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Relevant bibliographies by topics / Stock analysis

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Academic literature on the topic 'Stock analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Stock analysis"

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Patidar, Jaydeep. "Web Trade Analytics." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01704.

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This research introduces an innovative web application developed using the MERN (MongoDB, Express.js, React, Node.js) stack, enhanced with fundamental machine learning algorithms, designed to address the complexities of stock market analysis. The central focus is on creating a user-customizable dashboard, allowing investors to select specific stocks for real-time analysis, sentiment tracking, and future price prediction. The methodology integrates historical stock data with sentiment analysis sourced from news and social media. The machine learning algorithms leverage this data to generate buy

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Saputra, Darma, Irwan Trinugroho, and Faizul Mubarok. "Optimal portfolio strategy: A stock index-based analysis." Sebelas Maret Business Review 9, no. 2 (2025): 139. https://doi.org/10.20961/smbr.v9i2.95170.

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<p>The classification of stock indices published by Indonesia Stock Exchange (2021) has resulted in variations of stock indices, whereby a stock index may contain stocks that are the same, similar, or different from other stock indices. Based on the portfolio theory in Hartono (2014) and the Markowitz model in Lutfi and Hendrian (2020), variations or differences in portfolio performance can be influenced by the variations or differences in stock indices. This article analyzes the differences between optimal portfolio performances based on these variations of stock indices. Based on a sam

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

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

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

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

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Khan, Safi Ullah, and Syed Tahir Hijzi. "Single Stock Futures Trading and Stock Price Volatility: Empirical Analysis." Pakistan Development Review 48, no. 4II (2009): 553–63. http://dx.doi.org/10.30541/v48i4iipp.553-563.

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This study examines impact of the introduction of single stock futures contracts on the return volatility of the SSFs-listed underlying stocks. The study documents a significant decrease in return volatility for the SSFs-underlying stocks following the introduction of single stock futures contracts on the Karachi Stock Exchange. The multivariate analysis in which the spot trading volume, the futures trading volume and open interest were partitioned into news and informationless components, the estimated coefficient of expected futures volume component is statistically significant and negativel

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Mazher, Muhammad Ahmad, and Jauhari Dahalan. "Pakistan’s Infrastructure Capital-Growth Analysis." SEISENSE Journal of Management 4, no. 1 (2020): 1–12. http://dx.doi.org/10.33215/sjom.v4i1.486.

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Purpose- This empirical study facet at Pakistan for the period between 1960 and 2017 in the connection between public investment, public capital stock, private investment, private capital stock, and real GDP. Design/Methodology- Using theoretical and empirical literature assessment, to measure the impact of private investment, private capital stock, government investment, and government capital stock on Pakistan's real gross domestic product, we involved the ARDL Bound tests. Findings- A positive and significant connection was revealed between government investment, a private capital stock wit

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Yu, Junjie, Wenjia Sang, and Yiqian Tang. "Analysis of Apple Stock - Based on R." Frontiers in Business, Economics and Management 15, no. 3 (2024): 427–30. http://dx.doi.org/10.54097/sjaw2e59.

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With the continuous development of China's economy, the stock market is becoming increasingly mature, and stocks play a crucial role in the economic life. The development and changes in stocks can measure the economic development of enterprises. Meanwhile, stock investment has become a means for people to obtain economic benefits. The development of stocks is closely related to economic development. The fluctuation of stock prices can reflect the implementation of national economic policies and also comprehensively reflect the living conditions of residents. With the continuous improvement of

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Jia, Zuming. "Using Factor Analysis to Rank Several Chinese Public Stock." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 48–55. http://dx.doi.org/10.54097/hset.v16i.2237.

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The stock market currently plays an important role in the whole society and people ranging from a child to the old are all starting to explore and invest in the stock market. Since the stock market is unpredictable and fluctuates, people need to master more information about the stock in order to prevent the risks of investing and profit from the investment competition. In this paper, several indicators will be proceeded factor analysis by using SPSS, which can help people better understand how the stocks go and prevent them from losing too much because of ignorance. This method firstly perfor

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Shrikhande, Paresh, Raghu Ramani, and Rushi Bhalerao. "Stock Market Analysis and Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1254–63. http://dx.doi.org/10.22214/ijraset.2022.42239.

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Abstract: Stock price Analysis & Prediction is a one of the sought after and popular topic throughout the last decade. By using machine learning and deep learning (RNN & LSTM) methods to make stock price prediction using real time data. While using Deep learning functions to predict and analyze stock prices are becoming more prevalent in these recent days. Its observed and assumed that machine learning as well as deep learning methods with RNN and LSTM could produce accurate results in stock price prediction. That is why we would like to try our own methods for this project. We have us

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Huang, Yuran. "Analysis of China's Banking Market: Based on ARIMA Model." Advances in Economics, Management and Political Sciences 88, no. 1 (2024): 131–36. http://dx.doi.org/10.54254/2754-1169/88/20240893.

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China's A-share stock market has long been influenced by the banking sector, which holds a significant market capitalization. However, much of the current market research is macro-focused, which can result in a lack of correlation with individual stocks. Thus, this paper will begin with individual stocks and utilize the autoregressive integrated moving average (ARIMA) forecasting model to statistically predict their expected value. Additionally, it will analyze the errors of individual stocks and various statistical indices, and ultimately relate them to the fluctuations and patterns in the ba

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Dissertations / Theses on the topic "Stock analysis"

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Wongbangpo, Praphan. "Dynamic analysis on ASEAN stock markets." access full-text online access from Digital dissertation consortium, 2000. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?9982126.

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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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Lange, Joe. "An intraday analysis of stock market liquidity /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9906485.

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Wong, Michael C. S. "Technical analysis and market inefficiency a study of the Hong Kong stock market /." online access from ProQuest databases, 1997. http://libweb.cityu.edu.hk/cgi-bin/er/db/pqdiss.pl?9907800.

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Cheng, Wei. "Factor Analysis for Stock Performance." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-050405-180040/.

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Chen, Gang. "The Chinese stock market : an emperical analysis of market segmentation, inter-relationships and theoretical versus actual stock prices." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=165872.

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This thesis contributes to our knowledge of the behaviour of the Chinese stock market by offering an empirical investigation into issues such as market segmentation, inter‐relationships between Chinese stock markets and inter‐relationships with foreign stock markets. Basic questions which have been typically analysed for developed stock markets are considered in this thesis. These include an analysis of core concepts such as volatility; causal links with economic variables and the reasons why the theoretical stock price may be different from the actual stock price. Methodological methods inclu

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Pu, Hansong. "An Analysis of Preferred Equity Redemption Cumulative Stock." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc277588/.

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This dissertation examines whether Percs, Preferred Equity Redemption Cumulative Stocks, are properly priced regarding to the relevant securities, such as the underlying common stock, the long-term call option of the stock, and so on. Test results indicate that Percs were overpriced with respect to the equivalent packages composed of the relevant securities. Further tests on arbitrage restrictions show that transaction costs would prevent arbitrage profits. This dissertation also examines the market reactions to Percs offerings. Test results reveal that the market reactions to the announcement

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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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Chavarnakul, Thira. "The development of hybrid intelligent systems for technical analysis based equivolume charting." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Thira_Chavarnakul_Dissertation_2007_09007dcc803425db.pdf.

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Thesis (Ph. D.)--University of Missouri--Rolla, 2007.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed October 25, 2007) Includes bibliographical references.

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Mokoaleli-Mokoteli, Thabang. "Are analysts biased? : an analysis of analysts' stock recommendations for stocks that perform contrary to expectations." Thesis, Cranfield University, 2005. http://hdl.handle.net/1826/934.

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The finance literature suggests that analysts’ stock recommendations have negligible impact on market prices. Some studies suggest this lack of market impact may be partly driven by the affiliations between investment banks and the firms their brokerage arms cover (conflicts of interest). However, most of these studies fail to take into account other factors including institutional and trading issues and psychological biases which may well be just as important in influencing analysts when they gather, process and interpret information about stocks. The aim of the current study is to establish

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Books on the topic "Stock analysis"

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Domash, Harry. Fire your stock analyst!: Analyzing stocks on your own. FT Press, 2009.

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Thomsett, Michael C. Mastering fundamental analysis. Dearborn Finanical Pub., 1998.

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Edwards, Robert D., John Magee, and W. H. C. Bassetti. Technical Analysis of Stock Trends. CRC Press, 2018. http://dx.doi.org/10.4324/9781315115719.

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Edwards, Robert D. Technical analysis of stock trends. 6th ed. Prentice-Hall, 1992.

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Edwards, Robert D. Technical analysis of stock trends. 6th ed. J. Magee Inc., 1992.

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1901-, Magee John, and Bassetti W. H. C, eds. Technical analysis of stock trends. 9th ed. CRC Press, 2007.

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1901-, Magee John, and Bassetti W. H. C, eds. Technical analysis of stock trends. Taylor & Francis, 2013.

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Edwards, Robert D. Technical analysis of stock trends. BN Publishing, 2008.

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Edwards, Robert D. Technical analysis of stock trends. International Technical Analysis Publishers, 1991.

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Edwards, Robert D. Technical analysis of stock trends. 5th ed. J. Magee, Inc., 1987.

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Book chapters on the topic "Stock analysis"

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Dumiter, Florin Cornel, and Florin Marius Turcaș. "Stock Exchange Predictions." In Technical Analysis Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27416-9_7.

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Ramlall, Indranarain. "Stock Market Analysis." In Economics and Finance in Mauritius. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-39435-0_4.

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Hilborn, Ray, and Carl J. Walters. "Multispecies Analysis." In Quantitative Fisheries Stock Assessment. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3598-0_14.

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Busu, Mihail. "Stock Market." In Essentials of Investment and Risk Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15056-2_4.

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Hilborn, Ray, and Carl J. Walters. "Virtual Population Analysis." In Quantitative Fisheries Stock Assessment. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3598-0_10.

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Rothschild, Brian J., Steven G. Smith, and Helen Li. "The Application of Time Series Analysis to Fisheries Population Assessment and Modeling." In Stock Assessment. CRC Press, 2023. http://dx.doi.org/10.1201/9781003421252-7.

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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. "Technical Analysis Toolkit." In Stock Market Modeling and Forecasting. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5155-5_7.

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Wendt, Karen. "Critical analysis and Prognosis on the Development of SSEs." In Social Stock Exchanges. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99720-5_4.

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Hilborn, Ray, and Carl J. Walters. "Analysis of Body Size and Growth Data." In Quantitative Fisheries Stock Assessment. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3598-0_13.

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Conference papers on the topic "Stock analysis"

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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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Kowshik, S. B., Samhitha Nair, S. Kiruthika, and J. Joshan Athanesious. "StockAI - Stock Analysis Tool Agent." In 2024 International Conference on Computing and Intelligent Reality Technologies (ICCIRT). IEEE, 2024. https://doi.org/10.1109/iccirt59484.2024.10921844.

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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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S, Rahul, Shrinivas V. N, Brindha G, and Karthikeyan M. V. "Stock Market Analysis and Price Prediction." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031782.

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P, Nagaraj, M. Chaithanya Prabhu, Ch Yasaswitha, T. Visist, K. Mohana, and Varun Teja. "Stock Market Trend Analysis and Prediction Using Sentiment Analysis." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011720.

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S, Jeevitha, and Kriti Arya. "Stock Movement of Wipro Using Technical Analysis." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717324.

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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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Shah, Jay Mintu, Avani Dhagam, Aryan Wadhwa, Aravalli Atchuta Ram, and R. Bharathi. "Recommender Systems for Sector-Specific Stock Analysis." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10967744.

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Patil, Sharathchandra, S. Mahalakshmi, N. R. Vikas, V. N. Srajan, and M. Srinandan. "LLM-Driven Peer Competitor and Stock Analysis." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969856.

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Thakur, Nitin Kumar, Mandeep Kaur, and Keshav Gupta. "Stock Market Data Analysis Using LSTM Model." 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/otcon65728.2025.11070758.

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Reports on the topic "Stock analysis"

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Choi, Yun-Jung. Stock Market Analysis in Fashion Marketing Class. Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-754.

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Piskun, Alexandr, Володимир Миколайович Соловйов, and Sergio Piskun. Recurrence Quantification Analysis of Stock Market Crashes. University of Montreal, 2009. http://dx.doi.org/10.31812/0564/1135.

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Boudoukh, Jacob, Ronen Feldman, Shimon Kogan, and Matthew Richardson. Which News Moves Stock Prices? A Textual Analysis. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w18725.

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Hausman, Jerry, Andrew Lo, and A. Craig MacKinlay. An Ordered Probit Analysis of Transaction Stock Prices. National Bureau of Economic Research, 1991. http://dx.doi.org/10.3386/w3888.

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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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Zhao, Yao. Operations, Finance, and Stock Market Performance (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/v5ah6f65g6w1r1620.

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This seminar explores the interplay between operations, finance, and stock market performance. Participants will gain quantitative insights into how operational decisions impact financial outcomes and subsequently stock market movements, utilizing vast and free business and financial databases like SCDATA.AI for sophisticated data analysis and predictive analytics, and using recent examples like Amazon, Intel, Boeing and Tesla.

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Ruppert, David, and R. J. Carroll. Data Transformations in Regression Analysis with Applications to Stock - Recruitment Relationships. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada170183.

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O'Connor, Richard J., Mark D. Brickhouse, David McGarvey, H. D. Durst, and William R. Creasy. NMR Method for the Quantitative Purity Analysis of Neat Feed Stock Samples. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada406815.

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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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Bielinskyi, Andrii, Vladimir Soloviev, Serhiy Semerikov, and Viktoria Solovieva. Detecting Stock Crashes Using Levy Distribution. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3210.

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In this paper we study the possibility of construction indicators-precursors relying on one of the most power-law tailed distributions – Levy’s stable distribution. Here, we apply Levy’s parameters for 29 stock indices for the period from 1 March 2000 to 28 March 2019 daily values and show their effectiveness as indicators of crisis states on the example of Dow Jones Industrial Average index for the period from 2 January 1920 to 2019. In spite of popularity of the Gaussian distribution in financial modeling, we demonstrated that Levy’s stable distribution is more suitable due to its theoretica

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