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Journal articles on the topic 'Tool stock'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Sun, Yi, Quan Jin, Qing Cheng, and Kun Guo. "New tool for stock investment risk management." Industrial Management & Data Systems 120, no. 2 (2019): 388–405. http://dx.doi.org/10.1108/imds-03-2019-0125.

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Purpose The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior. Design/methodology/approach Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock. Findings It turns out that the lead–lag relation

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Kulkarni, Aseema, and Ajit More. "Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors." Oriental journal of computer science and technology 9, no. 3 (2016): 212–25. http://dx.doi.org/10.13005/ojcst/09.03.07.

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Prediction of stock prices using various computer programs is on rise. Popularly known in the field of finance as algorithmic trading, a radical transformation has taken place in the field of stock markets for decision making through automated decision making agents. Machine learning techniques can be applied for predicting stock prices. This paper attempts to study the various stock market forecasting processes available in the forecasting plugin of the WEKA tool. Twenty experiments have been conducted on twenty different stocks to analyse the prediction capacity of the tool.

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Shetty, Soumya, Janet Jyothi Dsouza, and Iqbal Thonse Hawaldar. "Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model." Investment Management and Financial Innovations 18, no. 4 (2021): 241–51. http://dx.doi.org/10.21511/imfi.18(4).2021.21.

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The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of select

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Suzuki, Makoto. "PSR—an efficient stock-selection tool?" International Journal of Forecasting 14, no. 2 (1998): 245–54. http://dx.doi.org/10.1016/s0169-2070(98)00030-2.

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Condrobimo, A. Raharto, Albert V. Dian Sano, and Hendro Nindito. "The Application Of K-Means Algorithm For LQ45 Index on Indonesia Stock Exchange." ComTech: Computer, Mathematics and Engineering Applications 7, no. 2 (2016): 151. http://dx.doi.org/10.21512/comtech.v7i2.2256.

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The objective of this study is to apply cluster analysis or also known as clustering on stocks data listed in LQ45 index at Indonesia Stock Exchange. The problem is that traders need a tool to speed up decision-making process in buying, selling and holding their stocks.The method used in this cluster analysis is k-means algorithm. The data used in this study were taken from Indonesia Stock Exchange. Cluster analysis in this study took data’s characteristics such as stocks volume and value. Results of cluster analysis were presented in the form of grouping of clusters’ members visually. Therefo

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Wan, Xiaole, Zhen Zhang, Chi Zhang, and Qingchun Meng. "Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index." Complexity 2020 (July 15, 2020): 1–19. http://dx.doi.org/10.1155/2020/7195494.

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The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respec

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Ko, Ching-Ru, and Hsien-Tsung Chang. "LSTM-based sentiment analysis for stock price forecast." PeerJ Computer Science 7 (March 11, 2021): e408. http://dx.doi.org/10.7717/peerj-cs.408.

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Investing in stocks is an important tool for modern people’s financial management, and how to forecast stock prices has become an important issue. In recent years, deep learning methods have successfully solved many forecast problems. In this paper, we utilized multiple factors for the stock price forecast. The news articles and PTT forum discussions are taken as the fundamental analysis, and the stock historical transaction information is treated as technical analysis. The state-of-the-art natural language processing tool BERT are used to recognize the sentiments of text, and the long short t

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Zandi, Gholamreza, Nik Khadijah Nik Abdul Rahman, Imran Ahmed Shahzad, and Sandy Low Bee Choo. "Non-linear relationship between control ownership and cumulative abnormal return: A new predictive tool for firms’ performance." Nurture 17, no. 4 (2023): 463–72. http://dx.doi.org/10.55951/nurture.v17i4.370.

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Purpose: The primary aim of the paper was to find out the non-linear relationship between ownership structure and the Cumulative Abnormal Return (CAR) of companies listed on the Muscat Stock Exchange (MSE) from 2013 to 2020. Design/Methodology/Approach: The Cumulative Abnormal Return (CAR) has been implied to determine the accuracy and ability of investors to predict stock performance and select an investment basket. Findings: The effect of Control Ownership (CO) on Cumulative Abnormal Return (CAR) of Muscat Stock Exchange (MSE) listed companies is negative which indicates that wit

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Koeberle, Alexander L., Ivan Arismendi, Whitney Crittenden, et al. "Otolith shape as a classification tool for Chinook salmon (Oncorhynchus tshawytscha) discrimination in native and introduced systems." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 7 (2020): 1172–88. http://dx.doi.org/10.1139/cjfas-2019-0280.

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Chinook salmon (Oncorhynchus tshawytscha) are widely distributed across the globe, with native stocks in the North Pacific Ocean and self-sustained populations in both the Northern and Southern hemispheres. In their native range, Chinook salmon face many conservation and management challenges, including depleted stocks, loss of genetic diversity, and hatchery influences, whereas naturalized range expansion poses a threat to novel ecosystems. Therefore, ways to improve stock discrimination would be a useful tool for fishery managers. Here, we evaluated otolith shape variation in Chinook salmon

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Van Thu, Ngo. "Absorption rate as a system risk measurement tool: evidence from the Viet Nam stock market." Science & Technology Development Journal - Economics - Law and Management 3, no. 1 (2019): 13–27. http://dx.doi.org/10.32508/stdjelm.v3i1.536.

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System risk is one of the problems concerned by many stock market researchers. Many different indicators have been used: the average price index Passcher, Laspeyres or Fisher. These indicators reflect the average price of stocks or a basket of representative stocks in the m arket. The models predicting the prices of these indexes are the measure of market risk. Recently, especially after the major financial crises, the plunge of the stock market indexes has been s een. There are two issues here: Firstly, whether a market index fully reflects systemic r isk. Secondly, any state of market risk i

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Wołowiec, Tomasz, Daniel Szybowski Szybowski, and Dariusz Prokopowicz. "METHODS OF DEVELOPMENT NETWORK ANALYSIS AS A TOOL IMPROVING EFFICIENT ORGANIZATION MANAGEMENT." International Journal of New Economics and Social Sciences 9, no. 1 (2019): 231–51. http://dx.doi.org/10.5604/01.3001.0013.3046.

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The analysis of the dependence network consists in the calculation of dates and time reserves of subsequent events, and then on the calculation of the time stocks during the execution of particular activities. In the dependency network, it is possible to calculate the earliest and the latest possible date of occurrence of each event and the possible reserve of time. Thanks to this, we will learn the stock of time related to individual activities. Those activities that do not have a stock of time (that is, the reserve is equal to zero) are called critical activities. All critical activities in

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Amalia, Farah, and Nindi Riyana Saputri. "Does Investor Sentiment Affect Islamic Stock Prices? Evidence From Indonesia." Jurnal Riset Ekonomi Manajemen (REKOMEN) 5, no. 2 (2022): 117–27. http://dx.doi.org/10.31002/rn.v5i2.5609.

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The Islamic finance industry in Indonesia has grown rapidly in the last decade, one of which is marked by the number of sharia stocks. Sharia stocks, based on the underlying principle, prohibit the involvement of investor sentiment which is often used as a consideration in investment decisions because there are elements of tadlees in it. This study examines the influence of investor sentiment on islamic stock prices index. This study aims to analyze whether Islamic stock price indices are influenced by investor sentiment. The representation of Islamic stock price indices are Indonesia Sharia S

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Louangrath, P.I. "Stock Price Analysis under Extreme Value Theory." Inter. J. Res. Methodol. Soc. Sci 1, no. 4 (2015): 51–67. https://doi.org/10.5281/zenodo.1321371.

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The objective of this paper is to provide a practical tool for stock price evaluation and forecasting under Extreme Value Theory (EVT). We reviewed three existing models: Mordern Portfolio Theory, Black-Scholes, and Jarrow-Rudd models. It was found that these models may not be effective tools where option contract is not part of the investment regime. The data used in this research consist of the daily close price from a period of 30 days from 100 companies in the SET100 index. From the sample distribution F(X), extreme values were separated into a group G(X). A tail index  was calculated

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Wu, Fu Zhong. "Tool Path Optimization of 2D Contour Considering Stock Boundary." Applied Mechanics and Materials 251 (December 2012): 169–72. http://dx.doi.org/10.4028/www.scientific.net/amm.251.169.

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Based on analyzing the existing algorithms, a novel tool path generation of 2D contour considering stock boundary is presented. Firstly the boundary points of stock are obtained by three-dimensional measuring machine. And the boundary curve is constructed by method of features identifying. The stock boundary is offset toward outside with tool diameter. An enclosed region is formed between the contour curves and the offset curves of stock boundary. The tool path is generated by form of parallel spiral by offsetting the stock boundary in the enclosed region. Finally the validity of present metho

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Molony, Brett W., R. Lenanton, G. Jackson, and J. Norriss. "Stock enhancement as a fisheries management tool." Reviews in Fish Biology and Fisheries 13, no. 4 (2003): 409–32. http://dx.doi.org/10.1007/s11160-004-1886-z.

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Molony, Brett W., R. Lenanton, G. Jackson, and J. Norriss. "Stock enhancement as a fisheries management tool." Reviews in Fish Biology and Fisheries 13, no. 4 (2005): 409–32. http://dx.doi.org/10.1007/s11160-005-1886-7.

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Kim, Kyung Soon, Jinwoo Park, Chune Young Chung, and Jin Hwon Lee. "Is Stock Split a Manipulation Tool? Evidence from the Korean Stock Market*." Asia-Pacific Journal of Financial Studies 41, no. 5 (2012): 637–63. http://dx.doi.org/10.1111/j.2041-6156.2012.01086.x.

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18

Bhattacharya, Biplab, Li Lin, Rajan Batta, and Pavani K. Ram. "Stock-out severity index: tool for evaluating inequity in drug stock-outs." Central European Journal of Operations Research 28, no. 4 (2019): 1243–63. http://dx.doi.org/10.1007/s10100-019-00634-z.

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19

Najaf, Abdul Rezha Efrat, Reisa Permatasari, and Ciptagusti Sila Sakti. "Evaluating the Usability of a Book Stock and Sales Recording Android App: System Usability Scale (SUS), Heuristic Evaluation, and Maze Tool Insights." Journal of Information Systems and Informatics 6, no. 3 (2024): 1774–89. http://dx.doi.org/10.51519/journalisi.v6i3.829.

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The implementation of digitalization in organizations is needed to apply technology and provide more value to customers. However, digitization still needs to be fully maximized in manually recording book stocks to record book stocks. Printed books are recorded as incoming books, sold books are sold as outgoing books and incoming and outgoing data are matched. Publishers still use Google Sheets as the primary tool for recording available book stock. However, human error, such as writing or data entry, often leads to inaccurate book stock information. Therefore, Peneleh Publishing needed an appl

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Nasution, Arbi Haza, Anggi Hanafiah, Winda Monika, Rajalingam Sokkalingam, Mohd Sham Mohamad, and Andry Alamsyah. "Assessing Lag-Llama in Probabilistic Time Series Forecasting for the Indonesian Stock Market." Advances in Artificial Intelligence and Machine Learning 05, no. 02 (2025): 3809–33. https://doi.org/10.54364/aaiml.2025.52216.

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Accurately predicting stock prices is crucial for investors and policymakers. This paper presents the first empirical evaluation of Lag-Llama, a novel probabilistic time series forecasting model, for predicting stock prices on the Indonesian Stock Exchange (IDX). By applying Lag-Llama to both univariate and multi-time series forecasts of key IDX stocks, we assess its ability to capture temporal patterns and market volatility, particularly in comparison to state-of-the-art models like DeepAR (RNN) and Temporal Fusion Transformer (TFT). Our results show that in fine-tuning scenarios Lag-Llama ac

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Ding, Song Lin, John P. T. Mo, and Daniel Yang. "Dynamic In-Process Stock Based Tool Path Generation for High Surface Finish Rough Machining." Key Engineering Materials 567 (July 2013): 59–65. http://dx.doi.org/10.4028/www.scientific.net/kem.567.59.

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This paper presents a new tool path generation strategy for rough machining based on the dynamic in-process stock model of the workpiece. Compared to conventional roughing method, the new tool paths result in a better surface finish but consume the same machining time. The cutter locations in the tool path are determined by removing the peak portion of the residual materials on the stock. The geometric information of remaining stocks is updated dynamically in the in-process model once each cutting pass is completed. The overall machining time is no longer than the conventional method since no

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22

Abhijit, Biswas. "Impact of Reliance Industry Stock Price on NIFTY 50 - Granger Causality Test." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 11 (2018): 367–76. https://doi.org/10.5281/zenodo.1490556.

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It is well known for the Indian Equity Market NIFTY 50 is the National Stock Exchange of India's benchmark broad based stock market Index. Full form of NIFTY 50 is "National Stock Exchange Fifty". Generally it represents the weighted average of fifty Indian Company Stocks; but right now it is fiftyone stocks and is one of the main stock indices in India. The study is an attempt to find the impact of Reliance Industries Limited(Reliance) stock price (one of the 51 stocks enlisted under NIFTY 50) on NIFTY 50. We have collected data mostly from i.) NSE and ii.) Yahoo finance. Annual

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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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g, Abhishek, Abinav k, Akshitha r, and Mrs .rejitha r. "Stock market Price Prediction Using Machine Learning and Deep learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44240.

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This project presents a dynamic Stock Market Price Prediction Website that utilizes Machine Learning (ML) and Deep Learning (DL) techniques to forecast future stock prices based on real-time and historical data. The system is designed with a full-stack architecture, featuring a responsive frontend using HTML, CSS, and Bootstrap, and a robust backend powered by Python (Django) with data storage handled through SQLite. For data acquisition, the project integrates the Yfinance API to fetch live and historical stock market data and uses BeautifulSoup to scrape the latest financial news articles. T

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Liang, Luocheng. "ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction in the US stock market." SHS Web of Conferences 196 (2024): 02001. http://dx.doi.org/10.1051/shsconf/202419602001.

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Absteact: Stock price forecasting is considered one of the most difficult tasks in financial forecasting. Combining ARIMA with neural networks helps to enhance the model’s predictive capabilities when dealing with complex, nonlinear time series data. Attention-based CNN-LSTM and XGBoost hybrid model achieves the accuracy of stock prediction results. However, the predictive effect of this hybrid model has only been confirmed by Chinese stock market data. Therefore, this paper proposes to use ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price of five differen

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Putrie, Veronica Clasrissa, and Himda Anataya Nurdyah. "Stock Making Investment Decisions Using the Capital Asset Pricing Model (CAPM) Analysis of the Business Index-27 on the Indonesian Stock Exchange." International Journal of Mathematics, Statistics, and Computing 2, no. 3 (2024): 95–101. http://dx.doi.org/10.46336/ijmsc.v2i3.119.

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The purpose of this study is to measure the ability of the Capital Asset Princing Model (CPAM) in analyzing investment decision making by predicting the risk and return that will be obtained by investors and helping investors in choosing efficient and inefficient stocks. CAPM is a measuring tool that can be used to determine the level of risk and return obtained and evaluate the rate of return on investment. The purposive sampling technique is used in selecting samples to be used in the study, namely companies listed on the Indonesia Stock Exchange and their shares are consistently included in

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Gazali, Masfar, Rahmadianti Thomas, and Matrodji Mustafa. "GARCH-M MODEL AND THE BEHAVIOR OF RISK-RETURN RELATIONSHIP IN INDONESIA STOCK MARKET." Jurnal Ilmiah Ekonomi Dan Bisnis 19, no. 2 (2022): 101–9. http://dx.doi.org/10.31849/jieb.v19i2.6315.

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This study examines the risk-return trade-off and volatility behaviour in Indonesia stock market. As the analytical tool this study uses GARCH-M model with symmetric GARCH(1,1). To obtain more reliable results, this study takes daily and weekly stock index as well as 5 individual stock returns from January 2004 to November 2020 as a sample. This study also investigates the results with two alternative mean equations, simple regression and AR(1) model. The first finding of this study is that in Indonesia stock market both in stock index and in individual stocks, the volatilities of return are t

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Kim, Sung-Dong. "Data Mining Tool for Stock Investors' Decision Support." Journal of the Korea Contents Association 12, no. 2 (2012): 472–82. http://dx.doi.org/10.5392/jkca.2012.12.02.472.

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Imisiker, Serkan, and Bedri Kamil Onur Tas. "Wash trades as a stock market manipulation tool." Journal of Behavioral and Experimental Finance 20 (December 2018): 92–98. http://dx.doi.org/10.1016/j.jbef.2018.08.004.

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Levi, D., M. G. Andreoli, E. Arneri, G. Giannetti, and P. Rizzo. "Otolith reading as a tool for stock identification." Fisheries Research 20, no. 2-3 (1994): 97–107. http://dx.doi.org/10.1016/0165-7836(94)90077-9.

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Mucci, Paul, Eun-Joo Lee, and Seung-Hwan Lee. "Stock Price Forecasting Using A Dependence Structure." European Journal of Mathematics and Statistics 3, no. 3 (2022): 21–29. http://dx.doi.org/10.24018/ejmath.2022.3.3.114.

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It is important to incorporate diverse dependence structures between stocks when managing a stock portfolio. Copulas are a useful statistical tool to capture dependence structure, dealing with both the linear and non-linear association that may occur in the tails of data. Financial time series datasets often exhibit volatility clustering that affects price forecasting accuracy. This work proposes the initial use of the principal component analysis followed by a copula and GARCH model that filters the effect of the volatility clustering in the series. For illustration, we consider ten banks fro

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Fauziyah and Evita Purnaningrum. "Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method." Jurnal Matematika MANTIK 7, no. 1 (2021): 20–30. http://dx.doi.org/10.15642/mantik.2021.7.1.20-30.

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Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization resu

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Vercelli, Monica, Luca Croce, and Teresina Mancuso. "An Economic Approach to Assess the Annual Stock in Beekeeping Farms: The Honey Bee Colony Inventory Tool." Sustainability 12, no. 21 (2020): 9258. http://dx.doi.org/10.3390/su12219258.

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For beekeepers, the beehive stock represents a fundamental means of ensuring the continuity of their activity, whether they are professionals or hobbyists. The evaluation of this asset for economic purposes requires knowledge of the rhythms and adaptations of honey bee colonies during the annual seasons. As in any breeding activity, it is necessary to establish the numerical and economic size of the species bred. Beekeepers are interested in this evaluation to monitor beehive stock. For keeping economic accounts of stock, a specific tool has been developed and proposed, here called the “Honey

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Nanang, Rusliana, Lokiteswara Setya Wardhani Ciptaning, Hidayat Andry, hastuti lestari Komarlina Dwi, and Rudiyanto Yayan. "Technical Analysis of Stocks; Using the Capital Asset Pricing Model (CAPM) To Assess Banking Share on the Indonesia Stock Exchange (2019-2021)." Account and Financial Management Journal 08, no. 04 (2023): 3150–57. https://doi.org/10.5281/zenodo.7818335.

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Abstract Stock are proof of ownership of the value of a company, stock are also one of the securities traded in the capital market. In addition,stocks are one of the investment instruments that have a high risk and return, however, they are still the most popular. This is evidenced by the increasing number of Indonesian investors every year. The purpose of this research is to assess efficient banking stocks and classify them. The analytical method in this research is used descriptive analysis method with a quantitative approach. The analytical tool used the Capital Asset Pricing Model (CAPM).

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Kumar, Ankush. "Stock Price Predictions Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48440.

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ABSRACT The goal of this research is to create a reliable ma- chine learning model that uses past market data to predict stock prices. This calls for the primary com- puter language to be Python and the use of special- ised tools like scikit-learn and yfinance. The inten- tion is to create a useful resource for those. Study finance or make stock market investments. This tool will assist them in making wise decisions. Accurately estimating the price of stocks is crucial for wise investment decisions. It lowers risks, assists investors in selecting the optimal stock combination, and may even inc

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Nicolle, Amandine, Roderic Moitié, Julien Ogor, et al. "Modelling larval dispersal of Pecten maximus in the English Channel: a tool for the spatial management of the stocks." ICES Journal of Marine Science 74, no. 6 (2016): 1812–25. http://dx.doi.org/10.1093/icesjms/fsw207.

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AbstractThe great scallop Pecten maximus supports one of the most important and valuable commercial fisheries around the British Isles and in the northwest of France, but the resource is mainly managed at the scale of each local fishing ground through a combination of European, national and local measures. To analyse the larval dispersal pathways and connectivity patterns among fishing grounds of the great scallop in the Celtic Sea and the English Channel, a particle tracking model was developed. The model combined a 3D physical circulation model that simulated currents and temperature fields

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D’Souza, Roshan M. "Tool Sequence Selection for 2.5D Pockets with Uneven Stock." Journal of Computing and Information Science in Engineering 6, no. 1 (2006): 33–39. http://dx.doi.org/10.1115/1.2161228.

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This paper describes an algorithm to select the cheapest tool sequence for machining 2.5D pockets using the milling process when the stock is uneven (noncylindrical). Uneven stock is generated when multiple setups are used to machine a prismatic part. Even though the pockets have flat bottom faces, the amount of material to be removed will vary along the depth of the pocket. This research has developed algorithms for finding accessible areas for tools, and pocket decomposition when the stock is uneven. Finally, it is shown that tool sequence selection problem can be formulated as the shortest

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Mohn, R. K., and R. W. Elner. "A Simulation of the Cape Breton Snow Crab, Chionoecetes opilio, Fishery for Testing the Robustness of the Leslie Method." Canadian Journal of Fisheries and Aquatic Sciences 44, no. 11 (1987): 2002–8. http://dx.doi.org/10.1139/f87-245.

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The principal tool for evaluating biomass and, hence, exploitation rate for Atlantic snow crab, Chionoecetes opilio, has been the Leslie method which is based on commercial catch rate and cumulative catch through the fishing season. The method assumes a dosed, homogeneous stock. However, a snow crab stock is often not closed because recruitment can occur during the fishing season as crabs molt into legal size. Also, stocks are not homogeneous and spatial heterogeneity causes fishermen to combine fishing and searching activities. A dynamic simulation of fishing/searching on a heterogeneous sess

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Kumari, Reenu, Anjana Gupta, and Abha Aggarwal. "Stock assessment using Cumulative Prospect Theory in DEA cross-efficiency model." Croatian operational research review 15, no. 1 (2024): 1–11. http://dx.doi.org/10.17535/crorr.2024.0001.

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Market volatility is becoming increasingly common as numerous factors are implemented in the financial system. As a result, portfolio managers and individual investors require reliable methods to assess stock performance. This study examines stock assessments using cross-efficiency evaluations in cases where negative data is present. An alternative approach to achieve this goal is to use an RDM DDF-based cross-efficiency model which oversees the negative data. We expand the RDM-based cross-efficiency analysis, which uses row and column average values to select portfolios and identify different

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Dumičić, Ksenija, and Berislav Žmuk. "Statistical Control Charts: Performances of Short Term Stock Trading in Croatia." Business Systems Research Journal 6, no. 1 (2015): 22–35. http://dx.doi.org/10.1515/bsrj-2015-0002.

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Abstract Background: The stock exchange, as a regulated financial market, in modern economies reflects their economic development level. The stock market indicates the mood of investors in the development of a country and is an important ingredient for growth. Objectives: This paper aims to introduce an additional statistical tool used to support the decision-making process in stock trading, and it investigate the usage of statistical process control (SPC) methods into the stock trading process. Methods/Approach: The individual (I), exponentially weighted moving average (EWMA) and cumulative s

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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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Yan, Sheng Yang. "Solving Portfolio Investment Model Based on Trade Data Stream in the Stock Market." Applied Mechanics and Materials 687-691 (November 2014): 5149–52. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.5149.

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Stock trading is a kind of modern economic movement in order to obtain high returns under risky investment activities. Stock value index only provides a tool for people with a measure of historical change of stock price. The paper presents a hybrid SAGA combined with dynamic penalty function algorithm is used to solve the model founded before. And also a more actual portfolio investment model, which is based on all requirements of the actual finical market such as no dividing stocks and all kinds of fees .The experiment results indicate that our algorithm has better searching ability and can a

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Fauzan, Ahmad, and Rindang Matoati. "Financial Ratios and Share Prices of JII70 Indexed Companies for the 2018-2020 Period." Management Journal of Binaniaga 6, no. 1 (2021): 23. http://dx.doi.org/10.33062/mjb.v6i1.422.

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Abstract: The sharia capital market in Indonesia has grown over the last five years. One of the members of the sharia capital market instrument is sharia shares. During the 2015-2020 period, the number of Islamic stock issuers continued to grow. The stock index is used by investors as a tool to choose stocks that suit their needs. IDX has issued three sharia stock indexes, and the most recent one is the JII70 index. A stock index is a collection of statistics about the price movement of a group of stocks that is evaluated periodically. One of the many factors that influence stock prices is the

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Deby Shintawulan Fransiska, Novita Rahmawati, Feby Galih Saputra, and Maria Yovita R Pandin. "STOCK DIVERSIFICATION (PORTFOLIO) STRATEGY TO INCREASE INVESTMENT RETURNS AND REDUCE RISK." Finance : International Journal of Management Finance 1, no. 2 (2023): 33–39. http://dx.doi.org/10.62017/finance.v1i2.14.

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The purpose of this article is to explain diversification and portfolio risk and the impact of diversification on portfolio risk and determine the number of stocks that make up the optimal portfolio. The sampling technique is purposive sampling and the sample is taken from 5 stocks of the IDX Energy Sector industry. The analysis tool uses a correlation matrix, expected return and risk for individual stocks and portfolios (5 stocks) and the Unknown Population Standard Deviation (σ) Hypothesis Test. The results of this study are: 1) The correlaion matrix table shows that stocks have a positive c

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Ohsawa, Yukio, Teruaki Hayashi, and Takaaki Yoshino. "Tangled String for Multi-Timescale Explanation of Changes in Stock Market." Information 10, no. 3 (2019): 118. http://dx.doi.org/10.3390/info10030118.

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This work addresses the question of explaining changes in the desired timescales of the stock market. Tangled string is a sequence visualization tool wherein a sequence is compared to a string and trends in the sequence are compared to the appearance of tangled pills and wires bridging the pills in the string. Here, the tangled string is extended and applied to detecting stocks that trigger changes and explaining trend changes in the market. Sequential data for 11 years from the First Section of the Tokyo Stock Exchange regarding top-10 stocks with weekly increase rates are visualized using th

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Gultom, Edra Arkananta, Kartika Dewi Sri Susilowati, and Anik Kusmintarti. "Design of a Stock Forecasting Dashboard using Python-Streamlit and FB Prophet with AI." Formosa Journal of Science and Technology 3, no. 11 (2024): 2445–64. https://doi.org/10.55927/fjst.v3i11.12216.

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This research aims to develop a stock price forecasting application using time series analysis with the Prophet model. The application retrieves historical stock data from Yahoo Finance (2015–present) for Indonesian stocks, which is then processed and analyzed to predict future prices. The study integrates yfinance for data collection, Prophet for forecasting, and Plotly for visualizing the results. The application allows users to select stocks and customize prediction periods (1–4 years). The findings indicate that while the model provides useful short-term predictions, its accuracy is limite

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Dewi, Risma. "January Effect Analysis on The Indonesian Stock Market (Case Study of the 2016-2020 LQ45 Index Stock)." Management Journal of Binaniaga 7, no. 1 (2022): 31–42. http://dx.doi.org/10.33062/mjb.v7i1.488.

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The January effect anomaly that occurred in the Indonesian stock market was inconsistent and only occurred in a few years. However, its existence is sufficient to create a potential negative return risk in the non-January trading month. So this research purposes to identify the characteristics of the January effect anomaly that occurs and its effect on abnormal stock returns in the long term of five years. The research sample contains 27 publicly listed company stocks in the LQ45 index from 2016 to 2020. The analytical tool used in this research is a multiple linear regression model with panel

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Jiang, Wantong. "Future trends of AI stocks prediction using ARIMA model." Theoretical and Natural Science 42, no. 1 (2024): 112–19. http://dx.doi.org/10.54254/2753-8818/42/2024ch0220.

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Abstract. This study uses an autoregressive integrated moving average (ARIMA) model to forecast the stock movements of artificial intelligence-related companies using IBM's historical stock price data from 2019 to 2024. Due to the high volatility and unique externalities of AI stocks, traditional financial models may not provide accurate forecasts. In this study, the ARIMA (1,1,0) model is chosen based on analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF). The prediction results indicate that IBM's stock price will trend upward in the short term. The model

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G, Kaveri, L. Tejashwini, and Manjunath K. "Role of Sentiment in Stock Forecasting." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1649–53. https://doi.org/10.22214/ijraset.2025.66656.

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Abstract: The Stocks uses the sentiment analysis in predicting stock market movements by analyzing data from sources like news, social media, and financial reports. Employing a Random Forest machine learning model, the system aggregates sentiment indicators, such as positive and negative emotions, to forecast stock trends and market fluctuations. The results show that sentiment analysis improves prediction accuracy, reduces investment risks, and supports data-driven decision-making. By integrating real-time data, the model adapts to changing market conditions and provides timely forecasts. Thi

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Hung, J. P., W. Z. Lin, K. D. Wu, and W. C. Shih. "Analyzing the Dynamic Characteristics of Milling Tool Using Finite Element Method and Receptance Coupling Method." Engineering, Technology & Applied Science Research 9, no. 2 (2019): 3918–23. http://dx.doi.org/10.48084/etasr.2463.

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This study aims to investigate the dynamic characteristics of a milling machine with different head stocks by using finite element (FE) method and receptance coupling analysis (RCA). For this purpose, five full finite element machine models, including vertical column, reformed head stock and feeding mechanism were created. With these models, the tool point frequency response functions were directly predicted. Another approach was the application of the receptance coupling method, in which the frequency response of the assembly milling tool was calculated from the receptance components of the i

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