Abstract
This study analyzes the impact of the information environment (IE) and credit default swap (CDS) transaction costs on information transmission between the stock and CDS markets. Using the daily regression analysis on the Korean firm’s stock and CDS data from 2004 to 2023, the results show that companies with superior IE in the stock market exhibit a larger and more sensitive total information flow from the stock market to the CDS market. Companies with lower transaction costs in the CDS market demonstrate faster information flow. In the case of companies with superior IE, fundamental information is reflected in stock prices with high weight and thus the CDS spreads change reflecting information about stock prices. According to this study’s findings, the primary factor influencing the information flow from the stock market to the CDS market is the information environment of the company in the stock market, rather than transaction costs in the CDS market.
Keywords
Citation
Park, H. and Park, Y.J. (2024), "Does information environment affect information spillover between the CDS and stock markets in Korea?", Journal of Derivatives and Quantitative Studies: 선물연구, Vol. 32 No. 2, pp. 145-158. https://doi.org/10.1108/JDQS-09-2023-0029
Publisher
:Emerald Publishing Limited
Copyright © 2024, Heewoo Park and Yuen Jung Park
License
Published in Journal of Derivatives and Quantitative Studies: 선물연구. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
With recent rise in interest rates, concerns about economic downturns have increased, leading to increased attention in the credit default swap (CDS) markets. According to the Financial Supervisory Service (FSS) of Korea [1], the CDS trading volume in 2022 reached a historic high of 26.6tn won. This is approximately three times higher than 7.9tn won in 2021 and 10.1tn in 2020. If high-interest rates persist, and the likelihood of corporate insolvency increases, the CDS market is expected to garner even more attention.
The CDS is a derivative instrument that allows a company to trade credit events. It is standardized compared with corporate bonds, enabling it to serve as a proxy for corporate credit risk. The CDS spreads are determined by changes in the value of a company with the underlying asset, establishing an inherent correlation with the stock price of the same company. Numerous prior studies (e.g. Acharya and Johnson (2007), Norden and Weber (2009), Wang and Bhar (2014), Hilscher et al. (2015), Park et al. (2021)) have highlighted the existence of a lead-lag relationship between the US stock and CDS markets, indicating mutual information flow.
Acharya and Johnson (2007) discovered that during periods of overall credit market deterioration or for firms experiencing credit deterioration, information flows from the CDS market to the stock market. Wang and Bhar (2014) demonstrate that during high market volatility, there is significant information flow from the stock market to the CDS market for investment-grade firms, while during low market volatility; this flow is significant for non–investment-grade firms. While there are no consistent research results regarding the direction of information flow, a general observation from the analysis of a large dataset of the US firms by Hilscher et al. (2015) indicates that informed trades primarily occur in the stock market because of high transaction costs, leading to information flow from the stock market to the CDS market. Furthermore, Park et al. (2021) discovered, through the analysis of stock prices and CDS spreads of the US firms, that the information environment of a company significantly influences the information transmission from the stock market to the CDS market. Bae et al. (2010) find that, in Korea, stock prices predict the CDS spreads in a negative direction, indicating that the stock market leads the CDS market.
This study aims to build upon the insights of Park et al. (2021) and extend them beyond the findings of Bae et al. (2010) in the Korean market. Specifically, we examined how the information environment of companies in the Korean market influences price movements from the stock market to the CDS market. According to Easley et al. (2002), companies have distinct information environments in the stock markets. We aim to demonstrate that variations in the information environment of companies can significantly influence the flow of information from the stock market to the CDS market. As the fundamental information of companies is reflected more smoothly in the stock market, investors in the CDS market are also expected to engage in more sensitively reflected transactions. Consequently, it is anticipated that the sensitivity and quantity of the information flow will increase. Additionally, this study examines how information flow changes when arbitrage opportunities are limited because of increased transaction costs in the CDS market. This analysis was compared with the impact of the information environment. Through these investigations, we aim to enhance our understanding of the process by which information from the stock market is reflected in the CDS market.
Following the methodology of Park et al. (2021), we explore the foundations of the factors influencing the information flow from the stock market to the CDS market using the two key concepts. The first concept is sensitivity, which signifies the overall quantity of information flow. The CDS spreads change when the value of a company with an underlying asset fluctuates. If the intrinsic value of the company is appropriately reflected in the stock price, the CDS spread will also be influenced by stock market information. In other words, as the degree to which the intrinsic value of a company is reflected in the stock price increases, the total quantity and sensitivity of the information flow reflected from the stock price to the CDS spread will also increase. Conversely, if the stock market reflects a significant amount of noise beyond the intrinsic value of the company, the total quantity of information flow from the stock market to the CDS market is expected to decrease. The second concept is the speed of information flow. When there are changes in fundamental managerial information, such as sales, profits and risks, both stock prices and the CDS spreads are expected to reflect this information similarly. However, due to differences in transaction costs for each company, the speed at which information is reflected can vary. Changes in fundamental information of companies are likely to be prioritized in companies with lower transaction costs before being reflected in companies with higher transaction costs.
In particular, the difference in the speed of information transmission due to variations in transaction costs among the companies is more significantly influenced by transaction costs in the CDS market. Generally, transaction costs in the CDS market are higher than those in the stock market, and it takes longer for fundamental information of companies to be reflected in the CDS market. In other words, for companies with higher transaction costs in the CDS market, arbitrage opportunities diminish, and the speed of information reflection in the CDS spreads slows down. However, as changes in a company’s fundamental information are ultimately reflected in the CDS spread of each company; differences exist only in the speed of information reflection, while the total quantity of information flow remains constant. We focus on the impact of a company’s information environment and the CDS transaction costs on the sensitivity (total quantity) and speed of information flow.
This study analyzes Korean companies with both the CDS spreads and stock prices from 2004 to 2023. The information flow between the CDS and stock markets was examined through the lead-lag relationship between credit protection return (CPR), a surrogate for CDS returns and stock returns. The corporate information environment was assessed using R2 (Dasgupta et al., 2010; Rajgopal and Venkatachalam, 2011; Li et al., 2014). Additionally, DELAY, which was influenced by Hou and Moskowitz (2005), was used in the analysis. R2 is a value obtained when conducting a regression analysis of a firm’s stock returns against the market and industry returns. A low R2 value indicates that the firm has a higher number of errors not reflected in its stock returns, implying a less favorable information environment. Furthermore, a high DELAY signifies lower liquidity and higher transaction costs, indicating an unfavorable information environment for the stock market.
The main findings of this study are summarized as follows: first, using recent data from our daily regression analysis, we discovered that stock returns significantly predict CPRs, with an approximate lag of five days in the Korean market. Second, when dividing companies based on their information environments and conducting the regression analysis, the sensitivity (total effect) of information transmission was higher for companies with favorable information environments. Third, as the transaction cost in the CDS market, represented by the CDS bid-ask spread, decreased, information in the stock market was reflected more rapidly in the CDS market.
The originality of this study lies in the following: first, it analyzes the sample with the longest period among studies on CDS for Korean firms, incorporating recent data. Second, while Park et al. (2021) explore the impact of asymmetric information on the information transmission from the stock market to the CDS market based on the US company data, this study analyzes the impact of a company’s information environment on the information transmission using Korean company data. Third, this is the first study to explore the impact of a company’s information environment on the information transmission from the stock market to the Korean CDS market. Studies on individual companies’ CDS, including those by Bae et al. (2010), generally have shorter sample periods and often do not incorporate recent data. Moreover, recent studies using current data have primarily focused on analyzing the information transmission between stock market indices and sovereign CDS spreads, rather than conducting analyses specifically related to individual companies. In addition, no existing studies have intricately analyzed information transmission from both speed and total effect perspectives, as conducted in this study. This study contributes significantly to the literature by utilizing recent Korean CDS data when interest in the CDS market is high, enhancing our understanding of price correlations between the stock and CDS markets.
The remainder of this paper is organized as follows: in Section 2, we explain the stock and CDS data used in our analysis as well as the variables related to the information environment. Section 3 conducts a daily regression analysis to analyze the information flow between the stock and CDS markets. Additionally, we examine how the results of the regression analysis vary based on a company’s information environment and CDS transaction costs. Finally, Section 4 summarizes the study and reviews its findings.
2. Data and variable description
2.1 CDS and stock price
We focused on 17 Korean companies for which the stock prices and CDS data were available simultaneously. The analysis covers October 2004–February 2023 and examines daily data for both CDS and stock prices. The CDS data used in the analysis pertain to unsecured senior US dollar-denominated bonds as the underlying assets, collected through Bloomberg. Daily CDS spreads were used for the five-year maturity spreads, which were the most actively traded and listed for the most prominent companies. The CDS bid-ask spread was computed using monthly data, with the calculation performed at the end of each month by dividing the difference between the ask and bid spreads by the average of the two values.
The yield generated in the CDS market is defined as CPR. CPR represents the inherent return obtained based on spread changes when trading a CDS. Following Hilscher et al. (2015), the CPR at time t is calculated as the yield from buying the CDS spreads at time t-1 and selling them at time t. This formula is expressed as follows:
We use CPR as the CDS return concept that corresponds to stock returns. Daily data on individual company stock, market and sector-specific index returns were collected using the Korea Exchange (KRX) information data system. Market returns are based on the KOSPI, whereas sector-specific index returns are derived from the industry-specific indices among the KOSPI constituents.
2.2 Information environment
The corporate information environment (IE) variable is computed using R2 and DELAY. R2 and DELAY indicate the high or low information environment of a company in the stock market, and they are derived through regression analysis formulas, such as Equations (2) and (3).
Here,
Regarding R2, various studies on the information environment have been conducted. Li et al. (2014) empirically find that companies with a high R2 have a superior information environment. Rajgopal and Venkatachalam (2011) demonstrate that companies with low R2 or high idiosyncratic volatility of stock returns have a poorer information environment. Dasgupta et al. (2010) argue that R2 tends to be higher when the information environment of stock prices is favorable. Kelly (2014) empirically demonstrates that in stocks with low R2, information-based investing becomes challenging, leading to reduced liquidity. In summary, a higher R2 value indicates a superior information environment for a company [2].
The second regression analysis equation is as follows:
Here,
According to Hou and Moskowitz (2005), when market frictions significantly impact stocks, there is a considerable delay in reflecting market information in stock prices. In other words, when market friction is high, DELAY tends to increase. Consequently, companies with high DELAY values are considered to have lower liquidity and incur higher transaction costs, indicating a less favorable information environment.
In this study, we referred to Park et al. (2021) to calculate the information environment (IE) variables using R2 and DELAY. Monthly rankings of the companies based on R2 (denoted as
2.3 Descriptive statistics
In Table 1, we examine the descriptive statistics of companies with high and low IE to understand the differences in their characteristics based on the corporate IE. High and low IE companies were defined each month using the median IE value for all companies. Panels A and B provide the descriptive statistics for these groups.
Table 1 presents the mean, first-quartile, median and third-quartile values for companies with high IE (Panel A) and low IE (Panel B) across various variables. The variables include CDS spread, market capitalization, CDS bid-ask spread, trading volume (daily averaged), market beta, leverage ratio, Amihud illiquidity index, R2 and DELAY. Market capitalization, trading volume, market beta and the Amihud illiquidity index were collected using the KRX information data system. The debt ratio is calculated as the ratio of total debt to total equity, using the data provided by FnGuide. The debt ratio was computed using annual financial information after excluding financial institutions.
The summary of the results from Table 1 is as follows: In Panel A, companies with high IE exhibit lower CDS spread, CDS bid-ask spread, debt ratio, Amihud illiquidity index and DELAY than those with low IE in Panel B. Additionally, Panel A shows higher market capitalization, trading volume, market beta and R2. In other words, companies with high IE appear to have characteristics such as larger market capitalization, higher trading volume and a lower illiquidity index.
3. Empirical analysis
3.1 Analysis for information flow from stock market to CDS market
In this section, we analyze the information flow from the stock market to the CDS market by examining the reaction of CPR to changes in stock returns. Numerous studies have reported on information flows from the US stock market to the CDS market. Hilscher et al. (2015) argue that this phenomenon in the CDS market is attributed to higher transaction costs than the the stock market, making arbitrage difficult and a slower speed of information reflection. Therefore, we investigate whether a similar lead-lag relationship in information reflection exists in the Korean stock and CDS markets. Specifically, we can examine the lead-lag relationship between the stock returns and CPR by conducting a regression analysis with CPR as the dependent variable.
Table 2 presents the coefficients
The interpretation of Table 2 can be illustrated with a simple example as follows: examining the results for a lag of five days (s = 5), the value of
In Table 2,
Moreover, the cumulative sum of coefficients
3.2 The impact of information environment
The information flow from the stock market to the CDS market can vary depending on how effectively a firm’s fundamental information is reflected in its stock prices. In other words, the extent to which intrinsic information is incorporated into stock prices may differ based on the informational environment of the firm in the stock market. The speed and sensitivity (magnitude) of the information transmission can vary accordingly.
In this study, we define the informational environment as a firm’s informational environment in the stock market. We examine how variations in stock returns contingent on IE manifest in differences, such as the lag in reflection on CPR, significance and other related aspects.
Table 3 presents the results of the regression analysis (Equation 5) for firms with high and low IE levels in the stock market. Specifically, each month, firms with an IE above the median are defined as those with a high informational environment, while those with an IE below the median are defined as those with a low informational environment. The regression coefficients
In
The regression results in Table 3 exhibit a similar pattern to Table 2 overall. The absolute value of the coefficient
When examining the results of Table 3 for companies with high and low levels of IE, the absolute values of the coefficient
The cumulative sum of the coefficients indicates that, overall, higher absolute values are observed for companies with high IE. For instance, at s = 5, the cumulative sum of coefficients for companies with high IE is −0.73, whereas for those with low IE, it is −0.49. Similarly, at s = 10, the cumulative sum of coefficients for companies with high and low IE is −0.68 and −0.50, respectively. In other words, higher absolute values of the cumulative sum result from a larger total amount of information transfer in companies with high IE. The flow of information from the stock market to the CDS market is more active in companies with high IE.
Examining the t-values, companies with high IE show statistical significance from s = 0 to 5, whereas for companies with low IE, significance is observed from s = 0 to 5, excluding s = 4. Moreover, for time lags of s = 6 and above, most values were either not statistically significant or exhibited positive values, lacking meaningful results. Essentially, there is a higher frequency of statistically significant outcomes for companies with high IE.
Furthermore, the absolute values of the coefficient differences presented in the last column tend to decrease as the time lag increases. For s = 0 and 1, the difference in coefficients is noted as −0.114 and −0.057, respectively, but for s = 5, this absolute value decreases to −0.004. Although there is a slight increase in the absolute values of coefficients for s = 6 and 7, interpreting these results as intuitively meaningful is challenging due to the lack of statistical significance in
In summary, companies with a higher level of IE in the stock market exhibit a more sensitive and statistically significant flow of information from the stock market to the CDS market than do companies with a lower IE level. This significance was particularly pronounced when the time lag was smaller. However, the cumulative sum of the coefficients indicates that, across all time lags, companies with a higher information environment consistently demonstrate a larger total flow of information. Therefore, the overall quantity of information flows is higher for companies in superior information environments.
The heightened sensitivity and substantial quantity of the information transmission from the stock market to the CDS market in companies with a high information environment can be attributed to the following reason: in stock markets, companies with a high-information environment witness a more accurate reflection of essential information related to changes in their business activities in stock prices. Consequently, stock price mispricing is reduced.
Certainly, investors in the CDS market are more likely to base their investments on fundamental information changes related to a company’s business activities rather than information already reflected in the stock market. However, as the information environment becomes more robust, resulting in reduced pricing errors in stock markets and more accurate reflections of fundamental company information, the CDS market tends to closely align with and reflect information from the stock market to a greater extent.
In essence, the total quantity of information flow is higher in companies with high IE, where fundamental information is fully reflected in stock prices. On the other hand, in companies with a low IE, even when there are changes in the business environment, fundamental information may not be smoothly reflected in stock prices, leading to a higher proportion of errors. Consequently, investors in the CDS market, focusing on fundamental information rather than stock prices, result in a decreased total quantity of information flow from the stock market to the CDS market.
Meanwhile, IE is derived from R2 and DELAY, defined as the sum of
3.3 The impact of CDS bid-ask spread
Differences in the transaction costs of individual companies in the CDS market can be a crucial factor influencing the information flow from the stock market to the CDS market. As transaction costs increase in the CDS market, it becomes more challenging for information reflected in the stock market to be readily incorporated into the CDS market. In other words, even if informed traders identify arbitrage opportunities in the CDS market, they engage in trading only if they can generate profits that exceed transaction costs. Various variables could represent transaction costs in the CDS market, but the bid-ask spread can serve as a direct indicator of transaction costs, representing the additional costs investors actually incur when buying or selling. We aim to investigate how the bid-ask spread among the individual companies in the CDS market influences the transmission of information from the stock market to the CDS market.
Table 4 presents the results of the regression analyses for companies with high and low CDS bid-ask spreads based on Equation (5). In this context, companies with high and low CDS bid-ask spreads are classified using the median of the monthly bid-ask spreads for all companies as the threshold. In Table 4, similar to Table 3, it presents regression analysis results, showing
Table 4 presents results that differ from those in Table 3. In Table 3, companies with high IE mostly exhibited larger absolute values for
On the contrary, as the time lag s increases, the magnitude of
The results from Table 4 suggest that, unlike Table 3, the transaction costs in the CDS market may not significantly impact the overall quantity of information flowing from the entire stock market to the CDS market. However, it can be interpreted that these transaction costs influence the speed of information flow. If transaction costs are low in the CDS market, investors can swiftly engage in arbitrage in the CDS market and thus, information reflected in stock prices is quickly incorporated without significant time lags, also reflecting in the CPR. Conversely, when transaction costs are high, investors face challenges in swift arbitrage, resulting in inadequate information transmission from the stock market to the CDS market for small time lags (s = 0, 1 and 2). In other words, a delay in information transmission occurs because of elevated transaction costs in the CDS market, leading to a difference in the speed of information flow. However, as discussed earlier in Table 3, companies with a superior information environment in the stock market have a higher proportion of fundamental information reflected directly in stock prices, leading to an increase in the quantity of information flow. In summary, the transaction costs in the CDS market influence the speed of information flow from the stock market to the CDS market. However, they do not have a significant impact on the overall quantity of the ultimate information flow.
3.4 Analysis for information flow from the CDS market to stock market
Until now, we have continued the discussion on the information flow from the stock market to the CDS market. However, it is also essential to examine the reverse direction information flow from the CDS market to the stock market. Wang and Bhar (2014) and Hilscher et al. (2015) demonstrate that the information flow from the CDS market to the stock market is not statistically significant. In contrast, Acharya and Johnson (2007) argue for the significance of information flow based on their analysis of 79 companies over specific points in time from 2001 to 2004. In our study, we investigate whether the CDS market leads the stock market during the analysis period for the selected Korean companies. We also explore how this information flow is influenced by changes in IE in the stock market. Specifically, we can examine the lead-lag relationship between stock returns and CPR through the following regression equation (6)
Table 5 presents the coefficients
In Table 5, only
Table 6 presents the results of regression equation (6) for companies with high and low IE levels in stock markets. Each month, companies with an IE above the median are defined as those with a high IE while those with IE below the median are defined as those with a low IE. The table shows the
In Table 6, statistically significant
The reason for significant regression coefficients only appearing for companies with high IE (s = 0,1) is believed to be associated with informed trading in the CDS market. In companies with high IE, informed trading is likely to occur in both the CDS and stock markets. This suggests that there is a lead time of approximately one day for information flow from the CDS market to the stock market, indicating that trading activities in the CDS market precede those in the stock market for high IE companies.
Although the regression coefficients for such information flow may be very small, suggesting limited economic significance, the results in Table 6 are intriguing in indicating that variations in the IE in the stock market may influence the information flow from the CDS market to the stock market.
4. Conclusion
This study analyzes the impact of a firm’s information environment on the information flow between the stock and CDS markets. Using the data from Korean companies spanning the period from 2004 to 2023, the analysis reveals that in companies with a superior information environment in the stock market, there is more pronounced sensitivity in the information transmission from the stock market to the CDS market. Furthermore, for companies with low CDS bid-ask spreads, the speed of information transmission was faster.
We examined the lead-lag relationship between the stock returns and CPR through a daily regression analysis. When exploring the changes in CPR in response to variations in stock returns, we observed that companies with high IE exhibited larger absolute values of regression coefficients and higher significance. This suggests that in companies with a superior information environment, where stock prices reflect fewer errors, the CDS market reacts more strongly to information in the stock market, leading to an increase in the overall volume of information transmission.
Companies with low CDS bid-ask spreads exhibit significantly larger absolute values of regression coefficients in the negative direction than companies with high CDS bid-ask spreads only when the time lag is small. In contrast, the cumulative sum of the coefficients considering a 10-day lag did not show any significant differences based on CDS bid-ask spreads. The transaction costs in the CDS market do not affect the overall volume of information transmission from the stock market to the CDS market. However, this creates a significant difference in the speed of information transmission.
We also examined the changes in stock returns in response to variations in CPR, but it was evident that the information flow from the CDS market to the stock market exists only on the concurrent days. However, in companies with a superior information environment in the stock market, we discover that information flow is significant up to a one-day lag.
This study is meaningful in discovering that the information environment in the stock market and the transaction costs in the CDS market have different impacts on the information flow from the stock market to the CDS market, using the data from Korean companies. In companies with low CDS bid-ask spreads, the speed of information flow from the stock market to the CDS market was shortened. Additionally, for companies with a superior information environment in the stock market, it was found that not only the speed of information flow but also the overall volume of information transmission increased. Therefore, the findings of this study suggest that the information environment of the company has a greater impact on the information flow from the stock market to the CDS market than the transaction costs in the CDS market.
Summary statistics
Mean | P25 | Median | P75 | |
---|---|---|---|---|
Panel A: High IE firms | ||||
CDS spread(bp) | 71.3 | 41.0 | 64.1 | 85.4 |
Size(trillion won) | 51.7 | 13.9 | 20.8 | 34.4 |
CDS bid-ask spread(bp) | 0.18 | 0.12 | 0.16 | 0.21 |
Trading volume(billion won) | 148.4 | 39.1 | 60.4 | 134.9 |
Beta | 1.00 | 0.62 | 0.99 | 1.37 |
Leverage ratio(%) | 130 | 65 | 92 | 154 |
Amihud illiquidity(10ˆ-6) | 26.1 | 10.7 | 20.9 | 31.8 |
R2 | 0.75 | 0.62 | 0.77 | 0.90 |
DELAY | 0.19 | 0.06 | 0.13 | 0.24 |
Panel B: Low IE firms | ||||
CDS Spread(bp) | 80.2 | 44.2 | 69.6 | 94.7 |
Size(trillion won) | 14.5 | 8.7 | 12.3 | 18.5 |
CDS bid-ask spread(bp) | 0.22 | 0.13 | 0.18 | 0.25 |
Trading volume(billion won) | 64.0 | 27.6 | 42.0 | 70.9 |
Beta | 0.78 | 0.36 | 0.75 | 1.17 |
Leverage ratio(%) | 181 | 78 | 119 | 174 |
Amihud illiquidity(10ˆ-6) | 35.1 | 18.2 | 28.3 | 42.7 |
R2 | 0.45 | 0.24 | 0.47 | 0.63 |
DELAY | 0.43 | 0.21 | 0.36 | 0.61 |
Source(s): Table by authors
Effects of equity returns on CPRs
Lag | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|---|
−0.218 | −0.178 | −0.084 | −0.063 | −0.043 | −0.041 | −0.017 | 0.039 | 0.013 | −0.019 | −0.005 | |
(−6.45)*** | (−6.07)*** | (−4.29)*** | (−3.16)*** | (−2.71)*** | (−2.78)*** | (−0.98) | (1.58) | (0.74) | (−1.25) | (−0.28) | |
−0.218 | −0.395 | −0.479 | −0.542 | −0.585 | −0.625 | −0.643 | −0.603 | −0.591 | −0.609 | −0.614 |
Source(s): Table by authors
Effects of the equity returns of high- and low-IE firms on CPRs
Lag | High | Low | High – Low | ||
---|---|---|---|---|---|
0 | −0.267 (−6.07)*** | −0.27 | −0.153 (−7.41)*** | −0.15 | −0.114 |
1 | −0.207 (−5.94)*** | −0.47 | −0.151 (−4.59)*** | −0.30 | −0.057 |
2 | −0.100 (−4.44)*** | −0.57 | −0.064 (−3.47)*** | −0.37 | −0.036 |
3 | −0.060 (−2.12)** | −0.63 | −0.055 (−2.17)** | −0.42 | −0.005 |
4 | −0.054 (−3.09)*** | −0.69 | −0.026 (−1.61) | −0.45 | −0.027 |
5 | −0.045 (−2.89)*** | −0.73 | −0.041 (−2.46)** | −0.49 | −0.004 |
6 | −0.020 (−0.89) | −0.75 | 0.004 (0.24) | −0.49 | −0.024 |
7 | 0.021 (1.34) | −0.73 | 0.070 (1.32) | −0.42 | −0.049 |
8 | 0.043 (2.11)** | −0.69 | −0.026 (−1.32) | −0.44 | 0.069 |
9 | −0.004 (−0.16) | −0.69 | −0.035 (−2.03)** | −0.48 | 0.031 |
10 | 0.012 (0.59) | −0.68 | −0.025 (−1.35) | −0.50 | 0.037 |
Source(s): Table by authors
Effects of the equity returns of high- and low-bid ask spread firms on CPRs
Lag | High | Low | High – Low | ||
---|---|---|---|---|---|
0 | −0.201 (−5.65)*** | −0.20 | −0.273 (−5.66)*** | −0.27 | 0.072 |
1 | −0.143 (−3.44)*** | −0.34 | −0.175 (−5.17)*** | −0.45 | 0.033 |
2 | −0.048 (−2.24)** | −0.39 | −0.077 (−2.78)*** | −0.53 | 0.029 |
3 | −0.072 (−2.99)*** | −0.46 | −0.067 (−2.88)*** | −0.59 | −0.004 |
4 | −0.022 (−0.92) | −0.48 | −0.014 (−0.63) | −0.61 | −0.008 |
5 | −0.071 (−2.48)** | −0.56 | −0.014 (−0.72) | −0.62 | −0.057 |
6 | 0.002 (0.09) | −0.55 | −0.028 (−1.30) | −0.65 | 0.030 |
7 | 0.037 (1.53) | −0.52 | 0.085 (1.53) | −0.56 | −0.047 |
8 | 0.003 (0.1) | −0.51 | 0.017 (0.74) | −0.55 | −0.015 |
9 | −0.026 (−1.63) | −0.54 | −0.005 (−0.26) | −0.55 | −0.021 |
10 | −0.004 (−0.15) | −0.54 | −0.007 (−0.29) | −0.56 | 0.003 |
Source(s): Table by authors
Effects of CPRs on equity returns
Lag | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|---|
−0.025 | −0.007 | −0.001 | −0.001 | −0.002 | −0.001 | −0.003 | 0.000 | −0.006 | −0.001 | 0.004 | |
(−1.74)* | (−1.55) | (−0.62) | (−0.32) | (−0.55) | (−0.21) | (−0.66) | (−0.09) | (−1.52) | (−0.31) | (1.21) | |
−0.025 | −0.032 | −0.033 | −0.034 | −0.036 | −0.037 | −0.040 | −0.040 | −0.046 | −0.047 | −0.043 |
Source(s): Table by authors
Effects of the CPRs for high- and low-IE firms on equity returns
Lag | High | Low | High – Low | ||
---|---|---|---|---|---|
0 | −0.055 (−3.71)*** | −0.05 | −0.011 (−1.27) | −0.01 | −0.044 |
1 | −0.015 (−3.78)*** | −0.07 | −0.003 (−0.90) | −0.01 | −0.012 |
2 | −0.002 (−0.48) | −0.07 | 0.000 (−0.18) | −0.01 | −0.002 |
3 | −0.001 (−0.19) | −0.07 | −0.001 (−0.53) | −0.01 | 0.000 |
4 | −0.003 (−0.57) | −0.07 | −0.001 (−0.40) | −0.02 | −0.001 |
5 | −0.002 (−0.24) | −0.08 | 0.001 (0.27) | −0.02 | −0.003 |
6 | 0.001 (0.14) | −0.08 | −0.008 (−1.30) | −0.02 | 0.008 |
7 | 0.000 (−0.06) | −0.08 | −0.003 (−0.56) | −0.03 | 0.002 |
8 | −0.005 (−0.93) | −0.08 | −0.004 (−0.62) | −0.03 | −0.002 |
9 | 0.001 (0.15) | −0.08 | −0.007 (−1.02) | −0.04 | 0.007 |
10 | 0.002 (0.63) | −0.08 | 0.007 (1.34) | −0.03 | −0.005 |
Source(s): Table by authors
Notes
The CDS trading volume is based on the financial derivatives trading status statistics table reported by the FSS. The financial institutions included in the FSS’s transaction performance aggregation are banks (domestic and foreign branches), financial investment firms (domestic and foreign), insurance companies and others (collective investment managers, futures companies, securities firms and credit card companies).
Meanwhile, according to prior studies such as Morck et al. (2000), Durnev et al. (2003) and Durnev et al. (2004), for companies with high R2, stock price movements do not reflect intrinsic fundamental information specific to the company, distinct from market returns. This suggests that the information environment may not be favorable in such cases. However, in this study, we aim to interpret the meaning of R2 following prior research such as Li et al. (2014), Rajgopal and Venkatachalam (2011), Dasgupta et al. (2010) and Kelly (2014), which argue that companies with low R2 indicate a poor information environment due to significant pricing errors, in addition to considering the results of basic statistics presented in Table 1.
The supplementary material for this article can be found online.
References
Acharya, V.V. and Johnson, T.C. (2007), “Insider trading in credit derivatives”, Journal of Financial Economics, Vol. 84 No. 1, pp. 110-141, doi: 10.1016/j.jfineco.2006.05.003.
Bae, K., Kang, H. and Lee, C. (2010), “The lead-lag relationship between the stock market and CDS market in Korea”, Korean Journal of Futures and Options, Vol. 18 No. 4, pp. 1-22, doi: 10.1108/jdqs-04-2010-b0001.
Dasgupta, S., Gan, J. and Gao, N. (2010), “Transparency, price informativeness, and stock return synchronicity: theory and evidence”, Journal of Financial Quantitative Analysis, Vol. 45 No. 5, pp. 1189-1220, doi: 10.1017/s0022109010000505.
Durnev, A., Morck, R., Yeung, B. and Zarowin, P. (2003), “Does greater firm-specific return variation mean more or less informed stock pricing?”, Journal of Accounting Research, Vol. 41 No. 5, pp. 797-836, doi: 10.1046/j.1475-679x.2003.00124.x.
Durnev, A., Morck, R. and Yeung, B. (2004), “Value-enhancing capital budgeting and firm-specific stock return variation”, Journal of Finance, Vol. 59 No. 1, pp. 65-105, doi: 10.1111/j.1540-6261.2004.00627.x.
Easley, D., Hvidkjaer, S. and O'Hara, M. (2002), “Is information risk a determinant of asset returns?”, Journal of Finance, Vol. 47 No. 5, pp. 2185-2221, doi: 10.1111/1540-6261.00493.
Hilscher, J., Pollet, J.M. and Wilson, M. (2015), “Are credit default swaps a sideshow? Evidence that information flows from equity to CDS markets”, Journal of Financial Quantitative Analysis, Vol. 50 No. 3, pp. 543-567, doi: 10.1017/s0022109015000228.
Hou, K. and Moskowitz, T.J. (2005), “Market frictions, price delay, and the cross-section of expected returns”, Review of Financial Studies, Vol. 18 No. 3, pp. 981-1020, doi: 10.1093/rfs/hhi023.
Kelly, P.J. (2014), “Information efficiency and firm-specific return variation”, The Quarterly Journal of Finance, Vol. 4 No. 4.
Li, B., Rajgopal, S. and Venkatachalam, M. (2014), “R2 and idiosyncratic risk are not interchangeable”, Accounting Review, Vol. 89 No. 6, pp. 2261-2295, doi: 10.2308/accr-50826.
Morck, R., Yeung, B. and Yu, W. (2000), “The information content of stock markets: why do emerging markets have synchronous stock price movements?”, Journal of Financial Economics, Vol. 58 Nos 1-2, pp. 215e260-260, doi: 10.1016/s0304-405x(00)00071-4.
Norden, L. and Weber, M. (2009), “The CO-movement of credit default swap, bond and stock markets: an empirical analysis”, European Financial Management, Vol. 15 No. 3, pp. 529-562, doi: 10.1111/j.1468-036x.2007.00427.x.
Park, H., Kim, T.S. and Park, Y.J. (2021), “Asymmetric information in the equity market and information flow from the equity market to the CDS market”, Journal of Financial Markets, Vol. 55, 100607, doi: 10.1016/j.finmar.2020.100607.
Petersen, M.A. (2009), “Estimating standard errors in finance panel data sets: comparing approaches”, Review of Financial Studies, Vol. 22 No. 1, pp. 432-480, doi: 10.1093/rfs/hhn053.
Rajgopal, S. and Venkatachalam, M. (2011), “Financial reporting quality and idiosyncratic return volatility”, Journal of Accounting Economics, Vol. 51 Nos 1-2, pp. 1-20, doi: 10.1016/j.jacceco.2010.06.001.
Wang, P. and Bhar, R. (2014), “Information content in CDS spreads for equity returns”, Journal of International Financial Markets Institutions and Money, Vol. 30, pp. 55-80, doi: 10.1016/j.intfin.2014.01.005.
Acknowledgements
This research was funded by the Hallym University Research Fund (No: HRF-202309-003). All remaining errors are the authors'.