The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of...The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.展开更多
Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been invol...Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.展开更多
Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth...Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.展开更多
A study was conducted in the forest area of Chittagong （South） Forest Division, Chittagong, Bangladesh for developing al- lometric models to estimate biomass organic carbon stock in the forest vegetation. Allometric...A study was conducted in the forest area of Chittagong （South） Forest Division, Chittagong, Bangladesh for developing al- lometric models to estimate biomass organic carbon stock in the forest vegetation. Allometric models were tested separately for trees （divided into two DBH classes）, shrubs, herbs and grasses. Model using basal area alone was found to be the best predictor of biomass organic carbon stock in trees because of high coefficient of determination （r^2 is 0.73697 and 0.87703 for 〉 5 cm to ≤ 15 cm and 〉 15 cm DBH （diameter at breast height） rang, respectively） and significance of regression （P is 0.000 for each DBH range） coefficients for both DBH range. The other models using height alone; DBH alone; height and DBH together; height, DBH and wood density; with liner and logarithmic relations produced relatively poor coefficient of determination. The allometric models for dominant 20 tree species were also developed separately and equation using basal area produced higher value of determination of coefficient. Allometric model using total biomass alone for shrubs, herbs and grasses produced higher value of determination of coefficient and significance of regression coefficient （r^2 is 0.87948 and 0.87325 for shrubs, herbs and grasses, respectively and P is 0.000 for each）. The estimation of biomass organic carbon is a complicated and time consuming research. The allometric models developed in the present study can be utilized for future estimation of organic carbon stock in forest vegetation in Bangladesh as well as other tropical countries of the world.展开更多
The southern Patagonian stock（SPS） of Argentinian shortfin squid, Illex argentinus, is an economically important squid fishery in the Southwest Atlantic. Environmental conditions in the region play an important role...The southern Patagonian stock（SPS） of Argentinian shortfin squid, Illex argentinus, is an economically important squid fishery in the Southwest Atlantic. Environmental conditions in the region play an important role in regulating the population dynamics of the I. argentinus population. This study develops an environmentally dependent surplus production（EDSP） model to evaluate the stock abundance of I. argentines during the period of 2000 to 2010. The environmental factors（favorable spawning habitat areas with sea surface temperature of 16–18°C） were assumed to be closely associated with carrying capacity（K） in the EDSP model. Deviance Information Criterion（DIC） values suggest that the estimated EDSP model with environmental factors fits the data better than a Schaefer surplus model without environmental factors under uniform and normal scenarios.The EDSP model estimated a maximum sustainable yield（MSY） from 351 600 t to 685 100 t and a biomass from 1 322 400 t to1 803 000 t. The fishing mortality coefficient of I. argentinus from 2000 to 2010 was smaller than the values of F（0.1） and F（MSY）. Furthermore, the time series biomass plot of I. argentinus from 2000 to 2010 shows that the biomass of I.argentinus and this fishery were in a good state and not presently experiencing overfishing. This study suggests that the environmental conditions of the habitat should be considered within squid stock assessment and management.展开更多
Accurate estimations of biomass and its temporal dynamics are crucial for monitoring the carbon cycle in forest ecosystems and assessing forest carbon sequestration potentials.Recent studies have shown that integratin...Accurate estimations of biomass and its temporal dynamics are crucial for monitoring the carbon cycle in forest ecosystems and assessing forest carbon sequestration potentials.Recent studies have shown that integrating process-based models(PBMs)with remote sensing data can enhance simulations from stand to regional scales,significantly improving the ability to simulate forest growth and carbon stock dynamics.However,the utilization of PBMs for large-scale simulation of larch carbon storage distribution is still limited.In this study,we applied the parameterized 3-PG(Physiological Principles Predicting Growth)model across the Mengjiagang Forest Farm(MFF)to make broad-scale predictions of the biomass and carbon stocks of Larix olgensis plantation.The model was used to simulate average diameter at breast height(DBH)and total biomass,which were later validated with a wide range of observation data including sample plot data,forest management inventory data,and airborne laser scanning data.The results showed that the 3-PG model had relatively high accuracy for predicting both DBH and total biomass at stand and regional scale,with determination coefficients ranging from 0.78 to 0.88.Based on the estimation of total biomass,we successfully produced a carbon stock map of the Larix olgensis plantation in MFF with a spatial resolution of 20 m,which helps with relevant management advice.These findings indicate that the integration of 3-PG model and remote sensing data can well predict the biomass and carbon stock at regional and even larger scales.In addition,this integration facilitates the evaluation of forest carbon sequestration capacity and the development of forest management plans.展开更多
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 touris...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.展开更多
Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
Pakistani marine waters are under an open access regime. Due to poor management and policy implications, blind fishing is continued which may result in ecological as well as economic losses. Thus, it is of utmost impo...Pakistani marine waters are under an open access regime. Due to poor management and policy implications, blind fishing is continued which may result in ecological as well as economic losses. Thus, it is of utmost importance to estimate fishery resources before harvesting. In this study, catch and effort data, 1996-2009, of Kiddi shrimp Parapenaeopsis stylifera fishery from Pakistani marine waters was analyzed by using specialized fishery software in order to know fishery stock status of this commercially important shrimp. Maximum, minimum and average capture production ofP. stylifera was observed as 15 912 metric tons （mr） （1997）, 9 438 mt （2009） and 11 667 mt/a. Two stock assessment tools viz. CEDA （catch and effort data analysis） and ASPIC （a stock production model incorporating covariates） were used to compute MSY （maximum sustainable yield） of this organism. In CEDA, three surplus production models, Fox, Schaefer and Pella-Tomlinson, along with three error assumptions, log, log normal and gamma, were used. For initial proportion （IP） 0.8, the Fox model computed MSY as 6 858 nat （CV=0.204, R^2=0.709） and 7 384 mt （CV=0.149, R^2=0.72） for log and log normal error assumption respectively. Here, gamma error produced minimization failure. Estimated MSY by using Schaefer and Pella-Tomlinson models remained the same for log, log normal and gamma error assumptions i.e. 7 083 mt, 8 209 mt and 7 242 mt correspondingly. The Schafer results showed highest goodness of fit R2 （0.712） values. ASPIC computed MSY, CV, R2, FMsv and BMsv parameters for the Fox model as 7 219 nat, 0.142, 0.872, 0.111 and 65 280, while for the Logistic model the computed values remained 7 720 mt, 0.148, 0.868, 0.107 and 72 110 correspondingly. Results obtained have shown that P. stylifera has been overexploited. Immediate steps are needed to conserve this fishery resource for the future and research on other species of commercial importance is urgently needed.展开更多
This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The margin...This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.展开更多
Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a gre...Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.展开更多
We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models...We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.展开更多
Two kinds of mathematical expressions of stock price, one of which based on certain description is the solution of the simplest differential equation (S.D.E.) obtained by method similar to that used in solid mechanics...Two kinds of mathematical expressions of stock price, one of which based on certain description is the solution of the simplest differential equation (S.D.E.) obtained by method similar to that used in solid mechanics,the other based on uncertain description (i.e., the statistic theory)is the assumption of Black_Scholes's model (A.B_S.M.) in which the density function of stock price obeys logarithmic normal distribution, can be shown to be completely the same under certain equivalence relation of coefficients. The range of the solution of S.D.E. has been shown to be suited only for normal cases (no profit, or lost profit news, etc.) of stock market, so the same range is suited for A.B_ S.M. as well.展开更多
This study developed a recruitment forecasting model based on a new concept of the stock recruitment relationship. No density-dependent effect in the relationship was assumed in the model, which showed that fluctuatio...This study developed a recruitment forecasting model based on a new concept of the stock recruitment relationship. No density-dependent effect in the relationship was assumed in the model, which showed that fluctuations in recruitment and spawning stock biomass of Japanese sardine in the northwestern Pacific can be explained mainly by environmental factors and the effects of fishing. The February Arctic Oscillation (AO) and sea surface temperature over the southern area of the Kuroshio Extension (30 - 35°N and 145 - 180°E;KEST) were used as the environmental factors. The recruitment forecasting model is proposed: The values for recruitment (), spawning stock biomass, (), in year t, forecast by this model accurately reproduced those estimated by tuning virtual population analysis (VPA), and the pattern of variability in the stock recruitment relationship was also reproduced well. In conclusion, a density-dependent effect does not necessarily have to be included to explain the large variations in recruitment and the spawning stock biomass of the Japanese sardine.展开更多
In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)methods.This study investigat...In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)methods.This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context.Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables.However,the inclusion of macroeconomic factors from the financial market,real economic activities,and investor sentiment leads to substantial improvements in the model performance.Notably,the degree of improvement varies with the specific measures of stock returns under consideration.Furthermore,our analysis indicates that,after the inclusion of macroeconomic factors,there is a dissimilarity in model performance,variable importance,and interaction effects among macroeconomic and firm-specific variables,particularly concerning abnormal returns derived from the Fama–French three-and five-factor models compared with excess returns.This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables.These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models,stock returns,and macroeconomic conditions in the domain of empirical asset pricing.展开更多
Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This...Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This study examines the dynamic,nonlinear correlations between Chinese stock volatility,trading volume,and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model(MS-VAR).The empirical findings are as follows:(1)The Chinese stock market can be divided into three regional systems:steady downward,steady upward,and high volatility.The three states have similar frequencies of occurrence,and their corresponding stable probabilities are not high,indicating that the Chinese stock market is unstable.(2)Asymmetric dynamic relationships exist between market volatility,investment return,and trading volume.For different regimes,while the effect of trading volume on volatility and return appears to be insignificant,the impacts of volatility and return on trading volume are considerably strong.(3)A regime-dependent,contemporaneous correlation between volatility and return is observed,which also reflects the behavior of the Chinese stock market“chasing up and down”.However,a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes,indicating that uncertainty in the Chinese stock market is closely related to information inflow.展开更多
The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock m...The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.展开更多
At present,the issues concerning agriculture,farmers and rural areas are increasingly prominent,and the demand of rural economic reform is increasing. In view of current development situation of rural areas,with refer...At present,the issues concerning agriculture,farmers and rural areas are increasingly prominent,and the demand of rural economic reform is increasing. In view of current development situation of rural areas,with reference to successful experience of Tangyue Village Model,this paper analyzed functions of rural stock cooperatives to agricultural development,farmers' income increase,and rural prosperity. Finally,it came up with feasible recommendations for rural reform and the issues concerning agriculture,farmers and rural areas.展开更多
Accurate evaluations of the burden distribution are of critical importance to stabilize the operation of blast furnace.The mathematical model and discrete element method(DEM)are two attractive methods for predicting b...Accurate evaluations of the burden distribution are of critical importance to stabilize the operation of blast furnace.The mathematical model and discrete element method(DEM)are two attractive methods for predicting burden distribution.Based on DEM,the initial velocities of the pellet,sinter,and coke were calculated,and the velocity attenuations of the above three particles between the burden and the chute were analyzed.The initial velocity and velocity attenuation were applied to a mathematical model for improving the accuracy.Additionally,based on the improved model,a scheme for rectifying the chute angles was proposed to address the fluctuation of the stock line and maintain a stable burden distribution.The validity of the scheme was confirmed via a stable burden distribution under different stock lines.The mathematical model has been successfully applied to evaluate the online burden distribution and cope with the fluctuation of the stock line.展开更多
Stock market trading is an activity in which investors need fast and accurate information to make effective decisions.But the fact is that forecasting stock prices by using various models has been suffering from low a...Stock market trading is an activity in which investors need fast and accurate information to make effective decisions.But the fact is that forecasting stock prices by using various models has been suffering from low accuracy,slow convergence,and complex parameters.This study aims to employ a mixed model to improve the accuracy of stock price prediction.We present how to use a random walk based on jump-diffusion,to obtain stock predictions with a good-fitting degree by adjusting different parameters.Aimed at getting better parameters and then using the time series model to predict the data,we employed the time series model to smooth the sequence utilizing logarithm and difference,which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure.According to the comparative analysis,which focuses on checking the mean absolute error,including root mean square error and R square evaluation index,we have drawn a clear conclusion that our mixed model prediction effect is relatively good.In the context of Chinese stocks,the hybrid random walk model is very suitable for predicting stocks.It can“interpret”the randomness of stocks very well,and it also has an unparalleled prediction effect compared with other models.Based on the time series model’s application in this paper,the abovementioned series is more suitable for predicting trends.展开更多
文摘The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.
文摘Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.
文摘Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.
文摘A study was conducted in the forest area of Chittagong （South） Forest Division, Chittagong, Bangladesh for developing al- lometric models to estimate biomass organic carbon stock in the forest vegetation. Allometric models were tested separately for trees （divided into two DBH classes）, shrubs, herbs and grasses. Model using basal area alone was found to be the best predictor of biomass organic carbon stock in trees because of high coefficient of determination （r^2 is 0.73697 and 0.87703 for 〉 5 cm to ≤ 15 cm and 〉 15 cm DBH （diameter at breast height） rang, respectively） and significance of regression （P is 0.000 for each DBH range） coefficients for both DBH range. The other models using height alone; DBH alone; height and DBH together; height, DBH and wood density; with liner and logarithmic relations produced relatively poor coefficient of determination. The allometric models for dominant 20 tree species were also developed separately and equation using basal area produced higher value of determination of coefficient. Allometric model using total biomass alone for shrubs, herbs and grasses produced higher value of determination of coefficient and significance of regression coefficient （r^2 is 0.87948 and 0.87325 for shrubs, herbs and grasses, respectively and P is 0.000 for each）. The estimation of biomass organic carbon is a complicated and time consuming research. The allometric models developed in the present study can be utilized for future estimation of organic carbon stock in forest vegetation in Bangladesh as well as other tropical countries of the world.
基金The National Natural Science Foundation of China under contract No.NSFC31702343the Science Foundation of Shanghai under contract No.13ZR1419700+4 种基金the Innovation Program of Shanghai Municipal Education Commission under contract No.13YZ091the National High-tech R&D Program of China(863 Program)under contract No.2012AA092303the Funding Program for Outstanding Dissertations in Shanghai Ocean Universitythe Funding Scheme for Training Young Teachers in Shanghai Colleges and the Shanghai Leading Academic Discipline Project(Fisheries Discipline)Involvement of Chen Yong was supported by SHOU International Center for Marine Studies and Shanghai 1000 Talent Program
文摘The southern Patagonian stock（SPS） of Argentinian shortfin squid, Illex argentinus, is an economically important squid fishery in the Southwest Atlantic. Environmental conditions in the region play an important role in regulating the population dynamics of the I. argentinus population. This study develops an environmentally dependent surplus production（EDSP） model to evaluate the stock abundance of I. argentines during the period of 2000 to 2010. The environmental factors（favorable spawning habitat areas with sea surface temperature of 16–18°C） were assumed to be closely associated with carrying capacity（K） in the EDSP model. Deviance Information Criterion（DIC） values suggest that the estimated EDSP model with environmental factors fits the data better than a Schaefer surplus model without environmental factors under uniform and normal scenarios.The EDSP model estimated a maximum sustainable yield（MSY） from 351 600 t to 685 100 t and a biomass from 1 322 400 t to1 803 000 t. The fishing mortality coefficient of I. argentinus from 2000 to 2010 was smaller than the values of F（0.1） and F（MSY）. Furthermore, the time series biomass plot of I. argentinus from 2000 to 2010 shows that the biomass of I.argentinus and this fishery were in a good state and not presently experiencing overfishing. This study suggests that the environmental conditions of the habitat should be considered within squid stock assessment and management.
基金funded by National Key Research and Development Program(2023YFD220080430&2017YFD0600404)。
文摘Accurate estimations of biomass and its temporal dynamics are crucial for monitoring the carbon cycle in forest ecosystems and assessing forest carbon sequestration potentials.Recent studies have shown that integrating process-based models(PBMs)with remote sensing data can enhance simulations from stand to regional scales,significantly improving the ability to simulate forest growth and carbon stock dynamics.However,the utilization of PBMs for large-scale simulation of larch carbon storage distribution is still limited.In this study,we applied the parameterized 3-PG(Physiological Principles Predicting Growth)model across the Mengjiagang Forest Farm(MFF)to make broad-scale predictions of the biomass and carbon stocks of Larix olgensis plantation.The model was used to simulate average diameter at breast height(DBH)and total biomass,which were later validated with a wide range of observation data including sample plot data,forest management inventory data,and airborne laser scanning data.The results showed that the 3-PG model had relatively high accuracy for predicting both DBH and total biomass at stand and regional scale,with determination coefficients ranging from 0.78 to 0.88.Based on the estimation of total biomass,we successfully produced a carbon stock map of the Larix olgensis plantation in MFF with a spatial resolution of 20 m,which helps with relevant management advice.These findings indicate that the integration of 3-PG model and remote sensing data can well predict the biomass and carbon stock at regional and even larger scales.In addition,this integration facilitates the evaluation of forest carbon sequestration capacity and the development of forest management plans.
文摘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.
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
基金Supported by the Earmarked Fund for Modern Agro-Industry Technology Research System of Chinathe Special Research Fund of Ocean University of China(No.201022001)
文摘Pakistani marine waters are under an open access regime. Due to poor management and policy implications, blind fishing is continued which may result in ecological as well as economic losses. Thus, it is of utmost importance to estimate fishery resources before harvesting. In this study, catch and effort data, 1996-2009, of Kiddi shrimp Parapenaeopsis stylifera fishery from Pakistani marine waters was analyzed by using specialized fishery software in order to know fishery stock status of this commercially important shrimp. Maximum, minimum and average capture production ofP. stylifera was observed as 15 912 metric tons （mr） （1997）, 9 438 mt （2009） and 11 667 mt/a. Two stock assessment tools viz. CEDA （catch and effort data analysis） and ASPIC （a stock production model incorporating covariates） were used to compute MSY （maximum sustainable yield） of this organism. In CEDA, three surplus production models, Fox, Schaefer and Pella-Tomlinson, along with three error assumptions, log, log normal and gamma, were used. For initial proportion （IP） 0.8, the Fox model computed MSY as 6 858 nat （CV=0.204, R^2=0.709） and 7 384 mt （CV=0.149, R^2=0.72） for log and log normal error assumption respectively. Here, gamma error produced minimization failure. Estimated MSY by using Schaefer and Pella-Tomlinson models remained the same for log, log normal and gamma error assumptions i.e. 7 083 mt, 8 209 mt and 7 242 mt correspondingly. The Schafer results showed highest goodness of fit R2 （0.712） values. ASPIC computed MSY, CV, R2, FMsv and BMsv parameters for the Fox model as 7 219 nat, 0.142, 0.872, 0.111 and 65 280, while for the Logistic model the computed values remained 7 720 mt, 0.148, 0.868, 0.107 and 72 110 correspondingly. Results obtained have shown that P. stylifera has been overexploited. Immediate steps are needed to conserve this fishery resource for the future and research on other species of commercial importance is urgently needed.
文摘This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.
文摘Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.
文摘We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
文摘Two kinds of mathematical expressions of stock price, one of which based on certain description is the solution of the simplest differential equation (S.D.E.) obtained by method similar to that used in solid mechanics,the other based on uncertain description (i.e., the statistic theory)is the assumption of Black_Scholes's model (A.B_S.M.) in which the density function of stock price obeys logarithmic normal distribution, can be shown to be completely the same under certain equivalence relation of coefficients. The range of the solution of S.D.E. has been shown to be suited only for normal cases (no profit, or lost profit news, etc.) of stock market, so the same range is suited for A.B_ S.M. as well.
文摘This study developed a recruitment forecasting model based on a new concept of the stock recruitment relationship. No density-dependent effect in the relationship was assumed in the model, which showed that fluctuations in recruitment and spawning stock biomass of Japanese sardine in the northwestern Pacific can be explained mainly by environmental factors and the effects of fishing. The February Arctic Oscillation (AO) and sea surface temperature over the southern area of the Kuroshio Extension (30 - 35°N and 145 - 180°E;KEST) were used as the environmental factors. The recruitment forecasting model is proposed: The values for recruitment (), spawning stock biomass, (), in year t, forecast by this model accurately reproduced those estimated by tuning virtual population analysis (VPA), and the pattern of variability in the stock recruitment relationship was also reproduced well. In conclusion, a density-dependent effect does not necessarily have to be included to explain the large variations in recruitment and the spawning stock biomass of the Japanese sardine.
文摘In the field of empirical asset pricing,the challenges of high dimensionality,non-linear relationships,and interaction effects have led to the increasing popularity of machine learning(ML)methods.This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context.Our findings reveal that neural network models exhibit consistent performance across different stock return measures when they rely solely on firm-specific characteristic variables.However,the inclusion of macroeconomic factors from the financial market,real economic activities,and investor sentiment leads to substantial improvements in the model performance.Notably,the degree of improvement varies with the specific measures of stock returns under consideration.Furthermore,our analysis indicates that,after the inclusion of macroeconomic factors,there is a dissimilarity in model performance,variable importance,and interaction effects among macroeconomic and firm-specific variables,particularly concerning abnormal returns derived from the Fama–French three-and five-factor models compared with excess returns.This divergence is primarily attributed to the extent to which these factor models remove the variance associated with the macroeconomic variables.These findings collectively offer valuable insights into the efficacy of neural network models for stock return predictions and contribute to a deeper understanding of the intricate relationship between factor models,stock returns,and macroeconomic conditions in the domain of empirical asset pricing.
基金This work was partially supported by the National Natural Science Foundation of China(Grant No.:72171192)the MOE Layout Foundation of Humanities and Social Sciences(Grant No.:22YJA790007)+1 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.:2021RC3057)the Youth Innovation Team of Shanxi University,and the Fundamental Research Funds for the Central Universities.
文摘Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This study examines the dynamic,nonlinear correlations between Chinese stock volatility,trading volume,and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model(MS-VAR).The empirical findings are as follows:(1)The Chinese stock market can be divided into three regional systems:steady downward,steady upward,and high volatility.The three states have similar frequencies of occurrence,and their corresponding stable probabilities are not high,indicating that the Chinese stock market is unstable.(2)Asymmetric dynamic relationships exist between market volatility,investment return,and trading volume.For different regimes,while the effect of trading volume on volatility and return appears to be insignificant,the impacts of volatility and return on trading volume are considerably strong.(3)A regime-dependent,contemporaneous correlation between volatility and return is observed,which also reflects the behavior of the Chinese stock market“chasing up and down”.However,a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes,indicating that uncertainty in the Chinese stock market is closely related to information inflow.
文摘The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.
基金Supported by Scientific Research Foundation of Hunan Provincial Department of Education(17A233)
文摘At present,the issues concerning agriculture,farmers and rural areas are increasingly prominent,and the demand of rural economic reform is increasing. In view of current development situation of rural areas,with reference to successful experience of Tangyue Village Model,this paper analyzed functions of rural stock cooperatives to agricultural development,farmers' income increase,and rural prosperity. Finally,it came up with feasible recommendations for rural reform and the issues concerning agriculture,farmers and rural areas.
基金financial support from the China Minmetals Science and Technology Special Plan Foundation(2020ZXA01)the National Natural Science Foundation of China(U1960205).
文摘Accurate evaluations of the burden distribution are of critical importance to stabilize the operation of blast furnace.The mathematical model and discrete element method(DEM)are two attractive methods for predicting burden distribution.Based on DEM,the initial velocities of the pellet,sinter,and coke were calculated,and the velocity attenuations of the above three particles between the burden and the chute were analyzed.The initial velocity and velocity attenuation were applied to a mathematical model for improving the accuracy.Additionally,based on the improved model,a scheme for rectifying the chute angles was proposed to address the fluctuation of the stock line and maintain a stable burden distribution.The validity of the scheme was confirmed via a stable burden distribution under different stock lines.The mathematical model has been successfully applied to evaluate the online burden distribution and cope with the fluctuation of the stock line.
基金supported by the 2020 Hunan Natural Science Foundation Project"Research on the Key Technologies of a Personalized Learning Platform for Higher Vocational Students Based on Self-Expanding Knowledge Base and Multimodal Portraits"(2020JJ7041)partly supported by the National Natural Science Foundation of China(No.72073041).
文摘Stock market trading is an activity in which investors need fast and accurate information to make effective decisions.But the fact is that forecasting stock prices by using various models has been suffering from low accuracy,slow convergence,and complex parameters.This study aims to employ a mixed model to improve the accuracy of stock price prediction.We present how to use a random walk based on jump-diffusion,to obtain stock predictions with a good-fitting degree by adjusting different parameters.Aimed at getting better parameters and then using the time series model to predict the data,we employed the time series model to smooth the sequence utilizing logarithm and difference,which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure.According to the comparative analysis,which focuses on checking the mean absolute error,including root mean square error and R square evaluation index,we have drawn a clear conclusion that our mixed model prediction effect is relatively good.In the context of Chinese stocks,the hybrid random walk model is very suitable for predicting stocks.It can“interpret”the randomness of stocks very well,and it also has an unparalleled prediction effect compared with other models.Based on the time series model’s application in this paper,the abovementioned series is more suitable for predicting trends.
