scholarly journals Empirical Analysis of AH-Shares

2016 ◽  
Vol 4 (4) ◽  
pp. 343-353
Author(s):  
Hongxing Yao ◽  
Kejuan Zhou
Keyword(s):  
Stock Market ◽  
Correlation Coefficient ◽  
Minimum Spanning Tree ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Dynamic Evolution ◽  
Chinese Stock Market ◽  
Coefficient Distribution ◽  
Industrial Sectors ◽  
The Difference

Abstract Recent studies of correlations in Chinese stock market have mainly focused on the static correlations in financial time series, and then we pay great attention to investigate their dynamic evolution of correlations. Our paper reports on topology of 41 AH-shares companies traded on Shanghai and Hong Kong Stock Exchange in Chinese stock market. We apply the concept of minimum spanning tree (MST) and hierarchical tree (HT) to analyze and reveal the dynamic evolution of correlations between different market sectors for the period 2008–2014. From these trees, we can detect that significantly industry clustering effects are in the stock network. We measure the linkage of different companies geared to different industrial sectors. We observe the evolution of AH-shares companies in the stock network based on the moving window technique and investigate the correlations by calculating the correlation coefficient distribution, mean correlation coefficient and mean distance of these companies with time. Therefore, through our analysis, we find that companies working in the same branch of production tend to make up cluster. The results present the difference and similarity between different industry sectors in different time periods.

Reporting human resources in annual reports

2015 ◽  
Vol 23 (3) ◽  
pp. 256-274 ◽  
Author(s):  
Monika Kansal ◽  
Mahesh Joshi
Keyword(s):  
Human Resources ◽  
Stock Exchange ◽  
Statistical Tests ◽  
Annual Reports ◽  
Public And Private ◽  
Content Type ◽  
Industrial Sectors ◽  
Significant Difference ◽  
The Difference ◽  
Training Programmes

Purpose – The purpose of this paper is to investigate the extent of corporate disclosure on human resources (HR) in the annual reports of top performing Indian companies. Design/methodology/approach – The paper explores the extent to which top 82 companies from India present information about HR in their annual reports. This study examines the annual reports of each of the top Indian firms listed on the Bombay stock exchange, using the “content analysis” method. Statistical tests have been performed to analyse the difference between the HR disclosure score across public and private sectors and disclosure variations among various industrial sectors. Findings – In-house training programmes has been noticed to be the favourite item of disclosure followed by safety awards/certifications and statements regarding cordial relations with the employees/unions. A majority of the Indian firms have ignored significant HR issues such as employee welfare fund, maternity/paternity leaves, holiday benefits, employee loans and adopting old age homes, etc. Overall, the study reflects low HR related disclosures. No statistically significant difference has been found between the mean HR disclosure from one industry to another and disclosure practices of the private and the public sector companies. Practical implications – The disclosure pattern of the Indian companies suggests that they only a few companies are concerned about employees’ welfare than the rest. This may motivate a change of the disclosure policy of the rest of the firms who may follow the reporting pattern of the most disclosing ones. Originality/value – This is first study on the disclosure of HR by the Indian corporate sector in the CSR domain with a disclosure analysis for a period of nine years . This research provides new directions for the literature in this area and may promote comparative studies on HR-based studies from different perspectives.

scholarly journals Portfolio Learning Based on Deep Learning

2020 ◽  
Vol 12 (11) ◽  
pp. 202
Author(s):  
Wei Pan ◽  
Jide Li ◽  
Xiaoqiang Li
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Composite Index ◽  
Series Data ◽  
High Dimensional ◽  
Chinese Stock Market ◽  
High Return ◽  
Stock Information

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

scholarly journals Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

scholarly journals Investor Sentiment and Stock Price Crash Risk in the Chinese Stock Market

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Binghui Wu ◽  
Yuanman Cai ◽  
Mengjiao Zhang
Keyword(s):  
Comparative Analysis ◽  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Crash Risk ◽  
Research Approach ◽  
Chinese Stock Market ◽  
The Impact ◽  
A Share

This paper uses the partial least squares method to construct the investor sentiment index in Chinese stock market. The Shanghai Stock Exchange 180 Index and the Shenzhen Stock Exchange 100 Index are used as samples. From the perspectives of holistic sentiment and heterogeneous sentiment, this paper studies the impact of investor sentiment on stock price crash risk. The results show that investor sentiment can significantly affect stock price crash risk in Shanghai and Shenzhen A-share markets, especially in the Shenzhen A-share market no matter from which perspective. And investor pessimism has a greater impact on stock price crash risk in the Shenzhen A-share market from the perspective of heterogeneous sentiment. Compared with the available researches, this paper makes two contributions: (i) the comparative analysis is adopted to discuss the differences between Shanghai and Shenzhen A-share markets, abandoning the research approach that takes the two markets as a whole in existing literature, and (ii) this paper not only studies the impact of investor holistic sentiment on stock price crash risk from a macro perspective, but also adds a more micro heterogeneous sentiment and conducts a comparative analysis.

scholarly journals O SETOR DE CONSUMO CÍCLICO SEGUNDO AÇÕES NEGOCIADAS NA B3: O SEGMENTO DE ELETRODOMÉSTICOS, PRODUTOS DIVERSOS,TECIDOS,VESTUÁRIOE CALÇADOS

2020 ◽  
Vol 3 (4) ◽  
pp. 37-46
Author(s):  
Rafael Gutierres Castanha ◽  
Andreia de Fatima Costa Miranda ◽  
Lucas Alves de Pontes
Keyword(s):  
Stock Market ◽  
Correlation Coefficient ◽  
Financial Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Methodological Approach ◽  
Price Variability ◽  
Pearson’S Correlation Coefficient ◽  
Home Appliances ◽  
The Stability

By analyzing a portion of the Brazilian financial market, according to the daily value of its shares traded on the largest stock exchange in the country, the B3 stock exchange, offering possibilities to understand more clearly the behavior of the stock market according to growth, decrease, and even the stability of the values traded on the stock market in question. Thus, this research presents an analysis using Pearson's correlation coefficient and offers elements to affirm or refute the idea of proximity between companies in the same sector or not. By proposing the application of this methodology in the segment of home appliances, miscellaneous products, and fabrics, clothing and footwear, it is possible to point out how closely these companies are interconnected in terms of stock price variability. Thus, the objective was to observe not only the behavior of the stock price of the companies of the sector in question during a given period, as well as the intensity of variation between the same measures by the correlation coefficient, but also to evaluate the use of this coefficient as a proposal. methodological approach to assess the proximity between the companies. As a result, it was concluded that the largest proximityis between companies of the same segment.

scholarly journals ANALISIS VARIANS MULTIVARIAT TERHADAP RETURN DAN RISIKO PORTOFOLIO YANG DITENTUKAN DENGAN ELTON GRUEBER PADBERG MODEL PADA EMPAT KELOMPOK INDEKS (LQ 45, SRI-KEHATI, JII, DAN ISSI)

2018 ◽  
Vol 4 (1) ◽  
pp. 32-52
Author(s):  
Baiq Nurul Suryawati
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Risk Index ◽  
Risk And Return ◽  
Single Index ◽  
Index Model ◽  
Single Index Model ◽  
Significant Difference ◽  
The Difference ◽  
Individual Index

AbstractThis research emphasizes the difference between risk and return on four group of index, which are LQ 45, SRI KEHATI, JII and ISSI. Test of significance conduct by doing Analysis of Varians Multivariate. The Analysis of Varians Multivariate results more accurate than repeatedly t-test among group. EGP Model mostly explained as Single Index Model in various textbook. Thus, Single Index Model only clarified influence of a Single Market Index for Individual Index, EGP Model use Reward to Volatility (RVOL) for measuring excess return to systematic risk.  The results shows that after 15 years from sharia index introduce in Indonesian Stock Exchange, it shows significant difference between sharia index and conventional stock market. However, LQ 45 shows it persistence as high return high risk index consistently. The Analysis VariansMultivariate also shows SRI KEHATI, as an ethic businesses representative in Indonesian Stock Exchange as a weaker index. SRI KEHATI shows that various group portfolio form by EGP Model could not exceed JII performance. Therefore, it concludes that indexes provide by capital market to facilitise the preference of investor whereas,it is tremendously various. To invest in stock market, investor need to clarify their wants and needs. Whether their wants is to gain more return or to accommodate their risk, and their preferrence to invest in various kind of business or  certain business such as business based on ethic or faith.Keywords: Analysis of Varians Multivariate; Risk and Return; EGP Model; Indexes; LQ 45; Sri-Kehati; Jakarta Islamic Index (JII); and Indeks Saham Syariah Indonesia (ISSI)

scholarly journals Chinese stock market sectoral indices performance in the time of novel coronavirus pandemic

2020 ◽  
Author(s):  
Venus Khim-Sen Liew ◽  
Chin-Hong Puah
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Descriptive Analysis ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Health Care Information ◽  
Stock Market Performance ◽  
Health Care Information Technology ◽  
Novel Coronavirus ◽  
Regulatory Commission

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 sectorial 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.

scholarly journals General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Bilal Ahmed Memon ◽  
Hongxing Yao ◽  
Rabia Tahir
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Spanning Tree ◽  
Oil And Gas ◽  
Minimum Spanning Tree ◽  
Centrality Measures ◽  
Political Event ◽  
Industrial Sectors ◽  
General Elections ◽  
Key Nodes

AbstractTo examine the interdependency and evolution of Pakistan’s stock market, we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange (KSE-100) index. Using the minimum spanning tree network-based method, we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan. Our results reveal a star-like structure after the general elections of 2018 and before those in 2008, and a tree-like structure otherwise. We also highlight key nodes, the presence of different clusters, and compare the differences between the three elections. Additionally, the sectorial centrality measures reveal economic expansion in three industrial sectors—cement, oil and gas, and fertilizers. Moreover, a strong overall intermediary role of the fertilizer sector is observed. The results indicate a structural change in the stock market network due to general elections. Consequently, through this analysis, policy makers can focus on monitoring key nodes around general elections to estimate stock market stability, while local and international investors can form optimal diversification strategies.

The multiscale analysis between stock market time series

2015 ◽  
Vol 26 (06) ◽  
pp. 1550071 ◽  
Author(s):  
Wenbin Shi ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Financial Time ◽  
Cross Correlation Analysis

This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.

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