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In June 2014, the asset under management of National Pension Service (NPS) of Korea reached over 444 trillion won. NPS forecasts that the asset size will gradually grow to around 2,561 trillion won until 2043. The NPS investment of domestic equities and fixed income securities have been already saturated. So the NPS started to expand in global investment. Accordingly, the worries have grown that NPS's trading in foreign exchange markets may lead to the instability in FX markets. This study has analyzed the influence of NPS's foreign exchange transactions in domestic FX market. The period of study was 54 months from Jan 2010 to June 2014. For detailed research, separate analysis was performed by full year and each year. Our main findings can be summarized as follows : There are statistically plus significant influences of the NPS’s trading volume on the estimated volatility of spot rate on the first half. However, there are minus significant influences on the second half. The NPS's FX trading is known to be systematically regulated by the financial planning of the investment committee. The result of this study shows that the regulation of the NPS’s FX trading minimizes the disturbances of the currency by maintaining the stable market expectation in FX markets.

There have been the concerns that leveraged and inverse ETFs contribute to the financial crisis of 2007~2008. Several researchers have investigated this important issue. However, there is no consensus yet whether leveraged and inverse ETFs destabilize a financial market. Financial stability is an important subject for policy makers, practitioners and academia. ETFs are one of the most important financial innovations. In particular, leveraged and inverse become more and more influential. Therefore, such lack of academic and practical consensus is a significant challenge. In this paper, we analyze whether leveraged and inverse ETFs affect the price and volatility of Korean market. Thus, our research contributes to the body of literature and to the design of public policies and trading strategies. Our research can also advance the development of ETF industry, one of the fastest growing and promising sector in the Korean financial market.

To overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).

The authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.

Using the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.

The authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

The purpose of this study is to hypothesize that cognitive biases such as nostalgia, rosy retrospection, overconfidence, fading-affect bias and prospect theory affect how to serve in the military. The behaviors of those expecting military service and those who have completed the service differ significantly in evaluating the self and social value of the human capital during the military service. This difference corresponds to the predictions of the cognitive-bias literature. The authors test propositions in option framework. This study’s experimental design proposes a novel military system, a hybrid of conscription and voluntary systems. This study’s results are consistent with the hypothesis, option theory and behavioral economics literature.

This study aims to empirically validate that a knowledge-based view (KBV) is an important framework to understand price discovery processes in initial public offerings (IPOs) by emphasizing the unique feature of knowledge creation jointly invoked by underwriters and institutional investors during the book building phase.

The authors decompose underwriters’ incremental knowledge acquisition into objective knowledge – acquired from premarket bids – and subjective knowledge – which is orthogonal to the objective knowledge. The authors implement a multiplicative heteroscedasticity model to analyze how each knowledge component relates to the level and volatility (as a proxy of pricing uncertainty) of post-issue returns. The authors take the 2007 regulatory change as a quasi-natural experiment in which institutional investors were incentivized to provide true information.

For Korean IPOs, the authors find that the objective (subjective) knowledge component reduces (increases) both pricing uncertainty and underpricing. The authors also observe that the efficacy of the IPO knowledge creation critically depends on the quality of the information provided by institutional investors, as anticipated by the KBV literature.

Using fine-grained knowledge measures, the authors provide original, compelling evidence that objective (subjective) knowledge formulated from the IPO knowledge-creation processes de facto alleviates (worsens) underwriters’ pricing difficulties. This reinforces the importance of knowledge-based mechanisms in managerial decision-making processes.

This paper aims to replicate 148 anomalies and to examine whether the performance of the Korean market anomalies is statistically and economically significant. First, the authors observe that only 37.8% anomalies in the universe of the KOSPI and the KOSDAQ and value-weighted portfolios have t-statistics that exceed 1.96. When the authors impose a higher threshold (an absolute value of t-statistics of 2.78), only 27.7% of the 148 anomalies survive. Second, microcaps have large impacts. The results vary significantly depending on whether the sample included stocks in the KOSDAQ and whether value-weighted or equal-weighted portfolios are used. The results suggest that data mining explains large portion of abnormal returns. Any tactical asset allocation strategies based on market anomalies should be applied very cautiously.

We contribute to the emerging literature on strategic corporate social responsibility (CSR) and its antecedents by undertaking a systematic analysis of the effect of rivalry on firm and industry CSR. We deal with the codetermination of competition and CSR by using instrumental variables in the firm-level analysis and by modeling it directly in the industry-level analysis. We find that higher intensity of rivalry and CSR of competitors increase firm CSR, ceteris paribus; however, in a more dynamic setting when firms can change their production output, more competition in fact decreases aggregate industry CSR. While seemingly contradictory, these findings suggest interesting implications for both managers and public policy makers.