Stock Indices and New Technologies

Stock indices were created to accurately reflect changes in the prices of stock exchange securities included in them.Indices are divided into price-weighted and value-weighted indices.In price-weighted indices,each security has the same impact on the index,which is the arithmetic mean of the stock values of the companies it comprises.A value-weighted index accounts for changes in the value of shareholdings.By choosing the right composition of share packages,one can change the impact of a given security on the index.Market dynamics also change the composition of the index by removing securities and replacing them with others.The impact on the index grows as the price of a given security increases.Recently,index values have been strongly influenced by new technology companies,which have greatly appreciated during the SARS-CoV-2 pandemic.The aim of this work is to show the changes in two indices in particular:US NASDAQ Composite,and the new competitive SSE STAR index,an index from the Shanghai Stock Exchange.

Spatial dynamics of aboveground carbon stock in urban green space:a case study of Xi'an,China

Greenhouse gas emission of carbon dioxide（CO2） is one of the major factors causing global climate change.Urban green space plays a key role in regulating the global carbon cycle and reducing atmospheric CO2.Quantifying the carbon stock,distribution and change of urban green space is vital to understanding the role of urban green space in the urban environment.Remote sensing is a valuable and effective tool for monitoring and estimating aboveground carbon（AGC） stock in large areas.In the present study,different remotely-sensed vegetation indices（VIs） were used to develop a regression equation between VI and AGC stock of urban green space,and the best fit model was then used to estimate the AGC stock of urban green space within the beltways of Xi＇an city for the years 2004 and 2010.A map of changes in the spatial distribution patterns of AGC stock was plotted and the possible causes of these changes were analyzed.Results showed that Normalized Difference Vegetation Index（NDVI） correlated moderately well with AGC stock in urban green space.The Difference Vegetation Index（DVI）,Ratio Vegetation Index（RVI）,Soil Adjusted Vegetation Index（SAVI）,Modified Soil Adjusted Vegetation Index（MSAVI） and Renormalized Difference Vegetative Index（RDVI） were lower correlation coefficients than NDVI.The AGC stock in the urban green space of Xi＇an in 2004 and 2010 was 73,843 and 126,621 t,respectively,with an average annual growth of 8,796 t and an average annual growth rate of 11.9%.The carbon densities in 2004 and 2010 were 1.62 and 2.77 t/hm2,respectively.Precipitation was not an important factor to influence the changes of AGC stock in the urban green space of Xi＇an.Policy orientation,major ecological greening projects such as ＂transplanting big trees into the city＂ and the World Horticultural Exposition were found to have an important impact on changes in the spatiotemporal patterns of AGC stock.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

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.

Effect of Distributional Assumption on GARCH Model into Shenzhen Stock Market： a Forecasting Evaluation

This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.