Relevant bibliographies by topics / Shanghai Securities Composite Índice

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Shanghai Securities Composite Índice'

Author: Grafiati
Published: 11 September 2021
Last updated: 12 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Shanghai Securities Composite Índice.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Shanghai Securities Composite Índice"

1

Wang, Rui Zhong. "Analysis of the Association for Shanghai Composite Index and Stock Index Futures." Applied Mechanics and Materials 644-650 (September 2014): 5672–75. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.5672.

Full text
Abstract:
In this paper, data mining association rules algorithms and techniques for relevance Shanghai CSI 300 Shanghai Financial Futures Exchange and the Shanghai Stock Index Futures Stock Exchange Composite Index were analyzed. The results show that the futures contracts and price movements highly positive correlation exists. The author believes that between the two since it is highly positive relationship, IF way of trading and settlement transactions should be fully consistent with the way the Shanghai Stock Exchange and deliver company's stock. Thus, equal opportunity traders in futures contracts and stock traders, more conducive to the development of China's securities market.
APA, Harvard, Vancouver, ISO, and other styles
2

Guan, Dahong. "Study on Relativity between China’s Nominal GDP and the Shanghai Securities Composite Index." Journal of Service Science and Management 11, no. 05 (2018): 527–42. http://dx.doi.org/10.4236/jssm.2018.115036.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Hongming. "The Impact of BW Emotional Index on China's A - Share Market Returns." Journal of Finance Research 1, no. 1 (October 16, 2017): 14. http://dx.doi.org/10.26549/jfr.v1i1.381.

Full text
Abstract:
Investor sentiment has its characteristics of the inherent complexity and changing. In this paper, through the analysis of the "investor sentiment and cross-sectional data on the impact of stock returns"[1] published by Baker and others in 2006, combining with the specific situation of China's securities market, and basing on the BW model to select Shanghai and Shenzhen 300 index turnover rate and other 5 emotional indices, and using the principal component analysis to build a monthly investor sentiment index of China's securities market. The principal component analysis of residual that calculated by a regression of emotional indicators and macroeconomic data is carried out, to get macroscopical emotional indicators that removes macro factors. Finally, OLS regression analysis is carried out with the Shanghai Composite Index and Shenzhen Component Index to find that the constructed emotional index has a significant effect on the yield of China's stock market, thus verifying the validity of the emotional index.
APA, Harvard, Vancouver, ISO, and other styles
4

吴, 仍康. "Forecast Analysis of Securities Index Based on Ridge Regression—In Case of Shanghai Composite Index." Business and Globalization 04, no. 02 (2016): 47–55. http://dx.doi.org/10.12677/bglo.2016.42007.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Liew, Venus Khim-Sen, and Chin-Hong Puah. "Performance of Shanghai Composite Index and Sector Indices in The Beginning of Novel Coronavirus Pandemic." Asian Journal of Finance & Accounting 13, no. 1 (June 10, 2021): 1. http://dx.doi.org/10.5296/ajfa.v13i1.18704.

Full text
Abstract:
This paper aims to quantify the effect of the deadly novel coronavirus (COVID-19) pandemic outbreak on Chinese stock market performance. Shanghai Stock Exchange Composite Index and its component sector indices are examined in this study. The pandemic is represented by a lockdown dummy, new COVID-19 cases and a dummy for 3 February 2020. First, descriptive analysis is performed on these indices to compare their performances before and during the lockdown period. Next, regression analysis with Exponential Generalized Autoregressive Conditional Heteroscedasticity specification is estimated to quantify the pandemic effect on the Chinese stock market. This paper finds that health care, information technology and telecommunication services sectors were relatively more pandemic-resistant, while other sectors were more severely hurt by the pandemic outbreak. The extent to which each sector was affected by pandemic and sentiments in other financial and commodity markets were reported in details in this paper. The findings of this paper are resourceful for investors to avoid huge loss amid pandemic outburst and the China Securities Regulatory Commission in handling future pandemic occurrence to cool down excessive market sentiments.
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Baogen, Guosheng Han, Shan Jiang, and Zuguo Yu. "Composite Multiscale Partial Cross-Sample Entropy Analysis for Quantifying Intrinsic Similarity of Two Time Series Affected by Common External Factors." Entropy 22, no. 9 (September 8, 2020): 1003. http://dx.doi.org/10.3390/e22091003.

Full text
Abstract:
In this paper, we propose a new cross-sample entropy, namely the composite multiscale partial cross-sample entropy (CMPCSE), for quantifying the intrinsic similarity of two time series affected by common external factors. First, in order to test the validity of CMPCSE, we apply it to three sets of artificial data. Experimental results show that CMPCSE can accurately measure the intrinsic cross-sample entropy of two simultaneously recorded time series by removing the effects from the third time series. Then CMPCSE is employed to investigate the partial cross-sample entropy of Shanghai securities composite index (SSEC) and Shenzhen Stock Exchange Component Index (SZSE) by eliminating the effect of Hang Seng Index (HSI). Compared with the composite multiscale cross-sample entropy, the results obtained by CMPCSE show that SSEC and SZSE have stronger similarity. We believe that CMPCSE is an effective tool to study intrinsic similarity of two time series.
APA, Harvard, Vancouver, ISO, and other styles
7

Zheng, Hongying, Zhiqiang Zhou, and Jianyong Chen. "RLSTM: A New Framework of Stock Prediction by Using Random Noise for Overfitting Prevention." Computational Intelligence and Neuroscience 2021 (May 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/8865816.

Full text
Abstract:
An accurate prediction of stock market index is important for investors to reduce financial risk. Although quite a number of deep learning methods have been developed for the stock prediction, some fundamental problems, such as weak generalization ability and overfitting in training, need to be solved. In this paper, a new deep learning model named Random Long Short-Term Memory (RLSTM) is proposed to get a better predicting result. RLSTM includes prediction module, prevention module, and three full connection layers. Input of the prediction module is a stock or an index which needs to be predicted. That of the prevention module is a random number series. With the index of Shanghai Securities Composite Index (SSEC) and Standard & Poor’s 500 (S&P500), simulations show that the proposed RLSTM can mitigate the overfitting and outperform others in accuracy of prediction.
APA, Harvard, Vancouver, ISO, and other styles
8

Zeng, Xin, Yuanyuan Ju, and Liucang Wu. "Statistical measure researches for the impacts of COVID-19 on Shanghai Securities Composite Index: based on mode regression model using skew-normal distribution." Journal of Physics: Conference Series 1883, no. 1 (April 1, 2021): 012041. http://dx.doi.org/10.1088/1742-6596/1883/1/012041.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Aguilar Córdova, Alfredo. "LAS BETAS CALCULADAS, LOS DILEMAS EN SU USO Y EL IMPACTO EN EL CAPM." Quipukamayoc 25, no. 47 (September 11, 2017): 123. http://dx.doi.org/10.15381/quipu.v25i47.13810.

Full text
Abstract:
La investigación permitió demostrar que las betas calculadas ofrecen diferentes valores dependiendo de la serie histórica de datos seleccionado por el analista, se observan resultados antagónicos con betas del activo más riesgosas que el mercado, pero al ampliar la selección de los datos histórica se convierten en betas menos riesgosas que el mercado en más del 50% de los casos observados. Los resultados confirman lo señalado por algunos investigadores en el pasado, la beta calculada con datos históricos no es una buena aproximación a la beta de la empresa. Se comprobó el impacto de la beta en el Costo del Capital a través del modelo CAPM en Estados Unidos debido a que existen múltiples betas dependiendo de la serie histórica de datos a tomar. Para tal fin, se ha realizado el cálculo de las betas a través de una regresión lineal utilizando algunas empresas cotizadas en los mercados asiáticos a través de la elección del índice Shanghai Composite, el europeo a través de la elección del índice IBEX 35 y el Norteamericano a través del índice Dow Jones, considerando para todos ellos una frecuencia diaria de los rendimientos y seleccionado una serie histórica de 3 años, 6 meses y 3 meses.
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Fang, Sai Tang, and Menggang Li. "Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market." Complexity 2021 (May 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6641298.

Full text
Abstract:
With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Shanghai Securities Composite Índice"

1

Yongzhe, Zhao. "Analysis of the relationship between the sentiment of retail investors and the performance of the chinese stock market." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20843.

Full text
Abstract:
Mestrado em Finanças
Ao contrário dos mercados de ações em países desenvolvidos, os mercados de ações chineses são principalmente composto por investidores de varejo. O comportamento do investimento no varejo é suscetível a emoções, que pode afetar o desempenho dos mercados de ações. Ao estudar a relação entre o dois tipos de mercados de ações, os investidores de varejo podem aumentar sua consciência do risco e investimento racional e a regulamentação dos mercados de capitais chineses também podem ser desenvolvidos de forma mais científica e saudável. Neste artigo, o método de computação afetiva é usado para quantificar o sentimento dos investidores de varejo registrados na Bolsa de Valores de Xangai. Então, a série temporal de sentimento de varejo, o preço de fechamento dos Valores de Xangai Índice Composto e o volume total de negociação da Bolsa de Valores de Xangai são organizado para análise e avaliado por meio de três métodos de análise, o modelo VAR, Correlação de Pearson e TLCC. As conclusões tiradas deste estudo são as seguintes: (i) Não há relação causal entre o sentimento dos investidores de varejo e o fechamento preço do Shanghai Securities Composite Index. (ii) Existe uma relação causal entre o sentimento do investidor de varejo e o volume total de negociação das Ações de Xangai Troca. (Iii) Há uma influência de defasagem mútua e forte correlação entre o sentimento dos investidores de varejo e a taxa de mudança do Shanghai Securities Composite Índice.
Unlike stock markets in developed countries, Chinese stock markets are mainly composed of retail investors. Retail investment behavior is susceptible to emotions, which can affect the performance of stock markets. By studying the relationship between the two types of stock markets, retail investors can increase their awareness of risk and rational investment, and the regulation of Chinese capital markets can also be developed more scientifically and healthily. In this paper, the affective computing method is used to quantify the sentiment of retail investors registered on the Shanghai Stock Exchange. Then, the retail sentiment time series, the closing price of the Shanghai Securities Composite Index, and the total trading volume of the Shanghai Stock Exchange are organized for analysis and assessed through three analysis methods, the VAR model, Pearson correlation, and TLCC. The conclusions drawn from this study are as follows: (i) There is no causal relationship between the sentiment of retail investors and the closing price of the Shanghai Securities Composite Index. (ii) There is a causal relationship between retail investor sentiment and the total trading volume of the Shanghai Stock Exchange. (iii) There is a mutual lag influence and strong correlation between the sentiment of retail investors and the changing rate of the Shanghai Securities Composite Index.
info:eu-repo/semantics/publishedVersion
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Weichen, and 陳威蓁. "The Study on Correlation and Hedge Effect among China Shanghai Securities Composite Index , Hong Kong Hang Seng China Enterprises Index and Hong Kong Hang Seng China Enterprises Index Future-The Application of Major Effect, VEC DCC GJR-GARCH Model and." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/21332064204087732303.

Full text
Abstract:
碩士
國立臺北大學
國際企業研究所
99
This study investigates the correlations among China Shanghai Securities Composite Index(SSCI), Hong Kong Hang Seng China Enterprises Index(HSCEI) and its Futures(HSIF) under the crisis of subprime mortgage and financial tsunami by using VEC DCC GJR-GARCH Model and VEC Copula GJR-GARCH Skewed-t Model. It also discusses the contagion effects of the crisis of subprime mortgage and financial tsunami on the China Shanghai Securities Composite index, Hong Kong Hang Seng China Enterprises Index and its Futures. The sample period of this study is from December 8, 2003 to February 28, 2011. The empirical results obtaining from the VEC DCC GJR-GARCH model verify that during the crisis of subprime mortgage and financial tsunami period, the correlation coefficients between SSCI- HCEI,SSCI- HSIF and HCEI-HSIF have increased. The results also indicated that the return and volatility correlation of these three markets are affected by the crisis of subprime mortgage and financial tsunami(contagion effect), rather than simply cross-market information transmission through the volatility spillovers between any two markets as mentioned above. Moreover, the estimated results signify that the hedge ratio and hedge performance of HSIF to their cash markets have increased during the subprime mortgage and financial tsunami period. The strategies effects of direct hedge are more than that of indirect hedge. In addition, the VEC Copula GJR-GARCH skewed-t model signifies the highly tail-dependency structure between SSCI-HCEI and SSCI-HSIF, and the double tail- dependency between HSCEI-HSIF. We also found that the market dependency between those any two markets have increased during the period of subprime mortgage crisis and financial tsunami. The hedge ratio and hedge performance estimated by this Copula Model are higher than those estimated in the VEC DCC GJR-GARCH Model.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Shanghai Securities Composite Índice"

1

Luo, Dancheng, and Yaqi Xue. "Research on the GARCH model of the Shanghai Securities Composite Index." In International Academic Workshop on Social Science (IAW-SC-13). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/iaw-sc.2013.35.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography