Search results

This study examines the bid/ask spread and its components in the KOSPI200 options market under the framework of the cross-market model, which utilizes the order flow information of both KOSPI200 futures and options markets. We also compare the results by the single-market model (MRR model; Madhavan et al., 1997) and by the cross-market model (Ryu (2011)’s extension). This comparison suggests that the cross-market approach can mitigate the underestimation of the permanent spread component of OTM options and the overestimation of the component of ITM options, which are often detected when we directly apply the single market model into the KOSPI200 options market where the ITM options are relatively illiquid while the OTM options are extremely liquid. We also find that the effect of the order flow information of the futures market on the option spread and its permanent spread component will vary depending on the option moneyness and the intraday time period. This implies that the order flow of the futures market has more significant effects if the degree of informed trading is relatively high.

The behavioral finance literature focuses on the effect of investor sentiment on the fundamental values of individual stocks. This study constructs a firm-level investor sentiment indicator based on transaction and price data for individual firms and shows that credit rating changes affect investor sentiment. We find the following empirical results. First, the response of investor sentiment to upgrades (downgrades) is significantly positive (negative). Second, the greater the magnitude of the downgrade is, the more negative the investor sentiment reaction is, although we do not find a similar result for upgrades. Third, cumulative abnormal returns around the event day are affected by cumulative abnormal sentiment before that day. This result suggests that the market reaction is affected by a combination of credit rating downgrades and investor sentiment.

This paper investigates the effects of introducing equity-linked warrants (ELWs) on the stock price, trading volume, volatility, and systematic risk (beta) by using the event study methodology. The study defines the event date as the announcement date as well as the listing date. In addition, whereas previous research has investigated only call ELWs, this study analyzes the effects of introducing both call and put ELWs.

The results provide no evidence of hedging effects of issuers before the announcement dates and information effects after the announcement dates. In addition, we can't find any significant changes of variables associated with the market completeness hypothesis near the listing dates.

However, the trading volume of the stock tends to increase in the days immediately following the listing of call ELWs, which may be due to the “informed trading effect”. The empirical results also provide support for the “diminishing short-sales restrictions” hypothesis related to the listing of put ELWs, which implies that short-sale restrictions can be reduced because put ELWs can provide investors with short positions in the underlying stock.

This paper investigates investment and hedging strategies using the KOSPI200 nighttime futures product which was launched at November 16th, 2009. To examine the performance of the investment strategies, we analyze one-minute transaction data of KOSPI200 daytime and nighttime futures from November 17th, 2009 to December 6th, 2012.

Our empirical results are as follows: First, the investment strategies using the nighttime futures significantly outperform the investment strategies based only on the daytime futures. Second, the investment and hedging strategies using the KOSPI200 nighttime futures are quite effective when investors have positions in the ETFs. Third, the empirical performance of the investment strategies using the nighttime futures is significantly related to volatility shocks. The strategies are more effective when the market is volatile.

This study analyzes small-sized asset owners’ optimal choice problems in selecting an outsourced chief investment officer (OCIO). While large-sized asset owners can select OCIOs through procurement auctions, it is difficult for small-sized asset owners to use this method. Instead, they access OCIO services by participating in an investment pool or utilizing OCIO funds. In this study, the authors compare the two OCIO selection methods. The authors construct an agent-based model for OCIO selection to reflect the heterogeneity in production efficiency and preferences. The results of this study imply that when the market has enough investment pools, the utility of all small-sized asset owners increases. To enhance the growth in the OCIO market, the investment pool should represent the preferences of small-sized asset owners and enable individual owners to find an appropriate OCIO.

This study examines the impact of social media investor sentiment on the stock market performance through qualitative and quantitative proxies.

The authors use a sample of daily stock performance related to S&P 500 Index for the period from December 18, 2017, to December 18, 2018. The social media investor sentiment was assessed through qualitative and quantitative proxies. For qualitative proxies, the study relies on three social media resources”: Twitter, Trump Twitter account and StockTwits. The authors proposed 3 methods to reflect investor sentiment. For quantitative proxies, the number of daily messages published from Trump Twitter account and StockTwits is considered as a signal of investor sentiment. For regression model, the study adopts the autoregressive distributed lagged to determine the relationships between the nonstationary series.

Empirical findings provide evidence that quantitative measures of investor sentiment have significant effects on S&P’500 performances. The authors find that Trump's tweets should be interpreted with caution. The results also show that the number of Trump's tweets on t−1 day have a positive effect on performance on day t.

Social media sentiment contains information for predicting stock returns and transaction activity. Since, the arrival of new information in capital markets triggers investor sentiment on social media.

This study investigates the investors’ sentiment through social media and explores quantitative and qualitative measures. The amount of information on social media reflects more the investor sentiment than content analysis measures.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0818