The Impact of US Stock Market on the Co-Movements of BRIC Stock Markets—Evidence from Linear Conditional Granger Causality

This paper investigates the impact of the US stock market on the co-movements among the BRIC stock markets using conditional Granger causality which allows a comprehensive exploration on direct and indirect causality. The results from linear conditional causality test show a strong influence of the US stock market on the co-movements of BRIC. Our findings identify the US stock market which is the main inner factor making major contributions to the co-movements among the BRIC stock markets. Further, this study provides robust evidence that the co-movements cannot be significantly influenced by the common information factor. These findings show a more complete picture of the relationships between the US and the BRIC stock markets, offering important implications for policymakers and investors.

Growth, Population Parameters and Stock Status of Sarotherodon galilaeus in Samandeni Reservoir, Burkina Faso

Mango tilapia, Sarotherodon galilaeus is one of the most caught fish species in the Samandeni multi-species fishing sites of which, few data on its biology and exploitation are available. The study aimed to Assess the stock status of S. galilaeus. Sampling was conducted from March, 2021 to February 2022 based on commercial fish catches to analyze growth parameters, first sexual maturity size and harvest status of the stock. A total of 572 specimens including 297 females and 275 males were examined. The stock assessment was performed by using the Length based Bayesian method of Biomass (LBB) and that of growth by the ELEFAN method. The growth parameters showed a seasonality of growth and females appeared to grow faster than males. On the other hand, males had a greater asymptotic length than females. Results on the estimated length of fish at first maturity showed that females firstly reached the maturity compared to males. The relative biomass (B/B0) estimated for the stock was higher than the relative biomass that produces maximum sustainable yield (BMSY/B0) indicating healthy biomass. In addition, the length at first sexual maturity was less than the length at the first catch, indicating the absence of overfishing of growth. In addition, extending the study to the various stocks of the reservoir would be important for the sustainable management of the Samandeni high economic fishing area.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.

Soil Organic Carbon Stock Variation under Different Soil Types and Land Uses in the Sub-Humid Noun Plain, Western Cameroon

This study was conducted to assess the current stock of soil organic carbon under different agricultural land uses, soil types and soil depths in the Noun plain in western Cameroon. Three sites were selected for the study, namely Mangoum, Makeka and Fossang, representative of the three dominant soil types of the noun plain (Andosols, Acrisols and Ferralsols). Three land uses were selected per site including natural vegetation, agroforest and crop field. Soil was sampled at three depths;0 - 20 cm, 20 - 40 cm, and 40 - 60 cm. Analysis of variance showed that soil type did not significantly influence carbon storage, but rather land uses and soil depth. SOCS decreased significantly with depth in all the sites, with an average stock of 66.3 ± 15.8 tC/ha at 0 - 20 cm, compared to an average stock of 33.3 ± 7.4 tC/ha at 40 - 60 cm. SOCS was significantly highest in the natural formation with 57.2 ± 19.7 tC/ha, and lowest in cultivated fields, at 37.7 ± 10.6 tC/ha. Andosols, with their high content of coarse fragments, stored less organic carbon than Ferralsols and Acrisols.

National Soil Organic Carbon Stocks Inventories under Different Mangrove Forest Types in Gabon

Gabonese’s estuary is an important coastal mangrove setting and soil plays a key role in mangrove carbon storage in mangrove forests. However, the spatial variation in soil organic carbon (SOC) storage remain unclear. To address this gap, determining the SOC spatial variation in Gabonese’s estuarine is essential for better understanding the global carbon cycle. The present study compared soil organic carbon between northern and southern sites in different mangrove forest, Rhizophora racemosa and Avicennia germinans. The results showed that the mean SOC stocks at 1 m depth were 256.28 ± 127.29 MgC ha−1. Among the different regions, SOC in northern zone was significantly (p p < 0.001). The deeper layers contained higher SOC stocks (254.62 ± 128.09 MgC ha−1) than upper layers (55.42 ± 25.37 MgC ha−1). The study highlights that low deforestation rate have led to less CO2 (705.3 Mg CO2e ha−1 - 922.62 Mg CO2e ha−1) emissions than most sediment carbon-rich mangroves in the world. These results highlight the influence of soil texture and mangrove forest types on the mangrove SOC stocks. The first national comparison of soil organic carbon stocks between mangroves and upland tropical forests indicated SOC stocks were two times more in mangroves soils (51.21 ± 45.00 MgC ha−1) than primary (20.33 ± 12.7 MgC ha−1), savanna and cropland (21.71 ± 15.10 MgC ha−1). We find that mangroves in this study emit lower dioxide-carbon equivalent emissions. This study highlights the importance of national inventories of soil organic carbon and can be used as a baseline on the role of mangroves in carbon sequestration and climate change mitigation but the variation in SOC stocks indicates the need for further national data.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

Co-movement in crypto-currency markets:evidences from wavelet analysis

We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods;employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin,Ethereum,Lite and Dashcoin.First,we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation.Further,Wavelet Local Multiple Correlation for the given cryptocurrency prices are estimated across different time-scales.From the results,it is found that that the correlation follows an aperiodic cyclical nature,and the crypto-currency prices are driven by Bitcoin price movements.Based on the results obtained,we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices,and any shocks in the latter is immediately transformed to the former.

COVID‑19 and tourism sector stock price in Spain:medium‑term relationship through dynamic regression models

The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

Dietary organic acids ameliorate high stocking density stress-induced intestinal inflammation through the restoration of intestinal microbiota in broilers

Background:High stocking density(HSD)stress has detrimental effects on growth performance,intestinal barrier function,and intestinal microbiota in intensive animal production.Organic acids(OA)are widely used as feed addi-tives for their ability to improve growth performance and intestinal health in poultry.However,whether dietary OA can ameliorate HSD stress-induced impaired intestinal barrier in broilers remains elusive.In this study,a total of 528 one-day-old male Arbor Acres broilers were randomly allocated into 3 treatments with 12 replicates per treatment including 10 birds for normal stocking density and 17 birds for HSD.The dietary treatments were as follows:1)Normal stocking density+basal diet;2)HSD+basal diets;3)HSD+OA.Results:HSD stress can induce increased levels of serum corticosterone,lipopolysaccharides,interleukin-1β,tumor necrosis factor-α,and down-regulated mRNA expression of ZO-1,resulting in compromised growth performance of broilers(P<0.05).Dietary OA could significantly reduce levels of serum corticosterone,lipopolysaccharides,interleukin-1β,and tumor necrosis factor-α,which were accompanied by up-regulated interleukin-10,mRNA expres-sion of ZO-1,and growth performance(P<0.05).Moreover,OA could down-regulate the mRNA expression of TLR4 and MyD88 to inhibit the NF-κB signaling pathway(P<0.05).Additionally,HSD stress significantly decreased the abundance of Bacteroidetes and disturbed the balance of microbial ecosystems,whereas OA significantly increased the abundance of Bacteroidetes and restored the disordered gut microbiota by reducing competitive and exploita-tive interactions in microbial communities(P<0.05).Meanwhile,OA significantly increased the content of acetic and butyric acids,which showed significant correlations with intestinal inflammation indicators(P<0.05).Conclusions:Dietary OA ameliorated intestinal inflammation and growth performance of broilers through restor-ing the disordered gut microbial compositions and interactions induced by HSD and elevating short-chain fatty acid production to inhibit the TLR4/NF-κB signaling pathway.These findings demonstrated the critical role of intestinal microbiota in mediating the HSD-induced inflammatory responses,contributing to exploring nutritional strategies to alleviate HSD-induced stress in animals.

Forecasting Stock Prices with an Integrated Approach Combining ARIMA and Machine Learning Techniques ARIMAML

Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Exploiting Data Science for Measuring the Performance of Technology Stocks

The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

Growth, and Stock Status Assessment of African Catfish, Clarias anguillaris (Linnaeus, 1758) from Burkina Faso Newly Man-Made Lake Samandeni

The Samandeni reservoir in Burkina Faso, impounded in 2017, hosts a significant diversity of fish, including the Clariidae family. The fish stocks have been exploited since 2019, when the reservoir was opened to fishermen. However, no assessment of the status of these stocks has been conducted. The present study focused on the dynamics of Clarias anguillaris exploitation in order to have reliable information that can contribute to the planning of its sustainable exploitation. Length-frequency data on 323 individuals were sampled from commercial catches from March 2021 to February 2022. The growth parameters were determined using ELEFAN method and the stock assessment was done using the Bayesian Length-Based Biomass (LBB) method. The growth analysis showed isometry for both male and female fishes with allometric coefficient value of 3.03, 3.01 and 3.17 respectively for mixed sexes, male and female. Estimates values (0.6 and 0.4) of the growth oscillation intensity indicate the existence of seasonal growth. The relative biomass (B/B0) estimated for C. anguillaris was less than the relative biomass that produces the maximum sustainable yield (BMSY/B0) indicating biomass overfishing. In addition, the length at first capture was less than the optimal length at first capture indicating a growth overfishing status. Therefore, it would be desirable to increase the mesh size of the fishing gear so that juveniles are not caught, which will ensure an ecological sustainability of the exploitation of the Clariidae.

Stock market prediction using deep learning algorithms

The Stock Market is one of the most active research areas,and predicting its nature is an epic necessity nowadays.Predicting the Stock Market is quite challenging,and it requires intensive study of the pattern of data.Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution.Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities.The Artificial Neural Network(ANN)or Deep Feedforward Neural Network and the Convolutional Neural Network(CNN)are the two network models that have been used extensively to predict the stock market prices.The models have been used to predict upcoming days'data values from the last few days'data values.This process keeps on repeating recursively as long as the dataset is valid.An endeavour has been taken to optimise this prediction using deep learning,and it has given substantial results.The ANN model achieved an accuracy of 97.66%,whereas the CNN model achieved an accuracy of 98.92%.The CNN model used 2-D histograms generated out of the quantised dataset within a particular time frame,and prediction is made on that data.This approach has not been implemented earlier for the analysis of such datasets.As a case study,the model has been tested on the recent COVID-19 pandemic,which caused a sudden downfall of the stock market.The results obtained from this study was decent enough as it produced an accuracy of 91%.

Eucalyptus carbon stock estimation in subtropical regions with the modeling strategy of sample plots–airborne LiDAR–Landsat time series data

Updating eucalyptus carbon stock data in a timely manner is essential for better understanding and quantifying its effects on ecological and hydrological processes.At present,there are no suitable methods to accurately estimate the eucalyptus carbon stock in a large area.This research aimed to explore the transferability of the eucalyptus carbon stock estimation model at temporal and spatial scales and assess modeling performance through the strategy of combining sample plots,airborne LiDAR and Landsat time series data in subtropical regions of China.Specifically,eucalyptus carbon stock estimates in typical sites were obtained by applying the developed models with the combination of airborne LiDAR and field measurement data;the eucalyptus plantation ages were estimated using the random localization segmentation approach from Landsat time series data;and regional models were developed by linking LiDAR-derived eucalyptus carbon stock and vegetation age(e.g.,months or years).To examine the models’robustness,the developed models at the regional scale were transferred to estimate carbon stocks at the spatial and temporal scales,and the modeling results were evaluated using validation samples accordingly.The results showed that carbon stock can be successfully estimated using the age-based models(both age variables in months and years as predictor variables),but the month-based models produced better estimates with a root mean square error(RMSE)of 6.51 t⋅ha1 for Yunxiao County,Fujian Province,and 6.33 t⋅ha1 for Gaofeng Forest Farm,Guangxi Zhuang Autonomous Region.Particularly,the month-based models were superior for estimating the carbon stocks of young eucalyptus plantations of less than two years.The model transferability analyses showed that the month-based models had higher transferability than the year-based models at the temporal scale,indicating their possibility for analysis of carbon stock change.However,both the month-based and year-based models expressed relatively poor transferability at a spatial scale.This study provides new insights for cost-effective monitoring of carbon stock change in intensively managed plantation forests.

The effect of overseas investors on local market efficiency:evidence from the Shanghai/Shenzhen–Hong Kong Stock Connect

Using a recent stock market liberalization reform policy in China—the Stock Connect—as a quasi-natural experiment,this study examines the effect of stock market liberalization on market efficiency.Employing a dataset of 17,086 Chinese listed firms covering 2009 to 2018,we find that stock market liberalization improves the market efficiency of the Chinese mainland stock market.We further explore the potential channels through which the Stock Connect can enhance the efficiency of the A-share(A-shares refer to shares issued by Chinese companies incorporated in China's Mainland,traded in the Shanghai Stock Exchange and the Shenzhen Stock Exchange.They are denominated in Chinese RMB(the local currency).A-shares were restricted to local Chinese investors before 2003,are open to foreign investors via the Qualified Foreign Institutional Investor,RMB Qualified Foreign Institutional Investor,or the Stock Connect programs.)market.The findings show that liberalizing capital markets could benefit local market efficiency by increasing stock price informational efficiency and improving corporate governance quality.The additional analysis shows that stock market liberalization has a significant and positive impact on local market efficiency,enhancing firm value and reducing stock crash risk.We conduct various robustness checks to corroborate our findings.This study provides important policy implications for emerging countries liberalizing capital markets for foreign investors.

Plant Diversity and Carbon Stock Assessment in an Informal Settlement of the City of Yaounde, Cameroon: The Case of Elig-Effa West

Due to rapid demographic growth, economic and technological changes, urban environments are highly exposed to the impacts of climate change and environmental catastrophes. Despite the pressure to which urban forests are exposed, they still play important roles through the service they provide: air quality, shade, and reduction of dioxide of carbon. The present study was carried out in the city of Yaoundé, Cameroon, especially in one of its suburb areas, Elig-Effa West, a neighborhood with spontaneous settlements. The study aimed at assessing the plant species diversity, and carbon sequestration potentials of diverse trees recorded using indirect methods. Six sampling plots of 100 × 100 m were established in the study area. Our results recorded a total of 16 species grouped into 12 families. Apocynaceae, Mimosaceae and Moraceae were the most represented families. The most represented species throughout the sampling plots were Mangifera indica, Persea americana, Annona muricata and Psidium gaujava, which are all fruiting trees. Carbon stock for the study area was estimated at 16.08 ± 5.60 tC with an average of 0.23 ± 0.08 tC/ha. The results also showed the species to be considered in a potential restoration program should be first fruiting trees, followed by non-fruiting trees useful to population, especially those that have their trunk peeled, a sign that they are used by the population. Nevertheless, informal settlements contribute to carbon sequestration, that well targeted urban reforestation will substantially improve.