• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.987-996
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• 2021

The development of Internet technology can affect firm value through the use of social media by business people. Nowadays, social media affect businesses of all sizes in several different ways. Despite the various benefits obtained by using social media, research at the organizational level and its impact on business performance have not grown as fast as desired. This research aims to examine the effect of social media on oil and gas firms' value. The research sample consists of 9 oil and gas firms listed on the Indonesian Stock Exchange 2013-2018. Social media proxies are firms' social media, other social media mentions, and social media sentiment. Firm value is measured by the market value to assets ratio. Data analysis uses a random-effect regression test. Based on the analysis, the social media account of a firm has a positive effect on firm value. It indicates that social media give advantages for oil and gas firms to give a signal of business prospect, make use of opportunities related to industry alliances, recruit employees globally, and c. On the other hand, the positive sentiment on social media has no effect on oil and gas firms' value.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.135-142
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• 2021

This study aims to examine the impact of audit committee characteristics on income management of companies listed on the Stock Exchange of Vietnam. Research data was collected from all 745 listed companies on Vietnam's stock market over four years, from 2015 to 2018. After excluding companies that did not qualify, there were 216 companies with 864 observations. With the help of dedicated software Stata 15, the impact of audit committee characteristics (through independent variables and control variables such as Audit Committee Independence, Auditing Committee size, Auditing Committee Expertise, Auditing Committee Meeting Frequency, Company Size, Financial Leverage, and Operating Cash Flow) to earning management through a multivariate regression model was determined. Research results from Vietnamese listed companies during this period show that the size and expertise of the audit committee are inversely related to the discretionary accruals representing earning management. At the same time, the research results also identify a positive relationship between firm size and earning management, and the inverse relationship between financial leverage, net cash flow from operating operations and earning management. However, the multivariate regression results do not find clear evidence of a relationship between audit committee independence and the audit committee meeting frequency to earning management.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.93-104
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• 2022

This study aims to investigate the factors that contribute to the sustainable development of 334 Vietnamese construction and real estate companies listed on the Stock Exchange of Vietnam over a 5-year period from 2016 to 2020. By using regression analysis with the support of STATA software through examining the financial statements, which involves looking into crucial ratios including capital structure, profitability, firm size, accounts receivable management, and tangible assets investment, this study sheds light on whether these accounting indicators could help predict the construction and real estate companies growing potential in the future. Nevertheless, these ratios slightly contribute to the explanation of the change in revenue growth ratio, with a result of 1.6%, indicating that the value relevance of accounting information provides a modest and insignificant effect on investment decisions. This is understandable because the Vietnamese construction and real estate market still has many shortcomings in handling unexpected events, as well as the industry's peculiarities related to major capital sources from bank loans. Based on this study, governmental authorities and business executives should plan appropriate risk management policies and measures to contribute to the sustainable development of construction and real estate companies.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.355-363
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• 2022

The debt maturity structure has a significant impact on a company's financial situation. Any debt maturity structure decisions substantially impact investment decisions due to changes in capital cost and dividend decisions due to cash flow consequences. This study used the system generalized method of moment (Sys-GMM) to investigate the debt maturity structure of real estate companies listed on the Ho Chi Minh Stock Exchange (HOSE) in the duration from 2008 to 20019. It found that the firm size, liquidity, and tangible assets affected the decision on debt maturity structure. The tangible asset had the most significant impact on the possibility for companies to access long-term loans. This finding revealed that the majority of the real estate companies listed on HOSE borrowed money from banks. Such decisions are most likely affected by the collateral. Another finding of the study is that financial institutions had a major impact on loan maturity structure, whereas the effects of the financial market were negligible. Besides, the real estate companies listed on HOSE seemed not to pay attention to changes in inflation, economic growth, and institutional qualities when deciding on the debt maturity structure.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.133-145
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• 2023

This paper examines whether investors have adapted to the venture capital(VC) investment style. VC firms invest in privately held companies and generate returns by selling them after the lock-up period expires. We analyze the impact on stock prices before and after the lock-up period expiration, and compare the Cumulative Abnormal Return(CAR) between the past period(2015-2017) and the recent period(2020-2022) to investigate the effect of the second venture boom. The main findings are as follows. First, unlike in the past, stock price returns around the lock-up period expiration have been lower than the KOSDAQ index in recent years. Second, the impact on stock prices is significant for both 1-month and 12-month lock-up periods. Specifically, it is confirmed that stocks held by venture capital and professional investors with a 1-month lock-up period respond in advance to their information after the second venture boom. Finally, we find that there is a difference in CAR depending on whether or not the company received VC investment after the second venture boom. Based on our findings, we suggest that VC firms need to revise their exit strategies to improve performance. This includes finding ways to reduce information asymmetry and fees, as well as developing strategies to mitigate market volatility. Additionally, the current lock-up period for VCs should be reconsidered as it may increase the risk of stock price decline. We recommend that the government revise the scope and duration of lock-up periods to protect investors after IPO.

• Journal of Environmental Policy
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• v.11 no.2
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• pp.17-54
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• 2012

This paper researched market responses for listed companies in several industries affected by the major oil spill accident off the coast of Taean, in December 7, 2007. The Taean accident triggered considerable concerns in people over the possibility of potential future regulation in shipbuilding and petroleum industries. However, the accident also provided an unexpected business opportunity for environmental clean-up industry and shipbuilding industry. The oil spill triggered the acceleration of the enactment of policies that require all new oil tankers to be constructed with double hull, which is interpreted as a good news for shipbuilding industry. Increased public pressure coupled with the prospect of tightened regulation is expected to decrease the market values of firms in the affected business fields. The stock prices of shipbuilding companies dropped after the incident but dramatically surged after the enactment of the policy in January 31, 2008. Our study also found that companies with more extensive prior environmental disclosure had less negative market reactions during the first sixteen days following the accident.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.281-288
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• 2019

This study examines the relationship between R&D intensity and disclosure. R&D activities are essential in bringing innovation to companies. However, R&D activities are naturally uncertain and increase information asymmetry. Thus, firms with high R&D activities are more likely to have the incentive to communicate the potential of R&D investment to the market through voluntary disclosure and, concurrently, resolve information asymmetry. Meanwhile, incentives to less voluntary disclosure exist because of the proprietary cost and the risk of competitiveness loss. Furthermore, the uncertainties inherent in R&D activities caused the possible decrease in the information accuracy. For the two opposing views, this study investigates the relationship between R&D intensity and disclosure frequency using the Regulation Fair Disclosure data in Korea. Moreover, the relationship between R&D intensity and usefulness of the information disclosed is also examined. Using firm sample listed in the 2011-2016 Korea Stock Market, results show that firms with high R&D intensity make disclosures more frequent. Subsequently, the analysis using forecast sample shows that management forecast error is higher in firms with high R&D intensity. This research contributes to the existing literature by presenting evidence that R&D intensity is a significant factor affecting manager's disclosure behavior and information usefulness.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.251-265
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• 2021

It seems a common sense that corporate social responsibility (CSR) is a key driver to attain business sustainability. Nevertheless, there has been little research on the performance of socially responsible activities, including economic and environmental responsibility activities in internationally diversified firms. The purpose of this study was to evaluate the effects of CSR activities on management performance. For this evaluation, an empirical analysis was conducted with a total of 2,520 cases, selected from companies listed on the Korea Composite Stock Price Index market for six years from 2013 to 2018. As proxies for management performance, financial date such as a total asset net profit ratio and a total asset-operating ratio were used. A multivariate regression analysis was conducted to test hypotheses. The results of this analysis indicated that firms in the CSR outstanding group are significantly higher than other groups in management performances. In addition, CSR activities of internationally diversified firms positively influence their total asset net profit ratio and total asset-operating ratio. The results suggested that CSR activities of these firms can play a significant role in enhancing management performances amid the economic status of Korea, where a degree of export dependency is high.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.