• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.317-327
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• 2024

Along with the rapid development of the database technology, as well as the widespread application of the database management systems are more and more large. Now the data mining technology has already been applied in scientific research, financial investment, market marketing, insurance and medical health and so on, and obtains widespread application. We discuss data mining technology and analyze the questions of it. Therefore, the research in a new data mining method has important significance. Some literatures did not consider the differences between attributes, leading to redundancy when constructing concept lattices. The paper proposes a new method of uncertain data mining based on the concept lattice of connotation difference degree (c_diff). The method defines the two rules. The construction of a concept lattice can be accelerated by excluding attributes with poor discriminative power from the process. There is also a new technique of calculating c_diff, which does not scan the full database on each layer, therefore reducing the number of database scans. The experimental outcomes present that the proposed method can save considerable time and improve the accuracy of the data mining compared with U-Apriori algorithm.

• Nonlinear Functional Analysis and Applications
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• v.29 no.3
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• pp.649-672
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• 2024

This paper explores the significance and implications of fixed point results related to orbital contraction as a novel form of contraction in various fields. Theoretical developments and theorems provide a solid foundation for understanding and utilizing the properties of orbital contraction, showcasing its efficacy through numerous examples and establishing stability and convergence properties. The application of orbital contraction in control systems proves valuable in designing resilient and robust control strategies, ensuring reliable performance even in the presence of disturbances and uncertainties. In the realm of financial modeling, the application of fixed point results offers valuable insights into market dynamics, enabling accurate price predictions and facilitating informed investment decisions. The practical implications of fixed point results related to orbital contraction are substantiated through empirical evidence, numerical simulations, and real-world data analysis. The ability to identify and leverage fixed points grants stability, convergence, and optimal system performance across diverse applications.

• The Journal of Economics, Marketing and Management
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• v.12 no.5
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• pp.91-103
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• 2024

Purpose: This study analyses the key variables that influence tourists' intention to re-participate in wellness tourism. To this end, a theoretical model is developed that is grounded in the theories of perceived value and perceived risk. Additionally, this study segments the market based on tourists' health consciousness and health status, examining the differences in the process of forming re-participation intentions. Research Design, Data, and Methodology: An online survey of 305 Japanese respondents was conducted, and the research model and hypotheses were validated using SmartPLS 4 and SPSS. Results: The findings illustrate that perceived functional, social, emotional, and epistemic values from previous wellness tourism experiences positively influence tourists' attitudes, whereas time risk negatively affects them. Furthermore, functional value and attitudes enhance re-participation intentions, whereas financial risk decrease them. Cluster analysis identified three groups: 'Health-Conscious but Unwell'; 'Not Health-Conscious and Unwell'; and 'Health-Conscious and Well'. Those who are 'Health-Conscious and Well' are more likely to re-participate if they are satisfied with the functional value of their wellness tourism experience. Conclusions: The findings of this study offer destination marketers and service providers valuable insights into how tourists form behavioural intentions and how to strategically allocate resources to maximise the potential of wellness tourism.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.181-205
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• 2024

Purpose - This study aims to examine the direct impact of corporate social responsibility initiatives on firm technological innovation and the moderating effect on the relationship between firm technological innovation and corporate value. Design/methodology/approach - This study collected 13,298 firm-year data by selecting A-share companies listed on the China Shenzhen Stock Exchange and Shanghai Stock Exchange from 2010-2017. This study runs the multivariate regression using random effect generalized least squares (GLS) regression model. Findings - The research results of this study are as follows. First, corporate social responsibility initiatives do not increase the firm technological innovation, but rather reduce it. Second, firm technological innovation generally improves corporate value, whether it is book value or market value. Third, corporate social responsibility initiatives reduce the positive influence of firm technological innovation on corporate value. Research implications or Originality - There may be discussions on whether Chinese patent application data is a good indicator of the innovation of Chinese companies, but previous studies prove that the number of patent applications has a significant correlation with R&D expenditures or financial performance. However, there is a clear limitation in that it is not possible to confirm the result of registration after a patent application, but it is expected that such limitations can be overcome by using patent registration information or detailed citation documents in the future.

• The Journal of Economics, Marketing and Management
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• v.12 no.4
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• pp.13-25
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• 2024

Purpose: Korea's construction industry has faced declining productivity and quality issues due to labor-intensive onsite construction and variables like weather, material price fluctuations, and labor shortages. The modular housing industry, introduced in Korea in 2003, offered benefits like reduced construction time and enhanced productivity through offsite manufacturing. However, its adoption remains limited due to high costs, quality concerns, and low consumer acceptance. Research Design, Data, and Methodology: This study explores the feasibility and impact of implementing smart factory technologies in the modular housing industry to overcome these barriers. Using survey data from 179 construction industry experts, the study employs frequency and regression analysis to identify key factors influencing the adoption of modular housing and the effectiveness of smart factories. Findings suggest that government-led educational programs and strong policy support are essential for successful implementation, enhancing productivity, reducing costs, and improving quality. Conclusions: The study emphasizes the need for standardization of modular housing, deregulation of relevant laws, and increased public awareness to stimulate market growth and innovation. Policy recommendations include financial support for modular manufacturers transitioning to smart factories, ensuring stable supply volumes, and promoting the benefits of modular housing to consumers. Integrating smart factory technologies can lead to significant advancements in the modular housing industry, contributing to the sustainable development and modernization of Korea's construction sector.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.1-20
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• 2023

This study attempted to identify the relationship between the investment determinants of accelerators and investment performance through empirical analysis. Through literature review, four dimensions and 12 measurement items were extracted for investment determinants, which are independent variables, and investment performance was adjusted to the cumulative amount of subsequent investment based on previous studies. Performance data from 594 companies selected by TIPS from 2017 to 2019, which are relatively reliable and easy to secure data, were collected, and the subsequent investment cumulative attraction amount, which is a dependent variable, was hypothesized through multiple regression analysis three years after the investment. As a result of the study, 'industrial experience years' in the characteristics of founders, 'market size', 'market growth', 'competitive strength', and 'number of patents' in the characteristics of products and services had a significant positive (+) effect. The impact of independent variables on dependent variables was most influenced by the competitive strength of market characteristics, followed by the number of years of industrial experience, the number of patents, the size of the market, and market growth. This was different from the results of previous studies conducted mainly on qualitative research methods, and in most previous studies, the characteristics of founders were the most important, but the empirical analysis results were market characteristics. As a sub-factor, the intensity of competition, which was the subordinate to the importance of previous studies, had the greatest influence in empirical analysis. The academic significance of this study is that it presented a specific methodology to collect and build 594 empirical samples in the absence of empirical research on accelerator investment determinants, and created an opportunity to expand the theoretical discussion of investment determinants through causal research. In practice, the information asymmetry and uncertainty of startups that accelerators have can help them make effective investment decisions by establishing a systematic model of experience-dependent investment determinants.

• Korean Journal of Construction Engineering and Management
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• v.15 no.1
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• pp.39-50
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• 2014

After the global financial crisis, domestic construction industry has gone through a rapid recession. This resulted in gradual market shift towards architectural remodeling. Architectural remodeling not only improves residential environment but it has many advantages such as increase of each unit's exclusive area, free space within the horizontal or extension of an annex building, and increase number of household through splitting the household of bigger pyeong, etc. However, in case of the Korean market for apartment remodeling, due to various regulations and problem with business promotion procedures, majority of business is slow despite the figure that remodeling volume is not that small. Also, feasibility study which decides to push ahead public house remodeling business will have a flaw using net present value's law; it has a flaw of not considering properties of each phase of remodeling business and future's uncertainty. Hence, this research will improve the problem of traditional value assessment method of net present value's law. It will also consider one of the real options such as binomial model in order to supplement NPV which is used in current feasibility study. This research was based on real successful cases of public house remodeling and it was possible for feasibility study which was more realistic and valid. This research provided foundation for development of Korean public house remodeling market. There is high anticipation of increasing the validity by improving the problems of current feasibility study and economic efficiency assessment.

• Management & Information Systems Review
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• v.12
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• pp.137-160
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• 2003

The association between accounting earnings and the stock price of an entity is the subject that has been most heavily researched during the past 25 years in accounting literature. Researcher's common finding is that there are positive relationships between accounting earnings and stock prices. However, the explanatory power of accounting earnings which was measured by $R^2$ of regression functions used was rather low. To be connected with these low results, The prior studies propose that there will be additional information, errors in variables. This study investigates empirically determinants of earnings response coefficients(ERCs), which measure the correlation between earnings and stock prices, using earnings level / change, as the dependent variable in the return/earnings regression. Specifically, the thesis tests whether the factors such as earnings persistence, growth, systematic risk, image, information asymmetry and firm size. specially, the determinable variables of ERC are explained in detail. The image / information asymmetry variables are selected to be connected with additional information stand point, The debt / growth variables are selected to be connected with errors in variables. In this study, The sample of firms, listed in Korean Stock Exchange was drawn from the KIS-DATA and was required to meet the following criteria: (1) Annual accounting earnings were available over the 1986-1999 period on the KIS-FAS to allow computation of variables parameter; (2) sufficient return data for estimation of market model parameters were available on the KIS-SMAT month returns: (3) each firm had a fiscal year ending in December throughout the study period. Implementation of these criteria yielded a sample of 1,141 firm-year observation over the 10-year(1990-1999) period. A conventional regression specification would use stock returns(abnormal returns) as a dependent variable and accounting earnings(unexpected earnings) changes interacted with other factors as independent variables. In this study, I examined the relation between other factors and the RRC by using reverse regression. For an empirical test, eight hypotheses(including six lower-hypotheses) were tested. The results of the performed empirical analysis can be summarized as follows; The first, The relationship between persistence of earnings and ERC have significance of each by itself, this result accord with one of the prior studies. The second, The relationship between growth and ERC have not significance. The third, The relationship between image and ERC have significance of each by itself, but a forecast code doesn't present. This fact shows that image cost does not effect on market management share, is used to prevent market occupancy decrease. The fourth, The relationship between information asymmetry variable and ERC have significance of each by. The fifth, The relationship between systematic risk$(\beta)$ and ERC have not significance. The sixth, The relationship between debt ratio and ERC have significance of each by itself, but a forecast code doesn't present. This fact is judged that it is due to the effect of financial leverage effect and a tendency of interest.