• Title/Summary/Keyword: 주가 수익률

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Attention to the Internet: The Impact of Active Information Search on Investment Decisions (인터넷 주의효과: 능동적 정보 검색이 투자 결정에 미치는 영향에 관한 연구)

  • Chang, Young Bong;Kwon, YoungOk;Cho, Wooje
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.117-129
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    • 2015
  • As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

A Study on Foreign Exchange Rate Prediction Based on KTB, IRS and CCS Rates: Empirical Evidence from the Use of Artificial Intelligence (국고채, 금리 스왑 그리고 통화 스왑 가격에 기반한 외환시장 환율예측 연구: 인공지능 활용의 실증적 증거)

  • Lim, Hyun Wook;Jeong, Seung Hwan;Lee, Hee Soo;Oh, Kyong Joo
    • Knowledge Management Research
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    • v.22 no.4
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    • pp.71-85
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    • 2021
  • The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

Simulation of Soil Moisture in Gyeongan-cheon Watershed Using WEP Model (WEP 모형을 이용한 경안천 토양수분 모의)

  • Noh, Seong-Jin;Kim, Hyeon-Jun;Kim, Cheol-Gyeom;Jang, Cheol-Hee
    • Proceedings of the Korea Water Resources Association Conference
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    • 2006.05a
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    • pp.720-725
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    • 2006
  • 토양수분은 식물의 생장 및 가용수자원 산정 등에 있어서 중요한 요소로서 토양층 상부의 수 m내에 존재하는 수분의 양을 일컫는다. 토양수분과 토양수분의 공간적 시간적 특징들은 증발, 침투, 지하수 재충전, 토양침식, 식생 분포 등을 지배하는 중요한 요소이다. 강우 등으로 인한 지면과 지표하에서의 순간적인 포화공간의 형성 및 유출의 생성 등을 포함하는 과정과 증발산 등은 모두 비포화대(vadose zone) 혹은 토양층에서의 토양수분의 함량에 크게 의존하게 된다(이가영 등(2005)). 분포형 수문모형은 유역을 격자단위로 세분화하여 매개변수를 부여하고, 증발산, 침투, 지표면유출, 중간유출, 지하수유출, 하도 흐름 등 여러 가지 수문요소를 해석하는 종합적인 수문모형이다. 지표면에 내린 강우가 증발, 침투, 유출될 지는 토양수분의 함량에 크게 의존하게 되며, 따라서 토양수분에 대한 적절한 모의가 분포형 수문모형의 정확도를 좌우하는 핵심이라 할 수 있다. 본 연구에서는 분포형 수문모형인 WEP 모형을 경안천 유역(유역면적: $575km^2$, 유로연장: 49.3㎞)에 적용하여 토양수분의 시공간분포를 모의하였다. 지점별 토양수분 모의결과, 토양 매개변수의 최대, 최소값 내에서 적절히 모의됨을 확인하였으나, 관측값이 없어 실질적으로 타당한지 여부는 검증하지 못하였다. 토양수분비율, 연간 증발산량, 지표면 유출량 공간분포를 비교한 결과, 토양수분비율이 연간 증발산량 모의에 직접적인 영향을 주는 것을 확인할 수 있었다. 일부격자에서는 토양수분이 지나치게 높게 모의되었는데, 지하수위와 관련있는 것으로 보이며, 구축된 자료가 부족한 지하대수층에 대한 정보부족이 토양수분 계산에도 영향을 준 것으로 보인다. 본 연구는 WEP 모형의 토양수분 해석능력에 대한 시험적용에 그 의의가 있으며, 향후 토양 및 지표하 매개변수 정보가 충분히 갖추어지고, 토양수분 관측결과 있는 대상유역에 대한 적용이 요구된다.-Moment 방법에 의해 추정된 매개변수를 사용한 Power 분포를 적용하였으며 이들 분포의 적합도를 PPCC Test를 사용하여 평가해봄으로써 낙동강 유역에서의 저수시의 유출량 추정에 대한 Power 분포의 적용성을 판단해 보았다. 뿐만 아니라 이와 관련된 수문요소기술을 확보할 수 있을 것이다.역의 물순환 과정을 보다 명확히 규명하고자 노력하였다.으로 추정되었다.면으로의 월류량을 산정하고 유입된 지표유량에 대해서 배수시스템에서의 흐름해석을 수행하였다. 그리고, 침수해석을 위해서는 2차원 침수해석을 위한 DEM기반 침수해석모형을 개발하였고, 건물의 영향을 고려할 수 있도록 구성하였다. 본 연구결과 지표류 유출 해석의 물리적 특성을 잘 반영하며, 도시지역의 복잡한 배수시스템 해석모형과 지표범람 모형을 통합한 모형 개발로 인해 더욱 정교한 도시지역에서의 홍수 범람 해석을 실시할 수 있을 것으로 판단된다. 본 모형의 개발로 침수상황의 시간별 진행과정을 분석함으로써 도시홍수에 대한 침수위험 지점 파악 및 주민대피지도 구축 등에 활용될 수 있을 것으로 판단된다. 있을 것으로 판단되었다.4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전략이고, 이 전략의 유용성은 투자자가 설정한 투자기간보다 더욱 긴 분석기간의 주식가격정보에 의하여 최대한 발휘될 수 있음을 확인하였다.(M1), 무역적자의

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A Comparative Study on the Discharge Measurement Methods at a Experimental Stream Downstream of Dam (댐 하류 시험하천에서의 유량 측정 방법간 비교)

  • Lee, Chan-Joo;Kim, Won;Kim, Dong-Gu;Kim, Chi-Young
    • Proceedings of the Korea Water Resources Association Conference
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    • 2006.05a
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    • pp.1338-1342
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    • 2006
  • 수자원의 계획과 관리를 위해 정확한 유량 측정은 무엇보다 중요하다. 이를 위해 다양한 유량 측정방법이 개발, 적용되고 있다. 본 연구는 국내 하천에 적용 가능한 다양한 유량 측정방법을 시험하천에 동시에 적용하여 유량 측정 방법간 비교를 목적으로 수행되었다. 비교를 위해 적용된 방법은 유속면적법, 부자법, ADCP법 등의 비연속적 방법과 기존 보를 이용한 방법, 초음파 유량계를 이용한 방법, 유속지수법, 실시간 경사면적법 등의 연속적 방법이다. 평저수기에 주로 적용될 수 있는 비연속적 방법인 유속면적법과 ADCP법의 비교에서는 유속면적법이 방류량 대비 평균 ${\pm}4.7%$의 오차를 가지며, ADCP법의 경우 ${\pm}4.6%$의 오차를 갖는 것으로 나타났다. 비연속적 방법과 연속적 방법을 동시에 비교하기 위해 평저수 5회, 홍수 2회를 포함하는 총 7회의 동시 유량측정이 수행되었다. 유속면적법과 ADCP법은 부적절하게 적용된 경우를 제외하면 오차는 대체로 10% 이내로 나타났다. 부자법의 경우 홍수시에만 적용되었으나 오차가 방류량 대비 20% 이상으로 다소 크게 나타났다. 연속적 방법은 기존 보의 경우 개발된 수 위-유량 관계의 이하의 저유량에 적용할 경우 오차가 다소 크게 산정되었으나 그 이외에는 대체로 10% 이내의 오차를 나타내었으며, 일부 저수위의 유속지수법을 제외하면 연속적 방법은 모두 오차가 10% 이내로 조사되었다. 향후 보다 장기간에 걸쳐 다양한 유량 범위에서의 검증이 필요하지만, 시험하천에서의 유량 측정 방법간 비교는 국내 하천에 적용할 수 있는 다양한 방법의 적용성을 평가하는데 기여할 수 있을 것으로 생각된다.향 범위는 최대 $0.07km^2$ 면적에 그 효과를 기대할 수 있을 것으로 판단되어, 남조 수화현상이 심화될 경우 인공순환에 의한 저감효과가 크지는 않을 것으로 예측된다. 조사 기간중 H호의 현존 식물플랑크톤량의 $60%{\sim}87%$가 수심 10m 이내에 분포하였고, 녹조강과 남조강이 우점하는 하절기에는 5m 이내에 주로 분포하였다. 취수탑 지점의 수심이 연중 $25{\sim}35m$를 유지하는 H호의 경우 간헐식 폭기장치를 가동하는 기간은 물론 그 외 기간에도 취수구의 심도를 표층 10m 이하로 유지 할 경우 전체 조류 유입량을 60% 이상 저감할 수 있을 것으로 조사되었다.심볼 및 색채 디자인 등의 작업이 수반되어야 하며, 이들을 고려한 인터넷용 GIS기본도를 신규 제작한다. 상습침수지구와 관련된 각종 GIS데이타와 각 기관이 보유하고 있는 공공정보 가운데 공간정보와 연계되어야 하는 자료를 인터넷 GIS를 이용하여 효율적으로 관리하기 위해서는 단계별 구축전략이 필요하다. 따라서 본 논문에서는 인터넷 GIS를 이용하여 상습침수구역관련 정보를 검색, 처리 및 분석할 수 있는 상습침수 구역 종합정보화 시스템을 구축토록 하였다.N, 항목에서 보 상류가 높게 나타났으나, 철거되지 않은 검전보나 안양대교보에 비해 그 차이가 크지 않은 것으로 나타났다.의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략

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An Optimized Combination of π-fuzzy Logic and Support Vector Machine for Stock Market Prediction (주식 시장 예측을 위한 π-퍼지 논리와 SVM의 최적 결합)

  • Dao, Tuanhung;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.20 no.4
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    • pp.43-58
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    • 2014
  • As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

An Analysis on the Influence of the Financial Market Fluctuations on the Housing Market before and after the Global Financial Crisis (글로벌 금융위기 전후 금융시장 변동이 주택시장에 미치는 영향 분석)

  • Kim, Sang-Hyeon;Kim, Jae-Jun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.4
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    • pp.480-488
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    • 2016
  • As the subprime mortgage crisis spread globally, it depressed not only the financial market, but also the construction business in Korea. In fact, according to CERIK, the BSI of the construction businesses plunged from 80 points in December 2006 to 14.6 points in November 2008, and the extent of the depression in the housing sector was particularly serious. In this respect, this paper analyzes the influence of the financial market fluctuation on the housing market before and after the Global Financial Crisis using VECM. The periods from January 2000 to December 2007 and January 2008 to October 2015, before and after the financial crisis, were set as Models 1 and 2, respectively. The results are as follows. First, when the economy is good, the Gangnam housing market is an attractive one for investment. However, when it is depressed, the Gangnam housing market changes in response to the macroeconomic fluctuations. Second, the Gangbuk and Gangnam housing markets showed different responses to fluctuations in the financial market. Third, when the economy is bad, the effect of low interest rates is limited, due to the housing market risk.

A Study on the Market Efficiency with Different Maturity in the Futures Markets (선물시장의 만기별 시장효율성에 관한 연구 - 베이시스간의 정보효과를 이용하여 -)

  • Seo, Sang-Gu;Park, Joung-Hae
    • Management & Information Systems Review
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    • v.35 no.2
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    • pp.273-284
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    • 2016
  • The objective of this study is to analyze the market efficiency in the futures markets. Although many previous studies have investigated market efficiency between spot and futures prices, that with different maturities has not been studied in the futures markets extensively. For our objective, this paper examines KOSPI200 stock index future market with different maturities. We analyze the dynamic serial relationship of the difference of basis between nearest-month contract and next nearest-month contract using dynamic regression analysis suggested by Kawamoto and Hamori(2011) Using the data from 2000. 1 to 2013. 12, the major empirical findings are as follows: First. the mean and standard deviation of basis of next nearest-month contract is bigger than those of nearest-month contract. Second, the t-period basis of nearest-month contract can be explained by (t-1)period basis of that. Third, the basis spread of t-period and (t-1)period have negative affect on the return of underlying assets. This result is very reasonable because two basis spreads are derived from same underlying assets. Finally, basis information of next nearest-month contract can be used for the prediction of nearest-month contract and spot market return.

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The Relationship Between DEA Model-based Eco-Efficiency and Economic Performance (DEA 모형 기반의 에코효율성과 경제적 성과의 연관성)

  • Kim, Myoung-Jong
    • Journal of Environmental Policy
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    • v.13 no.4
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    • pp.3-49
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    • 2014
  • Growing interest of stakeholders on corporate responsibilities for environment and tightening environmental regulations are highlighting the importance of environmental management more than ever. However, companies' awareness of the importance of environment is still falling behind, and related academic works have not shown consistent conclusions on the relationship between environmental performance and economic performance. One of the reasons is different ways of measuring these two performances. The evaluation scope of economic performance is relatively narrow and the performance can be measured by a unified unit such as price, while the scope of environmental performance is diverse and a wide range of units are used for measuring environmental performances instead of using a single unified unit. Therefore, the results of works can be different depending on the performance indicators selected. In order to resolve this problem, generalized and standardized performance indicators should be developed. In particular, the performance indicators should be able to cover the concepts of both environmental and economic performances because the recent idea of environmental management has expanded to encompass the concept of sustainability. Another reason is that most of the current researches tend to focus on the motive of environmental investments and environmental performance, and do not offer a guideline for an effective implementation strategy for environmental management. For example, a process improvement strategy or a market discrimination strategy can be deployed through comparing the environment competitiveness among the companies in the same or similar industries, so that a virtuous cyclical relationship between environmental and economic performances can be secured. A novel method for measuring eco-efficiency by utilizing Data Envelopment Analysis (DEA), which is able to combine multiple environmental and economic performances, is proposed in this report. Based on the eco-efficiencies, the environmental competitiveness is analyzed and the optimal combination of inputs and outputs are recommended for improving the eco-efficiencies of inefficient firms. Furthermore, the panel analysis is applied to the causal relationship between eco-efficiency and economic performance, and the pooled regression model is used to investigate the relationship between eco-efficiency and economic performance. The four-year eco-efficiencies between 2010 and 2013 of 23 companies are obtained from the DEA analysis; a comparison of efficiencies among 23 companies is carried out in terms of technical efficiency(TE), pure technical efficiency(PTE) and scale efficiency(SE), and then a set of recommendations for optimal combination of inputs and outputs are suggested for the inefficient companies. Furthermore, the experimental results with the panel analysis have demonstrated the causality from eco-efficiency to economic performance. The results of the pooled regression have shown that eco-efficiency positively affect financial perform ances(ROA and ROS) of the companies, as well as firm values(Tobin Q, stock price, and stock returns). This report proposes a novel approach for generating standardized performance indicators obtained from multiple environmental and economic performances, so that it is able to enhance the generality of relevant researches and provide a deep insight into the sustainability of environmental management. Furthermore, using efficiency indicators obtained from the DEA model, the cause of change in eco-efficiency can be investigated and an effective strategy for environmental management can be suggested. Finally, this report can be a motive for environmental management by providing empirical evidence that environmental investments can improve economic performance.

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Diffusion equation model for geomorphic dating (지형연대 측정을 위한 디퓨젼 공식 모델)

  • Lee, Min Boo
    • Journal of the Korean Geographical Society
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    • v.28 no.4
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    • pp.285-297
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    • 1993
  • For the application of the diffusion equation, slope height and maximum slope angle are calculated from the plotted slope profile. Using denudation rate as a solution for the diffusion equation, an apparent age index can be calculated, which is the total amount of denudation through total time. Plots of slope angle versus slope height and apparent age index versus slope height are useful for determining relative or absolute ages and denudation rates. Mathematical simulation plots of slope angle versus slope height can generate equal denudation-rate lines for a given age. Mathematical simulations of slope angle versus age for a given slope height, for equal denudation-rate at a particular profile site, and for comparing to other sites having controlled ages.

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A Study on Risk Parity Asset Allocation Model with XGBoos (XGBoost를 활용한 리스크패리티 자산배분 모형에 관한 연구)

  • Kim, Younghoon;Choi, HeungSik;Kim, SunWoong
    • 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.