• Management & Information Systems Review
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• v.35 no.1
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• pp.273-286
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• 2016

This study investigated the information contents of KOSPI200 Options for intraday big market movement by using minute by minute data. The major findings are summarized as follows; First, big market movement occurred more frequently during 9:00~10:00 and 14:00~14:50. These phenomena reflect market unstability just after opening and near closing. Second, VKSOPI is most closely associated with extreme changes such as KOSPI200 jumps. Third, VKOSPI is showed more predictive power with negative KOSPI200 jumps than KOSPI200 jumps. Fourth, VKOSPI showed predictive power for the positive and negative jumps up to 30 minutes before the jumps occurs. The purpose of this study is to explore the most recent topics in the field of finance, research on market microstructure. This study is an important contribution to investigate intraday information comprehensively in terms of market microstructure effects using the 15-year long-term and the high-frequency data(minute by minute). The results of this study are expected to contribute to detect intraday true jumps, proactive development of market risk indicators, risk management, derivatives investment strategy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.6
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• pp.55-69
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• 2018

As disaster risk and climate change volatility increase, there are more efforts to adapt to disasters such as forest fires, floods, and landslides. Most of the research, however, is about influence of human activities on disaster and there is few research on disaster adaptation for species. Previous studies focusing on biodiversity in selecting conservation areas have not addressed threats of disaster in the habitats for species. The natural disasters sometimes play role of drivers of ecological successions in the long run, but they might cause serious problems for the conservation of vulnerable species which are endangered. The purpose of this study is to determine whether soil loss (SL) is effective in selecting habitat management areas for amphibians and reptiles. RUSLE model was used to calculate soil loss (SL) and the distribution of each species (SD) was computed with MaxEnt model to find out the biodiversity index. In order to select the habitat management area, we estimated the different results depending if value of soil loss was applied or not by using MARXAN, a conservation priority selection tool. With using MARXAN, conservation goals can be achieved according to the scenario objectives, and the study has been made to meet the minimum habitat area. Finally, the results are expressed in two; 1) the result of soil loss and biodiversity with MATRIX method and 2) the result of regional difference calculated with MARXAN conservation prioritization considering soil loss. The first result indicates that the area with high soil loss and low species diversity have lower conservation values and thus can be managed as natural disturbances. In the area where soil loss is high and species diversity is also high, it becomes where a disaster mitigation action should be taken for the species. According to the conservation priorities of the second result, higher effectiveness of conservation was obtained with fewer area when it considered SL in addition to SD, compared to when considered only biodiversity. When the SL was not taken into consideration, forest area with high distribution of species were important, but when SL considered, the agricultural area or downstream of the river were represented to be a major part of habitats. If more species data or disaster parameters other than soil loss are added as variables later, it could contribute as a reference material for decision-making to achieve various purposes.

• 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 Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Korean journal of communication and information
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• v.43
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• pp.173-208
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• 2008

This study analyses the business performance according to the business diversification of Korean mass media enterprises from year 2003 to 2006. The conclusions drawn which could be divided into five main parts are the followings: First, newspaper companies pursue unrelated diversification in various industrial areas, in order to gain maximum profit while broadcasting companies exert themselves to provide better service by diversifying the major contents. Second, overall the interviewed companies display a constant decline in profit gained from their major business area thus establishing strategies to broaden their focus on diversification of any sort. Third, the researcher completed group analysis in regard of diversification measure resulting in division of three groups. The group which had the most immense diversification range gained the highest ROE, the lowest ROE volatility, and lesser probability of risk taking. The analysis adresses the companies broadening their business areas by researching and focusing on diversification are relatively stable in terms of the profit they gain. Fourth, the middle level group in terms of sales scale, debts, enterprise history, major share rate and high ROE group carry out diversification progressively. The sales scale affects positively to diversification, while the major share rate affects negatively to diversification. Fifth, in accordance to the research, diversification overall contributes to obtainance of successful outcome. Since there was not an immense amount of studies to be referred in the media area, the researcher interviewed and did panel discussion with numerous strategists and managers who are in charge of diversification of media companies. However, collection of only 4 years of data limits the research to be considered to be a generalized study, and does not reflect time gap between business diversification and business performance. Development is required in future studies to be established regarding the media companies' specificity different to other industries, classified the media companies into media types, and consider the time gap in the diversification activities and business performance.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Journal of International Area Studies (JIAS)
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• v.14 no.2
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• pp.499-524
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• 2010

The purpose of this study is to based on review of theoretical and empirical studies to assess the independence of central banks - the former Socialist republics, including the Russian Federation and Czech, Poland. In addition, the work is expected to clarify whether a link exists between independence and the most important economic indicators such as inflation, economic activity, the budget deficit. And The subject of this study are the formal and actual independence of national banks, as well as limiting factors: political and economic. Background investigation of the problem of independence of central banks from the fact that, according to many economists, it is essential to the successful development of a market economy. The effectiveness of any country's economy due to currency volatility, low inflation, high reliability of the banking system, etc. As far as the independence of monetary regulation contributes to these goals - one of the most actively debated issues in the world of economic theory and practice for a long time. The issue of central bank independence is extremely important for Russia, Czech, Poland. In the near future to the central bank has important tasks, among which are the transition to inflation targeting in the rejection of significant intervention in the foreign exchange market, as well as improving the sustainability of the national banking system. Transparency and independence of the Bank of Russia, Czech Republic, Poland, in my view, should be an important factor in achieving these goals. The countries of Czech Republic, Poland have already made a number of steps to bring the status of their banks to the European standards. Many other developing countries are also in the process of reforming their central banks and the improving conditions of their functioning. However, despite the fact that as a model for reform used by the central banks of countries with developed market economies, central banks in developing countries are still yet deprived of the legal, economic and political independence. A different situation exists in transition space. Because of significant differences in the views of the authorities in transition republics at the necessary level of independence of central banks and the exchange rate and monetary policy reform of monetary management in these countries led to different results.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.