Stock Fluctuations Research Articles

Comparison of Different Machine Learning Scenarios for Stock Price Prediction of Netflix

Buying stocks based on relatively accurate predictions to make a profit is what investors have been yearning for. However, due to the volatility and stochastic intrinsic of the stock, the price is also full of uncertainty, which is difficult to predict. With the improvement of computer performance and the popularization of machine learning methods nowadays, effective stock prediction methods emerge one after another. In this paper, random forest, XGBoost, and LSTM techniques are utilized for predicting the closing price of Netflix, which is one of the major stocks in Nasdaq and has been fluctuating this year. The closing price of the previous five days and the last day’s volume are used as inputs. The models are evaluated by MSE and R-square. According to the analysis, similar model evaluation results in random forest and LSTM indicate that the models are efficient. Furthermore, the prediction could be improved by considering the fluctuations of stocks simultaneously and introducing more variables in various dimensions. These results shed light on guiding further exploration of predicting stock price movements based on advanced machine learning methods.

Wal-Mart's Financial Analysis

Due to the impact of the epidemic, e-commerce is developing very rapidly. Under such a circumstance, this article would like to choose a company that may be greatly affected by the epidemic and starts an e-commerce business Under the pressure of the epidemic. After my understanding of e-commerce at home and abroad, this article finally chooses Walmart as the research object of this project. In this essay, this article used SWOT economic analysis to describe WalMart's development speed, development potential, and business barriers in recent years. After the economic analysis and stock analysis of Walmart, this article found that the company had been developing for almost 10 years before the outbreak of the epidemic, so Walmart must have done very well in both economy and management. These advantages also made previous losses during the pandemic less dramatic. Based on the stock fluctuations, this article found that there are two characteristics of the impact of the pandemic on Walmart; One is a short time, and the other is a big influence. This can also be seen in Walmart's response to the pandemic quickly.

Judging Stock Trends According to the Sentiments of Stock Comments in Expert Forums

Machine learning has been proven to be very effective and it can help to boost the performance of stock price predictions. However, most researchers mainly focus on the historical data of stocks and predict the future trends of stock prices by designing prediction models. They believe that past data must hide useful information in the future. Due to the lack of human participation, the result of this practice must be accidental. To solve this problem, we propose a novel model called Convolutional Neural Network with Sentiment Check (CNN-SC) in this paper. The model recommended by the authors refers to and expands upon the ideas of experts, and then takes the sentiment value in expert comments as the basis for stock price prediction. This model reflects the humanization of stock price prediction and eliminates the problem of a lack of supervision in machine learning. To demonstrate the effectiveness of our novel method, we compare it with five other popular and excellent methods. Although the C-E-SVR&RF and GC-CNN models are also quite effective, our results indicate the superiority of CNN-SC and it is accurately used to calculate the short-term (seven days later) stock price fluctuation of a single stock.

Financial Forecasting Method for Generative Adversarial Networks based on Multi-model Fusion

<p> To some extent, stock prices can reflect national economic development and residents’ living standards. However, current stock forecasts are mainly analyzed using the stock’s own price. It is found that the exchange rate and technical indicators are closely related to the fluctuations of stocks and stock indexes. In this study, a generative adversarial network (GAN) financial forecasting method based on multi-model fusion is proposed, which introduces the exchange rate and technical indexes of stock and stock index into the input data, and combines the characteristics of its own highest and lowest price, the closing price of stock or stock index is predicted by using the generated antagonistic network. The methods are as follows: firstly, the exchange rate features are de-noised by wavelet transform technology, the overall trend of exchange rate features is extracted, and the dimensionality of technical indicators is reduced by Principal Component Analysis (PCA). Then, a convolutional neural network (CNN) is used in the generator to extract the local features of the input data, and the attention mechanism is used to improve the prediction effect of the model. Finally, the prediction was made by long short-term memory network (LSTM). Convolutional neural network and multilayer fully connected neural network are used in the discriminant, and gelu is used as activation function in the hidden layer. This study selects the data of Zhuhai Port (000507.SZ), Xiamen International Trade (600755.SH), Orient Venture (600278.SH) and Shanghai Stock Exchange Index (SSE, 000001.SH) in the export industry from January 1, 2013 to December 31, 2019 as the experimental data set. The exchange rate price between RMB and US dollar at the corresponding time is selected as the exchange rate feature. Compared with the best in-depth learning financial prediction comparison model, the results show that the generation confrontation network proposed in this paper has improved by 3% to 15% in each experimental data set.</p> <p> </p>

SOIL RESPIRATION AS MICROBIAL RESPONSE TO THE ENDOGEN INPUT OF BIO-SYNTHESIZED ORGANIC MATTER AND ITS IMPLICATION IN CARBON SEQUESTRATION

Active C, as a measure of the level of chemical oxidation of organic matter, reflects the carbon available to microorganisms. Soil respiration, as a direct way to estimate edaphic microbial activity, could be a measure of the fluctuations of carbon stocks in soils. To determine the ability of soil respiration to evaluate such fluctuations we used soils with an increased content of organic carbon, constant optimal conditions, to eliminate the disturbing factors, analyzed in a short period of time. The influence of the specific decomposition rates of these soils was assessed by both spot determinations of soil respiration and analyzing the flux of CO2 from ex situ soil samples, under standard experimental conditions, to highlight carbon storage in such soils. Reference data can be accumulated through the analysis of these parameters, which compared with the results of quantitative/qualitative determinations regarding the changes in the content of microbial biomass, the content of fulvic sub-fractions, the fluorescence of dissolved organic material and the evolution of the content of siderophores, could be considered, by their own evolutions, as arguments in sustaining the use of respiration in the efficient estimation of carbon storage evolution in the soils. The analyses of these parameters were carried out in two phases, for comparing initial and final data of experiment (after 30 days). The soils had different levels of the respiration potential between phases. The level of soil respiration was reduced in time between 4.27-14.60%, in each soil. The CO2 flux showed, in time, a continuous decreasing trend in both soils. In the case of Mollic Histic Gleysol (Salinic), the coefficient of determination has the value R2=0.92 for the flux determined in the final phase. The levels of microbial biomass of both soils were increased significantly at the end of the experiment. In the case of Mollic Histic Gleysol (Salinic), microbial biomass increased from 456±23.12 μgC∙g-1 to 514±24.57 μgC∙g-1 soil. The fulvic sub-fractions A-D of both soils revealed significant accumulates of soluble organic compounds, with different molecular weights and complexity levels, after 30 days of incubations in standard conditions. The fluorescent components present in the water-extractable organic matter were highlighted by imagistic method. The highest degree of storages of newly bio-synthesized compounds of carbon was registered in organic matter of Mollic Histic Gleysol (Salinic). The intensity of siderophores biosynthesis increased over time, starting from an initial lower presence in the Mollic Gleysol (Salinic) (with Ø 11 mm halo), which were followed by an increasing of siderophores content and availability of iron, at the end of the experimental period. Accumulations of siderophores in the Mollic Histic Gleysol (Salinic) determined a Ø 31 mm halo diameter.

Estimating canopy height change using machine learning by coupling WorldView-2 stereo imagery with Landsat-7 data

ABSTRACT Canopy height change (CHC) is one of the key characteristics of forest dynamics, associated with the fluctuations in forest above-ground biomass and carbon stocks. Field measurements and Airborne Laser Scanning (ALS) point clouds can be used to detect CHC; however, they have limited availability in space and time, making it challenging to map CHC over large areas. Alternatively, very high-resolution (VHR) satellite stereo imagery plays an increasingly vital role in estimating fine-scaled digital surface models (DSMs) across landscapes. However, its capability and potential to estimate canopy height model (CHM) and CHC has not been widely explored. Using ALS-derived CHM in 2011 and 2015 and the four-year CHC as references, we evaluated stereo-based CHM and CHC from WorldView-2 over five woody parks in Columbus, Ohio, USA. We also integrated stereo-based CHM with vegetation indices from Landsat 7 to improve CHM and CHC estimation with machine learning methods. Our results showed that VHR stereo imagery captured similar spatial patterns of CHM with ALS data but significantly overestimated CHM. Moreover, the ALS-derived CHC ranged from 1.6±1.9 m (mean ± standard deviation) to 3.1±1.2 m, as compared to from -1.3±0.7 m to 1.1±1.7 m for stereo-based CHC, indicating the limitation of CHC estimation by stereo imagery alone. Among six widely used machine learning methods, Gradient Boosting Regression method provided the most reliable estimates of CHM, with a correlation coefficient R of 0.64 and a root-mean-square error (RMSE) of 3.1 m (11.1%). Stereo-based CHM and vegetation indices explained more than 70% of CHM variability, substantially improving the estimation of 4-year CHC. Our results suggested that VHR stereo imagery alone has limitations in estimating CHM and CHC. The combination of remote-sensing structural (stereo-based CHM) and spectral (vegetation indices) information improves the CHM and CHC estimations.

Short seller report and stock performance Empirical evidence from US-listed Chinese firms

In recent years, there have been a number of Chinese companies listing in the US market. At the same time, many of them delist from the US market. An important reason behind this is the issue of financial fraud. Most of these cases are due to short seller reports by activist short sellers. After short selling, financial and auditing problems are revealed, causing the share prices of these companies to collapse. This has raised concerns among US regulators about the quality of Chinese companies' auditing and prompted joint supervision by Chinese and US regulators. This paper uses Chinese companies listed in the US as samples to study the long and short term fluctuations of these companies' stock prices before and after the short seller reports were issued. After the short seller reports are released, the results show that the stock price can fall significantly in the short term, with the average CAR reaching 10%. Long term share prices are less affected, with 45% of short seller reports associating with negative price changes. This paper also examines the factors that influence the market's reaction to short seller reports. Companies that choose the accounting firms from the Big 4 see smaller share price declines in the short run compared to those that do not. In addition, small companies are more likely to recover from such negative events in the long run than large ones. The study of short selling reports is important. Some limitations and restrictions make it difficult for US regulators to fully monitor all Chinese companies. Therefore, individual investors can only obtain information through these short-selling reports, and then change their investment decisions to protect their interests.

Divergent Investor Perspectives and Volatility Risk -- Research Based on Stock Bar Public Opinion Data

Retail investors are the primary participants in the A-share market, with a large number, large differences in economic levels, and uneven accumulation of financial professions, so it is manifested in the large differences in opinion and investment behavior of individual investors, which influence the risk of stock market volatility. Due to the rise in popularity of mobile clients, browsing stock bar post content has become an essential way for retail investors to get investment trend. This paper analyzes the impact of public opinion on the fluctuation of Oriental Wealth Internet Stocks by focusing on micro-level individual stocks. In addition, the paper supplements empirical studies on stock volatility from the micro level of differences in investor opinion, providing market regulators with a reference for better understanding investor psychology and managing network public opinion. The empirical results show that: (1) Stock volatility is positively correlated with divergent investor opinions;(2) Post sentiment value is positively correlated with post comment volume;(3) Investor sentiment in different sectors affects stock prices to varying degrees.

Left-Tail Momentum of Korean Stock Markets

This study examines the relationship between left-tail risk and stock return in the Korean stock markets and ascertains whether there is a left-tail momentum phenomenon in which stocks with high left-tail risk have lower investment performance than those with low left-tail risk (Atilgan et al., 2020). The results reveal that left-tail risk has a significant negative relationship with stock returns for future holding periods and that the left-tail momentum is strongly observed in the Korean stock markets. The excess return of the Fama-French 3-factor model of the decile portfolio-based (H-L) zero-cost investment strategy using left-tail risk has a significant value of -0.67% to -0.73% per month on average. These results are robust even after controlling for changes in the left-tail risk measures or key related variables that could affect the left-tail momentum. In addition, the left-tail momentum has explanatory power regarding the fluctuations of stock returns independent of Fama-French 3-factors.

Population dynamics of the geographically defined metapopulations of brown shrimp Metapenaeus monoceros (Fabricius, 1798) from Indian waters

Population dynamics of the brown shrimp Metapenaeus monoceros (Fabricius, 1798) was studied along the Indian coast using catch and effort data of trawlers from 8 States and 2 Union Territories for the period 2007-2015. Classical length-based stock assessment methods and spatial database of the shrimp catch were used to derive fishing ground related conclusions. Inclusion of spatial data revealed the possibility of existence of metapopulations of M. monoceros in the region. Maximum sustainable yield (MSY) cannot be a reasonable reference point for deciding the fishing pressure in M. monoceros. By analysing stock fluctuations noticed during the study period with varying spawning stock biomass combinations, retention of 40% spawning biomass seems to be a safe option for sustaining M. monoceros production from Indian waters. Based on this, the study recommends reduction of fishing pressure along north-west, south-west and northeast coast by 70, 60 and 70% respectively from the base level of effort in 2017. The study illustrates that the spatial data from fishing ground, can serve as an additional tool to derive management options. The study also emphasises the need for further investigations on the possibilities of metapopulations of M. monoceros which is widely distributed in all agro-climatic zones along the Indian coast. Keywords: Indian coast, Metapopulation, M. monoceros, Spatial study, Stock assessment

The Month Effect in the Baltic and Nordic Stock Markets at Market-Level and Sector-Level

The purpose of this study is to analyze and compare the trends in the expression of the month effect in the Nasdaq Baltic and Nasdaq Nordic stock markets, as well as to examine whether the seasonal stock price fluctuations occur in all industrial sectors of these markets or are specific to certain sectors only. The OMX Baltic Benchmark, OMX Baltic, and OMX Nordic 40 indexes, which reflect the situation in the Baltic and Nordic stock markets, were used in the study to assess seasonality at the market level. To assess the seasonality in separate sectors of the Baltic and Nordic markets, we used sectoral indexes calculated in these markets. The data sample covers the period from January 2, 2004 through December 31, 2019. The methodology of the research used to examine seasonality in daily returns entails estimating a regression with dummies and GARCH (1,1) to capture month effects. Although the results of OLS and GARCH (1,1) analysis were quite different, they proved the existence of the month effect in both the Baltic and Nordic stock markets. The results of the analysis of seasonal fluctuations in stock prices at sector-level evidenced that month effects appear both in Baltic market and Nordic market, which allows earning abnormal returns in particular months for those who invested in stock of certain sectors. Our research has evidenced that trends in the volatility of stock prices in separate months in the Baltic countries are not stable and are characterized by greater instability as compared to the Nordic countries.

Numerical Approaches of Pricing European Options in the Cox-Ross-Rubinstein Models

The Cox-Ross-Rubinstein (CRR) market model is one of the simplest and easiest ways to analyze the option pricing model. CRR has been employed to evaluate a European Option Pricing (call options) model without complex elements, including dividends, stocks, and stock indexes. Instead, it considers only a continuous dividend yield, futures, and currency options. The CRR model is simple but strong enough to describe the general economic intuition behind option pricing and its principal techniques. Also, it gives us basic ideas on how to develop financial products based on current deviations and volatilities. The paper investigates the CRR model using numerical approaches with python code. It provides a practical event using the mathematical model to demonstrate the application of the model in the financial market. First, the paper provides a simple example to figure out the basic concept of the model. Only a two-period binomial model based on the introductory definitions of the call options makes us understand the concept more easily and quickly. Next, we used actual data on Tesla stock fluctuations from the Nasdaq website (See section 3). We developed the python code to make it easier to understand figures, including tables and graphs. The code allows us to visualize and simplify the model and output data. The code analyzes the stock data to evaluate the probability of the stock’s price increasing or decreasing. Then, it used the CRR model to estimate all possible cases for the stock’s prices and investigate the call and put option pricing. The code was based on the introductory code of binomial option pricing, but we improved it to get more information and provide more detailed results from the data. The detailed codes are provided in section 3 of the paper. As a result, we believe the CRR model is a fundamental formula, but the improved python code can suggest a new direction for evaluating the probability and investigating the value of the stocks. Also, we expect to develop the code to extend the Black Scholes Pricing model, increasing the number of periods.

Content and complexity of stakeholders’ mental models of socio-ecological systems

Stakeholders' interactions with environmental resources are influenced by their mental models of the socio-ecological system of the environmental resource. Individual differences in such mental models are particularly important to identify, as diverse mental models may be associated with different behaviour or policy preferences and affect collaborative conservation efforts. In the present work, we explore stakeholders' mental models of a socio-ecological system and assess content and complexity differences across fishing experience levels, migration status, and regions. We mapped Tanzanian fishers' (N = 185) mental models of the drivers of the Nile perch stock fluctuation at Lake Victoria. The findings show that (1) fishers’ mental models were complex and diverse, (2) mental models focused on the causal influence of destructive fishing activities, (3) mental model complexity, but not content, varied across regions, and (4) fishing experience and migration status were not consistently related to mental model complexity or content. These results have important implications for environmental resource management at Lake Victoria.

FinHGNN: A conditional heterogeneous graph learning to address relational attributes for stock predictions

Recent financial studies have shown that spillover effects of some market factors play a significant role in stock fluctuations. Previous studies, however, were incapable of capturing the spillovers of these relational market factors because they relied on a homogeneous graph that condenses these factors into firm node attributes and requires their spillover effects to follow firm relationship instead of themselves. This fact brings up a heterogeneous graph learning problem that requires multiple node types to transport different spillover effects. This study proposes a novel conditional heterogeneous graph neural network (FinHGNN) to capture multiple spillover effects in asset pricing with two uniquely designed mechanisms. First, it presents an efficient way to preserve the connectivity of relational attributes in graph learning, which is achieved by converting relational attributes into node variables to form a heterogeneous graph. Second, a conditional message-passing mechanism is proposed to handle multiple spillover effects simultaneously by messaging conditioned on different types of nodes and node attributes. This study paves the way for addressing the relational attributes in graph learning. Experiments on two real-world datasets demonstrate the advantages of the proposed framework over three classic and four state-of-the-art algorithms, including LSTM, GCN, HGNN, eLSTM, TGC, FinGAT, and AD-GAT.

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

With the rapid development of the global economy and stock market, stock investment has become a common investment method. People’s research on stock forecasting has never stopped. Accurately predicting the dynamic fluctuation of stocks can bring rich investment returns to investors while avoiding investment risks. Machine learning is a relatively important research field in artificial intelligence today, which is mainly used to study how to use machines to simulate human activities. In recent years, with the continuous development of the economy, machine learning under artificial intelligence has developed comprehensively in different fields, and it has been widely used in the field of the financial economy. Machine learning under artificial intelligence is currently widely used in stock market volatility dynamics and related research. This paper applied machine learning to the prediction of the dynamic relationship of Asian stock market volatility and established a model for predicting the dynamic relationship of stock market volatility under machine learning. By using statistical theory, linear support vector machines, generalizable bounds, and other algorithms, it provides the theoretical basis and feasibility analysis for the model. Through investigation and research, this paper found that compared with ordinary forecasting model methods, the stock volatility dynamic trend forecasting model based on machine learning has a relatively complete forecasting effect, and the accuracy of the machine learning forecasting model was up to 52%. The lowest was 39%, the average prediction accuracy was 46.5%, and the accuracy was improved by 16.8%. This showed that the introduction of machine learning prediction models in the dynamic prediction model of Asian stock volatility is relatively successful.

An empirical study on network conversion of stock time series based on STL method.

A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ( S T L) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time series are revealed from the topological characteristics of corresponding symbolic networks. The results show that: (1) both the weighted indegree and outdegree distributions obey the power-law distribution well; (2) fluctuations of stock closing price are revealed by related network topological properties, such as weighting degree, betweenness, pageranks, and clustering coefficient; and (3) stock closing price, in particular, periods such as financial crises, can be identified by modularity class of the symbolic networks. Moreover, the comparison between the S T L method and the visibility graph further highlights the advantages of the S T L method in terms of the time complexity of the algorithm. Our method offers a new idea to study the network conversion of stock time series.

PENGARUH VOLUME PERDAGANGAN SAHAM TERHADAP RETURN SAHAM DENGAN HARGA SAHAM SEBAGAI VARIABEL INTERVENING PADA PERUSAHAAN TRANSPORTASI YANG TERDAFTAR DI BEI TAHUN 2017-2020

Transportation companies are one of the sectors related to the development and economic development of countries in the world. Transportation companies are among the most important companies as the largest contributor to national development, resulting in the country's economy experiencing an increase along with the rise of increasingly sophisticated technology. The number of activities in the transportation services sector also has an impact that results in stock fluctuations. The purpose of this study was to determine the effect of Stock Trading Volume on Stock Returns with Stock Price as an intervening variable in transportation facilities listed on the IDX in 2017-2020. The population of this study is all transportation companies registered on the IDX in 2017-2020 as many as 47 companies. Sampling technique is determined by purposive sampling. Data analysis and hypothesis testing in this study using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The results of direct influence hypothesis test using Smart PLS 3.0 application showed that Stock Trading Volume has a significant negative effect on Stock Price, Stock Trading Volume has a negative but not significant effect on Stock Return, Stock Price has a positive but not significant effect on Stock Return and Stock Trading Volume on Stock Return through Stock Price has a negative but not significant effect.

Stochastic Modelling to Assess External Environmental Drivers of Atlantic Chub Mackerel Population Dynamics

The population dynamics of small and middle-sized pelagic fish are subject to considerable interannual and interdecadal fluctuations in response to fishing pressure and natural factors. However, the impact of environmental forcing on these stocks is not well documented. The Moroccan Atlantic coast is characterized by high environmental variability due to the upwelling phenomenon, resulting in a significant abundance and variation in the catches of small and middle-sized pelagic species. Therefore, understanding the evolution of stock abundance and its relationship with different oceanographic conditions is a key issue for fisheries management. However, because of the limited availability of independent-fishery data along the Moroccan Atlantic coast, there is a lack of knowledge about the population dynamics. The main objective of this study is to test the correlation between the environment conditions and the stock fluctuations trends estimated by a stock assessment model that does not need biological information on growth, reproduction, and length or age structure as input. To achieve this objective, the fishery dynamics are analyzed with a stochastic surplus production model able to assimilate data from surveys and landings for a biomass trend estimation. Then, in a second step, the model outputs are correlated with different environmental (physical and biogeochemical) variables in order to assess the influence of different environmental drivers on population dynamics. This two-step procedure is applied for chub mackerel along the Moroccan coast, where all these available datasets have not been used together before. The analysis performed showed that larger biomass estimates are linked with periods of lower salinity, higher chlorophyll, higher net primary production, higher nutrients, and lower subsurface oxygen, i.e., with an enhanced strength of the upwelling. In particular, acute anomalies of these environmental variables are observed in the southern part presumably corresponding to the wintering area of the species in the region. The results indicate that this is a powerful procedure, although with important limitations, to deepen our understanding of the spatiotemporal relationships between the population and the environment in this area. Moreover, once these relationships have been identified, they could be used to generate a mathematical relationship to simulate future population trends in diverse environmental scenarios.

Size-fractioned zooplankton biomass in the Barents Sea: Spatial patterns and temporal variations during three decades of warming and strong fluctuations of the capelin stock (1989–2020)

Zooplankton biomass has been monitored on joint Norwegian-Russian surveys in late summer and autumn since the 1980s. We report here on zooplankton biomass in three size fractions (<1, 1–2, and > 2 mm in screen mesh opening) obtained with WP-2 plankton net (180 μm mesh size) hauled vertically over the water column from near bottom to the surface for the period 1989–2020. The number of samples (stations) collected each year has been about 100–200, with a total number of 4543 stations for the whole data set. The size composition of zooplankton reflected by the three fractions has shown remarkable stability, with about 50% of biomass contained in the medium fraction (made up largely of Calanus species), about 1/3 in the small fraction (36%), and 16% in the large fraction. The depth integrated biomass was generally larger in basins compared to shallower bank areas. The temporal (interannual) pattern of change was characterized by a marked peak in biomass in 1994 and 1995 with values up to >20 g dry weight (dw) m−2, driven to large extent by the small size fraction. Subsequently the biomass decreased to lower values but with a divergence of relatively high values (10–15 g dw m−2) in the inflow area of Atlantic water in southwest, and low values (2–6 g dw m−2) in the central area. The difference is interpreted to reflect an increase in a second summer generation of Calanus finmarchicus in the Atlantic water and a decrease of C. glacialis in the central area. The zooplankton biomass fluctuated inversely with the biomass of the Barents Sea capelin (Mallotus villosus) stock, reflecting a top-down predation effect. However, biomass was also negatively correlated with temperature of the Atlantic water, suggesting an additional and confounding effect of climate variability and change. The decrease in biomass of the central area used as a forage area by capelin, was associated with a shift to dominance by the small size fraction. This is likely an effect of predation and could be associated with a lower trophic conversion efficiency from phytoplankton to planktivorous fish and higher trophic levels by smaller zooplankton (smaller copepods such as Pseudocalanus and others) compared to the larger Calanus species.

DYNAMIC SPILLOVER EFFECT OF COVID-19 EPIDEMIC ON DRUG INVENTORY IN AFFECTED COUNTRIES — FROM THE PERSPECTIVE OF INVESTOR ANXIETY

BackgroundSince the beginning of 2020, the global coronavirus pandemic in 2019 has lasted for nearly two years. According to the theory of stock market psychology, investors' judgment of stock value is mainly based on the reality of subjective reflection. In the event of unforeseen events, the changes of psychological factors will affect the anxiety of investors, affect the stock market, lead to stock price fluctuations, and affect the relevant listed industries and companies in the stock market. Due to the close relationship between the pharmaceutical industry and infectious diseases, the sudden outbreak and rapid spread of coronavirus disease in 2019 will undoubtedly bring great challenges and opportunities to the international pharmaceutical industry.Research Objects and MethodsAccording to the number of confirmed cases of coronavirus disease in 2019 published by the World Health Organization, the most seriously affected countries are China, the United States, the United Kingdom, India and Brazil. Based on TVP VAR model, impulse response diagrams with different lag stages and different time points are constructed. Using the daily newly confirmed cases and the pharmaceutical stock index of the most affected countries, this paper analyzes the dynamic spillover effect of coronavirus epidemic in 2019 on the pharmaceutical stock market of the most affected countries in the world. At the same time, it analyzes the correlation between investors' psychological state and venture capital level, especially the factors of investors' anxiety. The reliability and validity of QSC scale revised by Chinese Academy of Sciences were analyzed. The total correlation coefficients of venture capital decision-making, emotional anxiety, job stress and job satisfaction were greater than 0.5. The scale has high reliability. This paper takes emotional labor as independent variable and work pressure as dependent variable to explore the impact of emotional labor on work pressure. There was no significant difference between the experimental group (M = 3.250, SD = 0.684) and the control group (M = 3.222, SD = 0.946); There was no significant difference between the experimental group (M = 3.139, SD = 0.858) and the control group (M = 3.361, SD = 0.559); β A positive value indicates that the level of psychological state has a positive and significant impact on drug investment.ResultsThe impulse response diagram results of different lag stages show that the coronavirus disease epidemic in 2019 has a significant positive impact on the returns of pharmaceutical stocks in the UK, China and India. The coronavirus disease epidemic in 2019 mainly had a negative impact on the United States, but its negative impact was short-lived. The coronavirus epidemic in 2019 had a positive impact on Brazil in the early stage and a negative impact in the later stage. The results of impulse response diagrams at different time points show that the impact of the three important time points of the epidemic on the earnings of pharmaceutical stocks in the five countries fluctuates greatly in the early stage and tends to be stable in the later stage.ConclusionThe severity of coronavirus disease epidemic in 2019, as a global public health emergency, caused great panic all over the world, shook the minds of investors and triggered fluctuations in the stock market. The pandemic has had varying degrees of impact on drug inventory returns in the world's most affected countries. However, with the control of Internet public opinion and the stability of investor sentiment, the fluctuation of pharmaceutical stocks caused by coronavirus epidemic in 2019 will decline over time, but in the short term, the impact is significant. Taking the whole pharmaceutical industry as the research object, this paper can not reflect the impact of coronavirus epidemic in 2019 on different market segments of the pharmaceutical industry stock market, so there are some limitations. However, this study can intuitively reflect the impact of coronavirus disease epidemic in 2019 on the pharmaceutical industry. Based on stock market psychology, this study analyzes the economic psychological and behavioral characteristics of the public under the background of coronavirus epidemic in 2019, which will help to improve the emergency mechanism of China's stock market and formulate phased and differentiated economic incentive policies. It provides a theoretical basis for investors to reasonably deal with stock market fluctuations in an emergency.