• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.175-192
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• 2021

Purpose - In this study, we investigate the effects of government environmental subsidies and the globalization Chinese firms on their profitability using return on assets (ROA). Design/methodology/approach - In this study, a merged data including accounting, financial market, subsidization of the Chinese governments, local and the central, and export activities of 19,563 year-firms, for those listed on Shanghai Stock and Shenzhen Stock Exchange for 11 years from 2008 to 2018 is used. We collect subsidy data from RESSET database and financial data from CSMAR database. Then, we empirically test the test hypotheses using fixed effects models (FEM) separately and in a simultaneous equation model (SEM). Findings - Firstly, the globalization of Chinese firms has a negative impact on their profitability for some years after the year. Secondly, environmental subsidies just like other subsidies have ameliorating effects on financial performance for global firms. Such effects have lasted some years. Thirdly, environmental investments have a mostly negative impact on short- and long-term profitability for global firms. Lastly, the government's environmental subsidies in China have a positive effect on their profitability for both global and domestic firms. Research implications or Originality - We can infer that environmental investments with the help of the governmental subsidies can help Chinese firms deploy global strategies to expand markets to surpass competitors in the long run despite worsening profitability in global markets in the short run.

• 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.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.105-113
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• 2016

This study analyzed and verified panel data based on CSMAR (China Stock Market & Accounting Research) DB from 2002 to 2014 in order to find out significant differences of conservative accounting before and after Chinese companies adopted international accounting standards. Financial changes in companies can occur at the point of change in accounting standards, and as the difference would affect conservative accounting, it is important to understand conservatism in financial transaction. In this study, earnings per share and price, return on equity, and debt ratio were measured. As a result of analysis, conservative accounting has increased after the introduction of accounting standards, and as the debt ratio was higher, the proportion of conservative accounting was higher. Thus, at a certain point of change in accounting standards, companies apply conservative accounting in order to improve reliability in an unstable future financial environment. Therefore, this study is expected not only to practically influence business practice in changes in GAAP rules but also to provide useful guidance for future studies.

• The Journal of Industrial Distribution & Business
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• v.8 no.1
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• pp.5-13
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• 2017

Purpose - In this article, a dynamic model like a VAR is an appropriate choice for estimating the possible interrelationship between ownership structure and firm performance as a dynamic process. Research design, data, and methodology - Data of this work are collected from Chinese stock exchange including 350 Chinese-listed firms during the period of 1999-2012. We hypothesize that this interrelationship dynamically exists between ownership structure and firm performance. To examine the correlation, a panel Vector Auto-regression (PVAR) approach generated by GMM method is utilized to test the possible dynamic relation embedded in corporate governance. Another two dynamic analysis solutions such as orthogonalized impulse-response function and variance decomposition are also used simultaneously. Results - Findings of this study indicate the evidence that dynamically endogenous relationship exists between ownership structure and firm performance. Further, there is a dynamical correlation between investment and performance. Impulse response and variance decomposition illustrate that impact of a shock to variables themselves is the main source for their variability. Conclusions - The conclusion in this study is that there is a bidirectional and inter-temporal effect between proportion of ownership and corporate performance for a long run in accordance with impulse response function. Overall, our results suggest that corporate governance in China is more market oriented.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.1-22
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• 2022

Purpose - The purpose of this study is to examine the effect of VC investment on the IPO and post-IPO performance of Chinese firms. Design/methodology/approach - By utilizing CSMAR and VentureXpert database, we construct a firm-year panel data covering all listed firms in the Chinese stock market from 2006 to 2018. Findings - First, we find that VC-backed firms are significantly less underpriced than non-VC-backed firms. Our results show that the initial IPO-day return of VC-backed firms is 0.16% lower than that of non-VC-backed firms. Next, we find that VC-backed firms demonstrate significantly worse operating performance than non-VC-backed firms after the IPO. In the next three years following the IPO, VC-backed firms underperform non-VC-backed firms by 0.4% in terms of ROA and by 0.6% in terms of ROE. Research implications or Originality - Our results support the Grandstanding Hypothesis, among several competing hypotheses regarding the effect of VC investment, which suggests that VCs window dress their IPO firms for their early exit at the expense of a poor operating performance of the IPO firms after going public.

• Journal of Korea Trade
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• v.24 no.6
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• pp.1-18
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• 2020

Purpose - This study investigates whether a listing effect exists in cross-border M&As and whether the effect can be attributed to the uncertainty of the GDP growth rate in the target firm's home country. We apply a joint variable analysis using M&A announcement data from the Korea Exchange (KRX), Shanghai Stock Exchange (SSE), and the Taiwan Stock Exchange (TWSE) from 2004 to 2013. We also conduct an event study using the measure of the uncertainty of the GDP growth rate (based on IMF statistics) in 55 target countries. Design/methodology - We measure the abnormal return (AR) using the market-adjusted model. We test the significance of the AR and the cumulative abnormal return (CAR) using a one-sample t-test. We examine the characteristics of the CARs depending on whether the target company is listed by applying a difference analysis using CAR as a test variable. In addition, we set CAR (-5, +5) as a dependent variable to identify the cause of the listing effect, and test both the financial characteristic variables of the acquirer and the collective characteristic variables of the merger as independent variables in the multiple regression analysis. Findings - First, we find the listing effect of cross-border M&As in the KRX, SSE, and TWSE, which represent the capital markets in Korea, China, and Taiwan, respectively. This listing effect persists during the global financial crisis and has a negative effect on the wealth of acquiring shareholders, especially when the target countries are emerging markets. Second, greater uncertainty regarding the target countries' economic growth in cross-border M&As has a negative effect on the wealth of acquiring firms' shareholders. Third, our empirical analysis demonstrates that the listing effect is attributable to the fact that firms listed in a target country with greater uncertainty of economic growth are more directly and greatly exposed to uncertain capital markets through stock markets, than are unlisted firms. Originality/value - This study is significant in that it presents a new strategic perspective in the study of cross-border M&As by demonstrating empirically that the listing effect is attributable to the uncertainty regarding the economic development of the target firms' home countries.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Korea Convergence Society
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• v.6 no.1
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• pp.71-76
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• 2015

Using a sample of publicly-traded Chinese firms, this study examines a relationship between managerial ownership and corporate debt maturity decisions. China has transformed dramatically into a market capitalist economy over the past decades. However, so far, little attention has been paid to the role of professional managers. In this situation, this study explores the effect of stock grants to managers as incentive system by providing evidence that managerial ownership affects corporate debt maturity decisions. The findings are as follows: First, I find that like US firms, managerial ownership is negatively related to the proportion of long-term debt. Second, I divide the entire sample into two subsamples of state-owned and privately owned firms. For the privately owned firms, I find that there is a negative relationship between managerial ownership and the proportion of long-term debt. In contrast, for the state-owned firms, the relationship is positive and insignificant.

• International Journal of Industrial Entomology and Biomaterials
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• v.13 no.2
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• pp.51-55
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• 2006

Since 1970, China has become the biggest cocoon producer in the world, and made the highest historical record of cocoon output for 759,800 tons in 1995. However, in 1996 cocoon production reduced sharply to 470,900 tons. After a ten-year adjustment and reform, sericultural areas have shifted from developed regions to developing regions and from the east to the west. From 2000, the cocoon output has started to increase restoringly. By 2004 it recovered to 547,091 tons. With the development of market economy, sericulture management has been changed, including mulberry fields concentrated to the specializated households and cooperatives, cocoons produced in larger scale instead of individuals, Silkworm egg producing enterprises gradually changed into non-governmental joint-stock ones. The mechanism of market cocoon price has been gradually established. The management model of combination of trade, industry and agriculture is pushing and improving. It is the fruit of modern science and technology, especially sericultural basic research, that provides China's sericulture with the opportunity and vital force. China's sericulture, therefore, will continue to develop steadily in future.