• International Area Studies Review
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• v.22 no.3
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• pp.121-148
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• 2018

This paper examines the role of accounting conservatism on investment expenditure for non-financial Korean listed firms around the 2007-2008 global financial crisis using a differences-in-differences design. Specifically, this paper examines the association between an ex ante classification of firms by their level of accounting conservatism prior to the credit crisis and the ex post magnitude of the decline in investment. Consistent with prior literature, this study found that firms experienced a decline in their investment when hit by the financial crisis (Campello et al. 2010). And also this study found that firms with more conservative financial reporting experienced a smaller decline in investment activity following the financial crisis than did firms with less conservative financial reporting. Together, the results suggest that negative shocks to the supply of external finance hampers firm-level investment and that conservative financial reporting can lessen the sensitivity of firms' investment to such negative shocks. Next, this study shows that the magnitude of our findings is greater for firms more likely to suffer from underinvestment (as opposed to overinvestment). Firms that are financially constrained or have greater demand for external finance are more likely to experience underinvestment. Consistent with the predictions, this study finds stronger benefits of conservatism for firms that face relatively greater costs in raising external capital (i.e., financially constrained firms) or that have a relatively greater need to do so (i.e., firms that lack internal financial resources). This study also finds that the role for conservatism is greater in firms with a higher level of information asymmetry, consistent with the notion that conservatism mitigates financing frictions arising from information problems.

• Korean Journal of Heritage: History & Science
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• v.41 no.2
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• pp.79-97
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• 2008

Four Wangs' landscape style (四王山水畵風), which had appeared in Beijing in the early 18th century, widely spread to Korea and Japan in the 19th century and became a significant basis for developing new painting styles in both countries. It was first introduced to Korea by Shin Wi (申緯) and Kim Jeong-hee (金正喜) who associated with literary men of the Qing Dynasty. Being influenced by them directly or indirectly, Shin Myeong-yeon (申命淵), Yi Han-cheol (李漢喆), Yu Suk (劉淑), Changv Seung-eop (張承業), An Choog-sik (安中植), and Jo Seok-jin (趙錫晋) attempted to adapt Four Wangs' landscape style and it later became a main Stream painting style of the Korean painting circles. Based on Four Wangs' landscape style, their landscape paintings had something in common in that they captured natural features from a short distance using the Down-Up prospective and placed guardian mountains across mountain streams by making a tall tree in the right or left bottom of the canvas as the starting point. However, recently unveiled court painter An Geon-yeong (1841~1876)'s the Landscape Screen is remarkable in that it is based on Four Wangs' style, which was in fashion in the late 19th century, but shows different aspects from other Four Wangs' style paintings in terms of feature capturing, brush stroke and colors. While most of An Geon-yeong's existing paintings are small ones, this folding screen is a big piece consisting of six-fold landscape paintings. In particular, it shows new aspects by creating a serene and calm atmosphere through the description of various landscape scenes with thin brush strokes using glossy ink, by showing a macroscopic view in some paintings through feature capture using a birds-eye view method, and by giving life to the canvas through smoke and clouds. This painting style is considered to be linked with those of Wang Xue-hao (王學浩, 1754~1832), Tang Yifen (湯貽汾, 1778~1853) and Dai Xi (戴熙, 1801~1860), based on Four Wangs' style in the early 19th century's Jiangnan Circle (江南 畵壇), who tried to express the energy and vitality of real landscapes by going around China's well-known mountains and complementing painting styles with drawing from nature. Therefore, An Geon-yeong's six-fold Landscape Screen is very significant as a rare case proving the introduction and reception of Jiangnan Circle's Four Wangs' landscape style which was different in many aspects from Beijing Circle in the 19th century.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.