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Journal articles on the topic 'Stock intelligence'

Author: Grafiati

Published: 8 September 2021

Last updated: 29 July 2025

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1

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 & 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

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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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TSAI, CHIH-FONG, YUAH-CHIAO LIN, and YI-TING WANG. "DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES." International Journal on Artificial Intelligence Tools 18, no. 04 (2009): 603–11. http://dx.doi.org/10.1142/s0218213009000299.

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Stock trading activities are always very popular in many countries. Generally, investors with various backgrounds have different preferences over the stocks they trade. In literature, a number of studies examine the institutions' holding preferences for certain stock characteristics when choosing the security portfolio. However, very few studies investigate the stock trading preferences of individual investors. In this paper, we focus on two factors which affect the portfolio choices of investors, which are stock characteristics and investor features. In particular, a self-organizing map (SOM)

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Zhao, Yinuo. "Research on momentum strategy and contrarian strategy in AI stock prediction." Applied and Computational Engineering 29, no. 1 (2023): 125–32. http://dx.doi.org/10.54254/2755-2721/29/20231207.

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The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (E

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Subha, S. "Role of Artificial Intelligence in Stock Trading." Thiagarajar College of Preceptors Edu Spectra 7, S1 (2025): 44–47. https://doi.org/10.34293/eduspectra.v7is1-feb25.005.

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AI is transforming trading by democratizing access to sophisticated tools, making stock market investments more efficient. With machine learning and algorithmic trading, investors can analyze data and predict trends quickly. In India, AI's integration is growing, with SEBI enhancing its regulatory processes. As AI evolves, it promises significant growth in trading, while also posing risks like market volatility and misinformation. Earlier, investments in the stock market were mainly based on gut feelings and extensive research. Investors focused on a company's financial details and its stabili

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Sayed, Yaser El. "A Stock Market Prediction Module Based on Ann&Swarm Intelligence." Journal of Global Economy, Business and Finance 6, no. 11 (2024): 22–27. https://doi.org/10.53469/jgebf.2024.06(11).05.

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The forecasting of prices movements in financial market on daily or certain time period basis is one of the prime concerns and challenging for both researchers and investors. The characteristics of ripples of stock prices movements reflect unpredictable, non- stationary, non-linear, noisy, and chaotic tendency. A stock market is a public market for securities where the organized issuance and trading of company stocks take place either through exchange or over the counter in physical or electronic forms. It is now a day commonly known that huge amounts of capital are traded through stock market

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Haneffa Muchlis Gazali, Junisa Jumadi, Noor Rasyidah Ramlan, Nurmaisarah Abd Rahmat, Siti Nor Hazilawati Mohd Uzair, and Amirah Norliyana Mohid. "Application of Artificial Intelligence (AI) in Islamic Investments." Journal of Islamic Finance 9, no. 2 (2020): 70–78. https://doi.org/10.31436/jif.v9i2.485.

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This study examines the application of Artificial Intelligence (AI) in Islamic Investments. AI technology is very popular in both the conventional and Islamic banking systems as reflected in the contributions of AI in Islamic investment. The technology helps investors to analyse their stocks in terms of price levels, the current stability of each stock and the future price forecasts based on current price and stock data. The study is a conceptual discussion on the application of AI in Islamic investment, which focuses on the discussion of Text Mining, Algorithmic Trading, Stock Pick and Robo i

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Kong, Yoon, and Uddalok Sen. "Using Deep Learning Algorithms Prediction of the Closing Price of Stocks with Indication Features." International Journal of Engineering and Technology 16, no. 3 (2024): 143–48. http://dx.doi.org/10.7763/ijet.2024.v16.1271.

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Stock market price forecasting is currently a hot topic for research in the artificial intelligence field. It is quite challenging to correctly forecast stock market returns because of the financial stock markets’ significant volatility and non-linearity. Programmable methods of prediction are now more accurate at predicting stock values thanks to developments in artificial intelligence and computational power. In the present study, stock price data from five different sectors with 10 years of history has been collected, and the closing price for each stock has been predicted using Long Short-

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Jiang, Xiaotian. "LSTM Prediction and Portfolio Optimization for Artificial Intelligence Industry." Advances in Economics, Management and Political Sciences 38, no. 1 (2023): 192–97. http://dx.doi.org/10.54254/2754-1169/38/20231912.

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The launch of ChatGPT has overwhelmingly been revolutionizing the stock market. Of particular interests of stock traders and financial analysts, discovery about artificial intelligence stock market has become the main focus. The paper selected top worldwide artificial intelligence (AI) enterprises from Yahoo Finance and made future return forecasts with the long short-term memory networks (LSTM). The predicted information is employed in conducting portfolio optimization within the scope of mean-variance analysis to obtain an assessment of the portfolios performance. The outcomes illustrate tha

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Yu, Huashu. "The Application of a Backpropagation Neural Network for the Prediction of the New York Stock Exchange." Theoretical and Natural Science 87, no. 1 (2025): 300–305. https://doi.org/10.54254/2753-8818/2025.21033.

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Stocks have significantly impacted the market over the past century. Nevertheless, the domain of stock market prediction has consistently been perceived as a prospective yet predominantly ineffectual discipline. At the same time, artificial intelligence networks offer distinct advantages in data processing. Numerous scholars are convinced that artificial intelligence networks are instrumental in enhancing the accuracy of stock market predictions. Furthermore, they have effectively implemented sophisticated algorithms, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks

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Gurnani, Vandana, Prem Singh, Pradeep Haldar, et al. "Programmatic assessment of electronic Vaccine Intelligence Network (eVIN)." PLOS ONE 15, no. 11 (2020): e0241369. http://dx.doi.org/10.1371/journal.pone.0241369.

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eVIN is a technology system that digitizes vaccine stocks through a smartphone application and builds the capacity of program managers and cold chain handlers to integrate technology in their regular work. To effectively manage the vaccine logistics, in 2015, this technology was rolled-out in 12 states of India. This study assessed the programmatic usefulness of eVIN implementation in the areas of vaccine utilization, vaccine stock and distribution management and documentation across selected cold chain points. A pre-post study design was used, where cold chain points (CCPs) were selected usin

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K. V., Prof Roopa, Prof Sunitha B. K, Rojej Shrestha, et al. "A COMPARATIVE STUDY OF AN AI TO INVESTIGATE THE ROLE OF SENTIMENT ANALYSIS IN STOCK MARKET." International Journal of Social Science and Economic Research 08, no. 04 (2023): 594–605. http://dx.doi.org/10.46609/ijsser.2023.v08i04.003.

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Sentiment analysis has arisen as a new way to forecasting stock market trends by studying public opinion and media sentiment about individual stocks or firms. The application of artificial intelligence (AI) in sentiment research has allowed for more accurate and effective predictions of stock market trends. Yet, the efficiency of AI-based sentiment analysis in predicting stock market patterns is still being researched and debated.

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Mohapatra, Badri Narayan, Bhagwat Nagargoje, Prajwal Zurunge, and Suraj More. "ARTIFICIAL INTELLIGENCE IN STOCK MARKET INVESTMENT." Journal of Engineering Science 28, no. 3 (2021): 96–100. http://dx.doi.org/10.52326/jes.utm.2021.28(3).08.

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This study investigates the selection of stock from huge stock markets and by using good selection tools so that it will give a good return value. It helps investor to find an easy decision regarding their investment in stock market individually with effective collection of trading activities. Many artificial intelligence (AI) techniques are untested in the financial crisis scenario. This research really helpful to the investor in the stock selection and stock purchase decision. AI is also a one of the hottest topic for most industries, researchers and investors. The financial market is easy t

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Breitmayer, Bastian, Filippo Massari, and Matthias Pelster. "Swarm intelligence? Stock opinions of the crowd and stock returns." International Review of Economics & Finance 64 (November 2019): 443–64. http://dx.doi.org/10.1016/j.iref.2019.08.006.

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Muksalmina, Muksalmina, Ghadamfar Muflih Idroes, and Aga Maulana. "Artificial Intelligence in Islamic Finance: Forecasting Stock Indices with Neural Prophet." Indatu Journal of Management and Accounting 2, no. 2 (2024): 68–80. https://doi.org/10.60084/ijma.v2i2.232.

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Ensuring financial system stability is paramount, especially in markets guided by Sharia principles, where investor confidence and adherence to ethical standards play critical roles. The ability to accurately forecast stock movements within this framework not only supports informed investment decisions but also strengthens the overall stability of financial markets. This research employs the innovative Neural Prophet model to predict Islamic stock indices in Indonesia with remarkable accuracy and depth. The model demonstrates its capability not only in accurately forecasting trends but also in

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Khan,, Er Reshma, Gagan Ajit Singh, Shubham Behal, Kartik Sharma, and Saurabh Kumar. "Stock Prediction Using Machine Learning Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27220.

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The volatility and non-linear nature of the financial stock markets make it incredibly difficult to estimate stock market returns efficiently.. Investors need rapid access to precise information while trading stocks to make intelligent selections. Programable prediction approaches have demonstrated to be increasingly successful in forecasting stock prices with the introduction of artificial intelligence and better computing capacity. However, several variables impact the decision- making process as a stock market trades multiple stocks. Furthermore, it is impossible to forecast the behavior of

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K Bharne, Pankaj. "Stock Market Prediction Module Using Ann & Swarm Intelligence." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 195–200. http://dx.doi.org/10.21275/sr24526133327.

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Sano, Kazuo. "Intelligence and global bias in the stock market." Data Science in Finance and Economics 3, no. 2 (2023): 184–95. http://dx.doi.org/10.3934/dsfe.2023011.

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<abstract><p>Trade is one of the essential features of human intelligence. The securities market is the ultimate expression of it. The fundamental indicators of stocks include information about the effects of noise and bias on stock prices; however, distinguishing between them is generally hard. In this article, I present the fundamentals hypothesis based on rational expectations and detect the global bias components from the actual fundamental indicators using a log-normal distribution model based on the fundamentals hypothesis. The analysis results show that biases generally exhi

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Lee, Peiyuan, Zhigang Huang, and Yong Tang. "Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning." Security and Communication Networks 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/5972698.

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With the rapid development of the global economy and stock market, stock investment has become a common investment method. People’s research on stock forecasting has never stopped. Accurately predicting the dynamic fluctuation of stocks can bring rich investment returns to investors while avoiding investment risks. Machine learning is a relatively important research field in artificial intelligence today, which is mainly used to study how to use machines to simulate human activities. In recent years, with the continuous development of the economy, machine learning under artificial intelligence

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Fathi, Asmaa Y., Ihab A. El-Khodary, and Muhammad Saafan. "A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting." Revue d'Intelligence Artificielle 35, no. 6 (2021): 483–88. http://dx.doi.org/10.18280/ria.350606.

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The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stoc

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Kumar, Kumar, and Kumar Chandar S. "Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data." Fusion: Practice and Applications 16, no. 2 (2024): 178–89. http://dx.doi.org/10.54216/fpa.160211.

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Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sou

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Zhang, Ruihan. "AAPL Stock Investment Value Research." Highlights in Business, Economics and Management 1 (November 28, 2022): 193–203. http://dx.doi.org/10.54097/hbem.v1i.2560.

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This paper selects AAPL Co., Ltd., which has performed well in the field of artificial intelligence in recent years, as the research object, and analyzes it from both quantitative and qualitative aspects. First of all, this paper makes a theoretical analysis of the investment value of stocks, and proposes that the investment value of stocks has the characteristics of subjectivity, and analyzes financial indicators to determine whether the company's stocks have investment value. Then, the stock investment value is analyzed through stock valuation. Taking the price-earnings ratio model and the p

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Xu, Gaofeng, and Xinchen Lu. "Research on Predicting Stock Market Profitability Changes Based on Machine Learning." Frontiers in Computing and Intelligent Systems 10, no. 2 (2024): 25–28. http://dx.doi.org/10.54097/11awc370.

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The expansion of the market in our country has led to rapid economic growth, and many companies have begun to seek foreign investment for themselves. What investors are given is an invisible but very useful thing - stocks. However, stocks are unpredictable and subject to many influences, making it difficult for investors to predict whether the stock trend will decline or rise. It is uncertain whether investors will make a profit or lose money. The development of artificial intelligence technology has played a crucial role in driving stock model prediction. With the development and gradual matu

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Gatea, Alikhalaf. "EXPLORING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON FINANCIAL ACCOUNTING: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS." Financial and credit activity problems of theory and practice 6, no. 59 (2024): 167–79. https://doi.org/10.55643/fcaptp.6.59.2024.4565.

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The main aim is to investigate the impact of Artificial Intelligence (AI) on various aspects of disclosing financial information. The case study used a mixed methods approach, and a sample of stakeholders dealing in the Iraq Stock Exchange was taken during 2023 in Iraq. Data collection mainly included a survey conducted on 168 beneficiaries who trade stocks in the stock market, after which 22 clients were selected for personal interviews based on their voluntary willingness to participate in individual interviews. The results revealed beneficiaries prefer to use Artificial Intelligence to get

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Díaz, Raymundo, Efrain Solares, Victor de-León-Gómez, and Francisco G. Salas. "Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence." Applied Sciences 12, no. 8 (2022): 4067. http://dx.doi.org/10.3390/app12084067.

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Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of this work is two-fold: first, to describe an approach that comprehensively addresses the main activities carried out by practitioners during portfolio management (price forecasting, stock selection and portfolio optimization) and, second, to consider uptrends and downtrends in prices. Both aspects are rel

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HOBBS, ALLEN, and NIKOLAOS BOURBAKIS. "A NEURO-FUZZY ARBITRAGE SEMULATOR FOR STOCK INVESTING." International Journal on Artificial Intelligence Tools 05, no. 04 (1996): 473–84. http://dx.doi.org/10.1142/s0218213096000274.

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In this paper, the use of a neuro-fuzzy network model for the successful prediction of the price of a stock, given the fluctuations in the rest of the market that day, is presented. Based on the neural net’s prediction, the model then measures its success by simulating buying or selling that stock, based on whether the market’s price is determined over valued or under valued. The neural net itself is a modification of S.Y. Kung’s - a fuzzy based neural network. The neural model consistently averages over 20% A.P.R., and has been time tested over 6 years with several stocks.

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Borodin, A., R. El-Yaniv, and V. Gogan. "Can We Learn to Beat the Best Stock." Journal of Artificial Intelligence Research 21 (May 1, 2004): 579–94. http://dx.doi.org/10.1613/jair.1336.

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A novel algorithm for actively trading stocks is presented. While traditional expert advice and ``universal'' algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can ``beat the market'' and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.

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Amzile, Karim. "Artificial intelligence applied to African stock market." International Journal of Management Practice 17, no. 4 (2024): 463–77. http://dx.doi.org/10.1504/ijmp.2024.139678.

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Sanskriti, Harmukh, Mishra Mansi, Jain Satyam, Chawda Archit, Prasad Kauleshwar, and Kumar Bhawnani Dinesh. "Forecasting Stock Market Index using Artificial Intelligence." Journal of Advances in Computational Intelligence Theory 4, no. 1 (2022): 1–7. https://doi.org/10.5281/zenodo.6500420.

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<em>In this project, we attempt to implement the most popular Deep Learning technique for Time Series Forecasting since they allow for making reliable predictions on time series in many different problems. Instead of dealing with the data points collected randomly, we are using Time Series model to work upon a sequence of data points at a particular time interval. We are using three major modules to forecast the data, and they are Streamlit, Yahoo Finance, and Facebook Prophet. The user can select the number of years according to their convenience for prediction. The data is collected by yfina

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Apoorva, Ganapathy. "Pharmaceutical Company's Stock Leap through Machine Learning and AI Driven Input Injection." Annals of the Romanian Society for Cell Biology 25, no. 6 (2021): 11923–33. https://doi.org/10.5281/zenodo.5521690.

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Human intelligence simulation in machines programmed to think like humans and mimic their actions is referred to as artificial intelligence (AI). The term may also be used for any machine or computer that exhibits characteristics resembling the human mind, such as problem-solving and learning. The optimal feature of artificial intelligence is defined as the ability to rationalize, learn and take actions that have the highest potential to be most beneficial for the intended goal and objectives. Machine learning is the ability of computers to read and process data while learning from the da

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Rahman, Arista Widya, Nindi Vaulia Puspita, and Kartika Yuliari. "Emotional intelligence and behavioral biases on millennial stock trading decisions: a case study of Bibit investors." Manajemen dan Bisnis 23, no. 2 (2024): 326. http://dx.doi.org/10.24123/mabis.v23i2.801.

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This research explores the relationship between Em otional Intelligence (EI) and Behavioral Bias with Decision-Making Processes in Stock Trading among Millennial Investors. Data were collected through an online survey of 100 investors using the Bibit platform and analyzed using multiple linear regression and SPSS statistical software. The research findings indicate that Emotional Intelligence and Behavioral Bias significantly influence stock trading decisions, with a determination coefficient reaching 76%. This underscores the importance of understanding psychological factors in investment dec

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Choi, Sungyoon, Dongkyu Gwak, Jae Wook Song, and Woojin Chang. "Stock market network based on bi-dimensional histogram and autoencoder." Intelligent Data Analysis 26, no. 3 (2022): 723–50. http://dx.doi.org/10.3233/ida-215819.

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In this study, we propose a deep learning related framework to analyze S&P500 stocks using bi-dimensional histogram and autoencoder. The bi-dimensional histogram consisting of daily returns of stock price and stock trading volume is plotted for each stock. Autoencoder is applied to the bi-dimensional histogram to reduce data dimension and extract meaningful features of a stock. The histogram distance matrix for stocks are made of the extracted features of stocks, and stock market network is built by applying Planar Maximally Filtered Graph(PMFG) algorithm to the histogram distance matrix.

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Choi, Sungyoon, Dongkyu Gwak, Jae Wook Song, and Woojin Chang. "Stock market network based on bi-dimensional histogram and autoencoder." Intelligent Data Analysis 26, no. 3 (2022): 723–50. http://dx.doi.org/10.3233/ida-215819.

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Abstract:

In this study, we propose a deep learning related framework to analyze S&P500 stocks using bi-dimensional histogram and autoencoder. The bi-dimensional histogram consisting of daily returns of stock price and stock trading volume is plotted for each stock. Autoencoder is applied to the bi-dimensional histogram to reduce data dimension and extract meaningful features of a stock. The histogram distance matrix for stocks are made of the extracted features of stocks, and stock market network is built by applying Planar Maximally Filtered Graph(PMFG) algorithm to the histogram distance matrix.

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Zhu, He. "Stock Prediction Using Artificial Intelligence Technology: A Review." Advances in Economics, Management and Political Sciences 153, no. 1 (2025): 163–70. https://doi.org/10.54254/2754-1169/2024.19519.

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Artificial intelligence (AI) technologies have significantly transformed stock market prediction, offering novel approaches for financial forecasting. This review focuses on AI's integration into stock prediction, emphasizing key methodologies, such as machine learning (ML) and hybrid models, and emerging trends like quantum computing and blockchain technologies. The synergistic combination of AI and traditional financial analysis has yielded impressive improvements in accuracy. However, challenges such as data quality, overfitting, and legal concerns remain. This paper aims to provide insight

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Rathore, Anupriya, and Prof Priyanka Khabiya. "Predicting Stock Market Trends Employing Machine Learning: A Survey." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–12. http://dx.doi.org/10.55041/ijsrem24208.

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The stock prices depend both on time as well as associated variables and finding patterns in among the variables aid forecasting future stock prices which is often termed as stock market forecasting. Stock market prediction extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. For real life applications utilizing stock market data, it is necessary to predict stock market data with low errors and high accuracy. This needs design of appropriate artificial intelligence (AI) and machine learning (ML) based techniques which ca

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East, Robert, and Malcolm Wright. "Potential Predictors of Psychologically Based Stock Price Movements." Journal of Risk and Financial Management 17, no. 8 (2024): 312. http://dx.doi.org/10.3390/jrfm17080312.

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Investment in stocks is increasingly dependent on artificial intelligence (AI), but the psychological and social factors that affect stock prices may not be fully covered by the measures currently used in AI training. Here, we search for additional measures that may improve AI predictions. We start by reviewing stock price movements that appear to be affected by social and psychological factors, drawing on stock market behaviour during the COVID-19 pandemic. A review of processes that are likely to produce such stock market movements follows: the disposition effect, momentum, and the response

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Prof, Mayur Tembhurney, and Pise Sakshi. "Stack Market Prediction Using Machine Learning (ML) Algorithms." International Journal for Indian Science and Research Volume-1, Issue -1 (2022): 08. https://doi.org/10.5281/zenodo.6787069.

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In the world, stock marketing is one of the most important activities. The main objective of this paper is to predict the value of the stock market index Nifty 50 and compare the Algorithms which is best for Stock Market Prediction by comparing the graph of the four Algorithms. This Programing Language used is Python Programing Language. In this paper, we used a Machine Learning (ML) approach for training modules from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In this, we going to use four machine learning techniques called

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Noviandy, Teuku Rizky, Irsan Hardi, and Ghalieb Mutig Idroes. "Forecasting Bank Stock Trends Using Artificial Intelligence: A Deep Dive into the Neural Prophet Approach." International Journal of Financial Systems 2, no. 1 (2024): 29–56. https://doi.org/10.61459/ijfs.v2i1.41.

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This research aims to use Neural Prophet, a deep learning tool, to predict stock prices in the banking sector with high accuracy and useful insights. The model's capability in managing intricate temporal patterns differentiates it, garnering attention from researchers. The significance of this research lies in its potential to enhance stock price prediction precision, especially in the context of banking stocks, offering stakeholders’ deeper insights. The model's efficacy spans stable and volatile market behaviours, making it a valuable tool for informed decision-making in finance. Accurate pr

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V. V, Chornobaev. "Stock exchange activities in the conditions of the spread of artificial intelligence technologies." Economic Bulletin of Dnipro University of Technology 84 (December 2023): 134–38. http://dx.doi.org/10.33271/ebdut/84.134.

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Methods. During the research, the following methods were used: general and specific – when distinguishing the specifics of stock market activity in the conditions of the spread of artificial intelligence; comparison – to study distinctive features in the activity of Ukrainian stock exchanges; analysis and synthesis – while identifying the existing approaches in economic science to defining the essence of the concept of «technical analysis»; grouping – when classifying technical analysis methods used in stock market activity. Results. The article examines stock market activity in the context of

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Run, Xu. "The Principle of Investment in Sustainability Economy & Stocks." ISRG Journal of Arts Humanities & Social Sciences (ISRGJAHSS) II, no. I (2024): 181–83. https://doi.org/10.5281/zenodo.10642814.

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<em>The Artificial Intelligence invest in stocks has been prevailing over currently like LiuMai Excalibur etc.. Hence, the principle will be explored and discussed forwards deeply for us to understand the intrinsic relationship among those software. This study expresses the companies̖ stock ones & persons has been occupying each money respectively according to the cost produced from purchasing a certain share stocks from exchange centers. For the sake of the profit maximum by customers low price stock̖ more quantitative shares and good companies will afford secure and high profitability fo

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Lee, Eui-Bang, and Heon Baek. "Stock price prediction through an artificial intelligence model using basic, technical, and macroeconomic indicators." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 3, no. 1 (2024): 58–66. https://doi.org/10.54646/bijiam.2024.26.

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This study aims to analyze and predict future stock values more accurately. We proposed a stock price prediction model based on an artificial intelligence model using basic stock price data and technological and macroeconomic indicators. As a result of the experiment, the model using the features of adding technical indicators to the actual stock index (basic indicators) has better performance than the model using basic indicators and forecast performance by adding basic, technical, and macroeconomic indicators. Comparing artificial intelligence algorithms, the LightGBM model performed better

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Sutejo, Bertha Silvia. "THE LINKAGE OF STOCK TRADING DECISIONS, EI TRAITS, FINANCIAL LITERACY, AND RISK TOLERANCE ON GENERATION Z." EKUITAS (Jurnal Ekonomi dan Keuangan) 9, no. 1 (2025): 110–24. https://doi.org/10.24034/j25485024.y2025.v9.i1.6930.

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This study applied the dual process theory to explain the relationship between two systems in decision-making. The first system is associated with emotional intelligence (EI) traits. The second system is associated with financial literacy. Therefore, this study examines the two systems' effect on stock trading decisions by using a quantitative approach with non-probability and purposive sampling methods. The questionnaire was distributed to 350 Generation Z investors in Indonesia via Google Forms from July 2024 to September 2024. The study applied the SEM model with Amos statistical software a

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