The Style and Structure of Chinese Stock Market in 2005~2010: Based on Symbolic Principal Component Analysis

2012 ◽  
Author(s):  
Wen Long ◽  
Dingmu Cao
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Component ◽  
Component Analysis ◽  
Chinese Stock Market

scholarly journals Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Haifan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Stock Market ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Chinese Stock Market ◽  
Proposed Model ◽  
Bp Model

We investigate the statistical behaviors of Chinese stock market fluctuations by independent component analysis. The independent component analysis (ICA) method is integrated into the neural network model. The proposed approach uses ICA method to analyze the input data of neural network and can obtain the latent independent components (ICs). After analyzing and removing the IC that represents noise, the rest of ICs are used as the input of neural network. In order to forect the fluctuations of Chinese stock market, the data of Shanghai Composite Index is selected and analyzed, and we compare the forecasting performance of the proposed model with those of common BP model integrating principal component analysis (PCA) and single BP model. Experimental results show that the proposed model outperforms the other two models no matter in relatively small or relatively large sample, and the performance of BP model integrating PCA is closer to that of the proposed model in relatively large sample. Further, the prediction results on the points where the prices fluctuate violently by the above three models relatively deviate from the corresponding real market data.

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

scholarly journals A Hybrid Prediction Model of Kernel Principal Component Analysis, Support Vector Regression and Teaching Learning Based Optimization Techniques

2021 ◽  
pp. 17-25
Author(s):  
Mohammed Siddique ◽  
Tumbanath Samantara ◽  
Siba Prasad Mishra
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Prediction Model ◽  
Stock Price ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Forecasting of stock market is considered as one of the most decisive and critical tasks for the data scientists in financial domain. Stock market is one of exciting and demanding monetary activities for individual investors, and financial analysts. The stock market is an inter-connected important economic international business. Prediction of stock price has become a crucial issue for stock investors and brokers. The stock market is able to influence the day to day life of the common people. The stock price is based on the state of market stability. As the dormant high noises in the data impair the performance, reducing the noise would be competent while constructing the forecasting model. To achieve this task, integration of kernel principal component analysis, support vector machine with teaching learning based optimization algorithm is proposed in this research work. Kernel principal component analysis is able to remove the unnecessary and unrelated factors, and reduces the dimension of input variables and time complexity. The feasibility and efficiency of this proposed hybrid model has been applied to forecast the daily open prices of stock index of a leading Company. The performance of the proposed approach is evaluated with 3543 daily transactional (13th December 2001 to 4th December 2020) stocks price data from Bombay Stock Exchange (BSE). Empirical results show that the proposed model enhances the performance of the prediction model and can be used for taking better decision and more accurate predictions for financial investors.

scholarly journals Constructing Stock Market Performance Index for ASEAN 5 Countries: Principal Component Analysis Approach

2019 ◽  
Vol 2 (1) ◽  
pp. 77-85
Author(s):  
Kelvin Yong Ming Lee
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Performance Index ◽  
Market Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Annual Data ◽  
Sample Period ◽  
Stock Market Performance ◽  
Year 2000

This study develops the stock market performance index (SMPI) for ASEAN-5 countries, which include Indonesia, Malaysia, Thailand, Philippines, and Singapore. Along with that, principal component analysis is applied in developing the index. Annual data of ASEAN-5 countries ranging from the year 2000-2016 has been used for the purpose of analysis. The sources of data are the World Bank Database and Datastream. The results indicate that Singapore has the highest SMPI over the sample period, while Indonesia has the lowest SMPI over the sample period 2000 to 2016.

Do sukuk spur infrastructure development?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Houcem Smaoui ◽  
Karim Mimouni ◽  
Ines Ben Salah
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Large Scale ◽  
Banking System ◽  
Principal Component ◽  
Component Analysis ◽  
Market Development ◽  
Infrastructure Development ◽  
Content Type ◽  
Market Expansion

Purpose This paper aims to examine the effect do Sukuk Spur Infrastructure Development of Sukuk market expansion on infrastructure development for a sample of 15 emerging countries over the period 1997–2018. The paper also compares the role of Sukuk in infrastructure development to that of the size of the banking system, bond market development and stock market development. Design/methodology/approach A novel index of infrastructure development is constructed via principal component analysis. This index is regressed on Sukuk market development and other macroeconomic and institutional variables. To tackle the problems of heteroscedasticity and the existence of serial correlation in the residuals, the panel model is estimated using the generalized least squares (GLS) procedure with random effects and robust standard errors. Findings The evidence shows that a well-developed Sukuk market contributes to the expansion of the country’s infrastructure, whereas a larger banking system and a better capitalized stock market do not have any significant effect on infrastructure development. Surprisingly, well-developed bond markets jeopardize infrastructure expansion, thereby pointing to a potential crowding-out effect between Sukuk and bonds in financing infrastructure investments. Additionally, per capita GDP and education are positively related to infrastructure development, whereas inflation has a negative effect on the country’s proliferation of infrastructure. Originality/value This study uses a novel infrastructure index via principal component analysis and shows that Sukuk markets fill an important gap in the financing of large-scale and long-term projects. This result is novel and has not been documented in previous research.

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