• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.351-360
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• 2022

This study aims to determine the factors that can influence investors in selecting stocks in the Indonesian capital market to establish an optimal portfolio, and find phenomena that occurred during the COVID-19 pandemic so that buying interest / the number of investors increased in the Indonesian capital market. This study collection technique uses primary data obtained from the survey questionnaire and secondary data which is market data, stock price movement data sourced from the Indonesia Stock Exchange, Indonesian Central Securities Depository, and Bank Indonesia, as well as empirical literature on behavior finance, investment decision, and interest in buying stock. The method used in this research is the survey questionnaire analysis with the SEM (statistical approach). The results of the analysis using SEM show that investor behavior influences the stock-buying interest, investor behavior, and the stock-buying interest influences investor decision-making. However, risk management does not influence investor-decision making. This occurs when the investigator's psychological capacity produces more decision information by decreasing all potential biases, allowing the best stock selection model to be selected. When the investigator's psychological capacity creates more decision information by reducing biases, the optimum stock selection model can be chosen.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.139-169
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• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.481-504
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• 2006

VaR, a tail-related risk measure is now widely used as a tool for a measurement and a management of financial risks. For more accurate measurement of VaR, recently we are particularly concerned about the approach based on extreme value theory rather than the traditional method based on the assumption of normal distribution. However, many studies about the approaches using extreme value theory was done only for the univariate case. In this paper, we discuss portfolio risk measurements with modelling multivariate extreme value distributions by combining copulas and extreme value theory. We also discuss the estimation of ES together with VaR as portfolio risk measures. Finally, we investigate the relative superiority of EVT-copula approach than variance-covariance method through the back-testing of an empirical data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.2
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• pp.87-95
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• 2011

A Renewable Portfolio Standard(RPS) is a regulation that requires the increased generation of energy from renewable energy sources such as solar, wind, fuel cell, small hydro, biomass and geothermal. By environmental, technical and these regulatory reasons, the amount of renewable energy sources will be increased in a network. However, it is hard to assess risk of a transmission network with large scale renewable energy sources because the output characteristics of renewable energies are intermittent. This paper evaluates effects of a transmission system with supplemental large scale renewable energies into the existing system. To evaluate these effects, a methodology for risk level of components in a network is proposed considering steady state and contingency N-1 in this paper. We consider line current and bus voltage in each state of a network.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.29-43
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• 2022

This study measures the realized rate of return of venture capital(VC) fund at the level of investment agreement(as opposed to fund level returns reported by most of the relevant studies). It also measures the stock price return of the VC's portfolio firms (unlisted start-ups) at firm level(as opposed to fund returns) and its variance for the first time using unique data of the VC funds held by the Korean Venture Capital Association. Results of the analysis confirm that VC fund returns exceed individual stock price returns. Additionally, it is confirmed that VC portfolio firms exhibit a positive relationship between risk and return measured by total risk. Finally, we find that stock price returns at firm level are lower than that implied by the associated levels of risk. Consequently, this may make individual investors hesitate to directly buy unlisted startups' stocks even when investment in individual startup companies guarantees high risk-high returns relationship.

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

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.

• Journal of Food Hygiene and Safety
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• v.28 no.2
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• pp.115-123
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• 2013

Food safety management should be based on scientific evidences. FAO and WHO presented risk analysis as one of four principles in food safety management. WTO also admits the self safety regulation only when it is made on the basis of risk assessment. Without scientific analysis, tracing and eliminating the cause of food poisoning is impossible. Research and development plays a key role to produce scientific evidences. The Korean government ran over 40 programs in 11 agencies from 2008 to 2010. However, there is no statistics on food safety R&D at present. In this research, food safety projects conducted from 2008 to 2010 are listed up by means of analysing National Science and Technology Information Service (NTIS). The analytical criteria are the name of programs, national standard classification of science and technology, and keywords. As result, Korea Food and Drug Administration, Ministry for Food, Agriculture, Forestry and Fisheries, and Rural Development Administration play major role in the food safety R&D. The portion of more than one year projects should rise up in order to achieve the data for risk assessment, which is strongly required to improve. Besides, the research should be deeper so as to publish more SCI papers. The R&D portfolio should be changed in direction to raise up the portion of biological hazards such as norovirus. In order to do so, a large number of food safety programs should be emerged. The categories of food safety management and the hygiene/quality management of the agricultural and livestock products in the national standard classification of science and technology should be emerged because they are set up reflecting agencies' interests in spite of few differences between them.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.203-210
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• 2012

Traffic accident risk of young drivers(less than 25) is reported to have 8 times as high as that of middle aged drivers(between 30 and 49). Despite the rise of traffic accident risk, few have been attempted to take a look into driving characteristics of young drivers. The purpose of this paper is to analyze age-specific risks of young driver by means of database of insurance and vehicle inspection, thereby collecting data such as age, vehicle mileage, injuries and so on. We conducted Data-Mining(CART) and Portfolio analysis according to age groups(every 10 years). The conclusions which can be drawn from this empirical study are as follows: (1) Despite the fact that young drivers have low vehicle mileage, the rate of fatality is relatively high. (2) Being concerned of vehicle mileage, 24,000km of driving experience is thought to be critical in differing in fatality rate. Having annual average mileage fewer than 24,169 km, accident frequency is relatively lower than that exceeding 24,169 km(1,571 cases). Backed upon these, some recommendations about driver's license system for young driver to improve are given.

• Korean Business Review
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• v.20 no.1
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• pp.105-122
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• 2007

This paper identifies the dimensions of political risk on the basis of the classification between risk and uncertainties to implement the precise identification and assessment of the various types of political risk and develop the sound assessment model to accomplish their practical applications. This paper shows the concrete and detailed processes of deriving the assessment models and applying them with the microsoft excel spreadsheet, confirms the result of Butler and Joaquin(1998), and presents the methods of identifying the various combination effects of the political risk impact and the covariance relationship with the market portfolio return through the sensitivity analysis.