• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.165-188
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• 2009

This paper has suggested the methodology for the frontier portfolios and the optimal portfolio under the mean-VaR framework, not assuming the normal distribution and considering the investor's preferences for the higher moments of return distributions. It suggested the grid and rank approach which did not need an assumption about return distributions to find the frontier portfolios. And the optimal portfolio was selected using the utility function that considered the 3rd and the 4th moments. For the application of the methodology, weekly returns of the developed countries index, the emerging market index and the KOSPI index were used. After the frontier portfolios of the mean-variance framework and the mean-VaR framework were selected, the optimal portfolios of each framework were compared. This application compared not only the difference of the standard deviation but also the difference of the utility level and the certainty equivalent expressed by weekly expected returns. In order to verify statistical significances about the differences between the mean-VaR and the mean-variance, this paper presented the statistics which were obtained by the historical simulation method using the bootstrapping. The results showed that an investor under the mean-VaR framework had a tendency to select the optimal portfolio which has bigger standard deviation, comparing to an investor under the mean-variance framework. In addition, the more risk averse an investor is, the bigger utility level and certainty equivalent he achieves under the mean-VaR framework. However, the difference between the two frameworks were not significant in statistical as well as economic criterion.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.45-63
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• 2019

Until recently, most people have invested in a traditional portfolio consisting of stocks, bonds and real estates based on the three-division method of properties in Korea. However, this study analyzed the impact of the composition of a portfolio combining representative real estate indirect investment products such as Reits and real estate funds on the investment performance. For this purpose, the empirical analysis using the mean variance model, which is the most appropriate method for the portfolio composition, was used. For variables used in this study, mixed asset portfolios were classified into Portfolio A through Portfolio G depending on the composition of assets, and the price indices selected as Kospi, Krx bond, Reits Trus Y7, Hanwha-Lasal fund, and Office (Seoul). The results are as follows; first Portfolio D, which combined bonds, stocks, Reits and Real Estate funds, and Portfolio G, which added the office, the actual real estate, were shown to have the lowest risk. second, Portfolio B composed of bonds, stocks and Reits and Portfolio D with added real estate funds had the lowest risk while Portfolio F composed of bonds, stocks, offices and real estate funds, and Portfolio G with added Reits were the most profitable. As a result, it has been analyzed that it was more effective to compose a portfolio including Reits and real estate funds, which were real estate indirect investment products that eliminated the illiquidity limitation of real estates than real estates, the traditional three-division method of properties. Therefore, it is possible to minimize the risk of investors and reduce the cost of ownership of the real estate by solving the illiquidity problem that is the biggest disadvantage of the direct investment, In addition, it is considered that it is more necessary to reinvigorate the real estate indirect investment market where small amounts can be invested.

• Knowledge Management Research
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• v.22 no.4
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• pp.119-133
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• 2021

As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Land and Housing Review
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• v.11 no.2
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• pp.33-46
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• 2020

As the fastest growing office transaction volume in Korea, there's been a need for development of indicators to accurately diagnose the office capital market. The purpose of this paper is experimentally calculate to the office price index for effective benchmark indices in Seoul. The quantitative methodology used a Case-Shiller Repeat Sales Model (1991), based on actual multiple office transaction dataset with over minimum 1,653 ㎡ from Q3 1999 to 4Q 2019 in the case of 1,536 buildings within Seoul Metropolitan. In addition, the collected historical data and spatial statistical analysis tools were treated with the SAS 9.4 and ArcGIS 10.7 programs. The main empirical results of research are briefly summarized as follows; First, Seoul office price index was estimated to be 344.3 point (2001.1Q=100.0P) at the end of 2019, and has more than tripled over the past two decades. it means that the sales price of office per 3.3 ㎡ has consistently risen more than 12% every year since 2000, which is far above the indices for apartment housing index, announced by the MOLIT (2009). Second, between quarterly and annual office price index for the two-step estimation of the MIT Real Estate Research Center (MIT/CRE), T, L, AL variables have statistically significant coefficient (Beta) all of the mode l (p<0.01). Third, it was possible to produce a more stable office price index against the basic index by using the Moore-Penrose's pseoudo inverse technique at low transaction frequency. Fourth, as an lagging indicators, the office price index is closely related to key macroeconomic indicators, such as GDP(+), KOSPI(+), interest rates (5-year KTB, -). This facts indicate that long-term office investment tends to outperform other financial assets owing to high return and low risk pattern. In conclusion, these findings are practically meaningful to presenting an new office price index that increases accuracy and then attempting to preliminary applications for the case of Seoul. Moreover, it can provide sincerely useful benchmark about investing an office and predicting changes of the sales price among market participants (e.g. policy maker, investor, landlord, tenant, user) in the future.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.45-55
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• 2015

This study used for KOSPI, KOSDAQ, entertainment culture and digital contents index that is related to cultural contents industry. There was investigated the each stock price index and return trends for a total 597 weeks to July 2015 from March 2004. They looked the content-related stocks about investment worth to comparative analysis the return, volatility, correlation, synchronization phenomena etc. of each stock index. When we saw the growth potential of the cultural contents industry forward, looked forward to the investment possibility of related stocks. Analysis Result cultural content related stocks showed a higher rate after the last 2008 global financial crisis. Recent as high interest in the cultural contents industry, we could see that the investment merit increases slowly. In the future, the cultural content industry is expected to continue to evolve. The increase of investments value in the cultural content related businesses is much expectation.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.185-197
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• 2018

The purpose of this study is to analyze the Korean ETF market, which is experiencing a rapid increase in the number of stocks, to identify the degree of investment efficiency and to present investment directions. The methodology and procedure are ETF yield, change trends, correlation and regression analysis of the ETFs traded between 2010 and 2018. As a result, the total return of domestic ETFs was 3.51%, which was lower than the KOSPI growth rate and the return on equity ETFs was 4.03%, which was low. Leverage ETF yields were below 3%, which was low. The return on bond and currency ETFs was less than 1%. The most profitable ETFs were index ETFs, followed by domestic and leveraged ETFs. This study has contributed to establishing considerations when purchasing ETFs from the viewpoint of investors. Future research will present the direction of ETF investment more precisely.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1049-1060
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• 2009

The expansion of volatility in Korean Stock Market made it more difficult for the individual to invest directly and increased the weight of indirect investment through a fund. The purpose of this study is to construct the EIF(enhanced index fund) model achieves an excessive return among several types of fund. For this purpose, this paper propose portfolio optimization model to manage an index fund by using GA(genetic algorithm), and apply the trading amount and the closing price of standard index to earn an excessive return add to index fund return. The result of the empirical analysis of this study suggested that the proposed model is well represented the trend of KOSPI 200 and the new investment strategies using this can make higher returns than Buy-and-Hold strategy by an index fund, if an appropriate number of stocks included.

• The Journal of Industrial Distribution & Business
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• v.10 no.12
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• pp.35-42
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• 2019

Purpose : This study examines The impact of human resource investment in internal control on stock price crash risk. Effective internal control ensures that information provided is complete and accurate, financial statements are reliable. By overseeing management, internal control systems can reduce agency costs between management and outside parties. In Korea, firms have to disclose information about internal control systems. The working experience of human resources in internal control systems is also provided for interested parties. If a firm hires more experienced internal control personnel, it can better facilitate the disclosure of information. Prior studies reported that information asymmetry between managers and investors increases future stock price crash risk. Therefore, the longer working experience internal control personnel have, the lower probability stock crashes have. Research design, data and methodology : This study analyzed the association between the working experience of internal control personnel and crash risk using regression analysis on KOSPI listed companies for fiscal years 2016 through 2017. The sample consists of 1,034 firm-years of non-financial firms whose fiscal year end on December 31. Career spanning data of internal control personnel was collected from internal control reports. The professionalism(IC_EXP) was measured as the logarithm of the average working experience of internal control personnel in months. Negative conditional skewness(NSKEW) and down-to-up volatility (DUVOL) are used to measure firm-specific crash risk. Both measures are based on firm-specific weekly returns derived from the expanded market model. Results : We find that work experience in internal control environment is negatively related to stock price crashes. Specifically, skewness(NSKEW) and volatility (DUVOL) are reduced when firms have longer tenure of human resources in internal control division. The results imply that firms with experienced internal control personnel are less likely to experience stock price crashes. Conclusions : Stock price crashes occur when investors realize that stock prices have been inflated due to information asymmetry. There is a learning effect when internal control processes are done repetitively. Thus, firms with more experienced internal control personnel could manage their internal control more effectively. The results of this study suggest that firms could decrease information asymmetry by investing in human resources for their internal control system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.637-643
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• 2017

This study analyzed the Quality of Accounting Earning of Investor Relations(IR). For this, we utilized Book-Tax Difference Variability as the proxy of the level of the Quality of Accounting Earning. This study used 2,106 sample data from 2011 to 2016 on the listed firm on KOSPI(Korea Composite Stock Price Index). In short, the study results are as follows. Investor Relation(IR) has a negative relevance with Book-Tax Difference Variability, which agreed with the result of additional analysis using extra sample. According to these results, we can expect that Investor Relations(IR) firms will report more faithful Accounting Earning. This study makes the following fresh contribution to the field. The study result confirms how Investor Relation(IR) affects the Quality of Accounting Earning. We hope that this study will help the development of capital market.