• 제목/요약/키워드: Stock price movements

검색결과 44건 처리시간 0.03초

기술도입기업의 연구개발 집약수준에 따른 시장퇴출위험에 관한 실증연구 (Delisting risk of firm with a new technological innovation and research & development intensity)

  • 이포상
    • 디지털융복합연구
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    • 제17권10호
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    • pp.141-147
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    • 2019
  • 본 연구에서는 한국거래소(KRX)에 상장 등록된 기업을 대상으로 기술도입 공시가 이루어진 기업의 연구개발(R&D) 집약수준에 따른 주가움직임을 분석함으로써 미래 기업가치의 변화를 살펴보고 공시사건 이후에 나타날 수 있는 자본시장에서의 시장퇴출 가능성에 대한 실증분석을 실시하였다. 2002년 1월부터 2014년 12월까지 유가증권과 코스닥시장에서 기술도입공시가 발생한 기업을 주요 분석대상으로 하고 있다. 연구결과 기술도입 공시기업들 중 연구개발 집약수준이 생산성 수준에 비하여 상대적으로 높을수록 공시이후 음(-)의 주가흐름이 이어지고, 나아가 시장퇴출 가능성이 증대되고 있음을 확인 하였다. 이러한 연구결과는 개별기업 단위의 자산에서 차지하는 무형적 자산 요소의 비중 및 불확실성이 증가함에 따라 자본시장 리스크(risk)에 노출 될 수 있음을 보여주고 있으며, 자본시장의 여러 이해관계자들의 투자의사결정에 유용한 정보를 제공해 줄 수 있을 것으로 사료된다.

Trade Linkage and Transmission of Geopolitical Risks: Evidence from the Peace Progress in 2018

  • Taehyun Kim;Yongjun Kim
    • Journal of Korea Trade
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    • 제26권3호
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    • pp.45-62
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    • 2022
  • Purpose - Using unexpected changes in geopolitical tensions on the Korean peninsula as a quasi-natural experimental setting, we examine whether and how geopolitical risks travel across borders through firm-level imports and exports linkages. We also test whether the effects are driven by either imports or exports and assess whether firms can effectively hedge themselves against geopolitical risks. Design/methodology - We focus on a series of unanticipated geopolitical events taken place in Korea in 2018. Making use of the shocks to geopolitical climate, we identify five milestone events toward peace talks. We employ the event studies methodology. We examine heterogenous firm-level stock price reactions around key event dates depending on firms' exposure to geopolitical risks. As a measure of firms' exposure to geopolitical risks in Korea, we utilize a text-based measure of firm-level trade links. When a firm announces and discusses its purchase of inputs from Korea or sales of outputs to Korea in their annual disclosure filings, we define a firm to have a trade relationship with Korea and have exposure to Korean geopolitical risks. Similarly, we use a measure of a firm's hedging policies based on a firm's textual mention of the use of foreign exchange derivatives in their annual disclosure. Findings - We find that U.S. firms that have direct trade links to Korea gained significantly more value when the intensity of geopolitical risks drops compared to firms without such trade links to Korea. The effects are pronounced for firms purchasing inputs from or selling outputs to Korea. We find that the effectiveness of foreign exchange hedging against geopolitical risks is limited. Originality/value - We document the international transmission of geopolitical uncertainty through trade linkages. Export links as well as import links serve as important nexus of transmission of geopolitical risks across borders. Hedging strategies involving foreign-exchanges derivatives do not seem to insulate firms again geopolitical risks. With the recent movements of localization and reshuffling of the global value chain, our results suggest a significant impact of geopolitical risks in Korea on the construction of the global value chain.

SVM을 이용한 VKOSPI 일 중 변화 예측과 실제 옵션 매매에의 적용 (VKOSPI Forecasting and Option Trading Application Using SVM)

  • 라윤선;최흥식;김선웅
    • 지능정보연구
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    • 제22권4호
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    • pp.177-192
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    • 2016
  • 기계학습(Machine Learning)은 인공 지능의 한 분야로, 데이터를 이용하여 기계를 학습시켜 기계 스스로가 데이터 분석 및 예측을 하게 만드는 것과 관련한 컴퓨터 과학의 한 영역을 일컫는다. 그중에서 SVM(Support Vector Machines)은 주로 분류와 회귀 분석을 목적으로 사용되는 모델이다. 어느 두 집단에 속한 데이터들에 대한 정보를 얻었을 때, SVM 모델은 주어진 데이터 집합을 바탕으로 하여 새로운 데이터가 어느 집단에 속할지를 판단해준다. 최근 들어서 많은 금융전문가는 기계학습과 막대한 데이터가 존재하는 금융 분야와의 접목 가능성을 보며 기계학습에 집중하고 있다. 그러면서 각 금융사는 고도화된 알고리즘과 빅데이터를 통해 여러 금융업무 수행이 가능한 로봇(Robot)과 투자전문가(Advisor)의 합성어인 로보어드바이저(Robo-Advisor) 서비스를 발 빠르게 제공하기 시작했다. 따라서 현재의 금융 동향을 고려하여 본 연구에서는 기계학습 방법의 하나인 SVM을 활용하여 매매성과를 올리는 방법에 대해 제안하고자 한다. SVM을 통한 예측대상은 한국형 변동성지수인 VKOSPI이다. VKOSPI는 금융파생상품의 한 종류인 옵션의 가격에 영향을 미친다. VKOSPI는 흔히 말하는 변동성과 같고 VKOSPI 값은 옵션의 종류와 관계없이 옵션 가격과 정비례하는 특성이 있다. 그러므로 VKOSPI의 정확한 예측은 옵션 매매에서의 수익을 낼 수 있는 중요한 요소 중 하나이다. 지금까지 기계학습을 기반으로 한 VKOSPI의 예측을 다룬 연구는 없었다. 본 연구에서는 SVM을 통해 일 중의 VKOSPI를 예측하였고, 예측 내용을 바탕으로 옵션 매매에 대한 적용 가능 여부를 실험하였으며 실제로 향상된 매매 성과가 나타남을 증명하였다.

WHICH INFORMATION MOVES PRICES: EVIDENCE FROM DAYS WITH DIVIDEND AND EARNINGS ANNOUNCEMENTS AND INSIDER TRADING

  • Kim, Chan-Wung;Lee, Jae-Ha
    • 재무관리논총
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    • 제3권1호
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    • pp.233-265
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    • 1996
  • We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.

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