Stock Fluctuations Research Articles

Commentary: Demographic response of osprey within the lower Chesapeake Bay to fluctuations in menhaden stock

Commentary: Demographic response of osprey within the lower Chesapeake Bay to fluctuations in menhaden stock

EFFICIENT MARKET HYPOTHESIS DURING COVID-19 PANDEMIC: BRICS-T COUNTRIES

The impact of COVID-19 on financial markets has become a significant topic of research in the literature. Particularly, the declaration of the outbreak as a global pandemic by the World Health Organization on March 11, 2020, altered investors' risk perceptions and led to major fluctuations in stock markets. In this study, the response of stock indices in BRICS-T countries (Brazil, Russia, India, China, South Africa, and Türkiye) to the pandemic declaration was analyzed using the Event Study method. The event window was set as -5 to +5 days, and the estimation window was selected as the 90 days preceding the event. As a result of the analysis based on the comparison of normal and abnormal returns, it was found that all BRICS-T stock markets, except China, were not semi-strong form efficient. In addition, the results of the paired sample t-test applied to all stock markets demonstrated that the difference between abnormal returns calculated before and after COVID-19 was statistically significant. In this context, it was concluded that, unlike previous outbreaks, the COVID-19 pandemic led to significant fluctuations in financial markets. This study provides an important contribution to understanding the effects of the pandemic on emerging markets and examining investor behavior.

Predicting land cover changes and carbon stock fluctuations in Fuzhou, China: A deep learning and InVEST approach

Predicting the spatiotemporal dynamics of land cover and its carbon stock holds significant importance in guiding regional sustainable development, enhancing regional carbon stocks, and addressing global climate change. However, there is insufficient research on the quantitative relationships between various land cover types and carbon stock changes, as well as their future spatial predictions. Focusing on the core area of Fuzhou City, China, this study constructs a streamlined framework by coupling deep learning and the InVEST model to predict urban land cover and carbon stock changes in 2025 and 2035. The results show that: (1) The prediction model for land cover change has high applicability and can produce the simulated images with high accuracy. Impervious surface is expected to increase by 53 km2 in 2025 and 131 km2 in 2035 compared to 2020, resulting in considerable reductions in forest and cropland. (2) Carbon stocks of the study area are expected to decrease by 1.68 × 106t in 2035 compared to 2020 due to large amounts of high-carbon-density forests and croplands being converted into low-carbon-density impervious surfaces. (3) Multiple regression analysis reveals that forests have the largest impact on carbon stocks in the area, with a magnitude 5.25 times greater than impervious surfaces and 11.5 times greater than cropland, whereas impervious surfaces are the second most influential land cover type on carbon stock changes. Therefore, expanding forest areas becomes an essential initiative as forests could offset the carbon stock loss caused by impervious surface growth. This study provides scientific references for optimizing land-use planning and formulating policies for the development of low-carbon cities.

Substantiating the reliability conditions for the production process at metallurgical enterprises through the fault-tolerant functioning of the system «extraction of raw materials – technological railroad routes – metallurgical production»

The object of this study is the process of formation of insurance reserves at enterprises in the metallurgical industry. Under conditions of uneven supply of raw materials to metallurgical enterprises due to disruption of the transportation process or other reasons, there is a need to create insurance stocks in order to ensure the continuity of production. At the same time, it is necessary to take into account existing restrictions, such as the limited capacity of railroad sections and the impossibility of organizing parallel movement of trains, etc. The presence of these limitations makes it impossible to use classical methods for solving similar problems, such as linear programming. Therefore, to resolve the task, a simulation model was built, based on the discrete-event principle in the AnyLogic University Researcher environment using Oracle libraries and the Java SE compiler. With the help of the model, the process of rotation of dispatch routes at the railroad yard with multiple suppliers and one consignee was formalized. The optimization criterion was chosen to be the minimum deviations of fluctuations in reserves of iron ore concentrate and coke. Analysis of the simulation results revealed that the optimal size of the fleet of railroad routes on the selected rotation polygon is 30 units; at the same time, their utilization rate will be 65 %. It was also established that fluctuations in raw material stocks have a «natural character», which is confirmed by the normal distribution of the density of stock volumes. Under these conditions, the value of fluctuations in the volumes of the main raw materials will be ±13115 t/day for iron ore concentrate, and ±5298 t/day for coke. Reducing the range of fluctuation of raw materials volumes could make it possible to optimize the costs of creating stocks and streamline the transport work of the enterprise for providing raw materials

The Influence of Earning Per Share (EPS) and Price Earnings Ratio (PER) on Stock Prices in the Food and Beverage Sector on the Indonesian Stock Exchange (BEI) for the 2015-2019 Period

The objective of this study is to examine the impact of earnings per Share (EPS) and Price Earnings Ratio (PER) on the stock prices of companies in the food and beverage industry listed on the Indonesia Stock Exchange (BEI) from 2015 to 2019. EPS is utilized as a metric to gauge the profitability of each share, whereas PER characterizes the market's assessment of the earnings generated per share. The research employed a descriptive verification analysis method, utilizing a sample of seven prominent organizations within the researched sector.The data utilized is derived from the yearly financial reports of each company, specifically secondary data. The investigation employed multiple regression approaches to examine the hypothesis regarding the impact of EPS and PER on stock prices. The test utilizes the coefficient of determination to ascertain the extent to which the regression model can account for the fluctuations in stock prices.The research findings indicate a substantial correlation between EPS (Earnings Per Share), PER (Price Earnings Ratio), and firm share prices within the food and beverage industry on the IDX (Indonesia Stock Exchange). The R^2 value of 0.753 suggests that 75.3% of the changes in stock prices can be accounted for by the independent variables, EPS and PER. In this industry, share prices are primarily influenced by the profitability per share and market valuation.In addition, this study emphasizes that additional variables not investigated in this research can significantly impact stock prices, including macroeconomic conditions, market sentiment, government regulations, and other external factors. Stable macroeconomic conditions can lead to more favorable circumstances for increased stock values, whereas government policy or market sentiment alterations can profoundly influence a company's stock performance.The recommendations derived from this study are intended for investors and corporate executives operating in the food and beverage industry on the IDX. Investors are advised to consider the earnings per share (EPS) and price-to-earnings ratio (PER) components of their fundamental analysis to make better-informed investment choices. It is recommended that companies prioritize enhancing their financial performance and ensuring openness in their financial reporting to boost market trust and raise the value of their shares.Subsequent investigations might broaden the range by including supplementary variables like Dividend Yield or Return on Equity (ROE) while doing a more extensive analysis of qualitative elements that impact market perceptions of EPS and PER. This research contributes to understanding the factors that impact share prices in the Indonesian capital market, particularly within the food and beverage industry.

Stock market prediction employing ensemble methods: the Nifty50 index

<span lang="EN-US">Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.</span>

Relational Fusion-based Stock Selection with Neural Recursive Ordinary Differential Equation Networks

Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns. Recently, researchers have developed various (recurrent) neural network-based methods to tackle this problem. Without exceptions, they primarily leverage historical market volatility to enhance the selection performance. However, these approaches greatly rely on discrete sampled market observations, which either fail to consider the uncertainty of stock fluctuations or predict continuous stock dynamics in the future. Besides, some studies have considered the explicit stock interdependence derived from multiple domains (e.g., industry and shareholder). Nevertheless, the implicit cross-dependencies among different domains are under-explored. To address such limitations, we present a novel stock selection solution—StockODE, a latent variable model with Gaussian prior. Specifically, we devise a Movement Trend Correlation module to expose the time-varying relationships regarding stock movements. We design Neural Recursive Ordinary Differential Equation Networks (NRODEs) to capture the temporal evolution of stock volatility in a continuous dynamic manner. Moreover, we build a hierarchical hypergraph to incorporate the domain-aware dependencies among the stocks. Experiments conducted on two real-world stock market datasets demonstrate that StockODE significantly outperforms several baselines, such as up to 18.57% average improvement regarding SR.

The Evaluation of Impacts of Multi Factor Model on TTC Stock Price: a Case in Real Estate Industry in Vietnam

Objectives: The objective of this study is to analyze the impact of macroeconomic factors on the stock price of a real estate firm, specifically Thanh Cong TCC, in Vietnam from 2014 to 2020. The study aims to assess both positive and negative influences of five macroeconomic variables on stock prices and to understand the overall business health reflected by stock fluctuations. Methods: This study utilizes a data collection method involving statistics, analysis, synthesis, comparison, and quantitative analysis to generate qualitative comments and discussion. Econometric methods, particularly regression analysis, are employed to evaluate the quantitative results and assess the impact of macroeconomic factors on stock prices. Results: The quantitative research reveals several key findings. Firstly, the stock price of Thanh Cong TCC (Y) exhibits a negative correlation with the lending rate (R) and GDP growth, indicating that higher lending rates and lower GDP growth negatively impact stock prices. Conversely, there is a positive correlation between stock prices and the risk-free rate (Rf), consumer price index (CPI), and VNINDEX, suggesting that these factors positively influence stock prices. Furthermore, the study identifies GDP growth and lending rates as having the highest impact on firm stock prices. Conclusion: In conclusion, this study highlights the importance of considering macroeconomic factors in understanding stock price fluctuations in real estate firms in developing countries like Vietnam. The findings emphasize the significant impact of lending rates and GDP growth on stock prices, indicating their pivotal role in shaping the business health and overall economic conditions. By recognizing these influences, firms like Thanh Cong TCC can better navigate market dynamics and make informed strategic decisions to enhance their performance and resilience in the face of macroeconomic challenges.

A stock border compensation technique for gaseous energy scheduling in steel enterprises under uncertainty

This study proposes a stock boundary compensation method to address the uncertainty of gaseous energy in integrated steel plants. The uncertainty disrupts the balance of temporary storage, resulting in energy waste, environmental pollution, and disturbances in steel production. The method accurately compensates for the impact of gaseous energy uncertainty on storage. Firstly, this paper takes oxygen and byproduct gas systems as the examples to describe the gas energy system and its scheduling model. The storage deviation caused by gaseous energy uncertainty is quantitatively analyzed using formulas. Subsequently, this study formulates an optimization problem to determine the optimal margin for compensating the storage boundary using a data-driven approach. Comparative experiments demonstrate that the proposed method not only significantly enhances the safety of gaseous energy storage subject to uncertainty, but also outperforms traditional robust optimization and stock fluctuation optimization methods in terms of system robustness against uncertainty and operating cost optimality.

Erratum: Response of cetaceans to fluctuations of pelagic fish stocks and environmental conditions within the Celtic Sea ecosystem

Erratum: Response of cetaceans to fluctuations of pelagic fish stocks and environmental conditions within the Celtic Sea ecosystem

The loss of political connections and the fluctuation of corporate stock prices: Moderating effect based on media attention

This article examines the impact and mechanism of political connections on stock price fluctuations after the resignation of independent directors with “official” status, based on the exogenous influence of Document No. 18 of the Central Organization Department. Using panel data of A-share listed companies on the Shanghai and Shenzhen stock markets from 2012 to 2020, the experimental group and control group for political affiliation deficiency events were determined through propensity score matching (PSM), and a quasi natural experiment was constructed using differences in differences (DID) for empirical research. By examining the relationship between regional financial development, lack of political connections, and stock price fluctuations, we found that regions with higher levels of financial development are more prone to stock price fluctuations due to the lack of political connections, which is related to higher levels of debt financing in regions with higher levels of financial development. In addition, the increasing level of debt exacerbates conflicts and inconsistencies among stakeholders, which is not conducive to the stability of the company's stock price. Through the above research, relevant suggestions have been provided to enterprises, media, and governments. For example, enterprises should clarify the boundary between government and enterprise, focus on long-term strategic goals, build core competitiveness, and thereby enhance their own value; The media should play a correct role in information transmission and public opinion guidance, and play a positive role in the economic development of the industry; The government should promote market-oriented construction and establish positive government enterprise relationships.

Hedging Strategies with Derivatives after 2020

This paper examines the changes in the futures market and how Chinese companies have hedged in recent years. As is known to all, the world has experienced lots of events, such as the COVID-19 pandemic, the Ukraine Conflict, the economic recession, and the rising interest rate in the U.S. Under the risky circumstances, the foreign exchange market and commodity futures market have shown a larger volatility. During times of global economic turmoil, the investors in the financial markets are seeking opportunities to protect their investment portfolios and assets. Historically, the futures market can be seen as a safe haven for funds, mitigating losses caused by fluctuations in stock or commodity prices through proper hedging strategies. This paper analyses what the Chinese futures and spot markets are like and how Chinese companies use hedging tools to lock profits. Then, the paper gives suggestions and prospects for futures hedging and the development of markets.

Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.

The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.

Demographic response of osprey within the lower Chesapeake Bay to fluctuations in menhaden stock

Forage fish support the largest fisheries in the world and play a vital role in marine food webs by transferring energy from plankton to consumers within higher trophic levels. Growing commercial demand for these species and concern for the impact of over harvest on predator populations has driven a paradigm shift in management objectives from maximizing economic return to the establishment of ecosystem-based limits on harvest rates. How well current harvest policy supports noncommercial species like piscivorous birds remains poorly understood. We investigated the relationship between osprey breeding performance within the lower Chesapeake Bay and a menhaden stock index during a period (1974-2021) when the menhaden index fluctuated over 35-fold. Reproductive rate (young/pair/year), brood provisioning (fish/10-h) and the proportion of menhaden in the diet all declined during the study period. Indicators of food stress including brood reduction and nest failure increased during the study period. The population transitioned from reproductive surplus (demographic source) to reproductive deficit (demographic sink). A significant relationship between reproductive rate and the menhaden index suggests that osprey population viability requires that the menhaden stock be restored to 1980s levels. Current ecological reference points based on the food requirements of predatory fish are unlikely to protect the osprey population. We suggest the establishment of menhaden or reproductive thresholds designed to allow osprey to meet demographic targets (1.15 young/pair/year).

Different growth patterns reveal the potential origins of two Pacific saury (Cololabis saira) groups in the northwest Pacific Ocean

Identification of stock composition is essential for fish stock assessment and management. Previous studies have suggested that a mixed stock composition of Pacific saury Cololabis saira exists in the northwest Pacific Ocean, but the origins of the stock’s components are poorly understood. We analyze the otolith microstructure of Pacific saury collected in high-seas fishing grounds from June–November, 2016–2019, and estimate hatch date and reconstruct otolith growth trajectory before hyaline zone formation. Results confirm that Pacific saury recruiting to the high-seas fishing ground are mainly from a winter-spawned cohort, secondarily from an autumn-spawned cohort, but also with a small proportion from a spring-spawned cohort. Ages of medium (knob length 240–280 mm) and large (knob length > 280 mm) individuals are mainly 8–12 and 18–21 months, respectively. Two growth patterns are reported for individuals with different otolith growth axes in their early life stages. Each year, unsupervised random forest clustering analysis of otolith daily increment widths reveals two Pacific saury groups. One group, with younger age and a later hatching period, shows a consistently higher growth rate than the other, suggesting that the two groups have different hatch dates and locations. These findings permit more accurate prediction of Pacific saury recruitment, and improve the understanding of short-term stock fluctuations to enable more sustainable management of fisheries.

Variations in soil physico-chemical properties, soil stocks, and soil stoichiometry under different soil layers, the major forest region Liupan Mountains of Northwest China.

Liupan Mountains are an important region in China in the context of forest cover and vegetation due to huge afforestation and plantation practices, which brought changes in soil physio-chemical properties, soil stocks, and soil stoichiometries are rarely been understood. The study aims to explore the distribution of soil nutrients at 1-m soil depth in the plantation forest region. The soil samples at five depth increments (0-20, 20-40, 40-60, 60-80, and 80-100 cm) were collected and analyzed for different soil physio-chemical characteristics. The results showed a significant variation in soil bulk density (BD), soil porosity, pH, cation exchange capacity (CEC), and electric conductivity (EC) values. More soil BD (1.41 g cm-3) and pH (6.97) were noticed in the deep soil layer (80-100 cm), while the highest values of porosity (60.6%), EC (0.09 mS cm-1), and CEC (32.9 c mol kg-1) were reflected in the uppermost soil layer (0-20 cm). Similarly, the highest contents of soil organic carbon (SOC), total phosphorus (TP), available phosphorus (AP), total nitrogen (TN), and available potassium (AK) were calculated in the surface soil layer (0-20 cm). With increasing soil depth increment a decreasing trend in the SOC and other nutrient concentration were found, whereas the soil total potassium (TK) produced a negative correlation with soil layer depth. The entire results produced the distribution of SOCs and TNs (stocks) at various soil depths in forestland patterns were 0→20cm > 20→40cm > 40→60cm ≥ 60→80cm ≥ 80→100 cm. Furthermore, the stoichiometric ratios of C, N, and P, the C/P, and N/P ratios showed maximum values (66.49 and 5.46) in 0-20 cm and lowest values (23.78 and 1.91) in 80-100 cm soil layer depth. Though the C/N ratio was statistically similar across the whole soil profile (0-100 cm). These results highlighted that the soil depth increments might largely be attributed to fluctuations in soil physio-chemical properties, soil stocks, and soil stoichiometries. Further study is needed to draw more conclusions on nutrient dynamics, soil stocks, and soil stoichiometry in these forests.

Comparative Analysis of S&P 500 and NASDAQ: A Machine Learning Approach to Understanding Differential Market Sensitivity and Growth Stocks

The purpose of this study is to gain a deeper understanding of the dynamics and market sensitivity of technology stocks by constructing and predicting random stock portfolios in the S&P500 and NASDAQ indices using machine learning models. The article employs a rolling time window cross-validation approach to train the models, ensuring optimization based on continuously updated data. By comparing the predicted results with actual outcomes, the article analyze whether technology stocks possess characteristics of growth stocks and how key events impact the price fluctuations of technology stocks. The results also indicate the need to dynamically adjust the learning rate based on market characteristics, volatility, trends, and other factors to enhance the adaptability of the machine learning models in different conditions and better uncover the latent information within diverse data samples. Overall, the findings of this study underscore the importance of dynamically adjusting the learning rate of machine learning models under various market conditions, providing a fresh perspective for financial application research. Future research can further explore and develop cutting-edge methods related to optimizing the learning rate of models, such as adaptive learning rates, multi-task learning, and incremental learning, to more effectively cope with different market situations and similar issues in other fields.

Analysis of the Difference in Stock Price Between A-shares and American Stocks in Machine Learning

Contemporarily, stock market is the most representative financial investment tool in the world. The application of machine learning has had a significant impact on the development of society and economy as well as productivity, and has also been inextricably linked to the securities market. This study will analyse and compare the technological development of machine learning in the last five years, as well as the stock value data and stock price fluctuations of A-shares and American stocks in the field of machine learning. In this way, the machine learning technology may change the global stock market in the future, and the prospect of this technology in the future. This paper introduces three forecasting models, namely Light Gradient Boosting Machine (lightGBM) model, Convolutional Neural Networks (CNN) model and Long short-term memory (LSTM) model, and studies their influence on stocks and forecasting accuracy. Applying machine learning to financial investment is a two-edged sword, with advantages and disadvantages, opportunities and challenges, depending on whether and the measure to implement it.

Construction of International Trade and Investment Platform Based on Artificial Intelligence Technology

Abstract This paper completes the implementation of the risk control module of the international trade investment decision-making module through the construction process of the programmed international trade investment platform. Combining the decision tree algorithm and logistic regression algorithm to categorize and screen the factors affecting the fluctuation of international trade stock prices to obtain the international trade market fundamentals and technical factors that have a significant impact on international trade stock prices. The Kalman filter model is applied to improve the effective market factors screened so that the factor selection of the international trade investment model is more advantageous. After constructing the platform, in order to reflect its convenience, the RE, OLS, TI, TVD and BC models were used to analyze the export trade efficiency of the home country, respectively. The results show that the degree of facilitation of the host country’s trade and investment platform is positively correlated with the loss of the home country’s export trade efficiency (0.043), which is statistically significant at the 1% significance level, and that the loss of the home country’s export trade efficiency increases by 4.3% for every 1 unit increase in the degree of facilitation of the host country’s trade and investment platform. The construction of the international trade and investment platform increases the facilitation of export trade from trading partner countries to the host country, which indirectly increases the export trade efficiency loss of the home country.

Commercial resources of saffron cod <i>Eleginus gracilis</i> in the Russian waters of the Far-Eastern Seas

Saffron cod Eleginus gracilis, being a typical representative of the elittoral ichthyocene, is an important commercial species in the coastal areas of the Far-Eastern Seas. Small- and medium-sized vessels equipped with both active fishing gears as Danish seine or trawl and fixed nets are used for fishery of this species, it is also popular for amateur and sport fishing from the sea ice. The portion of saffron cod in the total landing is the largest in the Karaginsky fishing subzone (8.1 %) and West-Kamchatka fishing subzone (3.6 %). Annual catch of the species in the Far-Eastern Seas fluctuated in 2003–2022 from 16.7 . 103 to 50.6 . 103 t, on average 33.9 . 103 t. The bulk of this value was caught in the waters at Kamchatka, mostly at its western coast where the average catch was 10.5 . 103 t annually in the last decade. The main fishing grounds of saffron cod are located within the Kamchatka-Kuril, West-Kamchatka, East-Sakhalin and Karaginsky fishing subzones, whereas the stocks at Primorye and sparsely populated coasts of the western Okhotsk Sea and northwestern Bering Sea are underexploited. The saffron cod CPUE reaches 4.5 t per hour of trawling on the shelf of West Kamchatka. The biomass of saffron cod is estimated recently as 197.4 . 103 t in the West-Kamchatka subzone, 100.4 . 103 t in the Karaginsky subzone, 32.8 . 103 t in the West Bering Sea fishing zone, 17.2 . 103 t in the Petropavlovsk-Commander subzone, 17.1 . 103 t in the East-Sakhalin sunzone, and 8.7 . 103 t in the North Okhotsk Sea subzone. The species is distinguished by significant fluctuations of commercial stocks caused by both environmental impacts on its recruitment and fishing pressure. The strong year-classes appear usually in relatively warm or moderate environmental conditions. In modern times, abundance and biomass of almost all exploited stocks of saffron cod in the Far-Eastern Seas excess their average long-term level or fluctuate around, so the state of their populations is considered as quite satisfactory.