Relevant bibliographies by topics / Stock intelligence

Contents

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

Academic literature on the topic 'Stock intelligence'

Author: Grafiati
Published: 8 September 2021
Last updated: 29 July 2025

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Journal articles on the topic "Stock intelligence"

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Ho, Linh Tu, Christopher Gan, Shan Jin, and Bryan Le. "Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?" Journal of Risk and Financial Management 15, no. 7 (2022): 302. http://dx.doi.org/10.3390/jrfm15070302.

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We estimate and compare the impact of the coronavirus pandemic (COVID-19) on the performance of Artificial Intelligence (AI) and conventional listed firms using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the news of COVID-19 spread and the pandemic entered the first, second, third, and fourth months (24 February 2020, 23 March 2020, 20 April 2020, and 18 May 2020, respectively). The results show that the negative impact of COVID-19 on the AI stock market was less severe than on the co
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AH, Talebibanizi. "Using Intelligent Systems to Manage Risks and Reduce Financial Risks using Artificial Intelligence in Large Companies." Philosophy International Journal 7, no. 1 (2024): 1–19. http://dx.doi.org/10.23880/phij-16000319.

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This study was an attempt to examine the using intelligent systems to manage risks and reduce financial risks using artificial intelligence in large companies. The data collected from the data is collected from the stock organization and the stock Securities of Iran. Moreover, the data is collected from 17 companies for ten years and the data was collected through the variance formula and then the results were examined using the SSPS method. Variance formula is σ µ 2= xi- 2 /n ( ( )) ∧ ∧ ∑ . The data is completely obtained from a reliable and correct source, which is related to the Department
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Bagade, Mr Ketan, and Prof Varsha Bhosale. "Artificial Intelligence based Stock Market Prediction Model using Technical Indicators." International Journal of Innovative Technology and Exploring Engineering 11, no. 6 (2022): 34–39. http://dx.doi.org/10.35940/ijitee.f9915.0511622.

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The indian stock market is highly volatile and complex by nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy & Sell prices are predictable and investor can make above-average profits using efficient Technical Analysis (TA).Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company’s reputation, news, sentiments and other fundamental issues while stock indices are less affected by these issues. In this work, architecture of project is given.As a part of prediction model the Long Short-
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Mr., Ketan Bagade, and Varsha Bhosale Prof. "Artificial Intelligence based Stock Market Prediction Model using Technical Indicators." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 6 (2022): 34–39. https://doi.org/10.35940/ijitee.F9915.0511622.

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<strong>Abstract: </strong>The indian stock market is highly volatile and complex by nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy &amp; Sell prices are predictable and investor can make above-average profits using efficient Technical Analysis (TA).Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company&rsquo;s reputation, news, sentiments and other fundamental issues while stock indices are less affected by these issues. In this work, architecture of project is given.As a part of
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Zhao, Danxuan. "Forecasting Stock Prices with Artificial Intelligence." ITM Web of Conferences 70 (2025): 02023. https://doi.org/10.1051/itmconf/20257002023.

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The purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). To conduct the study, Wal-Mart stock data is utilized to accurately predict future stock prices. The results show that the MSE for the SimpleRNN test is higher, indicating weaker generalization. The MSE of th
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HADI, QAZI ABDUL. "Artificial Intelligence in Finance." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46550.

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ABSTRACT This research paper delves into the uses and Functioning of Artificial Intelligence (AI) in the Financial Sector. AI helps financial companies be smarter, faster and safer in their functioning with money and People. The paper talks about different ways AI is used in finance. For example: It helps decide if someone is likely to repay a loan (credit scoring), tries to guess how stock prices will change (stock market predictions), buys and sells stocks automatically (automated trading), and helps companies avoid losing money by spotting risks early (risk management). The goal of this res
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Apraj, Saurabh D. "A Review on Artificial Intelligence in Stock Market." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4358–60. http://dx.doi.org/10.22214/ijraset.2022.44946.

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Abstract: This paper essentially concentrates on the utilization of man-made consciousness and AI in the field of corporate share. The standards and qualities of KNN, k-Means, bisecting k-Means, and ANN algorithm are contemplated to analyse the impacts, similitudes and contrasts of various calculations. The calculations are carried out through Python programs for stock examination. As per the P/E proportion, profit rate, fixed resource turnover rate, net revenue and different marks of each stock, the stocks are characterized and grouped to anticipate the stock improvement prospects and give re
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Guan, Jialin. "The Application of Artificial Intelligence to Stock Forecasting: A Literature Review." SHS Web of Conferences 218 (2025): 02028. https://doi.org/10.1051/shsconf/202521802028.

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Due to the non-linearity, high volatility and noise characteristics of stock prices, the prediction of stocks has become a challenging issue. The results of stock prediction algorithms rely on the selected indicators, including financial indicators and market sentiment indicators, and the algorithm model. A large number of scholars have conducted studies and innovations from different perspectives respectively to optimize the prediction results. This paper reviews the development of artificial intelligence in stock application from two perspectives of index and algorithm model. Among them, the
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Patalay, Sandeep, and Madhusudhan Rao Bandlamudi. "Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning." Ingénierie des systèmes d information 26, no. 1 (2021): 87–93. http://dx.doi.org/10.18280/isi.260109.

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Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional
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Jaiswal, Rupashi, Kunal Mahato, Pankaj Kapoor, and Sudipta Basu Pal. "A Comparative Analysis on Stock Price Prediction Model using DEEP LEARNING Technology." American Journal of Electronics & Communication 2, no. 3 (2022): 12–19. http://dx.doi.org/10.15864/ajec.2303.

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In today's world, Artificial Intelligence and Deep Learning are getting popular regularly. The various applications areas of artificial intelligence are related to human activity. One of the general application areas of neural networks and artificial intelligence is prediction analysis. In this paper, the authors also have performed one comparative study based on artificial intelligence. Authors have performed stock market predictions using different models. In reality, stock markets are entirely volatile, so there is very much a requirement of good prediction analysis for judging the stocks p
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Dissertations / Theses on the topic "Stock intelligence"

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Budiakova, O., and V. Budiakov. "Use of artificial intelligence in the stock market." Thesis, Черкаський національний університет імені Богдана Хмельницького, 2021. https://er.knutd.edu.ua/handle/123456789/19411.

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In the near future, artificial intelligence will be a driving force for many areas of marketing and will give recommendations on products, communicate with consumers, create content. Businesses that have implemented artificial intelligence will be able to reduce staff costs and accelerate growth by bypassing their competitors.
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Castorina, Giovanni. "Artificial intelligence based hybrid systems for financial forecasting." Thesis, University of the West of England, Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365146.

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Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision
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Karathanasopoulos, Andreas. "Modeling and trading the Greek stock market with artificial intelligence models." Thesis, Liverpool John Moores University, 2011. http://researchonline.ljmu.ac.uk/6106/.

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The main motivation for this thesis is to introduce some new methodologies for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use some alternative computational methodologies named Evolutionary Support Vector Machine (ESVM), Gene Expression programming, Genetic Programming Algorithms and 2 hybrid combinations of linear and no linear models for modeling and trading the ASE20 Greek stock index using as inputs previous values of the ASE20 index and of four other financial indices. For comparison purposes, the tra
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Troeman, Reamflar Elvio Estebano, and Lisa Fischer. "Politics, Artificial Intelligence, Twitter and Stock Return : An Interdisciplinary Test for Stock Price Prediction Based on Political Tweets." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48436.

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As the world is gravitating toward an information economy, it has become more and more critical for an investor to understand the impact of data and information. One of the sources of data that can be converted into information are texts from microblogging platforms, such as Twitter. The user of such a microblogging account can filtrate opinion and information to millions of people. Depending on the account holder, the opinion or information originated from the designated account may lead to different societal impact. The microblogging scope of this investigation are politicians holding a Twit
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Shynkevich, Yauheniya. "Computational intelligence techniques for forecasting stock price movements from news articles and technical indicators." Thesis, Ulster University, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701435.

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Forecasting the future behaviour of market prices is an important area of research, which is exploited in asset allocation, risk management and algorithmic trading. Market behaviour is complex and influenced by many factors whose relationships are non-linear. The amount of financial data available for analysis is increasing substantially due to increased volumes of electronic trading, and market participants who are capable of extracting influential information from these massive amounts of data successfully benefit from using it in trading and investments. Computational intelligence technique
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Khoury, Pascal. "Traditional and swarm intelligence based algorithms for stock selection and risk modelling in emerging markets." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10042631/.

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Skolpadungket, Prisadarng. "Portfolio management using computational intelligence approaches : forecasting and optimising the stock returns and stock volatilities with fuzzy logic, neural network and evolutionary algorithms." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6306.

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Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraint
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Van, der Colff Francois. "An artificial intelligence model to predict financial distress in companies listed on the Johannesburg Stock Exchange." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/65515.

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As a prerequisite for an informed decision, a company’s financial results are undoubtedly one of the most important aspects to be considered in a financial distress prediction model. To rely purely on financial results for prediction is a risk. The dilemma is that financial variables are backward-looking and point-in-time measures of a company’s financial results. Ever-changing quantitative non-financial variables could enhance the decision-making process and should therefore be taken into consideration. For this research, an artificial intelligence model based on a unique combination of finan
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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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Wang, Fei, and 王緋. "Complex stock trading strategy based on parallel particle swarm optimization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49858889.

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Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this thesis, a complex stock trading strategy, namely Performance-based Reward Strategy (PRS), is proposed. PRS combines the seven most popular classes of
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Books on the topic "Stock intelligence"

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International, Conference on Artificial Intelligence Applications on Wall Street (2nd 1993 New York N. Y. ). The second annual International Conference on Artificial [Intelligence] Applications on Wall Street: Tactical & strategic technologies : proceedings. Software Engineering Press, 1993.

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1961-, Hudson Robert, and Littler Kevin, eds. The intelligent guide to stock market investment. Wiley, 1998.

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Matsumoto, Tōru. Japanese stocks: A guide for the intelligent investor. Kodansha International, 1989.

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Matsumoto, Tōru. Japanese stocks: A basic guide for the intelligent investor. Kodansha International, 1989.

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Isaacman, Max. Investing with intelligent ETFs. McGraw-Hill, 2008.

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Greer, Robert J. Intelligent commodity indexing: A practical guide to investing in commodities. McGraw-Hill, 2013.

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Leo, Kroon, Schoebel Anita, Wagner Dorothea 1957-, Zaroliagis Christos D. 1963-, and SpringerLink (Online service), eds. Algorithmic Methods for Railway Optimization: International Dagstuhl Workshop, Dagstuhl Castle, Germany, June 20-25, 2004, 4th International Workshop, ATMOS 2004, Bergen, Norway, September 16-17, 2004. Springer-Verlag Berlin Heidelberg, 2007.

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Briese, Stephen E. The commitments of traders bible: How to profit from insider market intelligence. J. Wiley & Sons, 2008.

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Briese, Stephen E. The commitments of traders bible: How to profit from insider market intelligence. J. Wiley & Sons, 2008.

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Cevelev, Aleksandr. Inventory and procurement management (rail transport). INFRA-M Academic Publishing LLC., 2024. https://doi.org/10.12737/2080065.

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The textbook outlines theoretical and practical issues of methodology of logistics and logistics of railway transport supply. The issues of practical activity of determining the need for material and technical resources of the track economy, inventory management and procurement, forecasting inventory volumes, as well as the introduction of a lean production system in the supply economy of the open Joint Stock company "Russian Railways" (JSC "Russian Railways"), aimed at providing unified approaches to the organization of the development and implementation of lean projects, are considered in de
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Book chapters on the topic "Stock intelligence"

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Amzile, Karim. "Artificial Intelligence and Stock Market." In Advances in Emerging Financial Technology and Digital Money. CRC Press, 2024. http://dx.doi.org/10.1201/9781032667478-4.

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Nguyen, Ngoc Kim Khanh, Quang Nguyen, and Marc Bui. "Mining Stock Market Time Series and Modeling Stock Price Crash Using a Pretopological Framework." In Computational Collective Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28377-3_53.

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Alnemer, Mohammed, and Abdalmuttaleb Al-Sartawi. "Artificial Intelligence and Stock Trading Decisions." In Studies in Computational Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43300-9_6.

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Nurmukhamedov, T., Z. H. Gulyamov, and A. Azimov. "Mathematical modeling of stock management." In Artificial Intelligence and Information Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781032700502-40.

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Tang, Junqing, Layla Khoja, and Hans Rudolf Heinimann. "Modeling Stock Survivability Resilience in Signed Temporal Networks: A Study from London Stock Exchange." In Studies in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72150-7_84.

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Hussain, Nisar, Amna Qasim, Zia-ud-din Akhtar, et al. "Stock Market Performance Analytics Using XGBoost." In Advances in Computational Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47765-2_1.

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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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Kaushik, Saroj, and Naman Singhal. "Pattern Prediction in Stock Market." In AI 2009: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10439-8_9.

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Fatima, Yasmeen, Mohammad Asif, Preethi Nanjundan, and Jossy P. George. "Predicting Stock Market Indexes with Artificial Intelligence." In Artificial Intelligence in Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003399292-17.

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Trippner, Paweł, and Rafał Jóźwicki. "The Efficiency of the Stock Exchange - The Case of Stock Indices of IT Companies." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87897-9_35.

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Conference papers on the topic "Stock intelligence"

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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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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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Bhatia, Shilpa, Deepanshu Bhardwaj, Akash Akash, and Lakshay Bhardwaj. "Prediction in Stock Market Value using Artificial Intelligence." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911380.

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Zhang, Yanting, Lei Yang, Shipei Du, Yu Sun, and Yaxin Hou. "Predicting financial and manufacturing stocks in China's stock market with random forest." In Second International Conference on Big Data, Computational Intelligence and Applications (BDCIA 2024), edited by Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3059615.

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Wen, Beibei, and Yuanyuan Zhao. "PSO-BP Neural Network Model in Stock Prediction." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721939.

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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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Bharadwaj, Gurudutt S., David Pratap, and Narayana Darapaneni. "Optimized Automated Stock Trading using DQN and Double DQN." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721810.

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Choudhary, Mudit, Vikas Bajpai, and Anukriti Bansal. "Stock Price Prediction Leveraging News Headlines." In 2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI). IEEE, 2024. http://dx.doi.org/10.1109/icetci62771.2024.10704186.

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Gao, Huaqi, and George Georgopoulos. "Stock Price Prediction Using Sentiment and Technical Analysis." In 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2024. https://doi.org/10.1109/wi-iat62293.2024.00147.

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Bhura, Shlok, Tanish Bhilare, and Kavita Kelkar. "Facilitating Stock Recommendations through Sentiment Analysis." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810912.

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Reports on the topic "Stock intelligence"

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Zhao, Yao. Supply Chain Analytics and Stock Market Intelligence. Instats Inc., 2025. https://doi.org/10.61700/8cx0l3vsfg5ea1342.

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This seminar delves into the intersection of supply chain management and stock market intelligence, highlighting how data analytics can reveal the influence of supply chains on financial and stock market performance. Participants will acquire advanced techniques, data and tools to analyze these dynamics, enhancing their research capabilities and understanding the strategic implications for businesses and investors.
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Dumas, Nathalie, Flourentzou Flourentzos, Julien BOUTILLIER, Bernard Paule, and Tristan de KERCHOVE d’EXAERDE. Integration of smart building technologies costs and CO2 emissions within the framework of the new EPIQR-web application. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541616188.

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The EPIQR method was developed between 1996 and 1998 within the framework of the European research programme JOULE II and with the support of the Swiss Federal Office for Education and Science. In its first versions, the EPIQR software and EPIQR+ that succeeded it, were desktop tools, allowing a precise diagnosis of the state of deterioration of an existing building and the elaboration of renovation scenarios including the different costs of the necessary works. However, deep refurbishment rate is still low. Climatic emergency state declared by most of the Swiss Cantons makes it necessary to s
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