• Proceedings of the CALSEC Conference
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• 1999.11a
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• pp.550-562
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• 1999

1990년 후반기에 금융시장의 두드러진 특징중의 하나의 금융의 증권화 현상일 것이다 다른 금융시장에 비해 급속한 성장을 하고 있는 곳이 주식시장이다(민상기, 1998). 이런 주식시장에서 각각의 투자자는 투자기업의 선정을 위해 기업분석과 구가의 예측을 위해 각종 변수에 대한 정보를 수집하여 이를 분석하게 된다 주식시장에 참여하는 투자자는 크게 나누어 보아 전문가로 편성된 기관투자자와 외국인 투자자 그리고 개인 투자자로 나누어 볼 수 있을 것이다 이건 투자자중에서 개인 투자자들은 기관투자자나 외국인 투자 전문회사와 비교해서 열악한 정보수집력과 분석력을 가지고 있다. 그 결과 투자수익률에서도 기환 투자자와 외국인 투자자에 비해 저조하게 나타난다. (중략)

• Management & Information Systems Review
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• v.34 no.1
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• pp.65-80
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• 2015

In previous studies concerning turnover, they argue individual stock's turnover must be identical to market portfolio's turnover under one condition where 2 funds separation theorem holds. In this kind of world, all market participants hold and trade the same portfolio and this should be only market portfolio. If one's trading portfolio's shape is different from market portfolio's, this would mean he or she has an advantage over others in information and this kind of information would be private. In accordance with this theory, we develop a metric which measures how far one's trading portfolio from market's and name it as Stock Selection by Investor(SSI). We apply this measurement to the various types of investor groups classified as individual, institutional and foreign who participate in Korea stock market. To test the validity of measure, we regress price ratio on this measurement using SUR method. As a result, individual investor group shows large number in SSI, but the coefficient in regression is not significant and economically meaningless. In case of institutional investor group, the coefficient proves to be significantly negative. We can infer from this fact that their trading is somehow far from informed trading. Stock selection activity by foreign investor groups proves to be informed trading by showing significantly positive coefficient and the magnitude of coefficient is economically meaningful, especially in sell activity.

• Management & Information Systems Review
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• v.32 no.3
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• pp.83-105
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• 2013

There are many literatures about the herding behavior of institutional investors but there is lack of literatures about the relation among several investor groups consisting of institutional investors. So we investigate the relation among sub-institutional investor groups like bank, insurance companies, pension funds using KRX intraday trading data of 2009. As the result, we find that foreign, individual, and securities firm investors trade in the opposite direction of other investor groups including pension funds. And pension, insurance, asset management, private equity funds, other companies, government, and banks are cross-mimicking each other, so we conclude that these investors make herding behavior. In 2009 institutional investors except securities firms make herding in a short period, and insurance, asset management, pension funds and other companies make herding and self-mimicking in all period, but there is no herding and mimicking after foreign investors.

• The Korean Journal of Financial Management
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• v.18 no.2
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• pp.215-244
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• 2001

한국 증권시장은 뉴욕증권거래소와 시장구조가 다르다. 미국 시장에서 많은 연구들은 스페셜 리스트의 고시 스프레드의 결정 요인과 성분에 관한 것이다. 그러나 한국 증권 시장에서는 스페셜 리스트가 없어서 고시 스프레드는 존재하지 않으며, 그 대신 많은 투자자들이 낸 지정가 주문이 시장 스프레드를 구성한다. 본 연구에서는 투자자들이 거래하면서 비용으로 부담하는 실효 스프레드를 거래 전 스프레드와, 접속매매에서만 나타나는 접속매매시 역선택 정보비용으로 구분하였으며, 다시 거래 전 스프레드를 주문처리 비용 및 역선택 비용으로 구분하였다. 두 성분의 역선택 비용은 투자자들간의 경쟁이 커지면 작아질 것으로 기대되었다. 동시호가와 접속매매 등 거래 방법에 따라 스프레드의 크기 및 구성이 달라질 것으로 예상하여 동시호가로만 거래가 체결된 시가와 오후 시가, 종가, 그리고 접속매매로만 거래가 이루어진 오전 종가, 오후 접속매매 종가로 구분하여 일별 수익률을 이용하여 검증하였다. 검증 결과 거래 전 스프레드에서 역선택 비용과 접속매매시 역선택 정보비용은 경쟁이 커질수록 작아진다고 할 수 있었다.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.163-199
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• 2007

This paper studies the stock market in which there are two types of investor, institutional and individual, whose information gathering and processing abilities are different. The institutional investor manages large funds and has powerful information sources. Whereas, the individual investor trades with a small amount of money and an information disadvantage. The model assumes that the institutional investor is more experienced and able to acquire relevant information earlier than the individual investor. On these assumptions, this paper shows a price continuation in the short run and a price reversal in the long run. The price continuation, or momentum, in the short run can be explained as follows. The early-informed institutional investor trades a stock, and as a result the stock price changes. Then the late-informed individual investor trades the same stock, and the stock price continues to move in the same direction in the short run. The reason for the price reversal in the long run is that since the individual investor has inferior information on the fundamental value of the stock, he tends to overreact to new information. So the stock price changes over its fundamental value initially and then regresses toward its fundamental value. In sum, both the price continuation and the price reversal are caused by the overreaction of the individual investor. The essay illustrates how these phenomena are stronger in the case where the proportion of the individual investor is higher. It also shows how the stock price goes up when the institutional investor buys a stock, while it goes down when the individual investor buys one.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• The Korean Journal of Financial Studies
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• v.9 no.1
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• pp.163-187
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• 2003

본 연구는 옵션가격 및 거래량 자료를 이용하여 옵션시장의 가격발견 기능에 대해서 분석을 시도하였다. 이를 위해 먼저 옵션가격과 거래량 정보가 현물시장을 선행하는 현상에 대해서 분석해 보았다. 옵션가격은 실제 현물지수를 약 1시간 정도 선행하는 것으로 관찰되었다. 콜옵션 가격이 풋옵션에 비해서 상대적으로 옵션시장에서 높게 거래되는 경우 이는 현물주식시장에서의 주가상승을 예고하는 것으로 나타났다. 옵션 거래량 정보 역시 현물시장의 가격움직임을 예측하는데 유효한 것으로 관찰되었다. 콜옵션의 풋옵션 대비 상대적인 거래증가는 투자자의 낙관적인 장세전망을 반영해 일단 현물지수의 상승을 야기하는 것으로 나타났으나 이후 투자자의 풋옵션을 통한 헤지(hedge) 수요의 증가로 이어지는 것으로 조사되었다. 두 번째로 본 연구는 이러한 옵션시장의 가격발견 기능을 이용하여 매매전략을 수립하고 이를 통하여 투자이익을 극대화시킬 수 있는지에 대해서 살펴보았다. 콜옵션 가격(거래량)이 풋옵션 가격(거래량)에 비해 고평가(증가) 되었을 경우 이는 주가상승을 미리 예고하고 있는 신호로 받아들어져 주식을 매입하고 반대로 콜옵션 가격(거래량)이 풋옵션 가격(거래량)에 비해 저평가(감소) 되었다면 주가하락을 예측하기 때문에 주식을 매도함으로써 투자이익을 증대시킬 수 있을 것이다. 실증분석 결과는 우선 옵션 가격정보를 이용하여 현물시장에서 지수 바스켓 포트폴리오를 매매하려는 전략은 30분 내외의 단기 투자에는 유효하나 그 이상의 투자기간을 가지는 경우에는 예상과는 다른 결과를 초래하였다. 반면 옵션시장에서의 콜옵션과 풋옵션의 상대적인 거래량 정보는 현물주식시장의 움직임을 예측하는데 옵션 가격정보에 비해서 보다 효과적인 것으로 판단되었다. 조사한 모든 일중 및 1일(overnight) 투자수익률에서 옵션 거래량의 상대적 비율에 의거한 투자전략은 통계적으로 유의한 투자수익률의 차이를 가져왔다.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.165-190
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• 2008

The degree of informational asymmetry relating to the expiration of index derivatives is usually increased as an expiration day of index derivatives approaches. The increase in the degree of informational asymmetry may have some effects on trading behavior of investors. To examine what the effects look like, 'life cycle of index derivatives' in this study is defined as three adjacent periods around expiration day: pre-expiration period(a week before the expiration day), post-expiration period(a week after the expiration day), and remaining period. It is inspected whether stock investor's trading behavior is changed according to the life cycle of KOSPI200 derivatives and what the reason of the changing behavior is. We have four results. First, trading behavior of each investor group is categorized into three patterns: ㄱ-pattern, L-pattern and U-pattern. The level of trading activity is low for pre-expiration period and normal for other periods in the ㄱ-pattern. L-pattern means that the level of trading activity is high for post-expiration period and normal for other periods. In the U-pattern, the trading activity is reduced for remaining period compared to other periods. Second, individual investors have ㄱ-pattern of trading large stocks according to the life cycle of KOSPI200 index futures while they show U-pattern according to the life cycle of KOSPI200 index options. Their trading behavior is consistent with the prediction of Foster and Viswanathan(1990)'s model for strategic liquidity investors. Third, trading pattern of foreign investors in relation to life cycle of index derivatives is partially explained by the model, but trading pattern of institutional investors has nothing to do with the predictions of the model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.189-195
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• 2014

In this research, we investigate the relationship between volatility and the trading volumes of trader types in the KOSPI 200 index stock market, futures market, and options market. Three types of investors are considered: individual, institutional, and foreign investors. The empirical results show that the volatility of the stock market and futures market are affected by the transaction information from another market. This means that there exists the cross-market effect of trading volume to explain volatility. It turns out that the option market volatility is not explained by any trading volume of trader types. This is because the option market volatility, VKOSPI, is the volatility index that reflects traders' expectation on one month ahead underlying volatility. Third, individual investors tend to increase volatilities, whereas institutions and foreign investors tend to stabilize volatilities. These results can be used in the areas of investment strategies, risk management, and financial market stability.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.161-171
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• 2021

According to the equity home bias theory, foreign investors are considered to have less information than native investors. However, as the economy becomes liberalized and overseas economic innovation has a great influence on the local economy, it is possible for foreign investors to invest as informed traders. This study analyzes whether information on trade amount by nationality has specific characteristics. The findings are summarized as follows. First, the increase in trading by foreign investors has negative effects on stock returns. There is no significant difference in these negative effects by nationality. This means that foreign investors show strong herd behavior regardless of nationality. Second, foreigners' investment activities increase stock price volatility, but the impact is not significant. Third, the behavior of foreign investors is still positive feedback. However, there are signs that positive feedback behavior may be changing, especially for funds from the United States and the Cayman Islands. Finally, tax haven zone funds have different investment strategies than other foreign investors. However, Cayman Islands funds, which are estimated to be closely related to Korea, are different from Luxembourg and Ireland funds. These findings undermine the fundamentals of the equity home bias theory.