Relevant bibliographies by topics / Stock Prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stock Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Stock Prediction"

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TOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network." Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, no. 2 (2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.

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One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep lea
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Li, Yung-Chen, Hsiao-Yun Huang, Nan-Ping Yang, and Yi-Hung Kung. "Stock Market Forecasting Based on Spatiotemporal Deep Learning." Entropy 25, no. 9 (2023): 1326. http://dx.doi.org/10.3390/e25091326.

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This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Short Term Memory (LSTM) and recent Transformer models lack the ability to directly incorporate spatial information, making the Spacetimeformer model a valuable addition to stock price prediction. This article uses the ten minute stock prices of the constituent stocks of the Taiwan 50 Index and the intraday data of individual stock on the Taiwan Stock Ex
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alan, Gun, Kavin Kumar, Su rya, and Kalai Chelvi. "Stock Market Prediction." International Academic Journal of Science and Engineering 9, no. 1 (2022): 18–22. http://dx.doi.org/10.9756/iajse/v9i2/iajse0909.

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Researchers have been investigating various approaches to accurately forecast stock market prices. Trading professionals can gain better insights regarding data, such as potential trends, by using useful prediction tools. Additionally, since the study predicts future market conditions, investors stand to gain significantly. Using machine learning algorithms for predicting is one such approach. The goal of this study is to increase the accuracy of stock market predictions made using stock valuation. Many academics have developed various approaches to address this issue, primarily using conventi
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Yang, Jiapeng. "Goldman Sachs’s Price Forecast Based on ARIMA and LSTM." Highlights in Business, Economics and Management 24 (January 22, 2024): 2194–201. http://dx.doi.org/10.54097/zk7c4c90.

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The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the
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Santhoshi, Mrs, Rianna Kristin M, Manojj D, and Rajeshwar v. "Stock Market Prediction." International Journal of Research Publication and Reviews 6, no. 5 (2025): 4091–95. https://doi.org/10.55248/gengpi.6.0525.1730.

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Jaiswal, Gourav. "Stock Prediction Model Using TensorFlow." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 99–103. http://dx.doi.org/10.22214/ijraset.2021.39207.

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Abstract: In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend available in market prediction technologies is that the use of machine learning that makes predictions on the basis of values of current stock exchange indices by training on their previous values. Machine learning itself employs completely different models to create prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Considering the factors are open, close, low, high and volume. Keyw
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Jagtap, Ajitkumar, Yash Patil, and Darshan Oswal. "Visualizing and Forecasting Stocks Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2562–66. http://dx.doi.org/10.22214/ijraset.2022.41846.

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Abstract: India's stock market is exceedingly changing and reductionism, which has a countless number of features that control the directions and trends of the stock price; therefore, prediction of uptrend and downtrend is a complex process. This paper point of view to demonstrate the use of recurrent neural network in finance to prediction of the closing price of a selected stock and analyse opinions around it in real-time. By combining both techniques, the submitted model can give buy or sell recommendation. In Stock Market Prediction, the aim is to predict the upcoming future value of the f
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BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU, and M. BHARGAV SAI. "STOCK PRICE PREDICTION." YMER Digital 21, no. 05 (2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.

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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of r
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Dong, Yuxin, and Yongtao Hao. "A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion." Electronics 13, no. 20 (2024): 4111. http://dx.doi.org/10.3390/electronics13204111.

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Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional d
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Singh, Aditya Kumar, Anurag Gupta, Faraz Rabbani, Abhijeet Yadav, and Mr Atma Prakash Singh. "Stock Prediction using LSTM Technique." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1352–55. http://dx.doi.org/10.22214/ijraset.2024.61815.

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Abstract: We attempt to use a machine learning approach to anticipate stock prices in this project. Whenit comes to stock price predictions, machine learning works well. The goal is to forecast future stock prices. make more accurate and better investment decisions We propose incorporating mathematical functions into stock prices. To arrive at an acceptable timescale, examine the prediction system, machine learning, and other external factors. delivers accurate stock predictions and lucrative trades There are some There are two types of stocks. Day trading is another name forintraday trading.
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Dissertations / Theses on the topic "Stock Prediction"

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Chatterjee, Arijit. "Stock Prediction Analyzing Investor Sentiments." Diss., North Dakota State University, 2017. http://hdl.handle.net/10365/26045.

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We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stock
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Fatah, Kiar, and Taariq Nazar. "Stock Market Prediction With Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293853.

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Due to the unpredictability of the stock market,forecasting stock prices is a challenging task. In this project,we will investigate the performance of the machine learningalgorithm LSTM for stock market prediction. The algorithmwill be based only on historical numerical data and technicalindicators for IBM and FORD. Furthermore, the denoising anddimension reduction algorithm, PCA, is applied to the stockdata, to examine if the performance of forecasting the stockprice is greater than the initial model. A second method, transferlearning, is applied by training the model on the IBM datasetand th
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Bahceci, Oktay, and Oscar Alsing. "Stock Market Prediction using Social Media Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166448.

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Stock Forecasting is commonly used in different forms everyday in order to predict stock prices. Sentiment Analysis (SA), Machine Learning (ML) and Data Mining (DM) are techniques that have recently become popular in analyzing public emotion in order to predict future stock prices. The algorithms need data in big sets to detect patterns, and the data has been collected through a live stream for the tweet data, together with web scraping for the stock data. This study examined how three organization's stocks correlate with the public opinion of them on the social networking platform, Twitter. I
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Ghodsi, Boushehri Ali. "Applying fuzzy logic to stock price prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ54332.pdf.

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Marques, Inês Filipa Rodrigues. "Machine learning in finance : stock market prediction." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19517.

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Mestrado em Finanças<br>Esta dissertação traz novas ideias na utilização de Machine Learning nos mercados financeiros, tendo por base o índice S&P500 durante o período 2000-2019. A dificuldade subjacente à aplicação das técnicas de Machine Learning é superada através da implementação de métodos automáticos de Machine Learning. Com esta implementação, investidores com pouco ou nenhum know-how podem tirar vantagem do uso destas técnicas. Nós investigámos a performance das técnicas de Machine Learning e comparámos com a performance de técnicas tradicionais de previsão de séries temporais, como o
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HALLGREN, FREDRIK. "On Prediction and Filtering of Stock Index Returns :." Thesis, KTH, Matematik (Inst.), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-35154.

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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.

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Modarres, Najafabadi Sayed Reza. "Prediction of stock market indices using machine learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40795.

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Financial time series prediction is a very important economical problem but the data available is very noisy. In this thesis, we explain the use of statistical and machine learning methods for stock market prediction and we evaluate the performance of these methods on data from the S&P/TSX 60 stock index. We use both linear regression and support vector regression, a state-of-art machine learning method, which is usually robust to noise. The results are mixed, illustrating the difficulty of the problem. We discuss the utility of using different types of data pre-processing for this task as wel
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Holmqvist, Carl. "Opinion analysis of microblogs for stock market prediction." Thesis, KTH, Teoretisk datalogi, TCS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233197.

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This degree project investigates if a company’s stock price development can be predicted using the general opinion expressed in tweets about the company. The project starts off with the model from a previous project and then tries to improve the results using state-of-the-art neural network sentiment analysis and more tweet data. This project also attempts to perform hourly predictions along with daily predictions in order to investigate the method further. The results show a decrease in accuracy compared to the previous project. The results also indicate that the neural network sentiment anal
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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.

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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This ap
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Books on the topic "Stock Prediction"

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I, Ellison George, ed. Stock returns cyclicity, prediction and economic consequences. Nova Science Publishers, 2009.

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I, Ellison George, ed. Stock returns cyclicity, prediction and economic consequences. Nova Science Publishers, 2009.

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J, Millard Brian. Channel analysis: The key to share price prediction. 2nd ed. J. Wiley, 1997.

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Goval, Amit. A comprehensive look at the empirical performance of equity premium prediction. National Bureau of Economic Research, 2004.

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Lin, Jie-Shin. The top management changes of construction firms: Stock price reaction and possibility of prediction. UMIST, 1996.

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McMillan, David G. Predicting Stock Returns. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69008-7.

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Hjalmarsson, Erik. Predicting global stock returns. Federal Reserve Board, 2008.

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Baker, Malcolm. Pseudo market timing and predictive regressions. National Bureau of Economic Research, 2004.

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Baker, Malcolm. Pseudo market timing and predictive regressions. National Bureau of Economic Research, 2004.

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Goyal, Amit. Predicting the equity premium with dividend ratios. National Bureau of Economic Research, 2002.

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Book chapters on the topic "Stock Prediction"

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Paluszek, Michael, and Stephanie Thomas. "Stock Prediction." In Practical MATLAB Deep Learning. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5124-9_10.

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Paluszek, Michael, Stephanie Thomas, and Eric Ham. "Stock Prediction." In Practical MATLAB Deep Learning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7912-0_10.

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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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Joyce, Philip. "Stock Price Prediction." In Practical Numerical C Programming. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6128-6_4.

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Hapsari, V. A. W., and R. Rokhim. "Stock movement prediction." In Contemporary Research on Business and Management. CRC Press, 2021. http://dx.doi.org/10.1201/9781003196013-12.

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Pardeshi, Karan, Sukhpal Singh Gill, and Ahmed M. Abdelmoniem. "Stock Market Price Prediction." In Applications of AI for Interdisciplinary Research. CRC Press, 2024. http://dx.doi.org/10.1201/9781003467199-11.

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Untwal, Nitin Jaglal, and Utku Kose. "Stock Prediction Applying LSTM." In Data Analytics for Finance Using Python. CRC Press, 2024. http://dx.doi.org/10.1201/9781032618241-15.

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Jagwani, Bhagwan, and Udai Bhan Trivedi. "Prediction of stock price." In Advances in Science, Engineering and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003641544-44.

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Kwon, Yung-Keun, and Byung-Ro Moon. "Evolutionary Ensemble for Stock Prediction." In Genetic and Evolutionary Computation – GECCO 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_120.

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Bai, Dingwei. "The Prediction of Stock Prices." In Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-304-7_77.

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Conference papers on the topic "Stock Prediction"

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Karadaş, Furkan, Bahaeddin Eravcı, and Ahmet Özbayoğlu. "Multimodal Stock Price Prediction." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013174500003890.

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Akgün, Halil İbrahim, and Ahmet Murat Özbayoğlu. "Stock Price Prediction Using Mamba." In 2024 15th National Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2024. https://doi.org/10.1109/eleco64362.2024.10847128.

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Aggarwal, Yash, Dolly Sharma, Aman Kumar Agrawal, and Saurabh Yadav. "Stock Prediction Using Artificial Intelligence." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912203.

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Sikarwar, Shailendra Singh, Shweta Jaswal, Rajeev Sharma, Ganesan G, Ranjan Ganguli, and Mukesh Kumar Mahawar. "Stock Price Prediction Using Transformers." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911285.

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Sohail, Amir, Hammad Afroz, J. N. Singh, Dipti Goel, and Laith H. Jasim. "Stock Price Prediction Using LSTM." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895015.

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Singh, Arun Kumar, Arjun Singh, Akshat Ishan, Ankit Kumar, Gaurav Kumar Dhari, and Bhaskar Kumar. "AI Based Stock Market Prediction." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895866.

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Mohmmad, Sallauddin, Ugge Reethu Varma, Thatikonda Sai Teja, Kadarla Mithrasri, Polsani Chaithra Sri, and Juluri Keerthan. "Stock Price Prediction using LSTM." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013311.

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Liu, Kuan-Hsien, Yu-Shiang Lin, Tsung-Jung Liu, and Wei-Shen Tai. "StockQM: A Cross-Frequency Dataset for Stock Prediction and a New Stock Prediction Model." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674208.

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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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Shankar, Metha, Sebastian Terence, Jude Immaculate, Anishin Raj M. M, and Vinoth Ewards. "Tata Motors Stock Price Prediction Using Predictive Artificial Intelligence." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717037.

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Reports on the topic "Stock Prediction"

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Mylavarapu, Vishnu Kalyan. News based prediction of Stock price. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-1054.

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performan
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Avery, Christopher, Judith Chevalier, and Richard Zeckhauser. The "CAPS" Prediction System and Stock Market Returns. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w17298.

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Han, Shangxuan. Stock Prediction with Random Forests and Long Short-term Memory. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1334.

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Cheng, Tingting, Jiti Gao, and Oliver Linton. Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.0318.

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Danylchuk, H., V. Derbentsev, Володимир Миколайович Соловйов, and A. Sharapov. Entropy analysis of dynamic properties of regional stock markets. Society for Cultural and Scientific Progress in Central and Eastern Europe, 2016. http://dx.doi.org/10.31812/0564/1154.

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This paper examines entropy analysis of regional stock markets. We propose and empirically demonstrate the effectiveness of using such entropy as sample entropy, wavelet and Tsallis entropy as a measure of uncertainty and instability of such complex systems as regional stock markets. Our results show that these entropy measures can be effectively used as crisis prediction indicators.
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Соловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a ch
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Соловйов, Володимир Миколайович, V. Saptsin, and D. Chabanenko. Markov chains applications to the financial-economic time series predictions. Transport and Telecommunication Institute, 2011. http://dx.doi.org/10.31812/0564/1189.

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In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different freque
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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matr
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Soloviev, Vladimir, Andrii Bielinskyi, Oleksandr Serdyuk, Victoria Solovieva, and Serhiy Semerikov. Lyapunov Exponents as Indicators of the Stock Market Crashes. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4131.

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Abstract:
The frequent financial critical states that occur in our world, during many centuries have attracted scientists from different areas. The impact of similar fluctuations continues to have a huge impact on the world economy, causing instability in it concerning normal and natural disturbances [1]. The an- ticipation, prediction, and identification of such phenomena remain a huge chal- lenge. To be able to prevent such critical events, we focus our research on the chaotic properties of the stock market indices. During the discussion of the re- cent papers that have been devoted to the chaotic beh
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