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For more than several decades, time series data have been in the center of attention for scholars to predict the future prices of the markets, the most fundamental and challenging of which has been the prediction of the price of the stock market. It is of great importance to note that the algorithms with the fewest errors in price predictions are more applicable. There have been more methods suggested for price prediction in the stock markets: time series data analysis, mathematical and statistical analysis, signal processing, pattern recognition and machine learning. One of the demerits of the aforementioned methods is failing to recognize sudden change of prices, in this regard, experiencing more errors is the consequence of such demerit. In this regard, to have the error solved, the DDQN algorithm, consisting of deep neural networks which includes LSTM-CNN layers, has been employed. Confronting price fluctuations, the agent has the privilege of having better performance by employing the advantages of LSTM-CNN layers. In this research, the algorithm has been carried out over Iranian Gold Market, including six various types of Gold, from 2009 to 2020. The results reveal the point that the given method is more precise in comparison with other suggested methods confronting sudden changes in prices. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government's target. Design/methodology/approach: The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)12 model for forecasting were determined based on model identification, estimation and diagnostics. Findings: SARIMA(1, 0, 1) (1, 1, 0)12 was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target. Research limitations/implications: This study provides preliminary ideas and insight into whether the government's target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna. Practical implications: The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry. Originality/value: This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government's target of 150,000 metric tonnes. [ABSTRACT FROM AUTHOR]

Despite the popular claim that broths are a good source of essential elements, few studies have been devoted to clarifying this narrative. In fact, there is growing concern about the risk of ingesting toxic elements. This study focused on assessing the content of important elements (Ca, Cd, Cu, Fe, K, Mg, Na, and Pb) in various types of broths (beef, fish, poultry, pork, and vegetable). The samples (n = 30) included broths of different origins, including laboratory-prepared broths and broths prepared by professional cooks from Czech school canteens. The broths were evaluated as potential sources of essential elements in relation to their reference daily intake, as well as their potential toxicity risk in relation to Cd and Pb. Although significant variability was observed in the contents of the investigated elements, the results generally contradicted the popular narrative that broths are a good source of Ca, Cu, Fe, and Mg, as their contents did not achieve even 5% of the recommended daily intake. The amount of Ca in the broths was minimal even compared to the amount in tap water. The consumption of broths poses minimal risk regarding the presence of Cd and Pb. [ABSTRACT FROM AUTHOR]

Purpose: Press reports have indicated that firms frequently underprice restricted stock and employee stock options. I test for underpricing of stock and options. Design/methodology/approach: I examined a sample of 5,333 private firm stock and option issuances between 1985 and 2017. I tested for underpricing using two approaches: assuming investors have no special market-timing ability and assuming instead they have perfect market-timing ability. Findings: I find evidence of widespread stock and option underpricing by private firms before they go public reflecting large discounts that exceed reasonable compensation for lack of marketability. Unreported underpricing is more frequent in the last pre-IPO private equity transactions that offer the last opportunity to give such discounts before the stock is publicly traded, but the discounts are greater in the earlier pre-IPO transactions where unreported discounts are presumably tougher for the SEC to detect. Underpricing is still detected even when the actual DLOMs are tested against a benchmark that assumes investors have perfect market-timing ability. Research limitations/implications: Firms frequently underprice restricted stock and employee stock options. Firms tend to underprice stock options more frequently than restricted stock, but restricted stock tends to be priced at deeper discounts when recipients are assumed not to have any special market-timing ability. Practical implications: Private firms issue restricted stock and options as incentive compensation. Lowballing the valuation transfers wealth from outside stockholders to employees/insiders. Wealth transfers take place through the issuance of equity claims to employees/insiders before firms go public. I found that more than a quarter of the DLOMs exceed the theoretical maximum by, on average, between 16% (median) and 20% (mean). This finding raises two questions worthy of investigation. First, to what extent do the frequency and magnitude of DLOMs above the theoretical maximum depend on whether a board of directors obtains an independent appraisal of a stock's fair market value? Second, if DLOMs above the theoretical maximum are observed even when the stock is independently appraised, how do appraisers justify such large DLOMs? Social implications: The wealth transfers that take place through the issuance of equity claims to employees/insiders before firms go public benefit employees/insiders at the expense of outside shareholders. Originality/value: My paper is the first to furnish evidence of widespread stock and option underpricing by private firms before they go public; demonstrate that the unreported underpricing is more frequent in the last pre-IPO private equity transactions that offer the last opportunity to give such discounts before the stock is publicly traded and show that the discounts are greater in the earlier pre-IPO transactions where unreported discounts are presumably tougher for the SEC to detect. [ABSTRACT FROM AUTHOR]

This study examines and compares the behaviors of private investors in cryptocurrency and stock trading platforms. To this end, we propose a Tobit model for private investors who navigate across cryptocurrency and stock trading apps and identify factors associated with cryptocurrency and stock trading platform usage. We apply our model to unique mobile app usage data from August 2020 to March 2021, covering two waves of the COVID-19 cxpandemic in South Korea and the global speculative cryptocurrency bubble. We find that cryptocurrency and stock private investors differ considerably, in terms of demographics as well as such behaviors as loss-aversion, optimism, addiction, herding, cross-trading, and response to the pandemic. Our analysis also discovers potential competition between cryptocurrency and stock trading platforms in targeting their customers, e.g., private investors. Furthermore, we reveal behavioral profiles of segments of private investors of cryptocurrency and stocks, so the trading platforms can discover them. This study offers important managerial implications for trading platforms, to target and manage their private-investor customers. [ABSTRACT FROM AUTHOR]

Euthynnus alletteratus, a crucial commercial resource for West African states in the Northeast Atlantic Ocean (ATNE), is of high research priority in the Atlantic Ocean, but its stock assessment is limited. This study aimed to explore the implications of integrating length, catch and abundance indices on the status of ATNE stock using the LIME model. We innovatively developed time series of abundance indices using tuna purse seine catches and efforts obtained from separate ICCAT datasets, alongside length-frequencies. The scenarios (S) S1 (length-only), S2 (length + catch + abundance), S3 (length + catch), and S4 (length + abundance) were considered. We observed that S1–S3 did not converge in LIME, except for S4. The stock's spawning potential ratio and mean length were below the target (SPR< 40%) and 95% maturity length (M95), respectively. By implication, the lack of convergence in S1–S3 portrayed data conflicts, which were mitigated by the abundance index in S4. A mean length above M95 is recommended when evaluating the stock using the length-only option in LIME. The stock was unsustainable, requiring management measures. Integrating the abundance index with length improved the performance of LIME for the stock, which estimate could be replicated for other species' assessments. [ABSTRACT FROM AUTHOR]

The European sardine (Sardina pilchardus) is under intense fishing pressure and exhibits distributional/abundance shifts linked to environmental change. The current understanding of population demographics needed for sustainable management is uncertain due to concerns that previous genetic studies lacked resolution and limited sampling of sardine north of the Bay of Biscay. To address these issues, we performed mtDNA sequencing and genome wide SNP analysis of samples collected across the Bay of Biscay, Celtic Sea, English Channel and North Sea. The complete SNP data reported a lack of structure throughout the sampled area compatible with high gene flow. A consensus suite of positive outlier SNPs was identified which reported a significant correlation with geographical distance with the largest differentiation between the southern Bay of Biscay and North Sea samples which also reported a significant mtDNA ΦST. While the roles of dispersal limitation and environmental heterogeneity underpinning this require further study, this adds to growing evidence that selection is influencing sardine population structure against a background of high gene flow. The results indicate that while there may be a level of demographic independence between North Sea and South Biscay sardine, the current delimitation of central (Biscay) and northern (Channel and Celtic Sea) operational stocks may misrepresent connectivity between the Biscay and Channel. The North Sea sample exhibited markedly lower mtDNA and nuclear variation than other samples. As sardine have only recently invaded the North Sea such reduced genetic variation is compatible with predictions for peripheral leading‐edge populations but contrasts with patterns for other small pelagic species and emphasises the need to consider species‐specific genetic structure in ecosystem‐based management. Nascent management of the North Sea sardine fishery must ensure that current low levels of genetic diversity are not eroded further as this may decrease the species adaptive potential and inhibit its expansion. [ABSTRACT FROM AUTHOR]

Trace elements are the essential mineral nutrients in grassland, however, we still know little about the distributions of trace elements in grassland with long‐term grazing exclusion. The contents, stocks, and proportions of iron (Fe), aluminum (Al), manganese (Mn), and boron (B) in green plant‐litter‐root‐soil were evaluated by enclosing for 18, and 39 years inside the fence (F18 and F39) and grazing outside the fence (F0) in Inner Mongolia grassland. The results showed that F18 and F39 decreased the stocks of Fe, Al, and Mn in green plant and root compared to F0 (p <.05), while increased the stocks of them in litter (p <.05). The stock of Fe, Al, and Mn in green plant at F39 was 28.6%, 13.9%, and 39.2% higher than that at F18. The stocks of four trace elements in first layer litter at F39 were increased by 12.7%–52.2% compared to F18, whereas the stocks of them in third layer litter were decreased by 32.2%–42.5%. The F18 obviously increased the stocks of Fe and Mn in soil, especially B (p <.05). While the stocks of these trace elements in soil at F39 were 9.1%–28.0% lower than that at F18, especially B (p <.05). In conclusion, the trace elements were mainly shifted from green plant and root to soil and third layer litter with 18‐year grazing exclusion. Compared to 18‐year grazing exclusion, the trace elements were shifted from third layer litter and soil to root with 39‐year grazing exclusion. [ABSTRACT FROM AUTHOR]

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spiny lobster is one of the highest-priced seafood worldwide. Its fishery contributes well to the country's income and is considered one of the important export items to other countries. However, its annual catch declined sharply. Studying the stock is important for proper fishery management because stock responds differently to overexploitation and adaptation, therefore different stocks need different conservation plan. In this study, the genetic diversity and population differentiation of 50 P. homarus (the most present spiny lobster subspecies in Oman) from two regions (Al-Ashkharah and Masirah) of Al Sharqyah governorate were studied using 49 microsatellite markers. The results showed that P. homarus from the two sites are genetically similar based on low Fst (0.009), AMOVA test (1% variation). In addition, they are admixed as it is shown by DAPC (Discriminate Analysis of Principal Component) and by the phylogenetic tree. In conclusion, P. homarus from the two sites can be considered as a single stock, therefore the same fishery management and conservation strategy can be used to manage the spiny lobster fishery in Al Sharqyah governorate. More studies with a larger sample size are necessary to support our findings. [ABSTRACT FROM AUTHOR]

Plants are able to naturally graft or inosculate their trunks, branches and roots together, this mechanism is used by humans to graft together different genotypes for a range of purposes. Grafts are considered successful if functional vascular connections between the two genotypes occur. Various techniques can evaluate xylem connections across the graft interface. However, these methods are generally unable to assess the heterogeneity and three‐dimensional (3D) structure of xylem vessel connections. Here we present the use of X‐ray micro‐computed tomography to characterize the 3D morphology of grafts of grapevine. We show that xylem vessels form between the two plants of natural root and human‐made stem grafts. The main novelty of this methodology is that we were able to visualize the 3D network of functional xylem vessels connecting the scion and rootstock in human‐made stem grafts thanks to the addition of a contrast agent to the roots and improved image analysis pipelines. In addition, we reveal the presence of extensive diagonal xylem connections between the main axial xylem vessels in 2‐year old grapevine stems. In conclusion, we present a method that has the potential to provide new insights into the structure and function of xylem vessels in large tissue samples. Summary statement: Successful grafting requires the formation of functional vascular connections between two different plants. We describe using X‐ray micro‐computed tomography to characterize the network of xylem vessels formed across the graft interface of natural root and human‐made stem grafts of grapevine. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Due to its potential to produce substantial returns and reduce risks, stock price prediction has garnered a lot of attention in the financial markets. OBJECTIVES: A comparison of neural network models for stock price prediction is presented in this research report. METHODS: Through this study, I aim to compare, on the basis of the precision and accuracy, the performance of different neural network models for stock price prediction. LSTM model along with RNN model accuracy in predicting the next day's stock price i.e., which model can predict closest to the actual value. RESULTS: It is found that LSTM works better than RNN in predicting a value closer to the actual open price stock value. CONCLUSION: A comparison between the models shows LSTM is the more accurate model. [ABSTRACT FROM AUTHOR]

In the construction industry, much of the construction process is controlled by materials, and material management has a direct impact on many performance criteria such as cost, time, quality, and efficiency. This study examined the impact on project performance of activities that make up the material management process: planning, supply, logistics, transport, stock, and waste control of building materials. Material management practices were measured using 15 criteria under five different headings, and project performance was measured using 13 criteria under three different headings, through surveys of industry personnel at various construction sites. Multiple regression analyses were performed on the data obtained. The impacts of material management practices on project time, project cost, project efficiency, and waste performance, as well as overall project performance were examined using multiple regression analyses. The analysis showed that material supply, material handling, and stock and waste control had positive impacts on overall project performance, while material planning and material logistics did not have statistically significant impacts. It is concluded that the results of the study will help to better understand the relationship between material management and project performance and will help stakeholders to improve their material management practices. [ABSTRACT FROM AUTHOR]

In any inventory and production management, deterioration of physical goods is one of the important factors put into consideration. Therefore, it is very important to control and maintain inventories for decaying items. We considered inventory problem for deteriorating items where demand is stock dependent and deterioration is controllable under two warehouse storage facility, one of the warehouses is owned and the other one is rented. Goods are displayed in both warehouses but demands are met from the rented warehouse until the level of goods get to zero then demands are met from owned warehouse. We derived the optimal inventory management policies for the cases where; the demand is constant function, there is no investment in technology and there is no investment in technology and demand is also constant. [ABSTRACT FROM AUTHOR]

Investor sentiment has the potential to serve as a predictive factor for cryptocurrency assets, yet its impact on volatility spillover across markets remains uncertain. This research investigates the influence of investor sentiment classification on the volatility spillover from stablecoins to conventional financial markets. Empirical findings reveal the role of stablecoins as net volatility receivers from financial assets during extreme fear sentiment conditions, suggesting that stablecoins may function as a potential safe-haven when investors experience significant fear, offering insights to investors and portfolio managers regarding the potential use of stablecoins as a risk mitigation tool within an investment portfolio.

Risk and volatility are two related factors in research regarding capital markets. Many factors influence the movement of shares and indices. Volatility is common and affects risk assessment. Stock price volatility is an important aspect of understanding market behavior, with high volatility reflecting rapid and unstable price fluctuations. This research investigates the GARCH model in assessing volatility on the IBM Stock Exchange. The method employed was the symmetric GARCH model. It focuses on univariate analysis using the GARCH econometric model. The GARCH model allows modeling stock price variance over time based on the assumption that the variance was influenced by past stock price variance. The stages of this research were (1) data collection, (2) data pre-processing, and (3) forecasting model implementation. The best model obtained was ARMA(4,2)-GARCH(5,6) with an AIC value of 4.1017. A lower AIC value indicates that the model explains the data better or more optimally. A diagnostic test found that the model was adequate because the residual distribution followed a straight line, which means it was normally distributed.

In daily life, banks usually not only offer their saving programs but also their stocks. This research focuses on designing a financial mathematical task for vocational high school (VHS) which is valid, practical, and has potential effects using saving and stock context. Pendidikan Matematika Realistik Indonesia (PMRI) is the approach used in this research. For the method, we used design research with a type of development studies. This research was conducted at VHS 1 at Jambi City with research subjects were the eleventh-grade students of accounting major. This research produces three financial mathematical tasks with the context of saving and stock. The results of research also found that by using saving and stock context, students became more understanding of real-world problems and wise in making decisions regarding financial problems. This research can also be used as a reference for financial mathematics teaching materials, especially for vocational high school teachers.

Results of the plankton survey conducted in the Gulf of Alaska, including EEZ of Canada, aboard RV Pacific Legacy No. 1 in March-April of 2020 are presented. The samples of plankton were collected in regular grid of stations and processed according to the methodologies adopted in TINRO. The mean total biomass of zooplankton was estimated as 301.9 mg/m3 , of phytoplankton — as 41.9 mg/m3 . The large-sized fraction of zooplankton dominated (on average 214.6 mg/m3 , or 71 % of the total biomass), the portions of the smalland medium-sized fractions were 16 % and 13 %, respectively. Because of spring blooming development, both phyto- and zooplankton biomasses were increased southward from 3.2 and 188.3 mg/m3 in the northern area of the gulf to 87.9–34.6 and 305.4–397.7 mg/m3 in its central and southern areas, on average. The biomass of small- and medium-sized fractions (presented by copepods Pseudocalanus, Oithona similis, eggs and nauplii of copepods, copepodites, juvenile pteropods, hyperiids and euphausiids, and larvae of siphonophore) had slightly increased in the southern area to 50–60 and 35–38 mg/m3 , respectively, whereas the biomass of largesized fraction presented mainly by copepods (49 %) and arrowworms (36 %) had increased from 118.9 mg/m3 in the north to 293.6 mg/m3 in the south. Boreal species, as Neocalanus cristatus, N. plumchrus/N. flemingeri, Eucalanus bungii, and Metridia pacifica dominated in the zooplankton biomass.The total stock of zooplankton in the Gulf of Alaska in March-April of 2020 was amounted in 39798 . 103 t that was higher than in February-March of 2019 (25517 . 103 t), obviously because of the spring bloom beginning. Correspondingly, distribution density of phyto- and zooplankton was 8.5 and 61.4 t/km2 in March-April of 2020 against 1.0 and 36.6 t/km2 in February-March of 2019. The largest increasing was noted for the small-sized fraction (in 2 times, to 9.7 t/km2 ) and medium-sized fraction (in 4 times, to 8.0 t/km2 ), whereas the increasing for large-sized animals was moderate — from 31.0 t/km2 in 2019 to 43.6 t/km2 in 2020, and the density of euphausiids did not increase (0.8 t/km2 in both years for Euphasia pacifica). The total stock of large-sized fraction had increased from 21650 . 103 t in 2019 to 28920 . 103 t in 2020.The diet of pacific salmon corresponded with the species composition and biomass of the large-sized fraction of zooplankton, with predominance of euphausiids, pteropods, jellyfish, hyperiids, and oikopleura. The main consumers were the most abundant fish groups, as chum salmon with size of 35–60 cm and coho salmon of 30–50 cm found mainly in the central and southern areas of the gulf. E. pacifica were the favorite prey for all salmon species (67–80 % of food weight), except coho salmon — this species preferred squids (77.6 %). Monthly grazing of zooplankton by salmons in the Gulf of Alaska is estimated as 30.175 . 103 tons that is an insignificant portion of its large-sized fraction stock, so the feeding conditions during the survey are considered as favorable for pacific salmons. However, the consumption is possibly underestimated because of low biomass of consumers in the winter-spring season. Spatial and temporal variations of fish aggregations driven by variability of water temperature and other abiotic factors should be accounted for adequate evaluation of the feeding conditions.

Copyright of Jurnal Manajemen dan Organisasi is the property of IPB University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Every organization needs inventory for the smooth running of its activities. It serves as a link between production and distribution processes. The investment in inventories constitutes the most significant part of current assets/working capital in most of the undertakings. Thus, it is essential to have proper control and management of inventories. The purpose of inventory management is to ensure availability of materials in sufficient quantity as and when required and to minimize investment in inventories. So, to understand the nature of inventory management of the organization, I took this Inventory Management as a topic for my project, to give findings and suggestions by adopting and analyzing different inventory control techniques. [ABSTRACT FROM AUTHOR]

Debtor's management is one of the key areas of financial decision-making. It is significant because, the management must see that an excessive investment in current assets should protect the company from the problems of stock-out. Current assets will also determine the liquidity position of the firm. The goal of Debtor's management is to manage the firm current assets and current liabilities in such a way that a satisfactory level of working capital is maintained. If the firm cannot maintain a satisfactory level of working capital, it is likely to become insolvent and may be even forced into bankruptcy. [ABSTRACT FROM AUTHOR]

This study proposes a method to infer the parameters of the constant elasticity of variance (CEV) model from the market values of stock after the extension from the asset process of the Merton model in the structural credit risk model to that of the CEV model. The state space model is used, which consists of an asset process (system equation) and the call option pricing a stock value (observation equation), for the inference. However, it is usually difficult to apply the Markov chain Monte Carlo (MCMC) method to estimate the parameters of the CEV model because the observation equation of the state space model has no analytical formula. Our method solves this parameter estimation problem by applying the MCMC combined with a finite difference method of partial differential equations, where the stock value obtained as a CEV option price is numerically solved. This study estimates the parameters from the real stock values of the US financial institutions as an empirical analysis. Furthermore, we analyze the default probability and measure the credit risk of bank portfolios. [ABSTRACT FROM AUTHOR]

Copyright of Kastamonu University Journal of Economics & Administrative Sciences Faculty / Kastamonu Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Kastamonu University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Competitiveness is one of the most significant challenges faced by companies to position themselves in the market and achieve excellence, thereby differentiating themselves from the competition. The Republic of Croatia is a developing country with a majority of small companies in its company structure. In terms of profits and employee numbers, large companies dominate the industry and economy as a whole. This paper aims to provide a theoretical basis and investigate the structure of companies in the Republic of Croatia, with a special focus on large companies listed on the stock exchange. Large companies are the carriers of the economy and have a significant influence on industry and investors who invest in stocks. The paper is based on stock trading data from the Zagreb Stock Exchange, with a special review and analysis of companies listed on the leading market. The results show the price range and business scope of the observed shares. Limitations of the research include a small sample size for the leading stock market and geographical limitations due to Croatia’s small market size. Future research should include an analysis of other companies listed on the stock exchange, as well as a comparison of large companies and stock market operations with countries in Eastern Europe and the European Union. Large companies have a significant impact on global economies, and this study’s findings can contribute to a better understanding and increased participation by stakeholders and potential investors in company operations available on the stock exchange. [ABSTRACT FROM AUTHOR]

Stock comprises materials, at different stages of processing, that are not currently in use in the company, but are maintained to meet future demands. Represents the set of resources used in the company's production process. To remain in the market, the company must have good inventory management and avoid excessive stocking or lack of materials for direct sales to the customer. The general objective of this article is to identify the importance of inventory management activities in the corporate logistics context. The work in question was carried out in a qualitative manner, through bibliographical research, having as its theoretical basis works by several renowned authors, including Chiavenato (2006); Ballou (2015); Fernandes (2012); Trigueiro (2016); Jardim (2014). As a result, we found that inventory management is a crucial element for the success of any business, whether small or large, regardless of the sector in which it operates. Therefore, we conclude that inventory management as a logistical activity is of paramount importance in the corporate world, given its complexity and marketing dynamism and contribution to achieving organizational objectives. [ABSTRACT FROM AUTHOR]

During the realization of the work, we encountered obstacles and a lack of literature, both in the theoretical and empirical aspects. Also, during the review of the literature, we also encountered a lack of local research in terms of addressing these problems by researchers of different profiles, which belong to the topic that will be analyzed in this study. The purpose of the paper is to review and analyze the empirical literature related to the impact of financial statements on management decision-making, but empirical research in this field of study is relatively few, so in general empirical research is lacking, which makes it more difficult further expanding the research in this paper. The aim was to evaluate the relevance of accounting practice with accounting rules based on the theoretical framework based on the review of empirical research. [ABSTRACT FROM AUTHOR]

Octopus cyanea (Gray, 1849), abundant in the South‐West Indian Ocean (SWIO), constitutes a vital resource for both subsistence and commercial fisheries. However, despite this socioeconomic importance, and recent indications of overfishing, little is known about the population structure of O. cyanea in the region. To inform sustainable management strategies, this study assessed the spatio‐temporal population structure and genetic variability of O. cyanea at 20 sites in the SWIO (Kenya, Tanzania, Mozambique, Madagascar, Mauritius, Rodrigues, and the Seychelle Islands) by complementary analysis of mitochondrial DNA (mtDNA) noncoding region (NCR) sequences and microsatellite markers. MtDNA analysis revealed a shallow phylogeny across the region, with demographic tests suggesting historic population fluctuations that could be linked to glacial cycles. Contrary to expectations, NCR variation was comparable to other mtDNA regions, indicating that the NCR is not a hypervariable region. Both nuclear and mtDNA marker types revealed a lack of genetic structure compatible with high gene flow throughout the region. As adults are sedentary, this gene flow likely reflects connectivity by paralarval dispersal. All samples reported heterozygote deficits, which, given the overall absence of structure, likely reflect ephemeral larval recruitment variability. Levels of mtDNA and nuclear variability were similar at all locations and congruent with those previously reported for harvested Octopodidae, implying resilience to genetic erosion by drift, providing current stock sizes are maintained. However, as O. cyanea stocks in the SWIO represent a single, highly connected population, fisheries may benefit from additional management measures, such as rotational closures aligned with paralarval ecology and spanning geopolitical boundaries. [ABSTRACT FROM AUTHOR]

In the context of the COVID‐19's outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safe‐haven under the pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets' returns significantly increased under COVID‐19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO's declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVID‐19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In terms of spillovers, we do find the safe‐haven function of Gold and Bitcoin. Comparatively, the safe‐haven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safe‐haven asset, as it remains robust during the current crisis of COVID‐19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic. [ABSTRACT FROM AUTHOR]

Abstract The European sardine (Sardina pilchardus) is under intense fishing pressure and exhibits distributional/abundance shifts linked to environmental change. The current understanding of population demographics needed for sustainable management is uncertain due to concerns that previous genetic studies lacked resolution and limited sampling of sardine north of the Bay of Biscay. To address these issues, we performed mtDNA sequencing and genome wide SNP analysis of samples collected across the Bay of Biscay, Celtic Sea, English Channel and North Sea. The complete SNP data reported a lack of structure throughout the sampled area compatible with high gene flow. A consensus suite of positive outlier SNPs was identified which reported a significant correlation with geographical distance with the largest differentiation between the southern Bay of Biscay and North Sea samples which also reported a significant mtDNA ΦST. While the roles of dispersal limitation and environmental heterogeneity underpinning this require further study, this adds to growing evidence that selection is influencing sardine population structure against a background of high gene flow. The results indicate that while there may be a level of demographic independence between North Sea and South Biscay sardine, the current delimitation of central (Biscay) and northern (Channel and Celtic Sea) operational stocks may misrepresent connectivity between the Biscay and Channel. The North Sea sample exhibited markedly lower mtDNA and nuclear variation than other samples. As sardine have only recently invaded the North Sea such reduced genetic variation is compatible with predictions for peripheral leading‐edge populations but contrasts with patterns for other small pelagic species and emphasises the need to consider species‐specific genetic structure in ecosystem‐based management. Nascent management of the North Sea sardine fishery must ensure that current low levels of genetic diversity are not eroded further as this may decrease the species adaptive potential and inhibit its expansion.

Abstract Trace elements are the essential mineral nutrients in grassland, however, we still know little about the distributions of trace elements in grassland with long‐term grazing exclusion. The contents, stocks, and proportions of iron (Fe), aluminum (Al), manganese (Mn), and boron (B) in green plant‐litter‐root‐soil were evaluated by enclosing for 18, and 39 years inside the fence (F18 and F39) and grazing outside the fence (F0) in Inner Mongolia grassland. The results showed that F18 and F39 decreased the stocks of Fe, Al, and Mn in green plant and root compared to F0 (p

The stock market is dynamic and highly chaotic due to its complicated nature. The prediction of future stock prices has gained the interest of investors and researchers. Numerous conventional and hybrid approaches were put forth. The projections performed poorly and strangely did not get any better. This study aimed to predict Philippine Stock Exchange closing prices using a proposed hybrid method. This proposed study aimed to improve the Artificial Neural Network (ANN) performance using the Kepler Optimization Algorithm, a metaheuristic algorithm, in conjunction with the Artificial Neural Network (ANN-KOA). ANN is a widely accepted technique in predicting. Still, the Kepler Optimization Algorithm can provide random initial inputs for the method through effective feature selection. It can identify superior subsets of input variables to integrate into ANN, enabling more accurate prediction. In this study, the ups and downs were grouped, and 12 technical indicators with varying lengths of days 3, 5, 10, 15, and 20 were used to decompose the original historical data, which are regular and smoother than the original data. The historical data was normalized into [-1, 1], so the predicted result would be 0 (decrease) or 1 (increase) otherwise. Finally, to obtain the train and test data, it was fused into five groups and tested using the ANN and ANN-KOA. The experiment's outcome of 0 (a declining stock price) indicates two alternative courses of action based on the two scenarios: holding if investors have already purchased it or purchasing if they haven't made a decision. In this proposed study, ANN-KOA resulted in higher accuracy than ANN. However, regarding the number of elapsed times, KOA-ANN provided a slower time than ANN. On the other hand, in terms of variations of lengths of the days, 3-5-10-KOA-ANN outperformed the variations of lengths of days: 3-5-10-ANN, 3-5-10-15-KOA-ANN, 3-5-10-15-20-KOA, and 3-5-10-15-20-KOA-ANN. In conclusion, this study suggested that it be utilized for other problems, such as predicting academic performance, diseases, floods, etc.

Investing in Sharia-compliant stocks is one of the rapidly growing investment options, making it a potential choice for investors' portfolios. Therefore, investors need to understand how to select an optimal composition of stocks in their portfolio. This research aims to calculate the expected return on Sharia-compliant stocks and determine the optimal portfolio. The data used in this study includes stocks within the Indonesian Sharia Stock Index (ISSI) in the energy and mining sectors from November 1, 2022, to October 30, 2023. The analytical models employed are the Arbitrage Pricing Theory (APT) and Mean-Value at Risk (Mean-VaR). Based on the research findings, seven stocks form the composition of the optimal stock portfolio. These stocks are AKRA, ANTM, PGAS, INCO, INDY, PTBA, and MDKA, with weights of 20.54%, 19.58%, 19.02%, 14.24%, 10.97%, 8.00%, and 7.66%, respectively. The expected return for the investor is 0.13% per day, with a corresponding risk of 0.23%.

The existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), Simple Exponential Smoothing (SES), deep learning models, and, most notably, univariate Long Short-Term Memory (LSTM) built based on univariate variable where the next lag of time series is leveraged for forecasting the next cycle of data. This paper takes this line of research to the next level by focusing on forecasting time series data based on “multivariate” modeling and analysis. To have a better insight of the performance of various deep learning-based models when multivariate analysis is performed, the paper builds and reports the forecasting accuracy for techniques such as the Transformer-based Multi-head Attention network, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM), Temporal Convolution Network (TCN), and conventional Vector Autoregressive (VAR) models. The findings revealed that the TCN model achieved the average lowest RMSE values of 0.0589 for stock data and 0.1554 for cryptocurrency data. Notably, the Multi-Head Attention model achieved average $R^{2}$ values of 0.92 for stock data and −1.98 for cryptocurrency data with respect to five variables (i.e., open, high, low, close and volume). According to the empirical studies conducted and reported in this paper, the transformer-based Multi-head Attention network outperformed other models such as LSTM, BI-LSTM, and more importantly conventional Vector Auto-Regression Models (VAR) in stocks and cryptocurrencies time series data where several variables were leveraged in building these multivariate-based models.