shrikant Panigrahi, and Sasikanta Tripathy. "Forecasting Nvidia Stock Trends: An Analytical Approach." In 2024 International Conference on Sustainable Islamic Business and Finance (SIBF). IEEE, 2024. https://doi.org/10.1109/sibf63788.2024.10883878.

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Tevare, Vaishnavi, and P. S. Revankar. "Forecasting Stock Prices with Stack Transformer." In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2023. http://dx.doi.org/10.1109/iccpct58313.2023.10245652.

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Louwerse, Victor, and Léon Rothkrantz. "Intraday stock forecasting." In the 15th International Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2659532.2659622.

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Reports on the topic "Stock Forecasting"

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Engle, Robert, and Joe Lange. Measuring, Forecasting and Explaining Time Varying Liquidity in the Stock Market. National Bureau of Economic Research, 1997. http://dx.doi.org/10.3386/w6129.

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Chatelais, Nicolas, Arthur Stalla-Bourdillon, and Menzie Chinn. Macroeconomic Forecasting using Filtered Signals from a Stock Market Cross Section. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30305.

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Chen, Joseph, Harrison Hong, and Jeremy Stein. Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices. National Bureau of Economic Research, 2000. http://dx.doi.org/10.3386/w7687.

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Ferreira, Miguel, and Pedro Santa-Clara. Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole. National Bureau of Economic Research, 2008. http://dx.doi.org/10.3386/w14571.

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Venäläinen, Ari, Sanna Luhtala, Mikko Laapas, et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361317.

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Climate change will increase weather induced risks to forests, and thus effective adaptation measures are needed. In Säätyö project funded by the Ministry of Agriculture and Forestry, we have summarized the data that facilitate adaptation measures, developed weather and climate services that benefit forestry, and mapped what kind of new weather and climate services are needed in forestry. In addition, we have recorded key further development needs to promote adaptation. The Säätyö project developed a service product describing the harvesting conditions of trees based on the soil moisture asses

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