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The jump in the value of the stock may result in a corresponding escalation of risk associated with the stock market. When a jump occurs in any particular market, such a risk can spread to other markets. This paper examines realized volatility and risk contagion of the jump between the Chinese mainland and Hong Kong market utilizing realized volatility and three realized jump separation methods. Correspondingly, the Markov regime‐switching approach is employed to exhibit the contagion effect of the jump risk in two economic states. This study employs the Shanghai–Shenzhen 300 Index and the Hang Seng Index as empirical research subjects. The results show that the Hong Kong market has a higher frequency of jumps compared to the mainland market. The MedRV method is found to be effective in identifying more jumps, while the mainland market displays a unidirectional contagion effect on Hong Kong. Under such circumstances, the risk associated with a continental market jump exhibits an unevenly contagious effect on the Hong Kong market. In conclusion, China needs to enhance its financial market risk management in order to avert the spread of escalating risk across markets. [ABSTRACT FROM AUTHOR]

Is relative valuation investing dead? Are markets "broken"? The authors attribute the long period of underperformance of value stocks to technological disruptions and inadequate accounting for intangible assets. They introduce a new methodology, data mining confidence bands, to provide transparency in backtesting performance and avoid overfitted models. Backtesting 24 strategies shows modest returns for valuation-based approaches, with improved results when incorporating momentum adjustments. Despite the diminished effectiveness of relative valuation signals over the past two decades, relative valuation investing remains viable. Successful investors must adapt by incorporating judgment, accounting adjustments, fundamental research, and awareness of technological and macroeconomic trends. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) 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.)

The object of research is a pneumatic spring of high-speed rolling stock of the railway. The method of experimental static testing of a pneumatic spring of high-speed rolling stock is presented. Based on experimental tests, the "force-strain" dependences of a pneumatic spring are obtained when the pressure gauge in the pneumatic spring changes from 2.5 atm up to 5.0 atm. Using a thermodynamic model, a quadratic equation is found to determine the effective area of a pneumatic spring. It is established that when the pressure gauge in the pneumatic spring changes from 2.5 atm up to 5.0 atm the effective area of the pneumatic spring varies from 0.231 m2 to 0.306 m2. The scientific novelty lies in the fact that for the first time, on the basis of static experiments, the change in the effective area of the pneumatic spring of high-speed rolling stock depending on the value of the gauge pressure in the spring was established. This will make it possible to determine the forces acting on the pneumatic spring in different operating conditions, ensuring the required level of safety of rolling stock. [ABSTRACT FROM AUTHOR]

The market value of a stock is a function of firm performance and market sentiment. Specific events can alter returns and present several challenges to market performances. Through an event study design, this study examines five real estate investment trusts (REITs) market indices' responses to market shock from the initial COVID-19 outbreak. The results revealed negative cumulative abnormal returns for lodging, health care, and retail REITs over the 20-day trading window. Considering their lower drawdown losses, the remaining REIT sectors showed greater resilience than their counterparts. Block bootstrapping confirmed the returns were significantly worse for all sectors, with retail and lodging sectors having the poorest returns. This study advances the literature on pandemic-related market crashes along with how COVID-19 affected cross-sector REIT equities compared to other market indices. REIT firms can use the findings to develop more effective policy measures as responses to pandemic market conditions. [ABSTRACT FROM AUTHOR]

Small stocks' time‐varying spreads predict future return gap between small and large stocks. To optimally exploit such predictability, the investor captures current risk premium by purchasing at large spreads with substantially reduced turnover; uses an aim‐in‐front‐of‐the‐target approach to trade‐off between future risk premium and current transaction costs; and meets hedging demand at low costs. Strong interaction between transaction costs and return predictability leads to large losses from myopic trading. Greater variability of the spread is advantageous for investors who trade optimally but detrimental for investors who trade myopically. The spread‐based return predictability significantly increases the investment value of small stocks. [ABSTRACT FROM AUTHOR]

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to forecast future asset values with higher accuracy. Machine learning, which consists of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to ensemble a model using Support Vector Machine (SVM) and Long-Short Term Memory model (LSTM) to predict the Nigerian Stock Exchange values. The main objective of this paper is to see in which precision a Machine learning algorithm can predict and how much the epochs can improve our model. [ABSTRACT FROM AUTHOR]

This study aims to examine the effect of Environmental, Social, and Governance (ESG) Performance on Firm Value with Company Size as a moderating variable in companies listed on the Indonesia Stock Exchange (IDX). This study uses an associative quantitative method with a sample consisting of 22 companies selected by purposive sampling from 2019 to 2021. The data was analyzed using multiple linear regression with classical assumption tests. The results showed that Environmental, Social, and Governance Performance had a positive and significant influence on Firm Value. Company size also acts as a moderating variable that strengthens the relationship between ESG Performance and Company Value. These results indicate the importance of implementing ESG principles to improve the competitiveness and sustainability of the company in the long term. [ABSTRACT FROM AUTHOR]

Exchange rate extreme shocks affect stock prices differently, depending on the sign and the magnitude of changes. This study, therefore, examines the asymmetric impact of multifarious exchange rate shocks on stock prices in sub-Saharan Africa between 1 March 2013 and 14 January 2023, using the novel, multiple thresholds nonlinear autoregressive distributed-lag (MTNARDL) model. This model estimates the effects of extremely small and extremely large positive and negative shocks in exchange rate on stock prices. The study partitioned the sample into pre-COVID-19 and COVID-19 era. The study's outcome indicates: first, exchange rate shocks below 25th percentile affect stock prices positively but above the 25th percentile and below the upper quantiles (75th percentile) the effects is mixed; second, at the upper quantiles (75th percentile), both exchange rate depreciation and appreciation adversely affect the value of stocks; third, the link between the series is highly sensitive to global shocks; fourth, causality result upholds the flow-oriented model in four out of the six countries. The policy implications are: (i) the responses of stock prices to exchange rates changes are sensitive to the sign and size of the shock, as well as sensitive to global shocks. Consequently, specific policy recommendations have been suggested. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Accounting & Auditing (2314-4793) is the property of Beni Suef 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.)

This study investigates how growth opportunity, investment opportunity set, and company size impact the valuation of digital banks that are publicly traded on the Indonesian Stock Exchange from 2018 to 2022. The sample selection method employed was full sampling, consisting of 13 companies as samples, and panel data regression analysis was utilized in this study with the assistance of E-views 12 software. According to the research findings, there are several discoveries. First, growth opportunities do not affect company value. Second, investment opportunity sets do affect company value. Third, the company size affects company value. [ABSTRACT FROM AUTHOR]

Indonesia's net zero emission target is being achieved through greenhouse gas (GHG) inventories using the Intergovernmental Panel on Climate Change (IPCC) method. This study aims to determine the potential contribution of the Brebes and Cilacap Regencies using the net zero target of Central Java Province in terms of carbon sequestration in various types of land cover. Land-cover data were collected from the RBI. Land cover use data were then multiplied by the constant value of carbon stocks to determine the potencies of carbon sequestration and storage. The potential contribution of carbon sequestration in Brebes Regency reaches 3,882,850.81 tons of CO2 eq or 4.60%, while Cilacap Regency has almost two times the potential for carbon sequestration in Brebes, which is 7,191,181.44 tons of CO2 eq, or equivalent to 8.52% of the total carbon emissions of Central Java Province which reached 84,435,352.5 tons of CO2 eq. This difference in carbon sequestration potential was most influenced by the presence of mangrove and terrestrial forests, with the highest value of carbon stock constants compared to other land uses. Overall, these two regencies can absorb almost 1/3 of the carbon emissions in Central Java Province; therefore, they can be prioritized in efforts to increase carbon sequestration at the provincial level. [ABSTRACT FROM AUTHOR]

This study provides step‐by‐step guidance for practitioners and local stakeholders on how to use existing study results to conduct benefit transfer (BT), and ultimately make informed predictions of how improvements in lake water clarity may benefit surrounding communities. The procedures are demonstrated using a publicly available meta‐dataset developed by the United States Environmental Protection Agency, and a subsequent meta‐analysis that synthesizes the literature on how improvements in water clarity impact home values. The BT procedures are demonstrated using a case study of 14 large lakes in Kosciusko County, Indiana. Lake‐specific average increases in home values, as well as the value of the housing stock in aggregate, are calculated for illustrative improvements in lake water clarity. This analysis provides a critical bridge to better connect high‐quality, academic research with real‐world policy analysis, and ultimately serves to better equip local governments and stakeholders to make more informed policy and land use decisions. [ABSTRACT FROM AUTHOR]

This paper examines the market responses to concurrent earnings and stock split announcements for evidence on the information content of stock splits. The majority of stock split research excludes splits announced with other information events due to confounding issues. However, it is difficult to extract the information content of splits by merely focusing on the standalone split announcement because stock splits are devoid of any information regarding firms' future cash flows. This study explicitly considers how a stock split is evaluated in conjunction with current earnings. This study shows that the market reacts more positively to earnings news concurrently announced with stock splits, consistent with the idea that splits are favorable news. Furthermore, this study finds that stock returns of concurrent split–earnings announcers exhibit a greater association with future cash flows, suggesting that investors should value stock splits favorably for more persistent earnings ahead. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Copyright of Gharee for Economics & Administration Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

The continuous growth of blockchain has brought revolutionary impact on different areas with characteristics like decentralization, trust-worthy and tampering-proof. In the financial field, forecasting stock market had remained a challenging task for researchers. A novel integration of convolution and LSTM along with attention mechanism is proposed for prediction of stock price for the last hour of a day based it's performance in six hours for making prediction in intraday trades. The proposed prediction model uses 4 different stocks of Apple, Facebook, Nike and Uber for data input. The input data is given to the combination of many convolutional and LSTM layers for predicting future value of stocks in short term. Furthermore, the proposed model combines the obtained data with LSTM layers output vectors enhanced for final prediction. The prediction is based on past value of stocks. Performance evaluation is done based on RMSE and computational time. The average values obtained were 0.091 and 11.57 seconds respectively, showing it's superiority from benchmark models. The obtained data is stored on blockchain for enhanced security. The model can further be tuned for predicting other markets and will be more efficient than market specific markets. [ABSTRACT FROM AUTHOR]

Depletion of groundwater stocks reduces both the flow of economic value and production of goods from this resource. One challenge in estimating the economic value of the stock of groundwater is that feedback effects from irrigation behavior affect resource conditions, which creates an endogeneity concern. This study estimates how changes in groundwater stocks affect the returns to agricultural land in the High Plains aquifer. We avoid bias from feedback effects by exploiting hydrologic variation in predevelopment saturated thickness. Simulation results reveal that the average annual present value of returns to land are expected to decrease in the High Plains region by $126.7 million in 2050 and by $266.0 million in 2100. When the initial saturated thickness is less than 70 feet, most of the economic impact (67%) of a decrease in the stock of groundwater occurs through an adjustment in irrigated acreage while 33% occurs through reduced irrigated rental rates. [ABSTRACT FROM AUTHOR]

Both the Residual Income Model (RIM) and Growth Value Model (GVM) combine the income-based approach and asset-based approach, and blend with the market-based approach, which employs regression analysis and market data to determine parameters in the models. Hence, they have more comprehensiveness than most approaches in the literature. The theoretical and empirical comparisons of RIM and GVM may have profound meaning to stock intrinsic value. The theoretical comparison shows that the ROE-P/B relationship is linear in RIM and power in GVM. When ROE equals the required return rate, whosever P/B is 1.0. When the ROE is small, the P/B estimated by GVM is still greater than 0; however, it may be negative by RIM, which is not reasonable. In a normal range (0 ~ 20%) of ROE, the two curves are rather close to each other. The empirical study showed that the GVM can more reasonablly fit the market data, especially when the ROE is rather small or great. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) technology in various fields has been a recent research hotspot. As a representative technology of AI, the specific application of machine learning models in the field of economics and finance undoubtedly holds significant research value. This article proposes Extreme Gradient Boosting Multi- Objective Optimization Model with Optimal Weights (OW-XGBoost) to comprehensively balance the returns and risks of investment portfolios. The model utilizes fusing label with optimal weights to achieve multi-objective tasks, effectively controlling the impact of various risk and return indicators on the model, thus improving the interpretability and generalization ability of the model. In the experiments, we tested the model using China A-share data from October 2022 to April 2023 and conducted a series of robustness tests. The results indicate that: (1) The OW-XGBoost outperforms the XGBoost Model with Yield as Label (YL-XGBoost), XGBoost Multi-Label Classification Model (MLC-XGBoost) in controlling risk or achieving returns. (2) OW-XGBoost performs better overall compared to baseline models. (3) The robustness tests demonstrate that the model performs well under different market conditions, stock pools, and training set durations. The model performs best in moderately fluctuating stock markets, stock pools comprising high market value stocks, and training set durations measured in months. The methodology and results of this study provide a new perspective and approach for fundamental quantitative investment and also create new possibilities and avenues for the integration of AI, machine learning, and financial quantitative research. [ABSTRACT FROM AUTHOR]

The contributions of asset selection and incremental leverage to buyout investment performance are more important than typically assumed or estimated to be. Buyout funds select small firms with distinct value characteristics. Public equities with these characteristics have high risk-adjusted returns relative to common factors. Adding incremental leverage to a publicly traded stock portfolio increases both risks and mean returns in this sample. Direct investments in private equity funds earn lower mean returns than a replicating strategy designed to mimic these key economic features of their investment process with public equities and brokerage loans. [ABSTRACT FROM AUTHOR]

In this paper, recurrent neural networks consisting of GRU and LSTM architectures are used to extract meaningful insights, characteristics, and specific patterns from previously observed, equally spaced, stock market data. The long-term dependency of nonlinear time series data can be learned using GRU and LSTM. In the first phase, the sliding window technique is used to analyse the daywise (i) open, (ii) high, (iii) low, and (iv) closing values of various stocks on the stock market to forecast the future. Performance comparisons show that the proposed GRU and LSTM networks outperform the existing models in terms of prediction accuracy. Multiple datasets were compared and the findings are: (1) the proposed model has a MAPE of 0.630, while the present model's is 1.748; (2) the MAPE of the proposed model is 0.6243, while that of the existing model is 1.92; (3) the recommended model has a MAPE of 0.7924, while the existing model's is 0.8587; (4) the recommended model's MAPE is 1.191, but the current model's MAPE is 2.99. In the second phase of the process, SMA, EMA, RSI, MACD, and ADX are chosen from among the many technical indicators and used in conjunction with OHLC to further optimise the models. [ABSTRACT FROM AUTHOR]

Introduction: The Arctic sea ice extent in September (when it is at its lowest) has declined 13% Q10 per decade, and the Arctic Ocean is becoming a more Atlantic-influenced system. Rapid climate-forced changes are taking place in many high-latitude marine ecosystems. The Barents Sea is one such highlatitude shelf ecosystem, between approximately 70° and 80°N in the Norwegian Arctic. The purpose of the current study was to estimate zoobenthic blue carbon across multiple habitats within the Barents Sea (trough, basin, shelf, and shallows), potentially providing values to aid ecosystem-based management of these areas under future climate change scenarios. Method: We tested this by capture and analysis of 947 high-resolution (each 405.7 × 340.6 mm, 12 MB, 5 megapixels) seabed images at 17 sites with latitudinal cline, linked to a collection of corresponding oceanographic data. Biotas within these images were identified to one of the 14 functional groups and the density was calculated. Mean stored carbon per individual was assigned by ash mass (AM) and ash-free dry mass (AFDM) of individuals caught within Agassiz trawl deployments at the same sites. Results: Trough sites, except for one site (B16), have a low quantity of zoobenthic blue carbon compared with the shallow, shelf, and basin habitats. Discussion: The results of a previous study focused entirely on trough habitats and are therefore difficult to scale up as the basis for a meaningful estimate of across-habitat zoobenthic blue carbon in the Barents Sea. Compared with the trough and the basin, the shelf and shallow habitats of the Barents Sea are also subjected to more trawling events through demersal fisheries and showed higher zoobenthic blue carbon stock values. [ABSTRACT FROM AUTHOR]

Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can contain valuable information, and their removal may adversely impact the prediction performance of models. In this study, a novel method called Two-Level Entropy Ratio-Based Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (2LE-CEEMDAN) is proposed for the first time to effectively denoise time series data. Financial time series with high noise levels are utilized to validate the effectiveness of the proposed method. The 2LE-CEEMDAN-LSTM-SVR model is introduced to predict the next day's closing value of stock market indices within the scope of financial time series. This model comprises two main components: denoising and forecasting. In the denoising section, the proposed 2LE-CEEMDAN method eliminates noise in financial time series, resulting in denoised intrinsic mode functions (IMFs). In the forecasting part, the next-day value of the indices is estimated by training on the denoised IMFs obtained. Two different artificial intelligence methods, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), are utilized during the training process. The IMF, characterized by more linear characteristics than the denoised IMFs, is trained using the SVR, while the others are trained using the LSTM method. The final prediction result of the 2LECEEMDAN-LSTM-SVR model is obtained by integrating the prediction results of each IMF. Experimental results demonstrate that the proposed 2LE-CEEMDAN denoising method positively influences the model's prediction performance, and the 2LE-CEEMDAN-LSTM-SVR model outperforms other prediction models in the existing literature. [ABSTRACT FROM AUTHOR]

This article challenges the argument that stocking densities should not be regulated in aquaculture. The authors provide evidence to show that regulating stocking density is important for fish welfare and can be effectively used in aquaculture. They discuss different perspectives on stocking density and its relevance to animal welfare, as well as its potential as a tool for legislation in aquaculture. The document provides a list of references related to environmental enrichment in fish aquaculture and fish welfare, including scientific articles, books, regulations, and standards. It emphasizes the importance of considering fish welfare in aquaculture practices and the potential benefits of applying fish behavior knowledge to improve production and welfare. [Extracted from the article]

This study provides the generalization of fuzzy real numbers by imposing the elevator's condition upon it's legs. Our aim is to construct three types of Lift Fuzzy Real Numbers, an extension of h-generalized fuzzy real numbers, to indicate medical signals, stock market values, and commercial establishment profits over time. It explores concepts like ɛ-cut, strong ɛ-cut, β-level set, and convexity, and presents a graphical representation based on profit earned by three industries. Appropriate numerical examples are provided to support the new ideas. It's interesting to note that Lift Fuzzy Real Numbers are also used to represent real numbers. Additionally, the connections between the Lift Fuzzy real numbers have been established. The new fuzzy real numbers offer an advantage in representing data sets not represented by existing fuzzy numbers. [ABSTRACT FROM AUTHOR]

This research investigates the intrinsic value of stocks of companies within the oil extraction sector. The focus is on publicly traded corporations part of "Big Oil": ExxonMobil Corporation, Chevron Corporation, TotalEnergies SE, BP plc, Shell plc, and ENI S.p.A. The numerous fundamental analysis intrinsic value of stocks models is being applied to the Big Oil consortium. This article stands out for its unique focus on utilizing dividend discount models, earnings models, and cash flow models to calculate the intrinsic value of stocks in major oil companies, which is currently missing in existing studies as of April 2024. Through this approach, it not only forecasts stock prices but also provides investment recommendations, offering fresh insights into stock evaluation and uncovering potential investment prospects within these companies according to intrinsic value of stocks. Specific mathematical models for the intrinsic value of stocks are applied to each examined company for this calculation. The intrinsic value of stocks in companies recommended for investment was found to be undervalued by 7.25% for dividend discount models, 8.25% for earnings models, and 36.75% for cash flow models compared to their current market values. The assessment of intrinsic value of stocks reveals the suitability of investing in these companies: TotalEnergies SE, BP plc, Shell plc, ENI S.p.A. These results offer valuable insights for stock investors in the oil sector, as well as organizations under investigation and other stakeholders in the oil extraction industry. [ABSTRACT FROM AUTHOR]

The stock market is an important segment of the economy that circulates a large volume of assets. Several factors may affect the stock market transactions, leading to fluctuations in the stock values that may pose a problem for those who seek to forecast future stock values and maximize their profits. This issue is more serious when the stock values present the concept drift phenomenon, which means that the stock value's patterns change over time. In this work, we aimed to evaluate whether machine learning-based predictors that incorporate mechanisms to deal with concept drift are suitable for stock market forecasting. To do so, a historic database of stock prices of 10 companies, negotiated in the Brazilian stock exchange and collected over 20 years, was used. We compared the performance of predictors based on different paradigms, with and without mechanisms to deal with concept drift, and the results showed that, although the strategies that handle concept drift demand longer computational times, they also tend to present smaller prediction errors. The highlight was the EOS-D approach, which had the best performance in 6 of the 10 stocks analyzed considering one-to-one comparisons. [ABSTRACT FROM AUTHOR]

Copyright of Polish Journal of Management Studies is the property of Czestochowa University of Technology, Faculty of Management 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.)

We explore insider trading at multinational firms and find multinational firm insiders make larger trades followed by larger abnormal returns relative to those at domestic firms. Multinational firm insiders profitably purchase underpriced, value stocks with low past returns. They trade more profitably, not only because of market pricing opportunities, but also because of the advantageous timing of informed trades before earnings announcements. Insider trading profits are highest at multinational firms with foreign sales in regions culturally and linguistically distinct from the United States. These findings are consistent with opportunistic insiders strategically profiting from difficult to process foreign information. [ABSTRACT FROM AUTHOR]

Motivated by an empirical analysis of stock reaction to COVID-19 pandemic, we propose a generalized mediation model with high-dimensional potential mediators to study the mediation effects of financial metrics that bridge company's sector and stock value. We propose an estimation procedure for the direct effect via a partial penalized maximum likelihood method and establish its theoretical properties. We develop a Wald test for the indirect effect and show that the proposed test has a χ 2 limiting null distribution. We also develop a partial penalized likelihood ratio test for the direct effect and show that the proposed test asymptotically follows a χ 2 -distribution under null hypothesis. A more efficient estimator of indirect effect under complete mediation model is also developed. Simulation studies are conducted to examine the finite sample performance of the proposed procedures and compare with some existing methods. We further illustrate the proposed methodology with an empirical analysis of stock reaction to COVID-19 pandemic via exploring the underlying mechanism of the relationship between companies' sectors and their stock values. [ABSTRACT FROM AUTHOR]

Accounting literature boasts a long list of studies on the impact of earnings information on stock prices. Recently, this strand of literature has been given a new twist by exploring the dynamics of earnings-returns relation at the aggregate level. The knowledge about this relationship is particularly useful for investors who hold diversified portfolios. The paper tests if the aggregate earnings-returns relationship is different when stocks are grouped according to different characteristics such as size, value, profitability and investment. The results suggest that the relation between aggregate earnings and stock returns is relatively stronger for large-cap and growth stocks as compared to small-cap and value stocks. These findings are valuable for investors and fund managers who use factor-based strategies to manage their investments. [ABSTRACT FROM AUTHOR]

The aim of the study was to investigate the relationship between selected macroeconomic variables and the values of representative stock market indices for Central and Eastern European countries in the period Q1 2004 -- Q4 2021. The results, based on the Johansen cointegration test, revealed that the selected macroeconomic variables have an impact on the value of stock market indices on the long term. These results are attributed to the importance of the state of the macroeconomic environment for stable business activity. The reason for this is that macroeconomic stability provides better grounds for predicting the development of the market situation and fiscal and monetary policy. The application of VECM estimations and the Granger causality test indicate that the selected macroeconomic variables affect the values of European stock market indices on the long term rather than on the short term. These findings may reflect the expectations of subjects and/or the consequences of policy measures, whose the impacts can only be estimated and may manifest with a significant delay. [ABSTRACT FROM AUTHOR]

Objective: Rapid changes in the world's oceans make assessment of fish population responses to multiple stressors, especially on scales relevant to management, increasingly important. I used an existing agent‐based, spatially explicit model of Atlantic Croaker Micropogonias undulatus in the northern Gulf of Mexico to examine how temperature, hypoxia, and ocean acidification, singly and in combinations, affect long‐term population dynamics. Methods: I performed a factorial simulation experiment with each stressor at three levels and analyzed various treatment combinations to assess the additivity and multiplicity of interactions. The response variables were long‐term equilibrium (final year) values of spawning stock biomass (SSB), recruitment, weight at age, and two measures of stock productivity (recruits per SSB and maximum recruitment) derived from the spawner–recruit relationship fitted to model output. I used the single‐stressor effects from the experiment to predict how the response variables would change when all three stressors were changed. Single‐stressor effects were combined as the sum of the fractional changes (additive scale) and the product of ratios of changes (multiplicative scale) and compared to the responses in simulations with all stressors imposed. Result: Analyzing the factorial design for two‐way and three‐way interactions showed that there were many interactions on the additive scale but very few on the multiplicative scale. Thus, the responses to multiple stressors were well predicted from single stressor effects when combined as multiplicative effects. Conclusion: I discuss how the lack of strong interactions could be due to model assumptions, the structure of the model, or oversimplified representation of stressor effects. Alternatively, the model and analysis may be sufficiently realistic and weak interactions on the multiplicative scale may be common. This would reduce a complicated multi‐factor situation to a series of more tractable single‐factor effects. A critical next step is to determine how we can a priori identify situations of low interactions (i.e., predictable from single‐stressor effects) without having to already know the multi‐stressor response. Impact statementThe prevailing view is that population responses to multiple stressors are complex. Simulations of temperature, dissolved oxygen, and ocean acidification effects using a croaker population dynamics model showed that population responses to all three stressors together were predictable by combining the effects of each stressor alone. [ABSTRACT FROM AUTHOR]

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment. [ABSTRACT FROM AUTHOR]

This paper introduces a moderated‐mediation model to investigate the relationship between green innovation, as measured by green patent stocks, and firms' stock market value, as measured by Tobin's Q. Using longitudinal data drawn from 351 heavily polluting firms in China's A‐share market from 2007 to 2018, we find that green innovation is likely to affect firms' stock market value positively. A 1% increase in the green innovation over assets index will enhance by 0.18% the Tobin's Q at the firm level. Environmental compliance costs significantly mediate the green innovation‐firms' stock market value association by 8.5%. We also emphasize the positive moderating role of technological collaboration with public research organizations on green innovation‐environmental compliance costs linkage. Our results emphasize that the recurrent question in the extant literature "does it pay to be green?" should be substituted by "how and under what conditions does it pay to be green for firms?" and further stress how firms obtain sustainable development in the environment and economy through green innovation. Implications for firms and investors are also proposed. [ABSTRACT FROM AUTHOR]

In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a rally thereafter. We also introduce a model-based unified tail dependence measure to directly model and compare various tail dependence patterns. Using regression analysis of the upper and lower tail dependence simultaneously, we have identified some interesting intraday tail dependence patterns, such as interactions between the upper and lower tail dependence over time among growth and value stocks and in different market regimes. Our results indicate that tail dependence tends to peak towards the end of the regular trading hours, and, counter-intuitively, upper tail dependence tends to be stronger than lower tail dependence for short-term returns during a market sell-off. Furthermore, we investigate how the Fama–French five factors affect the intraday tail dependence patterns and provide plausible explanations for the occurrence of these phenomena. Among these five factors, the market excess return plays the most important role, and our study suggests that when there is a moderate positive excess return, both the upper and lower tails tend to reach their lowest dependence levels. [ABSTRACT FROM AUTHOR]

If target prices reflect the true values of stocks, they should direct prices towards intrinsic values. But analysts' optimism and use of less sophisticated valuation methods have been found to impede target price informativeness. Contrary to conventional belief, we propose that, due to analysts' optimism, target prices are closer to intrinsic values, and hence more informative, when investor sentiment is low. Accordingly, we find that the association of target prices with future returns is highest when investor sentiment is low and target prices are inferred to be based on sophisticated valuation methods. When investor sentiment is high, however, we find that the association of target prices with future returns approaches zero, suggesting that analyst optimism drives target prices away from intrinsic values, irrespective of the implied valuation method. Further, investors' reactions to target price revisions are strongly positive and seemingly irrational with high investor sentiment—which potentially destabilizes markets. [ABSTRACT FROM AUTHOR]

A bootstrap and a subsequent injected alpha analysis were conducted on 1,221 Chinese mutual funds that were active at some point between July 2001 and July 2021. The results show that most active managers achieve a positive risk-adjusted return. Additionally, we find that this phenomenon is primarily attributable to local (i.e., Chinese) fund managers. We argue that one explanation for the different levels of risk-adjusted returns observed is the information asymmetry between foreign and local fund managers. Additional results support this view, as fund managers primarily investing in small- to mid-cap and value stocks provide a superior performance, which inherently exhibit greater information asymmetry. The findings are contrary to those from similar studies in developed markets, where only a few active managers demonstrate actual skill in their performance. [ABSTRACT FROM AUTHOR]

We examine the characteristics of housing markets under adaptive and heterogeneous expectations. Model agents have finite horizons, and their borrowings are constrained by the collateral value of housing stock. Our model shows that expectation‐driven housing price dynamics constantly change the direction of movement. The steady‐state process of housing prices follows an endogenous oscillation process, and the magnitude of the cycles can be amplified by external shocks. Our quantitative results imply that (i) short‐term positive and long‐term negative serial correlations in housing price changes are inherent, (ii) house prices and expected house price movements are positively correlated, and (iii) fluctuations in housing prices are not fully explained by fundamentals. [ABSTRACT FROM AUTHOR]