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Journal articles on the topic 'Stock analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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8

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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10

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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Venkatesan, P. "National Stock Exchange Vs Bombay Stock Exchange: A Comparative Analysis." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (2018): 659–61. http://dx.doi.org/10.31142/ijtsrd19030.

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Chandrashekhar R, Chandrashekhar R. "Analysis of Free Stock Recommendations." Indian Journal of Applied Research 3, no. 6 (2011): 349–52. http://dx.doi.org/10.15373/2249555x/june2013/116.

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Latipov, Ulugbek, and Wei Wang. "Analysis of Stock Market Prediction." International Journal of Science and Research (IJSR) 10, no. 6 (2021): 763–66. https://doi.org/10.21275/mr21607160538.

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BURTNYAK, Ivan, and Roman RUSYN. "ANALYSIS OF STOCK MARKET DYNAMICS." Herald of Khmelnytskyi National University. Economic sciences 314, no. 1 (2023): 55–59. http://dx.doi.org/10.31891/2307-5740-2023-314-1-7.

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The article analyzes the application of information technologies to the analysis of the stock market, namely to the study of the dynamics of the Ukrainian currency exchange rate, which will allow us to draw a conclusion about the market as a whole. Exploratory data analysis was used to review and analyze financial data. It is an approach to summarizing, visualizing, and gaining insight into the important characteristics of a data set. When analyzing and predicting the dynamics of complex financial systems, one cannot do without such a powerful tool as the Python programming language and neural

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Nanaware, Shubham. "Comparative analysis on three companies stock prices." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41279.

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: - The report provides an in-depth evaluation and analysis of three company stocks to help guide investment decisions. The analysis is based on a combination of three different company (Maruti Suzuki, Tech Mahindra, Tata Motors) stock rate data from year 2019-2023. The analysis considers factors, Open rate of stock, Close rate of stock, Low difference, High difference, VWAP (Volume weighted average price), Volume of overall stock rate. This Project also help to analyse the difference between the stock rates of all the three companies Keywords: - Changes in price rate of stock from 2019-2023,

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Hilborn, R. "Apparent Stock Recruitment Relationships in Mixed Stock Fisheries." Canadian Journal of Fisheries and Aquatic Sciences 42, no. 4 (1985): 718–23. http://dx.doi.org/10.1139/f85-092.

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When several stocks of differing productivities are fished together and combined for stock recruitment analysis, the estimated productivity and size of the stock depends strongly on the previous exploitation history. As a mixed stock is harvested harder, it appears smaller in total size but more productive per individual. I analysed the mechanism behind this change. Passive feedback management policies perform well on mixed stocks, when starting from unexploited conditions. When starting from an overexploited condition, passive feedback management will fail to allow the less productive stocks

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Li, Sijia, Tai Peng, and Mingzhe Si. "The Portfolio Analysis in Hong Kong Stocks." Advances in Economics, Management and Political Sciences 4, no. 1 (2023): 418–32. http://dx.doi.org/10.54254/2754-1169/4/2022914.

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Maximizing returns is always peoples investment goal. Stocks have some hedging capabilities, and their prices also fluctuate widely, making them popular in-vestments for investors. As a result, Stock prices have fluctuated wildly, leading to uncertain investments and uncertain returns. To compute and analyze the maximum returns of stocks in Hong Kong stocks, this paper takes The Hang Seng index, an important indicator of Hong Kong stock market prices as the scene. We calculate the variance of these stocks using the Fama experiment. And We examined the normal distribution and i.i.d. of the data

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Larasati, Btari Gavrilla, C. Ambar Pujiharjanto, and Nilmawati Nilmawati. "Analysis Of Stock Return Anomaly On The Indonesia Stock Exchange Based On Market Capitalization." Journal of Business Innovation and Research 2, no. 2 (2024): 195. http://dx.doi.org/10.31315/jubir.v2i2.12031.

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There has been a well-known market anomaly in the stock market called the firm size effect. This theory explains that small-cap stocks may provide greater stock returns than big-cap stocks. This research aimed to test the firm size effect theory on 827 stocks listed on the Indonesia Stock Exchange (IDX) during January 2 to June 27, 2023. The research sample was divided into big-cap and small-cap categories based on the calculation of average market capitalization, then the average value of stock returns from both categories were statistically compared. The result showed that the average values

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U Amadi,, I., C. P. Ogbogbo, and B. O Osu,. "Stochastic analysis of stock price changes as markov chain in finite states." Global Journal of Pure and Applied Sciences 28, no. 1 (2022): 91–98. http://dx.doi.org/10.4314/gjpas.v28i1.11.

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In this work, stochastic analysis of Markov chain model used to examine stock price formation in finite states. The data was subjected to 5-step transition matrix for independent stocks where transition matrix replicated the use of 3-states transition probability matrix. This enables us proffer precise condition of obtaining expected mean rate of return of each stock. Out of the four stocks studied, stock (1), stock (2), stock (3) and stock (4), it was also discovered that stock (1) has the highest mean rate of return:4.0548 and Stock (4) has the best probability of price increasing in the nea

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Pulkkinen, Henni, and Samu Mäntyniemi. "Maximum survival of eggs as the key parameter of stock–recruit meta-analysis: accounting for parameter and structural uncertainty." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 4 (2013): 527–33. http://dx.doi.org/10.1139/cjfas-2012-0268.

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Despite their name, hierarchical stock–recruit meta-analyses are often parameterized in terms of steepness, which depends not only on the assumed stock–recruitment relationship but also on the recruit–spawner relationship. This parameterization requires assumptions about the reproductive potential of the recruit that are not desirable if the focus of the study is limited to the spawning–recruitment phase instead of the full life cycle. Thus, usage of steepness should be avoided in studies that aim to produce informative priors for the stock–recruit relationship for use in studies of other salm

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Addinpujoartanto, Nur Ariefin. "ANALYSIS OF JANUARY EFFECT ON BIG STOCK COMPANIES AND SMALL STOCK COMPANIES AT INDONESIA STOCK EXCHANGE." International Journal of Business, Humanities, Education and Social Sciences (IJBHES) 1, no. 2 (2019): 47–56. http://dx.doi.org/10.46923/ijbhes.v1i2.40.

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January Effect is one of market anomaly where the stock returns in January are higher than other months. Some of causes the January Effect are the actions of investor who carry out tax-loss selling and windows dressing. In addition, investors have different views to choose stocks, based on market capitalization dan risk. This study is purposed to find the January Effect in the Indonesia Stock Exchange and January Effect on small company stock is stronger than large company stock. The data is normally distributed using the One-Sample Kolmogorov-Smirnov test. The test using the OLS method with d

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Pragathi, DrYVS Sai, M. V. S. Phani Narasimham, and B. V. Ramana Murthy. "Analysis and implementation of realtime stock prediction using reinforcement frameworks." Journal of Physics: Conference Series 2089, no. 1 (2021): 012045. http://dx.doi.org/10.1088/1742-6596/2089/1/012045.

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Abstract Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get rea

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ISHII, YASUYUKI. "THE ROLE OF PATENT, CITATION AND OBJECTION STOCKS IN THE PRODUCTIVITY ANALYSIS OF R&D — USING JAPANESE COMPANY DATA." International Journal of Innovation Management 14, no. 05 (2010): 947–63. http://dx.doi.org/10.1142/s1363919610002945.

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In this paper, I examine the role of patent stock, citation stock and objection stock in the analysis of R&D productivity and the exact interrelationships between those stocks by using new data on 100 large Japanese companies. I find, first, that citation and objection stock represent the knowledge stock of the company well, whereas patent stock itself is an ineffective proxy for the knowledge stock. Objection stock seems to be the best proxy for the knowledge stock. Second, citation and objection stocks are effective in playing the role of control factors for successful R&D stock, whe

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Jane, Vedant, Rigved Makode, Shaijal Nachane, Vedakshi Korde, and Prof. Devendra G. Ingale. "Stock Management in Multicity NGO's with Data Analysis." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 2 (2024): 175–78. https://doi.org/10.5281/zenodo.11174101.

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<em>The concept behind the development of system for Goonj is to maintain the stock, related to relief material, rehabilitation material, etc. Goonj is working at various disaster area like flood affected, landslide, cloud burst, droughts, cyclones, tsunami, etc. The purpose of this system is to provide the complete details of the stock available with the organization, this system will also help the organization about tracking of stock supply, stock availability and stock delivery also which will help in tracking the stock available at different store and warehouse of the organization. This sy

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Xiao, Qianyi, and Baha Ihnaini. "Stock trend prediction using sentiment analysis." PeerJ Computer Science 9 (March 20, 2023): e1293. http://dx.doi.org/10.7717/peerj-cs.1293.

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These days, the vast amount of data generated on the Internet is a new treasure trove for investors. They can utilize text mining and sentiment analysis techniques to reflect investors’ confidence in specific stocks in order to make the most accurate decision. Most previous research just sums up the text sentiment score on each natural day and uses such aggregated score to predict various stock trends. However, the natural day aggregated score may not be useful in predicting different stock trends. Therefore, in this research, we designed two different time divisions: 0:00t∼0:00t+1 and 9:30t∼9

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Madhavan, Joel Paul. "Banking Operations Through Data Analysis." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03519.

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Abstract— In the evolving landscape of personal finance, users often rely on multiple disjointed applications to manage expenses, monitor stock portfolios, and receive investment advice. This paper evaluates the usability and effectiveness of integrating these core functionalities into a single, unified platform. We developed a full-stack web application that combines an Expense Tracker, a Stock Visualizer, and a Stock Suggestor. The goal is to eliminate the need for switching between various financial tools, thereby improving convenience, reducing cognitive load, and enhancing financial decis

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Liu, Xingcong. "Stock Analysis and Portfolio Optimization." Advances in Economics, Management and Political Sciences 25, no. 1 (2023): 176–86. http://dx.doi.org/10.54254/2754-1169/25/20230495.

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This paper investigates financial time series from US stock markets from a quantitative perspective. The returns of all stocks are clustered with K-means with five centroids; in each group, the store with the maximum return is selected. For all five chosen supplies, construct the portfolio with different stock weights and optimize the combination with Monte Carlo to mitigate risk and maximize the Sharpe ratio. Comparing the weights under different stock weights concludes that the optimal portfolio can be obtained with the maximized Sharpe ratio scenario. More interestingly, the consequences ba

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Adithya, Vishnu. "Stock Market Analysis Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47888.

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Abstract— The stock market price prediction and classification is Stock market price prediction is a common problem in finance that involves using historical data to forecast future prices of stocks or other financial instruments. The goal of stock market price prediction is to identify profitable trading opportunities and make informed investment decisions, to resolve this issues different machine learning algorithm is implemented for predict the model accuracy of stock market price level The problem statement for stock market price prediction involves identifying the factors that influence s

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Suryana. "Analysis of Stock Prices: A Case Study of Indonesia Stock Exchange." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3249–54. http://dx.doi.org/10.37200/ijpr/v24i2/pr200634.

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Vuković, Marija, Snježana Pivac, and Zoran Babić. "Comparative analysis of stock selection using a hybrid MCDM approach and modern portfolio theory." Croatian Review of Economic, Business and Social Statistics 6, no. 2 (2020): 58–68. http://dx.doi.org/10.2478/crebss-2020-0011.

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Abstract The problem of selecting an optimal set of investment stocks is of a huge interest for both individual and institutional investors. This paper compares the hybrid multiple criteria decision making (MCDM) approach to selecting the best stock to invest in, with the stock selection using modern portfolio theory (MPT). When selecting stocks, it is very important to thoroughly analyse stocks, according to multiple criteria, including their equity market indicators, as well as financial indicators. The objective of the research is to compare the stock selection using a hybrid MCDM approach

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Vuković, Marija, Snježana Pivac, and Zoran Babić. "Comparative analysis of stock selection using a hybrid MCDM approach and modern portfolio theory." Croatian Review of Economic, Business and Social Statistics 6, no. 2 (2020): 58–68. http://dx.doi.org/10.2478/crebss-2020-0011.

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AbstractThe problem of selecting an optimal set of investment stocks is of a huge interest for both individual and institutional investors. This paper compares the hybrid multiple criteria decision making (MCDM) approach to selecting the best stock to invest in, with the stock selection using modern portfolio theory (MPT). When selecting stocks, it is very important to thoroughly analyse stocks, according to multiple criteria, including their equity market indicators, as well as financial indicators. The objective of the research is to compare the stock selection using a hybrid MCDM approach a

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Guan, Lisha, Yong Chen, Robert Boenish, Xianshi Jin, and Xiujuan Shan. "Improving data-limited stock assessment with sporadic stock index information in stock reduction analysis." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 5 (2020): 857–68. http://dx.doi.org/10.1139/cjfas-2018-0500.

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As most exploited fisheries lack a coherent time series of biomass index, development of data-limited stock assessment methods such as stock reduction analysis (SRA), is critical for fishery stock assessment due to their modest data requirements for estimating stock status and overfishing catch limits. In this study, we propose that sporadic time series of biomass indices, if available, may be fully utilized to inform priors of recent relative biomass (BT/B1) for data-limited stocks. We evaluated the performance of SRA incorporating this index-based prior by comparing two other common SRA prio

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Janulevičienė, Alma. "The analysis of the stock of the Republican scientific-technical library." Knygotyra 22, no. 15-2 (2024): 70–77. http://dx.doi.org/10.15388/knygotyra.1988.36564.

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Nowadays, the investigation of the questions of the scientific management of library stocks has gained great urgency (increased in importance). The tasks of increasing the efficiency of the formation and usage of library stocks highlight the necessity for systematic research into their peculiarities and tendencies of development. This article analyzes the peculiarities and tendencies of the development of the stocks of the Republican Scientific-Technical Library of Lithuania's Scientific-Research Institute of Scientific-Technical Information and Technical-Economic Research. The multifaceted re

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Wood, Chris C. "Utility of Similarity Dendrograms in Stock Composition Analysis." Canadian Journal of Fisheries and Aquatic Sciences 46, no. 12 (1989): 2121–28. http://dx.doi.org/10.1139/f89-262.

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Similarity dendrograms are useful for revealing clustering patterns among reference stocks used in stock composition analysis. Simulations with simple mixtures of known composition indicate that clustering patterns based on genetic differences among reference stocks have a greater influence on the reliability of maximum likelihood estimates of mixture composition than either the number of reference stocks in the analysis or the size of the mixture samples. Estimated proportions for reference stocks that form tight ("problem") clusters in a similarity dendrogram tend to be biased and this bias

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Lieksnis, Raimonds. "MULTIFACTOR ASSET PRICING ANALYSIS OF THE BALTIC STOCK MARKET." Ekonomika 89, no. 4 (2010): 85–95. http://dx.doi.org/10.15388/ekon.2010.0.964.

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This study investigates whether the Fama–French three-factor asset pricing model is applicable for explaining cross-sectional returns of stocks listed in the Baltic stock exchanges. Findings confirm the validity and economic significance of the three-factor model for the Baltic stock market: only investors who chose to invest in value stocks during the reference period achieved positive returns by matching or beating the returns of the stock market index. The monthly returns of 8 Latvian, 13 Estonian and 27 Lithuanian company stocks are analyzed for the time period from June 2002 till February

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Seo, Kanghyeon, Zhipeng Dong, and Yoojeong Song. "Neural Network-Based CSI300 Stock Prediction: Feature Importance and Attention Mechanism Analysis." Electronics 14, no. 9 (2025): 1729. https://doi.org/10.3390/electronics14091729.

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In this study, neural networks are utilized to develop a stock price prediction model based on the constituent stocks of the China Securities Index 300 (CSI300). This research investigates various prediction methods and models through experiments, comparing their advantages, limitations, and applicability to improve the accuracy and efficiency of stock price forecasting. Furthermore, we analyze the characteristics of CSI300 constituent stocks and explore the relationships among key variables and influential factors, enhancing our understanding of stock market behavior. Additionally, we explore

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Cui, Xiaodong, Jun Hu, Yiming Ma, Peng Wu, Peican Zhu, and Hui-Jia Li. "Investigation of stock price network based on time series analysis and complex network." International Journal of Modern Physics B 35, no. 13 (2021): 2150171. http://dx.doi.org/10.1142/s021797922150171x.

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Complex network is now widely used in a series of disciplines such as biology, physics, mathematics, sociology and so on. In this paper, we construct the stock price trend network based on the knowledge of complex network, and then propose a method based on information entropy to divide the stock network into some communities, that is, a gathering study of stock price trend. We construct time series networks for each stock in Chinese A-share market based on time series network model, and then use these networks to divide the stock market into communities. We find that the average trend of stoc

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Zhao, Cheng, Ping Hu, Xiaohui Liu, Xuefeng Lan, and Haiming Zhang. "Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction." Mathematics 11, no. 5 (2023): 1130. http://dx.doi.org/10.3390/math11051130.

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The ability to predict stock prices is essential for informing investment decisions in the stock market. However, the complexity of various factors influencing stock prices has been widely studied. Traditional methods, which rely on time-series information for a single stock, are incomplete as they lack a holistic perspective. The linkage effect in the stock market, where stock prices are influenced by those of associated stocks, necessitates the use of more comprehensive data. Currently, stock relationship information is mainly obtained through industry classification data from third-party pl

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Harimbawa, Dipta, Moh Hasanudin, Bagas Putra Pradana, and Afiat Sadida. "Design and Development of Information Systems Supporting Stock Investors "Batch of Automatic Stock Analysis System"." Ilomata International Journal of Management 3, no. 2 (2022): 222–41. http://dx.doi.org/10.52728/ijjm.v3i2.455.

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Many information systems make it easier for stock investors to make decisions to buy shares. But no information system provides an easy-to-understand display and exclusive features to make it easier for investors to make decisions and find information about stock investments, especially for novice investors. Therefore, this study aims to design and build a web-based information system that can assist stock investors in making decisions and seeking information about stock investments. The design of this information system is equipped with a stock overview, moving average analysis, analysis of M

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Nur Safitri, Indah Nur, Sudradjat Sudradjat, and Eman Lesmana. "STOCK PORTFOLIO ANALYSIS USING MARKOWITZ MODEL." International Journal of Quantitative Research and Modeling 1, no. 1 (2020): 47–58. http://dx.doi.org/10.46336/ijqrm.v1i1.6.

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A common problem that often occurs in investment is the selection of the optimal portfolio according to the wishes of investors. This thesis ueds the Markowitz Model as a basis to formed a model to choose the optimal portfolio that provided the lowest risk. Efforts to minimize risk were carried out by conducting a diversification strategy. After the selection of several companies with the criteria of capitalization value and DER (Debt Equity Ratio), a combination of stocks is formed to form a portfolio. The formed portfolio was then analyzed to determine the optimal proportion of each stock. U

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Zhang, Aibo. "Portfolio Optimization of Stocks – Python-Based Stock Analysis." International Journal of Education and Humanities 9, no. 2 (2023): 32–38. http://dx.doi.org/10.54097/ijeh.v9i2.9584.

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With the development of big data, blockchain artificial intelligence, and other technologies, the development of the financial industry also plays a great role in promoting the development of digital finance is also developing rapidly, the huge amount of financial data, the laws behind, randomness, the complexity have increased the difficulty of processing our data. The financial industry is also increasingly in need of data processing talents. For financial data such as: intra-day high-frequency data and stock price and volume data processing, Python has the points of fast calculation speed,

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Smitha, Kurian, Anand, Das Anushka, Basavaraj, and N. Chandana. "A Survey on Stock Price Prediction Using AI/ML." Recent Trends in Cloud Computing and Web Engineering 5, no. 1 (2023): 19–25. https://doi.org/10.5281/zenodo.7695478.

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<em>Need of Stock have become huge with Increase in popularity of Stock market in Digital world. Prediction and Analyzing stock can benefit People to think before buying or selling stocks. So, A New Stock Price Prediction through Deep Learning Algorithms has been prognosticated and visualized. Through this system we can predict of any company’s stock in the world. </em>

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Li, Tianhao. "Stock prediction and analysis based on machine learning algorithms." Applied and Computational Engineering 50, no. 1 (2024): 15–22. http://dx.doi.org/10.54254/2755-2721/50/20241142.

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The stock market has consistently remained a focal point of substantial concern for investors. Nevertheless, due to the intricate, tumultuous, and often noisy nature of the stock market, forecasting stock trends presents a formidable obstacle. To augment the accuracy of stock trend predictions, the author adopts a combination of the Long Short-Term Memory (LSTM) neural network and a noise reduction technique known as Ensemble Empirical Mode Decomposition (EEMD). This composite model is employed to develop predictions for the daily stock price increases, aiming to provide more precise insights

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T., Aditya Sai Srinivas, Vinod Kumar Y., Sravanthi Y., and Dwaraka Srihith I.V. "Stock Duel: Python's Play in Comparative Market Analysis." Journal of Advancement in Parallel Computing 7, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10081375.

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<i>The provided Python code offers a comprehensive framework for conducting a comparative analysis of stocks in the financial market. Using the Yahoo Finance API, it fetches historical stock price data for specified stocks (e.g., Apple and Microsoft) within a defined timeframe. The script calculates and visualizes key metrics, including daily returns and cumulative returns, allowing users to assess the performance of selected stocks. Additionally, it conducts statistical analysis by computing mean returns, standard deviations, and correlation coefficients to quantify the relationship between t

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Kong, Minseok, and Jungmin So. "Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning." Applied Sciences 13, no. 1 (2023): 633. http://dx.doi.org/10.3390/app13010633.

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There are several automated stock trading programs using reinforcement learning, one of which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL agents and make an ensemble with three different actor–critic algorithms: Advantage Actor–Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). This novel idea was the concept mainly used in this paper. However, we did not stop there, but we refined the automated stock trading in two areas. First, we made another DRL-based ensemble and employed it as a new trading agent. We named

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Nur Iqmal Ibrahim, Siti, Siti Aida Muhammad, and Mimi Hafizah Abdullah. "A Network Analysis of the Stock Market in Malaysia, Singapore and Indonesia." International Journal of Engineering & Technology 7, no. 4.1 (2018): 99. http://dx.doi.org/10.14419/ijet.v7i4.1.28234.

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In this study, we investigate the stock market network among the stocks traded in Malaysia, Singapore and Indonesia using the minimal spanning tree approach. Based on the market capitalization, the monthly adjusted closing prices from 2016 until 2017 of 10 companies for each stock market are chosen to construct the network, and the most influential stocks between Malaysia, Singapore and Indonesia stock markets are identified. Findings of this study show that 3 out of 30 companies are identified as the most influential in the Malaysia, Singapore and Indonesia stock market.

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Koljonen, Marja-Liisa. "Distinguishing between resident and migrating Atlantic salmon (Salmo salar) stocks by genetic stock composition analysis." Canadian Journal of Fisheries and Aquatic Sciences 52, no. 4 (1995): 665–74. http://dx.doi.org/10.1139/f95-067.

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The possibility of using the genetic stock identification (GSI) method to distinguish between individual Atlantic salmon (Salmo salar) stocks and stock groups in Finnish catches was studied. In the Baltic Sea, the Atlantic salmon is a target of a mixed-stock fishery, and information about stock composition would be valuable for the management of the species. The salmon catches on the Finnish west coast consist of two seasonally variable components: a group of northern stocks migrating through the area to the Baltic main basin and the resident Neva salmon. The migratory component includes two e

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G, Sharmila, and Kabirdoss Devi. "AN ANALYSIS OF BEST PERFORMING STOCKS OF BANKING SECTOR THROUGH TECHNICAL ANALYSIS." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT & SOCIAL SCIENCE 07, no. 02(II) (2024): 17–24. http://dx.doi.org/10.62823/7.2(ii).6511.

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This research conducts an in-depth analysis of the best performing stocks within the banking sector using technical analysis methods. Through the examination of historical price data, trading volumes, and various technical indicators, the study aims to identify patterns and trends that contribute to the success of these stocks. Key factors such as moving averages, support and resistance levels, and momentum indicators are analyzed to determine the stocks' performance and potential future movements. The findings provide valuable insights into the effectiveness of technical analysis in predictin

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Chen, Junshi. "Stock Selection Based on Valuation, Growth, Profitability and Payout Ratio Analysis." BCP Business & Management 38 (March 2, 2023): 919–25. http://dx.doi.org/10.54691/bcpbm.v38i.3797.

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Stock selection is one of the most popular topics among both inexperienced and sophisticated investors. However, it is always difficult to make a decision from various kinds of stocks with limited purchasing power. Therefore, this study focuses on stock selection among seven particular stocks. The selection was be based on comparisons of indices of valuation, profitability, growth, and payout. Domino’s Pizza Inc stood out from the first part of the comparison in light of its stability and constant growth. MV Oil Trust stood out from the second part of the comparison on account of its high divi

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Balaji Naidu, Bharath, K. Bindhu Madhavi, B. Siva Venkata Harshith, and P. Sowmya. "Stock Market Analysis and Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44099.

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The project focuses on leveraging an LSTM neural network to predict stock market behaviour, addressing the challenges posed by market volatility and complexity. LSTM's capability to handle long-term dependencies make it suitable for analysing historical stock prices and forecasting future trends. The model’s effectiveness is assessed through its accuracy and ability to generalize across various stocks and market conditions. Initial results indicate that the LSTM model can identify significant trends and patterns, though its accuracy is influenced by the quality of input data. Future enhancemen

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