• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.439-441
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• 2020

기존의 균등배분, 마코위츠, Recurrent Reinforcement Learning 방법들은 수익들을 최대화하거나 위험을 최소화하고, Risk Budgeting 방법은 각 자산에 목표 리스크를 배분하여 최적의 포트폴리오를 찾는다. 그러나 이 방법들은 미래의 최적화된 포트폴리오를 잘 찾아주지 못하는 문제점들이 있다. 본 논문은 자산 배분을 위한 Deterministic Policy Gradient 기반의 Actor Critic 모델을 개발하였고, 기존의 방법들보다 성능이 우수함을 검증한다.

• KDI Journal of Economic Policy
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• v.32 no.1
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• pp.27-49
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• 2010

This paper provides insight into some important features of the intergenerational resource allocation in Korea, before and after the financial crisis in 1997-98. Data sets of three periods before and after the financial crisis (1996, 2000, and 2005) were used to compare the results. This research particularly addresses two related issues: i) the generational effects of economic crisis, and ii) the capacity of age reallocation systems to spread economic risks across generations. The results show tremendous consumption smoothing and resource reallocation by age, during and after the financial crisis. Private education and private health consumption decreased for children between 1996 and 2000. However, the decrease in private education and private health consumption was mitigated by the increase in public consumption. It appears that the public sector did not only mitigate the adverse impact of the economic crisis on consumption, but it also reduced the widening disparity amongst generations. Within transfers, the public transfers for the elderly increased substantially as the private transfers decreased rapidly. Finally, there was a big increase in the asset-based reallocation of the elderly. The increase in asset-based reallocation was mainly due to an increase in asset income between 1996 and 2000, but it was almost entirely due to a decrease in saving (i.e. an increase in dissaving) between 2000 and 2005. This suggests that Korean elderly seemed to have some degree of supporting system during the crisis, even without sufficient pension benefits. The increased reliance on asset accumulation will be critical in the long-run in Korea, as public pension funds diminish due to population aging.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.45-59
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• 2022

Purpose - This study investigates the performance of investment strategies incorporating estimated stock market cycle based on a lead-lag relationship between business cycle and stock market cycle, thereby deriving empirical implications on risk management. Design/methodology/approach - The data period ranges from June 1953 to September 2022 and de-trended short rate, term spread, credit spread, stock market volatility are considered as major input variables to estimate business cycle and stock market cycle by applying probit model. Based on the estimated stock market cycle, two types of strategies are constructed and their performance relative to the benchmark is empirically examined. Findings Two types of strategies based on stock market cycle are considered: The first strategy is to long(short) on stocks when stock market stage is expected to be an expansion(a recession), and the second one is to long on stocks(bonds) when expecting an expansion(a recession). The empirical results show that the strategies based on stock market cycle outperforms a simple buy and hold strategy in both in-sample and out-of-sample investigation. Also the out-of-sample evidence suggests that the second strategy which is in line with asset allocation is more profitable than the first one. Research implications or Originality The strategies considered in this study are based on the estimated stock market cycle which only depends on a few easily available financial variables, thereby making easier to establish such a strategy. It implies that investors enhance investment performance by constructing a relatively simple trading strategies if they set their position on stocks or choose which asset class to buy conditioning on stock market cycle.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.231-248
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• 2023

The purpose of this study is to trigger a discussion on the educational gap between regions in school library resources. To this end, differences and correlations between other resources invested in the school library and output results were analyzed according to manpower allocation. There was a positive correlation between the number of books, the budget, the number of seats, the number of borrowed materials, and the number of students. It was analyzed that manpower allocation had a negative correlation with the number of subjects in which the ratio of students, the lowest grade in the achievement evaluation, was more than 1/2. As a result of examining the staffing according to the 「School Library Promotion Act」 by regional characteristics, it was found that the allocation rate was statistically significantly higher in the order of metropolitan area, and provincial unit. Depending on the regional characteristics, there were differences in net asset per household as well as differences in school library manpower assignment rates. In contrast, the large cities with relatively affluent school library manpower assignment rates were found to be higher. Therefore, based on the survey contents of this study, it was emphasized that the manpower stipulated in the 「School Library Promotion Act」 should be deployed as soon as possible even in relatively poor areas to bridge the educational gap between regions.

• Journal of the Korean Home Economics Association
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• v.28 no.1
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• pp.67-87
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• 1990

This study was designed with the purpose to analyze the bias and the factor structure of Need of Living and to clear the influence of related variables on Need on Living. The Degree of Importance(Di) and of sufficiency(Ds) of 65 indicators which were the constituent components of Need of Living and were extracted from the 51 preceeding studies was estimated by the 1084 samples including 614 urban and 470 rural residents through the questionaire. The indicators with higher Di and lower Ds than average were considered to show the high level of Need of Living. The main results are as follows; 1. Generally speaking, the level of Di was higher than that of Ds. Specially this was serious in the case of the indicators related with social-economic equity, employment, housing and environment. 2. Di level of the indicators such as physical health, judicial equality, the life of planned expenditure was highest. Specially the equity of income allocation was considered to be more important than the increase of income or asset itself. 3. Ds level of the indicators such as the life of plannel expenditure, the development of transportation and communication and the relationship between parents and children was highest, adn that of the use of leisure time, socialactivity, economic equality and social welfare was lowest. 4. Through the oblique rotation of Factor analysis, 12 factors were extracted (total eigen value 32.663, total variance 50.251%). Specially Factor 1 which was christened as the equality and development of social-economic life was related with 11 indicators and its common variance was 51.68%. 5. The rural residents, the lower income group and the lower educated group, who were told of being under disadvantage and unfair treatment of social-economic status, showed higher suffiency on the equity of income allocation, the freedom of living and expression, and judicial equality. 6. Generally speaking, the urban residents, the group under 39 years old, the higher educated group had more intensive Need of Living than the other groups.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Journal of Industrial Distribution & Business
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• v.5 no.2
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• pp.13-20
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• 2014

Purpose - The purpose of this study is to empirically investigate the impact of capital structure on firm performance. Research design, data, and methodology - This study examined the impact of capital structure on the performance of cement companies listed on the Karachi Stock Exchange during the period 2009-2013. The authors hypothesize that there is a negative relationship between capital structure and firm performance. To examine the association, the authors run a Pearson correlation and multiple regression analysis. Results - Results reveal a strong negative relationship between debt to asset and firm performance variables (GPM, NPM, ROA, and ROE). Further, there is a positive relationship between debt to equity and firm performance variables (GPM and NPM), anda negative relationship between debt to equity and firm performance variables (ROA and ROE). Moreover, capital structure variables significantly impact firm performance. Conclusions - This study concluded that financial analysts and managers should emphasize on the optimal level of capital structure and efficient utilization and allocation of resources to achieve the targeted level of productive efficiency in business.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.21-34
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• 2018

The study tests the Fama and French three-factor model by using the newly created Islamic equity style indices. Based on a dataset from May 2006 to April 2011, the three-factor model is tested based on returns of Islamic unit trust funds using the Generalized Method of Moments (GMM) methodology. The sample period is also divided between periods before and after the Global Financial Crisis in August 2008 to test for robustness, and the Bai and Perron (2003) multiple structural break test was used to determine the structural break in the series. The analysis shows that the Fama and French model is valid for Islamic unit trust funds before and after the collapse of Lehman Brothers. The result further indicates the reversal of size effect. As for trading strategies, value funds outperform growth funds by annualized 3.13 percent for the full period. During pre-crisis period, value funds perform better than growth funds while in post-crisis, size factor yields better return than other strategies. As policy suggestion, fund managers need to be aware of the reversal of size effect, and they need to ensure a more transparent stock selection process so that investors can make an informed decision in their asset allocation.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.297-309
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• 2021

In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.