The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related field...The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.展开更多
Finding the best model to predict the trend of stock prices is an issue that has always garnered attention,and it is also closely related to investors’investment dynamics.Even the commonly used autoregressive integra...Finding the best model to predict the trend of stock prices is an issue that has always garnered attention,and it is also closely related to investors’investment dynamics.Even the commonly used autoregressive integrated moving average(ARIMA),extreme gradient boosting(XGBoost),and long short-term memory(LSTM)have their own advantages and disadvantages.We use mean squared error(MSE)to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve.However,the LSTM model still needs to improve in terms of performance so as to reduce the bias.We anticipate the discovery of more models that are apt for predicting stocks in the future.展开更多
Single-layer MoS_(2)produced by mechanical exfoliation is usually connected to thicker and multilayer regions.We show a facile laser trimming method to insulate single-layer MoS_(2)regions from thicker ones.We demonst...Single-layer MoS_(2)produced by mechanical exfoliation is usually connected to thicker and multilayer regions.We show a facile laser trimming method to insulate single-layer MoS_(2)regions from thicker ones.We demonstrate,through electrical characterization,that the laser trimming method can be used to pattern single-layer MoS_(2)channels with regular geometry and electrically disconnected from the thicker areas.Scanning photocurrent microscope further confirms that in the as-deposited flake(connected to a multilayer area)most of the photocurrent is being generated in the thicker flake region.After laser trimming,scanning photocurrent microscopy shows how only the single-layer MoS_(2)region contributes to the photocurrent generation.The presented method is a direct-write and lithography-free(no need of resist or wet chemicals)alternative to reactive ion etching process to pattern the flakes that can be easily adopted by many research groups fabricating devices with MoS_(2) and similar twodimensional materials.展开更多
文摘The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.
文摘Finding the best model to predict the trend of stock prices is an issue that has always garnered attention,and it is also closely related to investors’investment dynamics.Even the commonly used autoregressive integrated moving average(ARIMA),extreme gradient boosting(XGBoost),and long short-term memory(LSTM)have their own advantages and disadvantages.We use mean squared error(MSE)to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve.However,the LSTM model still needs to improve in terms of performance so as to reduce the bias.We anticipate the discovery of more models that are apt for predicting stocks in the future.
基金supported by the National Natural Science Foundation of China(62174059,52250281 and 91963102)the Hong Kong Research Grant Council(15300619)+3 种基金the Science and Technology Projects in Guangzhou(202201000008)Guangdong Science and Technology Project-International Cooperation(2021A0505030064)Guangdong Provincial Key Laboratory of Optical Information Materials and Technology(2017B030301007)the Joint Funds of Basic and Applied Basic Research Foundation of Guangdong Province(2019A1515110605)。
基金Financial supports from the National Natural Science Foundation of China(NSFC)(Nos.62011530438 and 61704129)supported by the Key Research and Development Program of Shaanxi(No.2021KW-02),the fundamental Research Funds for the Central Universities(No.JB211409 and 20109215605)+6 种基金the fund of the State Key Laboratory of Solidification Processing in Northwestern Polytechnical University(No.SKLSP201612)funding by European Research Council(ERC)through the project 2D-TOPSENSE(GA 755655)European Union's Horizon 2020 research and innovation program(Graphene Core2-Graphene-based disruptive technologies(No.881603)Graphene Core3-Graphene-based disruptive technologies(No.956813))EU FLAG-ERA through the project To2Dox(No.JTC-2019-009)the Comunidad de Madrid through the project CAIRO-CM project(No.Y2020/NMT-6661)the Spanish Ministry of Science and Innovation through the project(No.PID2020-118078RB-I00).O.Ç.acknowledges the European Union's Horizon 2020 research and innovation program under the grant agreement 956813(2Exciting).S.P.acknowledges the fellowship PRE2018-084818.
文摘Single-layer MoS_(2)produced by mechanical exfoliation is usually connected to thicker and multilayer regions.We show a facile laser trimming method to insulate single-layer MoS_(2)regions from thicker ones.We demonstrate,through electrical characterization,that the laser trimming method can be used to pattern single-layer MoS_(2)channels with regular geometry and electrically disconnected from the thicker areas.Scanning photocurrent microscope further confirms that in the as-deposited flake(connected to a multilayer area)most of the photocurrent is being generated in the thicker flake region.After laser trimming,scanning photocurrent microscopy shows how only the single-layer MoS_(2)region contributes to the photocurrent generation.The presented method is a direct-write and lithography-free(no need of resist or wet chemicals)alternative to reactive ion etching process to pattern the flakes that can be easily adopted by many research groups fabricating devices with MoS_(2) and similar twodimensional materials.
