• Journal of Intelligence and Information Systems
• /
• v.16 no.2
• /
• pp.19-32
• /
• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The Korean Journal of Financial Studies
• /
• v.3 no.1
• /
• pp.233-265
• /
• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.

• Information Systems Review
• /
• v.9 no.1
• /
• pp.105-120
• /
• 2007

With the fast development of the Internet and the increasing dependence on information infrastructures, companies are faced with various information security threats such as information leakages, modifications, and information breaches. South Korea is one of the leading countries in the Internet usage, but is ranked relatively low when it comes to information security. In fact, many Korean firms have suffered financial losses and damaged corporate images from the information security breaches. However, because of the difficulties in quantifying the costs of the information security breaches, Korean companies tend to delay their investment decisions on information security. The purpose of this study is to measure the cost of information security breach and the economic value of security investment using the event study methodology. Our results show that the announcement of an information security breach negatively influenced the market value of the corresponding company. The effect was statistically significant at the significance level of p=0.05. The breached companies lose, on average, 0.86% of their market values on the day of the announcement - an average loss in market capitalization of $55 million. On the other hand, the investment on information security had no effect on the stock price or the market value of the firm.

• Journal of Fashion Business
• /
• v.1 no.3
• /
• pp.60-70
• /
• 1997

The purpose of this study is to survey the sales of brassieres positively those of elderly women's (aged 50 or older) ones in particular through 72 sales outlets and thereby, in order to present the more comfortable brassiere models which can serve to reinstate elderly women's constitution and provide the useful basic data to brassiere makers and distributors for their business. The results of this survey and the suggestions therefrom can be summarized as follows; 1) Brassieres usually sell at 10,000-20,000 wons, which allows for 15% or more margin rate. Brassieres are disposed through bargain sales once or twice every year where their price are discount 10% or higher. Meanwhile, the majority of the brassieres distributors maintain more than 15% stock rate. The accumulated stocks are primarily disposed through return to makers or bargain sales. About 15% of the brassieres sold are returned by consumers to distributors to be replaced. 2) About a half of distributors operate some or other types of sales education programs. Most of these distributors feel that their educational program have been effective which suggests the effectiveness of sales educational program. On the other hand, 83.3% of the distributors operate in-house repair shops, while the absolute majority of them brief their customers on how to wear brassieres or clean them. 3) Because elderly women's understanding of brassieres sizes is very poor, they tend to ask help of the 'sales people about their sizes before purchasing and proper one personally. In other words, it has been disclosed that old women respond positively to seller's recommendation for their brassiere sizes. 4) It has been found that the brasseries sizes purchased by old women most are. 85A, 90A and 85B in their order, which suggests that the most popular size for under bust circumference is 85~90cm, while their primary cup size is "A". 5) The type of brasseries favored most by elderly women is the "full-cup" type, while their most favorite brassiere design is a soft and simple one. The colors preferred most by them are white, beige and pink in their order. 6) When being consulted by elderly women, sales people experience various difficulties due to their poor understanding of sizes and complaint about prices. Lastly, it has been found through this survey that elderly women want to see some sales promotion material featuring their brassiere sizes and their production arid ask the brasseries makers to produce more diverse brasseries sizes.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.243-249
• /
• 2020

This study analyzes the earnings management that can occur in the process of public offering in the process of SMEs reducing cost of capital, risks and seeking opportunities for direct financing. Since a company is subject to strict supervision during the IPO process, it is possible to prevent the phenomenon that the company value evaluated in the market is underestimated, or to perform earnings management in consideration of overestimation. This study attempted to verify the degree of earnings management through discretionary accruals and actual earnings management values that can affect the earnings ratio of the IPO of a company. For this study, total accruals were calculated and analyzed through discretionary accruals, sales, costs, and actual earnings management adjustments from production activities. As a result of the analysis, discretionary accruals, which are the countermeasures for earnings management during the listing process, have a positive(+) relationship in both the stock price return and the sales adjustment value, which can be viewed as a factor that induces high valuation. As a result of this, there may be a risk of adverse selection for the benefit amount, and information asymmetry may exist for public offering stocks. This study can provide useful guidelines for evaluating corporate value to domestic SMEs and investors that do business with Chinese companies as well as China through the current and type of earnings management of Chinese listed companies.

• KDI Journal of Economic Policy
• /
• v.30 no.1
• /
• pp.171-209
• /
• 2008

The Great Depression is one of the most important economic incidents in the twentieth century. A significant and long-lasting impact of this event is the rise of the government intervention to the economy. Under the catastrophic downturn of the economic condition worldwide, people required their government to play an active role for economic recovery, and this $mentalit{\acute{e}}$ prolonged even after the Second World War. Social science textbooks taught at Korean high schools mostly referred to the Great Depression for explaining the reason of government intervention in economy. However, the mainstream view commonly found in the textbooks provides a misleading theological interpretation. It argues that inherent flaws of the market economy causes over-production/under-consumption, and that this mismatch ends up with economic crisis. The chaotic situation was resolved by substitution of the governments for the market, and the New Deal was introduced as the monumental example ('laissez-faire economy ${\rightarrow}$over-production${\rightarrow}$the Great Depression${\rightarrow}$government intervention${\rightarrow}$economic recovery'). Based on economic historians' researches for past three decades, I argue that this mainstream view commits the fallacy of ex-post justification. Unlike what the mainstream view claims, the Great Depression was neither the result of the 'market failure', nor the recovery from the Great Depression but was due to successful government policies. For substantiating this claim, I suggest three points. First, blaming the weakness or instability of the market economy as the cause of the Great Depression is groundless. Unlike what the textbooks describe, the rise of the U.S. stock price during the 1920s cannot be said as a bubble, and there was no sign of under-consumption during the 1920s. On the contrary, a new consensus emerging from the 1980s among economic historians illustrates that the Great Depression was originated from 'the government failure' rather than from the 'market failure'. Policymakers of European countries tried to return to the gold standard regime before the First World War, but discrepancies between this policy and the reality made the world economy vulnerable. Second, the mainstream view identifies the New Deal as Keynesian interventionism and glorifies it for saving the U.S. economy from the crisis. However, this argument is not true. The New Deal was not Keynesian at all. What the U.S. government actually tried was not macroeconomic stabilization but price and quantity control. In addition, New Deal did not brought about economic recovery that people generally believe. Even after the New Deal, industrial production or employment level remained quite low until the late 1930s. Lastly, studies on individual New Deal policies show that they did not work as they were intended. For example, the National Industrial Recovery Act increased unemployment, and the Agricultural Adjustment Act expelled tenants from their land. Third, the mainstream view characterizes the economic order before the Great Depression as laissez-faire, and it tends to attribute all the vice during the Industrial Revolution era to the uncontrolled market economy. However, historical studies show that various economic and social problems of the Industrial Revolution period such as inequality problems, child labor, or environmental problems cannot be simply ascribed to the problems of the market economy. In conclusion, the remedy for all these problems in high school textbooks is not to use the Great Depression as an example showing the weakness of the market economy. The Great Depression should be introduced simply as a historical momentum that had initiated the growth of government intervention. This reform of high school textbooks is imperative for enhancing the right understanding of economy and history.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.