Examining Systematic Risk with Principal Component Analysis: An Empirical Analysis in Hong Kong Stock Market

2017 ◽  
Vol 12 (2) ◽  
pp. 1-13
Author(s):  
Raymond Kwong ◽  
Ricky K.L. Mak
Keyword(s):  
Principal Component Analysis ◽  
Hong Kong ◽  
Stock Market ◽  
Empirical Analysis ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Hong Kong Stock Market

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.

scholarly journals Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

2021 ◽  
Vol 13 (2) ◽  
pp. 513-543
Author(s):  
Rogelio ◽  
Salvador Torra Porras ◽  
Enric Monte Moreno
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Stock Exchange ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Computational Techniques ◽  
Arbitrage Pricing Theory ◽  
Pricing Theory ◽  
Systematic Risk Factors

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.

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