• Journal of the Economic Geographical Society of Korea
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• v.17 no.2
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• pp.280-295
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• 2014

This paper analyzes the topological structure of housing market networks with an application of minimal spanning tree method into apartment sales markets in the Capital Region over the period 2003.7-2014.3. The characteristics of topological network structure gained from this application to some extent share with those found in equity markets, although there are some differences in their intensities and degrees, involving a hierarchical structure in networks, an existence of communities or modules in networks, a contagious diffusion of log-return rate across nodes over time, an existence of correlation breakdown due to the time-dependent structure of networks and so on. These findings could be partially attributed to the facts that apartments as a quasi-financial asset have been strongly overwhelmed by speculative motives over the period investigated and they can be regarded as a housing commodity with the highest level of liquidity in Korea.

• The Science & Technology
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• v.33 no.8 s.375
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• pp.16-17
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• 2000

뉴욕증권거래소의 객장은 집어던지는 매매전표, 주문하는 거래인의 함성 그리고 미친듯이 두드리는 컴퓨터 건반 소리로 온통 난장판같다. 그러나 이런 소란 속에서 객장부 수석차장 안 앨런은 마치 딴 세상에서 살고 있는 것처럼 조용히 자기 일에만 몰두하고 있다. 강철-유리로 격리된 작은 방에서 그녀는 소음과 번쩍이는 게시판의 숫자에는 개의치 않고 모든 거래현황을 컴퓨터모델로 보여 주는 1.8m ×1.2m 크기의 납작한 디스플레이패널에 정신을 집중하고 있다. 앨런은 최근 신설한 이 '뉴욕증시 가상거래객장'에서 마우스를 움직여 특정한 주식이나 주식집단의 시세와 거래량의 실시나 변화를 나타내는 아이콘을 찍으면 궁금한 가격변동이나 거래패턴을 그 자리에서 알 수 있다. '가상거래객장'의 울긋불긋한 그래픽과 심벌들을 통해 그녀는 거래소의 '맥동'을 언제든지 모니터할 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.781-790
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• 2015

The Parrondo paradox is the counter-intuitive phenomenon: when we combine two losing games we can win the game or when we combine two winning games we can lose the game. In this paper, we assume that an investor adopts the rule of the history-dependent Parrondo game for investment in the stock market. Using the KRX (Korea Exchange) data from 2012 to 2014, we found the Parrondo paradox in the stock trading: the redistribution of profits among accounts can turn the decrease of the expected cumulative profit into the increase of the expected cumulative profit. We also found that the opposite case, namely the reverse Parrondo effect, can happen in the stock trading.

• Korea Real Estate Review
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• v.28 no.2
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• pp.21-34
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• 2018

The purpose of this study is to investigate the effect of macro-finance variables on the correlation between the housing and stock markets because understanding the nature of time-varying correlations between different assets has important implications on portfolio allocation and risk management. Thus, we adopted the AG-DCC GARCH model to obtain time-varying, conditional correlations. Our sample ranged from January 2004 to November 2017. Our empirical result showed that the coefficients on asymmetric correlation were significantly positive, implying that correlations between the housing and stock markets were significantly higher when changes in the housing price and stock returns were negative. This finding suggested that the housing market has less hedging potential during a stock market downturn, when such a hedging strategy might be necessary. Based on the regression analysis, we found that the term spread had a significantly negative effect on correlations, while the credit spread had a significantly positive effect. This result could be interpreted by the risk premium effect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.123-130
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• 2021

With the recent developments in computer technology, there has been an increasing interest in the field of machine learning. This also has led to a significant increase in real business cases of machine learning theory in various sectors. In finance, it has been a major challenge to predict the future value of financial products. Since the 1980s, the finance industry has relied on technical and fundamental analysis for this prediction. For future value prediction models using machine learning, model design is of paramount importance to respond to market variables. Therefore, this paper quantitatively predicts the stock price movements of individual stocks listed on the KOSPI market using machine learning techniques; specifically, the reinforcement learning model. The DQN and A2C algorithms proposed by Google Deep Mind in 2013 are used for the reinforcement learning and they are applied to the stock trading strategies. In addition, through experiments, an input value to increase the cumulative profit is selected and its superiority is verified by comparison with comparative algorithms.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.623-632
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• 2018

Both investors and researchers are attentive to the prediction of stock price movement directions since the accurate prediction plays an important role in strategic decision making on stock trading. According to previous studies, taken together, one can see that different factors are considered depending on stock markets and prediction periods. This paper aims to analyze what data mining techniques show better performance with some representative index and stock price datasets in the Korea stock market. In particular, extreme gradient boosting technique, proving itself to be the fore-runner through recent open competitions, is applied to the prediction problem. Its performance has been analyzed in comparison with other data mining techniques reported good in the prediction of stock price movement directions such as random forests, support vector machines, and artificial neural networks. Through experiments with the index/price datasets of 12 years, it is identified that the gradient boosting technique is the best in predicting the movement directions after 1 to 4 days with a few partial equivalence to the other techniques.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.255-267
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• 2011

The aim of this study is to utilize system trading for making investment decisions and use technical analysis and Dynamic Time Warping (DTW) to determine similar patterns in the frequency of stock data and ascertain the optimal timing for trade. The study will examine some of the most common patterns in the futures market and use DTW in terms of their frequency (10, 30, 60 minutes, and daily) to discover similar patterns. The recognized similar patterns were verified by executing trade simulation after applying specific strategies to the technical indicators. The most profitable strategies among the set of strategies applied to common patterns were again applied to the similar patterns and the results from DTW pattern recognition were examined. The outcome produced useful information on determining the optimal timing for trade by using DTW pattern recognition through system trading, and by applying distinct strategies depending on data frequency.

• The Korean Journal of Financial Management
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• v.9 no.2
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• pp.101-147
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• 1992

증권거래소(證券去來所)는 시황에 따라 위탁증거금율(委託證據金率)을 탄력적으로 변경 운용함으로써 시장의 수급을 조절하는 등의 시장관리수단의 하나로 이용하여 공정한 시세형성을 기하고자 설립시부터 증권회사로 하여금 매매의 위탁시 위탁증거금을 징수하도록 규정하고 증거금율을 상황에 따라 신축적으로 운용하여 1962년 이후에만도 무려 32회이상 변경하였다. 따라서 문제의 핵심은 위탁증거금징수가 주식시장에서의 과잉투기행위를 근절시키고 주가변동율(株價變動率)(stock volatility)을 감소시켜 공정거래질서(公正去來秩序)를 확보하는데 기여하고 있는지의 여부가 된다. 이 점은 특히 미국(美國)에서 1987년 10월 소위 '검은 월요일(Black Monday)'당시 갑작스러운 주가폭락과 시장체계의 붕괴사태이후 금융시장의 발전을 모색하는 정책당국자들과 학자들사이에 새로운 주목을 받기 시작하였다. Salinger(1989)와 Schwert(1989)는 위탁증거금율(委託證據金率)의 변경과 주가변동율(株價變動率)의 감소와는 아무런 인과관계가 없다고 결론을 내리고 있다. 특히 Schwert는 거래일시중단시책마저도 주가변동율에 별 효과가 없다고 주장하면서 금융공황과 관련된 거래일시중단은 주가변동을 큰 폭으로 증가시켜왔으나 금융공황을 동반하지 않은 기래일시중단은 높은 주가변동율과 무관함을 밝히고 있다. Hardouvelis(1991)는 그러나 위탁증거금율을 상승시키면 주가변동율이 낮아지며, 결과적으로 주가가 본원적가치(本源的價値)로부터 일탈하는 현상도 줄어든다는 사실을 통계적으로 입증하고, 위탁증거금의 징수가 시장을 교란하는 악성투기행위를 억제시키는데 매우 효과적인 정책수단이라고 주장하고 있다. 본 연구는 우리나라 주식시장에서 과잉투기현상을 억제하여 시장의 안정을 확보하는 기능으로서의 위탁증거금제도에 대해 그 경제적 효과여부를 규명하는 실증분석을 행하였다. 이 논문에서는 Schwert(1989)와 Hardouvelis(1991)의 방법을 원용하여 두가지 서로 다른 방법으로 주가변동율을 측정하여 비교하였다. 통계적 기법은 기본적으로 다변량(多變量) 회귀분석법(回歸分析法)을 택하였다. 분석의 결과로 매우 흥미로운 실증상(實證上)의 규칙성(規則性)을 발견하였다. 즉 현금시장(cash market)의 위탁증거금율이 높아지면 실제주가변동율(實際株價變動率)과 초과주가변동율(超過株價變動率)이 감소되고, 또한 유행(流行)의 경우와 마찬가지로 본원적 가치로부터의 괴리가 작아진다. 이 결과에 따르면 위탁증거금의 징수는 그 제도의 취지에 부합되고 있다. 다만 제도운용상의 이유이거나 혹은 우리나라 주식시장의 투자자들이 비합리적인 투자형태를 보임에 따라 그 정책적 효과는 때로 역기능적인 결과로 초래하였다. 그럼에도 불구하고 이 연구결과를 통하여 최소한 주식시장(株式市場)에서 위탁증거금제도는 그 제도적 의의가 여전히 있다는 사실이 확인되었다. 또한 우리나라 주식시장에서 통상 과열투기 행위가 빈번히 일어나 주식시장을 교란시킴으로써 건전한 투자풍토조성에 저해된다는 저간의 우려가 매우 커왔으나 표본 기간동안에 대하여 실증분석을 한 결과 주식시장 전체적으로 볼 때 주가변동율(株價變動率), 특히 초과주가변동율(超過株價變動率)에 미치는 영향이 그다지 심각한 정도는 아니었으며 오히려 우리나라의 주식시장은 미국시장에 비해 주가가 비교적 안정적인 수준을 유지해 왔다고 볼 수 있다.

• Management & Information Systems Review
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• v.34 no.5
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• pp.23-47
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• 2015

In the Korea stock market, individual investors have perceived stock as short arbitrage investment, not long-term investment strategy. In order to reinforce stock market transparency and soundness, it is important to enforce the measures for stock market management. Especially, stock market event caused by financial policy can be given individual investors negative information regarding a stock trading. Thus, it is a need for investigating whether comprehensive review of listing eligibility is influenced on individual investors' responses and stock behaviors in respect of effectiveness. The purpose of this study to examine the relations between such stock market management and transitional aspect of individual investors' trading types and response on the based of pre- and post-event occurrence. Using an dataset of user's text messages on 9 firms posted on the firm-based social media (i.e., Naver, Daum, Paxnet) over the period 2009 to 2014. And we performed text-clustering and topic modeling according to keywords for classifying into investors group and non-investors groups and two types of investors were categorized depending on main topic transition by event windows in Comprehensive review of listing eligibility. The results indicated that a variety of stockholders existed in the stock. And the ratio of non-investors group was on the decrease, on the other hand, the proportion of investors group veer onto the side of pre-pattern after comprehensive review of listing eligibility. A distinctive feature of our study is to explain the influence of stock market management on response changes of individual investors as well as to categorize in accordance with time progression. Implications an suggestions for future research were also discussed.