• 제목/요약/키워드: Daily Stock Returns

검색결과 78건 처리시간 0.023초

한국 증권시장의 주가변동성에 관한 실증적 연구 (An Empirical Study on the Stock Volatility of the Korean Stock Market)

  • 박철용
    • 산학경영연구
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    • 제16권
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    • pp.43-60
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    • 2003
  • 본 연구에서는 French, Schwert, & Stambaugh와 Schwert의 연구에 사용된 방법을 이용하여 한국 증권시장에서 주식수익률의 변동성의 특징을 분석하였다. 본 연구에 사용된 모형은 주식시장의 변동성의 시계열 특성에 대한 보다 조직적 분석을 제공한다. 간단히 말하면, 이 모형들은 일별 수익률로부터 자기회귀 및 계절적 영향을 제거함으로써 예기치 못한 수익률을 추정할 수 있게 한다. 그리고 나서 자기회귀 및 계절적 모형에 예기치 못한 수익률의 절대값을 이용하여 주가변동성을 예측하였다. 분석결과 첫째, 총체적 주식수익률의 움직임에 대한 지속성은 미약하고, 자기회귀모형에 비정상성이 있을 수 있음을 알 수 있었다. 또한, 일별 주가변동성의 움직임이 주식수익률의 움직임보다 훨씬 예측가능하다는 것을 발견하였다. 둘째, 변동성의 증가가 미래 기대수익률을 증가시킨다는 증거는 미약하고, 변동성이 시차 주식수익률과 관계가 있다는 사실을 알 수 있었다.

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Measuring COVID-19 Effects on World and National Stock Market Returns

  • KHANTHAVIT, Anya
    • The Journal of Asian Finance, Economics and Business
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    • 제8권2호
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    • pp.1-13
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    • 2021
  • Previous studies have found the significant adverse effects of coronavirus disease 2019 (COVID-19) on stock returns and volatility. The effects varied with the confirmed cases and deaths. However, the extent of the effects have never been measured exactly. This study proposes a measurement model for the COVID-19 effects. In the proposed model, stock returns in the COVID-19 period are weighted averages of pre-COVID-19 normal returns and COVID-19-induced returns. The effects are measured by the contributing weights of the COVID-19-induced returns. Kalman filtering is used to estimate the model for the world and Chinese markets, in combination with 10 markets - five most affected countries (United States, India, Brazil, Russia, and France) and five best recovering countries (Hong Kong, Australia, Singapore, Thailand, and South Korea). The sample returns are daily, obtained from the closing Morgan Stanley global investable market indexes. The full period is from September 24, 2018, to October 30, 2020, whereas the COVID-19 period is from November 18, 2019, to October 30, 2020. The contributing weights are significant and close to 100% for all markets. The COVID-19-induced returns replace the pre-COVID-19 normal returns; they are negatively auto-correlated and highly volatile. The COVID-19-induced returns are new normal returns in the COVID-19 period.

기업합병의 성과에 영향을 주는 요인에 대한 실증적 연구 (The Gains To Bidding Firms' Stock Returns From Merger)

  • 김용갑
    • 경영과정보연구
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    • 제23권
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    • pp.41-74
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    • 2007
  • In Korea, corporate merger activities were activated since 1980, and nowadays(particuarly since 1986) the changes in domestic and international economic circumstances have made corporate managers have strong interests in merger. Korea and America have different business environments and it is easily conceivable that there exists many differences in motives, methods, and effects of mergers between the two countries. According to recent studies on takeover bids in America, takeover bids have information effects, tax implications, and co-insurance effects, and the form of payment(cash versus securities), the relative size of target and bidder, the leverage effect, Tobin's q, number of bidders(single versus multiple bidder), the time period (before 1968, 1968-1980, 1981 and later), and the target firm reaction (hostile versus friendly) are important determinants of the magnitude of takeover gains and their distribution between targets and bidders at the announcement of takeover bids. This study examines the theory of takeover bids, the status quo and problems of merger in Korea, and then investigates how the announcement of merger are reflected in common stock returns of bidding firms, finally explores empirically the factors influencing abnormal returns of bidding firms' stock price. The hypotheses of this study are as follows ; Shareholders of bidding firms benefit from mergers. And common stock returns of bidding firms at the announcement of takeover bids, shows significant differences according to the condition of the ratio of target size relative to bidding firm, whether the target being a member of the conglomerate to which bidding firm belongs, whether the target being a listed company, the time period(before 1986, 1986, and later), the number of bidding firm's stock in exchange for a stock of the target, whether the merger being a horizontal and vertical merger or a conglomerate merger, and the ratios of debt to equity capital of target and bidding firm. The data analyzed in this study were drawn from public announcements of proposals to acquire a target firm by means of merger. The sample contains all bidding firms which were listed in the stock market and also engaged in successful mergers in the period 1980 through 1992 for which there are daily stock returns. A merger bid was considered successful if it resulted in a completed merger and the target firm disappeared as a separate entity. The final sample contains 113 acquiring firms. The research hypotheses examined in this study are tested by applying an event-type methodology similar to that described in Dodd and Warner. The ordinary-least-squares coefficients of the market-model regression were estimated over the period t=-135 to t=-16 relative to the date of the proposal's initial announcement, t=0. Daily abnormal common stock returns were calculated for each firm i over the interval t=-15 to t=+15. A daily average abnormal return(AR) for each day t was computed. Average cumulative abnormal returns($CART_{T_1,T_2}$) were also derived by summing the $AR_t's$ over various intervals. The expected values of $AR_t$ and $CART_{T_1,T_2}$ are zero in the absence of abnormal performance. The test statistics of $AR_t$ and $CAR_{T_1,T_2}$ are based on the average standardized abnormal return($ASAR_t$) and the average standardized cumulative abnormal return ($ASCAR_{T_1,T_2}$), respectively. Assuming that the individual abnormal returns are normal and independent across t and across securities, the statistics $Z_t$ and $Z_{T_1,T_2}$ which follow a unit-normal distribution(Dodd and Warner), are used to test the hypotheses that the average standardized abnormal returns and the average cumulative standardized abnormal returns equal zero.

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COVID-19 Pandemic and the Reaction of Asian Stock Markets: Empirical Evidence from Saudi Arabia

  • SHAIK, Abdul Rahman
    • The Journal of Asian Finance, Economics and Business
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    • 제8권12호
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    • pp.1-7
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    • 2021
  • The study examines the influence of COVID-19 on the stock market returns of Saudi Arabia. The data was analyzed through event study methodology using daily price data of Tadawul All Share Index (TASI). The study examines the behavior pattern of the Saudi Arabian stock market in different phases during the event period by selecting six-event windows with a range of 10 days. The results report a negative Abnormal Return (AR) of -0.003 on the event date, while the abnormal returns reversed the next day to 0.005 positively. The result of Cumulative Abnormal Return (CAR) is negative and significant at the 1 percent level in all the six-event windows starting from the event date to day 59 after the event for the TASI index. Even though the influence of the COVID-19 pandemic decreased after 30 days of the event date, it increased during the last ten days of the event window. The stock market volatility of Saudi Arabia increased during the post-event period compared to the pre-event period with a negative mean return of -0.326 and a greater standard deviation. In a conclusion, the study found a significant influence of the COVID-19 pandemic on the stock market returns of TASI.

The Day of the Week Effect in Chinese Stock Market

  • Lu, Xing;Gao, Han
    • The Journal of Asian Finance, Economics and Business
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    • 제3권3호
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    • pp.17-26
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    • 2016
  • This study investigates daily stock market anomalies in Chinese stock market, using nine most representative stock indices over an eleven year time period spanning from pre-financial crisis era to six years into the financial crisis. This research is the first to test the presence of the day of the week effect on stock returns in the Chinese stock exchanges during the financial crisis. We find that the day of week effects have been strongly significant in Chinese stock exchanges since 2004. However, unlike the previously found negative Monday effect and positive Friday effect in the U.S., Chinese stock market shows positive returns on Mondays and negative returns on Tuesdays. More importantly, the negative Tuesday effect is only significant after the inception of financial crisis. The results indicate a positive effect on Mondays and a negative effect on Thursdays. More importantly, we find a negative Tuesday effect during the financial crisis, which suggests a spillover of the Monday effect from the U.S. stock market. Our results shed some light on the degree of market efficiency in the largest emerging capital market in the world, and its increasingly close relationship with the U.S. capital market.

An Empirical Investigation on the Interactions of Foreign Investments, Stock Returns and Foreign Exchange Rates

  • Kim, Yoon-Tae;Lee, Kyu-Seok;Shin, Dong-Ho
    • Communications for Statistical Applications and Methods
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    • 제9권1호
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    • pp.141-154
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    • 2002
  • Foreign investors'shares and their influences on the Korean stock market have never been larger and greater before since the market was completely open to foreign investors in 1992 Quantitatively and qualitatively as well, as a result, changes in the patterns of foreign investments have caused enormous effects on the interactions of major macroeconomic indices of the Korean economy. This paper is intended to investigate the causal relations of the four variables, foreigners'buy-sell ratios, stock returns, ₩/$ exchange rates and $\yen$/$ exchange rates, over the two time periods of the pre-IMF (1996.1.1-1997.8.15) and the post-IMF (1997.8.16-2000.6.15) based on the daily data of the variables. Granger Causality Test, Forecast Error Variance Decomposition(FEVD) using VAR model and Impulse Response Function were implemented for the empirical analysis.

The COVID-19 Pandemic and Instability of Stock Markets: An Empirical Analysis Using Panel Vector Error Correction Model

  • ABDULRAZZAQ, Yousef M.;ALI, Mohammad A.;ALMANSOURI, Hesham A.
    • The Journal of Asian Finance, Economics and Business
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    • 제9권4호
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    • pp.173-183
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    • 2022
  • The objective of this research is to examine the influence of the COVID-19 pandemic on stock markets in a few developing and developed countries. This study uses daily data from January 2020 to May 2021 and obtained from World Health Organization and Thomson Reuters. The secondary data was evaluated through panel econometric methodology that includes different unit root tests, and to analyze the long-run relationship between variables, panel cointegration techniques were applied. The long-run causality among variables was examined through Panel Vector Error Correction Model. The overall findings of this study suggest a long-run association exists between several cases and death with the stock returns of the GCC and other stock markets. Furthermore, the VECM model also identified a long-run causality running from COVID cases and death towards the stock rerun of both sets of stock markets. However, a subsequent Wald test yielded mixed results, indicating no short-run causality between cases and deaths and stock returns in both groups; however, in the case of GCC, several COVID-19 cases are having a causal impact on stock markets, which is notable in light of the fact that the death rate in GCC is significantly lower than in many developed and developing countries.

Dependence Structure of Korean Financial Markets Using Copula-GARCH Model

  • Kim, Woohwan
    • Communications for Statistical Applications and Methods
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    • 제21권5호
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    • pp.445-459
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    • 2014
  • This paper investigates the dependence structure of Korean financial markets (stock, foreign exchange (FX) rates and bond) using copula-GARCH and dynamic conditional correlation (DCC) models. We examine GJR-GARCH with skewed elliptical distributions and four copulas (Gaussian, Student's t, Clayton and Gumbel) to model dependence among returns, and then employ DCC model to describe system-wide correlation dynamics. We analyze the daily returns of KOSPI, FX (WON/USD) and KRX bond index (Gross Price Index) from $2^{nd}$ May 2006 to $30^{th}$ June 2014 with 2,063 observations. Empirical result shows that there is significant asymmetry and fat-tail of individual return, and strong tail-dependence among returns, especially between KOSPI and FX returns, during the 2008 Global Financial Crisis period. Focused only on recent 30 months, we find that the correlation between stock and bond markets shows dramatic increase, and system-wide correlation wanders around zero, which possibly indicates market tranquility from a systemic perspective.

국내 주식시장에서 주가급락위험이 기대수익률에 미치는 영향 (Left-tail Risk and Expected Stock Returns in the Korean Stock Market)

  • 전용호;반주일
    • 한국콘텐츠학회논문지
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    • 제21권11호
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    • pp.320-332
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    • 2021
  • 본 연구는 국내 주식시장에서 개별종목의 주가급락위험을 과거 1년간 일별수익률의 VaR(Value-at-Risk) 통계량으로 정의하고, 주가급락위험이 기대수익률에 미치는 영향을 분석하였다. 결과는 다음과 같이 요약된다. 첫째, 전체 종목을 전월의 주가급락위험의 크기 순으로 10개의 포트폴리오로 나눈 후, 주가급락위험이 가장 높은 포트폴리오를 매수하고 가장 낮은 포트폴리오를 공매도하여 매월 구성한 무비용 포트폴리오는 월평균 -2.29%의 수익률(주가급락위험 프리미엄)을 나타낸다. 둘째, Fama-MacBeth 횡단면 회귀분석에서 기업규모, 장부가대시장가비율, 시장베타, 유동성, 최대수익률, 고유변동성, 왜도 등의 다양한 기업특성변수를 통제한 후에도 전월의 주가급락위험은 금월 수익률에 대해 유의한 음(-)의 설명력을 갖는다. 셋째, 최근 1개월 이내에 주가급락폭이 큰 종목일수록 다음 달 수익률이 더 낮다. 넷째, 전월 시장수익률의 변동성과 주가급락위험 프리미엄의 크기는 음(-)의 상관관계를 갖는다. 이러한 결과는 주가급락위험에 대해 투자자들이 과소반응하는 경향으로 인해 주가급락위험이 높은 종목일수록 주가가 고평가된다는 행태재무학적 관점에서의 가설을 지지한다.

Is it possible to forecast KOSPI direction using deep learning methods?

  • Choi, Songa;Song, Jongwoo
    • Communications for Statistical Applications and Methods
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    • 제28권4호
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    • pp.329-338
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    • 2021
  • Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.