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- 검색키워드
- 저자 : Bonha Koo (검색결과 6 건)
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Dividend Month Premium in the Korean Stock Market
( Bonha Koo ),( Joon Chae ) 한국파생상품학회(구 한국선물학회) 2020 선물연구 Vol.28 No.2
The dividend month premium is the phenomenon that firms have abnormal returns in predicted dividend month. This study aims to examine the dividend month premium in the Korean stock market, using common stocks listed on the KOSPI and KOSDAQ from January 1999 to December 2016. Abnormal returns are estimated using the following asset price models: capital asset pricing model, Fama-French three-factor model and the Fama-French-Carhart four-factor model. This study finds positive abnormal returns in predicted dividend months, and even for the within-firm portfolio that buys stocks in the predicted dividend months and sells the same stocks in other months. The price impact and the subsequent reversals are greater with lower liquidity and higher dividend yield, implying that the price pressure from dividend-seeking investors affects this dividend month premium. Also, the risk-adjusted returns with the pre-declaration stock are smaller compared to the post-declaration stock, suggesting the necessity to improve the cash dividend policy of post-declaration for market efficiency.
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Whose option ratios contain information about future stock prices?
( Bonha Koo ),( Ryumi Kim ) 한국파생상품학회 2024 선물연구 Vol.32 No.1
Using the next-day and next-week returns of stocks in the Korean market, we examine the association of option volume ratios - i.e. the option-to-stock (O/S) ratio, which is the total volume of put options and call options scaled by total underlying equity volume, and the put-call (P/C) ratio, which is the put volume scaled by total put and call volume - with future returns. We find that O/S ratios are positively related to future returns, but P/C ratios have no significant association with returns. We calculate individual, institutional, and foreign investors’ option ratios to determine which ratios are significantly related to future returns and find that, for all investors, higher O/S ratios predict higher future returns. The predictability of P/C depends on the investors: institutional and individual investors’ P/C ratios are not related to returns, but foreign P/C predicts negative next-day returns. For net-buying O/S ratios, institutional net-buying put-to-stock ratios consistently predict negative future returns. Institutions’ buying and selling put ratios also predict returns. In short, institutional put-to-share ratios predict future returns when we use various option ratios, but individual option ratios do not.
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인터넷 검색량을 이용한 이익공시에서의 투자자 정보수요 연구
구본하(Bonha Koo),김류미(Ryumi Kim) 한국인터넷전자상거래학회 2020 인터넷전자상거래연구 Vol.20 No.1
Recent studies use internet search volume as a measure of investor information demand and attention. We examine that factors that influence investor information demand around earnings announcements which are representative events for investors to need to know regarding profits of firms. We show investor information demand captured by abnormal internet search frequency significantly increases around earnings announcements. We also find that firms with higher unexpected earnings, more individual turnover, and smaller market capitalization have more investor information demand. To reveal market reaction to earnings surprises and search intensity, the cumulative abnormal returns are calculated for the earnings announcement date and the post-earnings announcement. Our evidence indicates that firms with more investor information demand and attention have stronger announcement-day reactions, but only earnings surprises have positive relation with cumulative abnormal returns for post-earnings periods.
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Market returns and Investor sentiment measured by Internet search volume
Joon Chae,Hyungjoo Kim,Bonha Koo 한국재무학회 2017 한국재무학회 학술대회 Vol.2017 No.05
We propose a new measure of investor sentiment, using weekly Internet search volume data of words in Korea from NAVER DataLab—Financial and Economic Attitudes Revealed by Search (FEARS) index, which reflect households’ economic concerns. We find that the FEARS index: (1) relates to a negative contemporary return and reverses after three weeks; (2) coincides with a temporary increase in volatility; (3) simultaneously induces a shift in trading behavior from risky to safe assets, which reverses after three weeks; and (4) is mostly affected by individual investors. Overall, these results are consistent with behavioral finance, which suggests that investor sentiment leads to mispricing that is subsequently corrected.
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Hyoung-Goo Kang,Kyounghun Bae,Joon Chae,Bonha Koo 한국경영정보학회 2019 한국경영정보학회 학술대회논문집 Vol.2019 No.11
We examine how investor emotions and Bitcoin price influence each other using high-frequency data and NLP analysis. Emotions regarding Bitcoin are extracted from an online chatting window at one of the largest cryptocurrency exchanges in Korea. To control for global factors, we analyze relative Bitcoin prices, Korean premium, and differences between Korean exchange and other global prices. We aggregate intraday high-frequency data at five-minute intervals from October 8, 2017 to January 23, 2018. The identified emotions predict the return and volatility of Korean premium five minutes ahead. The results are economically significant: simple trading strategies using the relationship between emotions and Bitcoin prices generate profits. Consequently, investor emotions drive Bitcoin prices, suggesting irrational crypto-markets that rational speculators can exploit, but policy makers need to address.
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Informational Content of Exchange Flows in Cryptocurrency Markets
Kyoung-hun Bae,Hyoung-Goo Kang,Jaehyun Kim,Bonha Koo 한국자료분석학회 2020 Journal of the Korean Data Analysis Society Vol.22 No.3
How predictable are the prices and liquidities of cryptocurrencies listed at different exchanges? We analyze price, trading volume, and in-and-out flows of Bitcoin and Ethereum at multiple crypto-exchanges using cross-sectional analysis. We examine the intraday data at hourly intervals from January 1st, 2018 to September 30th, 2019. The results show that returns, trading volumes, and net flows in different crypto-exchanges predict each other significantly. In particular, the movement in big exchanges presents a disproportionately large predictive impact on the movement in other markets. Therefore, inefficiencies are prevalent in crypto-markets, and the cross-sectional analysis for exchanges is essential for traders in the market.
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