• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.185-196
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• 2022

The purpose of this study is to examine the effects of split ratings on earnings management through cost adjustments based on asymmetric cost behaviors. Using a sample of 2,027 Korean firm-year observations over the 2002-2019 period, we analyze whether a firm deliberately reduces discretionary costs, such as selling, general, and administrative (SG&A) expenses, to improve profits when it receives multiple ratings from credit rating agencies (CRAs). While examining earnings management incentives in the presence of split ratings, we also investigate the moderating effects of Chaebols, Korea's unique corporate governance structure. We find that split-rating firms show less stickiness in SG&A costs compared to non-split-rating firms when sales decrease. This result implies the deliberate reduction of discretionary costs to improve earnings in the presence of split ratings, which are more likely to change in future credit assessments. We also find that the incentives for earnings management of split-rating firms are limited in Chaebol firms, which have high levels of socio-economic surveillance and support affiliated firms through the internal market of corporate groups. This study contributes to existing research by identifying new determinants of cost behavior by using the framework of asymmetric cost behavior in relation to earnings management incentives.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.55-67
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• 2018

The organizational politics strengthened resistance to change. This study explores how the organizational political perception and fairness in split firms affect resistance to change. The analysis showed that the boss's and colleague's politics is minimized by its distributional fairness and procedural fairness. The organizational politics strengthened resistance to change. Additionally, mediating effects of fairness were analyzed. As a result, it was revealed that the perception of organized politics by bosses and that colleagues' perception of organizational politics affected resistance to change based on distributional fairness. However, it was shown that procedural fairness had no mediating roles between organizational politics and change resistance. There is a need to pay keen attention to distributional fairness to minimize the resistance to changes of organizational members at the split. If distributional fairness is not secure, Members will be a political behavior.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.67-79
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• 2019

This study examines the impact of corporate governance on capital structure decisions based on a large panel of Chinese listed firms. Using the system Generalized Method of Moments (GMM) estimator to control for unobserved heterogeneity, endogeneity, and persistency in capital structure decisions, we document that the ownership structure plays a significant role in determining leverage ratios. More specially, we find that managerial ownership has a positive and significant impact on firms' leverage, consistent with the incentive alignment hypothesis. We also find that managerial ownership only affects the leverage decisions of private firms in the post-2005 split share reform period. State ownership negatively influence leverage decisions implying that SOEs may face fewer restrictions in equity issuance and may receive favourable treatments when applying for seasoned equity ¿nancing, thus use less debt. Furthermore, our results show that while foreign ownership negatively influences leverage decisions, legal person shareholding positively influences firms' leverage decisions only for state controlled firms. We also find that the board structure variables (board size and the proportion of independent directors) do not influence firms' capital structure decisions. Our findings suggest that recent ownership reforms have been successful in terms of providing incentive to managers through managerial shareholdings to take risky financial choices.

• Journal of Korea Trade
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• v.25 no.6
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• pp.57-77
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• 2021

Purpose - International strategic alliance has been regarded as a strategic decision made by firms' managerial problems and ensure performance growth. From the perspective of the proactive behavior for changing strategies in a global market, this study aims to identify whether performance feedback influences firms' decisions to pursue strategic alliances. This study examines the effects of performance feedback on performance when firms use strategic alliances. Design/methodology - To analyze the impact of performance feedback on forming an international strategic alliance, this study adopt the concept of performance feedback to develop a research model and our hypotheses. Thus, this study used a two-stage least squares unbalanced panel data analysis with random effects. This study is based on 24,543 observations from Korean manufacturing firms from 2007 to 2016. Findings - The results show that firms pursue the formation of strategic alliances more actively, if their past financial and R&D performance are lower than their aspiration level, based on the result of performance feedback. An in split sample analysis for examining the effect of a firm's technology sophistication based on the OECD's classification, negative innovation performance discrepancy has positive effects on the probability of international alliance in high-tech and medium-high-tech industries. Financial performance also improves when a firm decides to form a strategic alliance based on the results of performance feedback. Originality/value - This research extends recent efforts to better understand the effect of performance feedback on firms' performance when they use strategic alliances. These findings suggest that the CEOs and managers of firms should consider the performance feedback perspective when deciding to pursue a strategic alliance to improve performance. In other words, the decision-makers in a firm must analyze and consider various complex variables inside and outside the firm and expand such subjects of examination to more complex and dynamic factors.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5184-5190
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• 2010

This study chiefly aims to find out the efficient ways of organizing teams of architectural design projects. In order to reach this goal, this study analyzed (1) specialties of students' projects during semesters in architectural design studios, (2) teaching methods relevant to architectural design education, (3) division of role play in architectural studios and organizing method that students prefer. Besides, in order to clarify the principles of practical project team organization and to find out more efficient methods in team organization, this study executed archival research on (1) specialties of team organization rules and principles in architectural firms, (2) types and merits of projects teams, (3) efficient methods of dividing roles. For the students project of the studio, it is desirable to switch the roles of team leader and staff members of the team to make members' feeling of participating the decision making of the project progress. In architectural design team, since the high productivity was valued beyond individual preferences, the team organization and and work split was done on the basis of (1) experiences, (2) design ability, (3) schedule of the project, etc. However, for the continuous motivation and enthusiasm for the firms, it is required to study efficient ways of team organization.

• 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.