• Title/Summary/Keyword: Stock Portfolio

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Impact of Oil Price Shocks on Stock Prices by Industry (국제유가 충격이 산업별 주가에 미치는 영향)

  • Lee, Yun-Jung;Yoon, Seong-Min
    • Environmental and Resource Economics Review
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    • v.31 no.2
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    • pp.233-260
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    • 2022
  • In this paper, we analyzed how oil price fluctuations affect stock price by industry using the non-parametric quantile causality test method. We used weekly data of WTI spot price, KOSPI index, and 22 industrial stock indices from January 1998 to April 2021. The empirical results show that the effect of changes in oil prices on the KOSPI index was not significant, which can be attributed to mixed responses of diverse stock prices in several industries included in the KOSPI index. Looking at the stock price response to oil price by industry, the 9 of 18 industries, including Cloth, Paper, and Medicine show a causality with oil prices, while 9 industries, including Food, Chemical, and Non-metal do not show a causal relationship. Four industries including Medicine and Communication (0.45~0.85), Cloth (0.15~0.45), and Construction (0.5~0.6) show causality with oil prices more than three quantiles consecutively. However, the quantiles in which causality appeared were different for each industry. From the result, we find that the effects of oil price on the stock prices differ significantly by industry, and even in one industry, and the response to oil price changes is different depending on the market situation. This suggests that the government's macroeconomic policies, such as industrial and employment policies, should be performed in consideration of the differences in the effects of oil price fluctuations by industry and market conditions. It also shows that investors have to rebalance their portfolio by industry when oil prices fluctuate.

An Empirical Study on Korean Stock Market using Firm Characteristic Model (한국주식시장에서 기업특성모형 적용에 관한 실증연구)

  • Kim, Soo-Kyung;Park, Jong-Hae;Byun, Young-Tae;Kim, Tae-Hyuk
    • Management & Information Systems Review
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    • v.29 no.2
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    • pp.1-25
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    • 2010
  • This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

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A Study on the Prediction Model of Stock Price Index Trend based on GA-MSVM that Simultaneously Optimizes Feature and Instance Selection (입력변수 및 학습사례 선정을 동시에 최적화하는 GA-MSVM 기반 주가지수 추세 예측 모형에 관한 연구)

  • Lee, Jong-sik;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.147-168
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    • 2017
  • There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

A study on the efficient application of the replicating portfolio according to the tax imposition within K-OTC market for activating financial transactions of small-medium and venture business (중소 벤처 기업의 금융거래 활성화를 위하여 K-OTC 시장에서 조세부과에 따른 복제포트폴리오의 효율적 활용에 대한 연구)

  • Yoo, Joon-soo
    • Journal of Venture Innovation
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    • v.1 no.1
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    • pp.83-98
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    • 2018
  • This paper makes a theoretical approach to the differences between transaction tax and capital gains tax when the financial instruments are traded and imposed taxes in K-OTC market, a newly emerging off-board market. Since it is difficult to reduce risk to the level which investors would like to pursue - depending on the taxation methods of portfolio-composed financial instruments - when it comes to forming a synthetic bond to hedge risk, this paper also seeks for effective taxation methods to make this applicable. First of all, to thoroughly review the taxation balance of synthetic bonds, this paper analyzed the effects of the transaction tax and capital gains tax imposed upon synthetic bonds according to the changes in final stock price and strike price in K-OTC market, and analyzed after-tax profit differences among them depending on whether income tax deduction took place or not. As a result of the research upon the tax gap in transaction tax and capital gains tax according to the changes of final stock prices, it was shown that imposing transaction tax is more likely to be effective for some level of risk hedging with replicating portfolio considering taxation policies and financial markets, since the effect of the transaction tax has a much lower tax gap than that of capital gains tax. In addition, in relation to whether income tax deduction was permitted or not, it was proved that the effect of the transaction tax and the capital gains tax vary depending on the variation in the strike price. Above all, it was shown that if the strike price is lower than the stock price, the transaction tax will be less affected by the existence of income tax deduction than the capital gains tax, while both will be equally affected by the existence of income tax deduction if the strike price is higher than the stock price. Further study would be to demonstrate the validation of this in the K-OTC market with actual financial instruments and, also, to seek for a more systematic hedging method by using a ratio analysis approach to the calculation of the option transaction tax

The Stock Portfolio Recommendation System based on the Correlation between the Internet Stock Message Board and the Stock Market (인터넷 주식 토론방과 주식 시장의 상관관계 분석을 통한 투자 종목 선정 시스템)

  • Lee, Yun-Jung;Kim, Gunwoo;Woo, Gyun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2014.04a
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    • pp.967-970
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    • 2014
  • 인터넷 게시판이나 트위터 같은 온라인 매체는 쉬운 접근성과 실시간 특성으로 어떤 사건에 대한 사용자들의 반응이 즉각적으로 나타난다. 또한, 실시간으로 엄청난 양의 데이터가 생성되고 있어 이 데이터를 잘 분석한다면 실제 사회에서 나타나는 다양한 현상들에 대해 파악할 수 있다. 최근 주식 시장에서도 이러한 온라인 데이터들을 분석하여 주가 변동이나 주식 시장 상황을 이해하려는 연구가 시도되고 있다. 이 논문에서는 주식 토론방의 게시물과 주가 사이에 어떤 상관관계가 있는지를 분석하고, 이를 이용한 주식 투자 종목 추천 시스템을 제안하고자 한다. 먼저 주가와 주식 토론방 게시물들 사이의 상관관계를 분석하기 위해서 KOSPI200에 속한 회사 중 55개의 회사를 대상으로 주가와 주식 토론방 게시물을 분석하였다. 2008년부터 2013년까지 6년 동안 각 회사의 주가와 게시물의 상관관계를 분석한 결과 개별 주가와 게시물 수 사이에는 특별한 상관관계가 나타나지 않았다. 하지만 주가와 게시물 수의 상관관계가 높을수록 주식 수익률이 높은 경향을 보였다. 이 논문에서는 주가와 게시물 수의 상관관계 정보를 이용한 투자 종목 추천 알고리즘을 제안하였고, 모의투자 실험을 통해 제안 방법의 효율성을 보였다. 2008년 1월부터 2013년 12월까지의 주가와 주식 토론방 데이터를 이용한 모의투자 실험에서 제안 방법으로 구성한 포트폴리오의 1개월 평균 수익률은 약 1.82%로, 주식 네트워크 특성을 이용한 기존 방법보다 약 0.64% 높은 수익률을 보였다. 또한, 마코위츠의 효율적 포트폴리오와 KOSPI200 수익률보다 각각 약 0.85%와 1.48% 높게 나타났다.

A Study on Global Blockchain Economy Ecosystem Classification and Intelligent Stock Portfolio Performance Analysis (글로벌 블록체인 경제 생태계 분류와 지능형 주식 포트폴리오 성과 분석)

  • Kim, Honggon;Ryu, Jongha;Shin, Woosik;Kim, Hee-Woong
    • Journal of Intelligence and Information Systems
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    • v.28 no.3
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    • pp.209-235
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    • 2022
  • Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

Structural Change in Real Estate Market (IMF 이후의 부동산시장의 구조변화)

  • 서승환;김갑성
    • Journal of the Korean Regional Science Association
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    • v.15 no.3
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    • pp.33-51
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    • 1999
  • After the 1997 currency crisis, the real estate prices had been rapidly dropped and the deregulation in the Korean real estate merket has been performed. It is analyzed whether these transactions caused a structural change in real estate market, or not. The Pettitt test shows there exists a turing point in real estate prices in 1998. It is found that the degrees of co-movement between the change in real estate prices and real GDP growth rate are increased. Consequently, the factor, represented as real GDP growth rate, determining the market fundamental of real estate prices will effect on the behavioral pattern and the real estate prices in the long run. While the factors determining the portfolio selection behaviors, such as interest rate and stock prices, will cause short-term variations.

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A Theoretical Study on Risk - focused on systematic risk- (위험에 관한 이론적 연구 -체계적 위험을 중심으로-)

  • 김원기
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.2 no.2
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    • pp.115-124
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    • 1979
  • The purpose of this study is theoretical research on risk. The research is focused on systematic risk. Chapter I is objective of this study, Chapter II includes definition and measurement of risk. Chapter III introduces attitudes toward risk and classification of risk. Chapter IV discusses Portfolio theory, Capital market line and Shape and Lintner model The objective of firm is assumed to maximize its value. In a world of uncertainty, value is not determined by earnings alone, the degree of risk involved with the streams of earnings. Financial manager has to consider the risk in order to maximize the value of firm. Total risk can be classifier into two parts : Systematicrisk and unststematic risk by Sharpe. Systematic risk is important because investors can't diversify it. Blume and Jensen measured f and they testified that the f is stationary over the time For further study, Korean stock mark has to take emperical study about $\beta$ and its stationarity.

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Valuation of Options in Incomplete Markets (불완전시장 하에서의 옵션가격의 결정)

  • Park, Byungwook
    • Journal of the Korean Operations Research and Management Science Society
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    • v.29 no.2
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    • pp.45-57
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    • 2004
  • The purpose of this paper is studying the valuation of option prices in Incomplete markets. A market is said to be incomplete if the given traded assets are insufficient to hedge a contingent claim. This situation occurs, for example, when the underlying stock process follows jump-diffusion processes. Due to the jump part, it is impossible to construct a hedging portfolio with stocks and riskless assets. Contrary to the case of a complete market in which only one equivalent martingale measure exists, there are infinite numbers of equivalent martingale measures in an incomplete market. Our research here is focusing on risk minimizing hedging strategy and its associated minimal martingale measure under the jump-diffusion processes. Based on this risk minimizing hedging strategy, we characterize the dynamics of a risky asset and derive the valuation formula for an option price. The main contribution of this paper is to obtain an analytical formula for a European option price under the jump-diffusion processes using the minimal martingale measure.

A Method for Portfolio Construction Using a Clustering Technique on the Stock Market Networks (주식시장 네트워크에서 클러스터링 기법을 이용한 포트폴리오 구성 방법)

  • Chun, Bong-Hwan;Kim, Eun-Kyung;Jung, In-Jun;Woo, Gyun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2012.04a
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    • pp.1396-1399
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    • 2012
  • 본 논문은 주식 투자 포트폴리오를 구성하기 위해 클러스터링 기법을 이용하는 방법을 제안한다. 클러스터링 기법은 패턴 공간 상의 특징 벡터로 표현된 패턴 데이터를 몇 개의 부분집합으로 나누는 작업을 의미한다. 본 연구에서는 주식시장 네트워크에 클러스터링 기법을 적용하여 안정성과 수익률이 높은 포트폴리오를 구성하는 방법을 제안한다. 그리고 추천 클러스터의 투자 적합여부를 데이터를 통해 확인한다. 2007년 주식 데이터를 대상으로 실험한 결과, 추천 클러스터의 수익률이 전체 수익률을 상회함을 확인할 수 있었다.