Pub Date : 2023-09-25DOI: 10.1108/cfri-12-2022-0250
Xiao Yao, Dongxiao Wu, Zhiyong Li, Haoxiang Xu
Purpose Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction. Design/methodology/approach Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques. Findings The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL). Research limitations/implications It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies. Originality/value The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.
{"title":"On the prediction of stock price crash risk using textual sentiment of management statement","authors":"Xiao Yao, Dongxiao Wu, Zhiyong Li, Haoxiang Xu","doi":"10.1108/cfri-12-2022-0250","DOIUrl":"https://doi.org/10.1108/cfri-12-2022-0250","url":null,"abstract":"Purpose Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction. Design/methodology/approach Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques. Findings The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL). Research limitations/implications It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies. Originality/value The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135768788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1108/cfri-04-2023-0088
Shaun Shuxun Wang
Purpose This paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies. Design/methodology/approach This paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth. Findings The author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment. Research limitations/implications The integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative. Practical implications Once supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit. Social implications The integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks. Originality/value This paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.
{"title":"Funding startups using contingent option of value appreciation: theory and formula","authors":"Shaun Shuxun Wang","doi":"10.1108/cfri-04-2023-0088","DOIUrl":"https://doi.org/10.1108/cfri-04-2023-0088","url":null,"abstract":"Purpose This paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies. Design/methodology/approach This paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth. Findings The author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment. Research limitations/implications The integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative. Practical implications Once supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit. Social implications The integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks. Originality/value This paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134996737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PurposeAccording to the Government Response tracker (oxCGRT) index, the overall government response, stringency, economic support, containment and health policies to COVID-19 from January 2020 to December 2022. The main objective of this paper is to explore how stock market performance is affected by these polices, respectively.Design/methodology/approachThe authors employ EGARCH and autoregressive distributional lag (ARDL) models to test the impact of epidemic prevention policy implementation on stock market returns, volatility and liquidity and make cross-country comparisons for six important world economies.FindingsFirstly, the implementation of various preventive policies hurts stock market returns and increases volatility, but there are a few indicators that have no effect or have an easing effect in some countries. Secondly, health policies exacerbate market volatility and have a stronger effect than other policy indicators. Thirdly, In China and the USA, anti-epidemic policies have been shown to worsen liquidity, while in Japan they have been shown to improve liquidity.Originality/valueFirst, enrich the growing body of COVID-19 research by comprehensively examining whether and how government prevention policies affect stock market returns, volatility and liquidity. Second, explore the impact of different types of intervention policies on stock market performance, separately.
{"title":"COVID-19, various government interventions and stock market performance","authors":"Helong Li, Huiqiong Chen, Guanglong Xu, Weiming Zhang","doi":"10.1108/cfri-03-2023-0068","DOIUrl":"https://doi.org/10.1108/cfri-03-2023-0068","url":null,"abstract":"PurposeAccording to the Government Response tracker (oxCGRT) index, the overall government response, stringency, economic support, containment and health policies to COVID-19 from January 2020 to December 2022. The main objective of this paper is to explore how stock market performance is affected by these polices, respectively.Design/methodology/approachThe authors employ EGARCH and autoregressive distributional lag (ARDL) models to test the impact of epidemic prevention policy implementation on stock market returns, volatility and liquidity and make cross-country comparisons for six important world economies.FindingsFirstly, the implementation of various preventive policies hurts stock market returns and increases volatility, but there are a few indicators that have no effect or have an easing effect in some countries. Secondly, health policies exacerbate market volatility and have a stronger effect than other policy indicators. Thirdly, In China and the USA, anti-epidemic policies have been shown to worsen liquidity, while in Japan they have been shown to improve liquidity.Originality/valueFirst, enrich the growing body of COVID-19 research by comprehensively examining whether and how government prevention policies affect stock market returns, volatility and liquidity. Second, explore the impact of different types of intervention policies on stock market performance, separately.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43057378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-29DOI: 10.1108/cfri-11-2022-0224
Q. Lu, Yangyang Deng, Xinyi Wang, Aiping Wang
PurposeAs an effective tool to promote rational resource allocation and facilitate the development of green management practices such as enterprise digital innovation, the green credit policy has recently gained extensive attention. The purpose of this paper is to analyze the relationship between green credit policies and the digital innovation of enterprises, and to further explore the mechanism of action between them and their boundary conditions.Design/methodology/approachBased on micro-level data on Chinese firms from 2007 to 2019, this paper constructs a difference-in-differences (DID) model to investigate the impact and intrinsic mechanisms of green credit policies on firms' digital innovation and its boundary conditions, with the help of a quasi-natural experiment, i.e. the Green Credit Guidelines.FindingsGreen credit policies inhibit digital innovation and fail to compensate for innovation. The analysis of the mechanism shows that the implementation of green credit policies has a negative impact on digital innovation by increasing the financing constraints faced by firms, and has also a crowding-out effect on R&D investment, resulting in a disincentive to digital innovation. Further analysis reveals that the negative impact of green credit policies on digital innovation is more pronounced in state-owned enterprises, enterprises without financially experienced executives, and in the eastern regions of China.Originality/valueThis study provides empirical evidence to understand the effectiveness and mechanism of influence of green credit policies on enterprise digital innovation, providing also a basis to further improve green credit policies.
{"title":"The impact of China's green credit policy on enterprise digital innovation: evidence from heavily-polluting Chinese listed companies","authors":"Q. Lu, Yangyang Deng, Xinyi Wang, Aiping Wang","doi":"10.1108/cfri-11-2022-0224","DOIUrl":"https://doi.org/10.1108/cfri-11-2022-0224","url":null,"abstract":"PurposeAs an effective tool to promote rational resource allocation and facilitate the development of green management practices such as enterprise digital innovation, the green credit policy has recently gained extensive attention. The purpose of this paper is to analyze the relationship between green credit policies and the digital innovation of enterprises, and to further explore the mechanism of action between them and their boundary conditions.Design/methodology/approachBased on micro-level data on Chinese firms from 2007 to 2019, this paper constructs a difference-in-differences (DID) model to investigate the impact and intrinsic mechanisms of green credit policies on firms' digital innovation and its boundary conditions, with the help of a quasi-natural experiment, i.e. the Green Credit Guidelines.FindingsGreen credit policies inhibit digital innovation and fail to compensate for innovation. The analysis of the mechanism shows that the implementation of green credit policies has a negative impact on digital innovation by increasing the financing constraints faced by firms, and has also a crowding-out effect on R&D investment, resulting in a disincentive to digital innovation. Further analysis reveals that the negative impact of green credit policies on digital innovation is more pronounced in state-owned enterprises, enterprises without financially experienced executives, and in the eastern regions of China.Originality/valueThis study provides empirical evidence to understand the effectiveness and mechanism of influence of green credit policies on enterprise digital innovation, providing also a basis to further improve green credit policies.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41441638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 10.1108/cfri-12-2022-0248
M. T. M. Garcia, Ana Jin Ye
PurposeThe aim of this paper is to study the relationship between banks' ownership structure and their risk-taking behavior as well as the impact of banking regulation on banks' approach to taking risk, after the 2008 financial crisis.Design/methodology/approachThe empirical analysis considers a sample of listed banks from European Union (EU) countries, over the period of 2011–2016 and uses the generalized least squared (GLS) random effect (RE) method, following Baltagi and Wu (1999) and Pathan (2009).FindingsThe authors find that the structure of the board of directors can influence bank risk behavior but not the ownership concentration. No significant relation was found between the influence of the regulatory environment and bank risk, i.e., stricter regulation has no effect on risk taking by banks.Originality/valueThe paper contributes to the literature in risk measures and banks' corporate governance. It also considers the impact of regulatory framework on banks' risk-taking behavior. The aim of this empirical analysis was to examine in greater detail these subjects and the dynamics between them after the significant structural changes in the macroeconomic environment and in the financial system, particularly with regards the regulatory and supervisory framework following the 2008 financial crisis, using data from European Union countries.
{"title":"Risk-taking by banks: evidence from European Union countries","authors":"M. T. M. Garcia, Ana Jin Ye","doi":"10.1108/cfri-12-2022-0248","DOIUrl":"https://doi.org/10.1108/cfri-12-2022-0248","url":null,"abstract":"PurposeThe aim of this paper is to study the relationship between banks' ownership structure and their risk-taking behavior as well as the impact of banking regulation on banks' approach to taking risk, after the 2008 financial crisis.Design/methodology/approachThe empirical analysis considers a sample of listed banks from European Union (EU) countries, over the period of 2011–2016 and uses the generalized least squared (GLS) random effect (RE) method, following Baltagi and Wu (1999) and Pathan (2009).FindingsThe authors find that the structure of the board of directors can influence bank risk behavior but not the ownership concentration. No significant relation was found between the influence of the regulatory environment and bank risk, i.e., stricter regulation has no effect on risk taking by banks.Originality/valueThe paper contributes to the literature in risk measures and banks' corporate governance. It also considers the impact of regulatory framework on banks' risk-taking behavior. The aim of this empirical analysis was to examine in greater detail these subjects and the dynamics between them after the significant structural changes in the macroeconomic environment and in the financial system, particularly with regards the regulatory and supervisory framework following the 2008 financial crisis, using data from European Union countries.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46798098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-07DOI: 10.1108/cfri-03-2023-0061
Onur Polat
PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in ter
本研究旨在研究2018年7月1日至2023年2月19日期间主要加密货币、NFT代币和DeFi资产之间的时变回报和波动性相互联系，并确定不同投资组合构建技术下的最佳投资组合配置和对冲有效性。这项工作研究了2018年7月1日至2023年2月19日期间主要加密货币、NFT代币和DeFi资产之间的时变回报和波动性相互联系。为此，Antonakakis等人(2020)的基于时变参数向量自回归(TVP-VAR)的连通性方法。该方法是Diebold - yilmaz (DY)方法(Diebold and Yılmaz, 2014)的扩展版本，比原始DY具有优势。首先，与DY不同，它不需要选择特定的窗口大小。其次，它对异常值具有鲁棒性。此外，继Broadstock et al.(2022)之后，作者在不同的投资组合构建场景下估计了时变的最优投资组合权重和对冲有效性。研究结果表明:(1)整体连通性指数显著反映了众所周知的金融/地缘政治危机事件;(2)主要加密货币(ETH、BTC和BNB)是收益冲击的最大发送者，而LINK和BTC是波动冲击的最大发送者/接受者;(3)加密货币、nft和DeFi在收益和波动性连通性方面形成了不同的集群组;(4)在2022年加密货币崩溃和FTX申请破产前后估计的连通性网络具有最强的回报和波动性相互联系;(5)不同投资组合构建技术计算出的最优投资组合策略具有相似的基序，除个别短期回落外均具有持续的增长路径。研究的局限性/启示本研究的发现为投资者、利益相关者和决策者提供了一些政策建议。首先，该研究的基于时间的动态相互联系可以帮助市场参与者进行最优投资组合决策。特别是，在整个事件中，特别是在金融/地缘政治动荡期间，DeFi资产和nft的持续净接收作用支撑了它们的避险潜力(Umar等人，2022a, b)。最后，由于总连通性指数(tci)在金融/地缘政治爆发时期容易显著增加，这些工具对政策制定者监测风险很有价值。知识的贡献至少有三方面。首先，考虑到最近突出的金融/地缘政治事件，作者重点关注2018年7月和2023年2月主要加密货币、nft和DeFi资产之间的动态时间相互联系。其次，作者估计了围绕金融/地缘政治爆发的动态连通性的网络拓扑结构，并在相互联系方面对它们进行了比较。最后，计算了不同投资组合构建技术下的时变最优投资组合配置和套期保值效果。
{"title":"Dynamic interlinkages between cryptocurrencies, NFTs, and DeFis and optimal portfolio investment strategies","authors":"Onur Polat","doi":"10.1108/cfri-03-2023-0061","DOIUrl":"https://doi.org/10.1108/cfri-03-2023-0061","url":null,"abstract":"PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in ter","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45240109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-14DOI: 10.1108/cfri-02-2023-0039
Yang Gao, Wanqi Zheng, Yaojun Wang
PurposeThis study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.Design/methodology/approachThe authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.FindingsThe empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.Originality/valueThe results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.
{"title":"Sectoral risk contagion and quantile network connectedness on Chinese stock sectors after the COVID-19 outbreak","authors":"Yang Gao, Wanqi Zheng, Yaojun Wang","doi":"10.1108/cfri-02-2023-0039","DOIUrl":"https://doi.org/10.1108/cfri-02-2023-0039","url":null,"abstract":"PurposeThis study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.Design/methodology/approachThe authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.FindingsThe empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.Originality/valueThe results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46295598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.1108/cfri-12-2022-0277
Oumayma Gharbi, Yousra Trichilli, M. Boujelbene
PurposeThe main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.Design/methodology/approachThe authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.FindingsThe wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.Research limitations/implicationsThis study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.Practical implicationsThe study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.Originality/valueTo the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.
{"title":"Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications","authors":"Oumayma Gharbi, Yousra Trichilli, M. Boujelbene","doi":"10.1108/cfri-12-2022-0277","DOIUrl":"https://doi.org/10.1108/cfri-12-2022-0277","url":null,"abstract":"PurposeThe main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.Design/methodology/approachThe authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.FindingsThe wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.Research limitations/implicationsThis study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.Practical implicationsThe study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.Originality/valueTo the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49073654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-15DOI: 10.1108/cfri-02-2023-0021
Nicholas Addai Boamah, E. Opoku, Stephen Zamore
PurposeThe study investigates the co-movements amongst real estate investments trust (REITs). This study examines the co-movements between the world and individual countries' REITs and the co-movements amongst country-pair REITs. This study explores the responsiveness of the REITs markets' co-movements to the 2008 global financial crisis (GFC), the coronavirus disease 2019 (COVID-19) pandemic and the Russian–Ukraine conflict.Design/methodology/approachThe study employs a wavelet coherency technique and relies on data from six REITs markets over the 1995–2022 period. FindingsThe evidence shows a generally high level of coherency between the global and the country's REITs. The findings further indicate higher co-movements between some country pairs and a lower co-movement for others. The results suggest that the REITs markets increased in co-movements around the 2008 GFC, the COVID-19 pandemic and the Russian–Ukraine conflict. These increased co-movements mostly lasted for a short period suggesting REITs markets contagion around these global events. The results generally suggest interdependence between the global and the country's REITs. Additionally, interdependence is observed for some of the country-pair REITs.Originality/valueThe evidence indicates that REITs markets respond to global events. Thus, the increasing co-movement amongst REITs observed in this study may expose domestic REITs to global crisis. However, this study provides opportunities for minimising the cost of capital for real estate projects. Also, REITs provide limited diversification gains around crisis times. Therefore, countries need to open the REITs markets to global investors whilst pursuing policies to ensure the resilience of the REITs markets to global events. Investors should also take note of the declining geographic diversification gains from some country-pair REITs portfolios.
{"title":"The integration of real estate investment trust: a wavelet coherency analysis","authors":"Nicholas Addai Boamah, E. Opoku, Stephen Zamore","doi":"10.1108/cfri-02-2023-0021","DOIUrl":"https://doi.org/10.1108/cfri-02-2023-0021","url":null,"abstract":"PurposeThe study investigates the co-movements amongst real estate investments trust (REITs). This study examines the co-movements between the world and individual countries' REITs and the co-movements amongst country-pair REITs. This study explores the responsiveness of the REITs markets' co-movements to the 2008 global financial crisis (GFC), the coronavirus disease 2019 (COVID-19) pandemic and the Russian–Ukraine conflict.Design/methodology/approachThe study employs a wavelet coherency technique and relies on data from six REITs markets over the 1995–2022 period. FindingsThe evidence shows a generally high level of coherency between the global and the country's REITs. The findings further indicate higher co-movements between some country pairs and a lower co-movement for others. The results suggest that the REITs markets increased in co-movements around the 2008 GFC, the COVID-19 pandemic and the Russian–Ukraine conflict. These increased co-movements mostly lasted for a short period suggesting REITs markets contagion around these global events. The results generally suggest interdependence between the global and the country's REITs. Additionally, interdependence is observed for some of the country-pair REITs.Originality/valueThe evidence indicates that REITs markets respond to global events. Thus, the increasing co-movement amongst REITs observed in this study may expose domestic REITs to global crisis. However, this study provides opportunities for minimising the cost of capital for real estate projects. Also, REITs provide limited diversification gains around crisis times. Therefore, countries need to open the REITs markets to global investors whilst pursuing policies to ensure the resilience of the REITs markets to global events. Investors should also take note of the declining geographic diversification gains from some country-pair REITs portfolios.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49178032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1108/cfri-07-2022-0126
M. Usman, W. Akhter, A. Haque
PurposeThis paper aims to investigate the spillover effects of jump and crash events among Chinese nonfinancial firms.Design/methodology/approachThis sample consists of more than 1.5 million weekly observations of over 3,000 Chinese listed firms over the period 1991–2015. The authors utilize univariate tests to compare the post-event performance of matched peer and non-peer control firms and cross-sectional regressions of their abnormal returns/cumulative abnormal returns (ARs/CARs) and returns on assets (ROAs).FindingsThe authors find that extreme risk-adjusted abnormal stock returns (stock price crashes and jumps) generate statistically significant ARs/CARs in the same directions in industry, size, leverage, and geographical location matched peer firms in Chinese stock market. Further tests reveal that peer firms' response to the crash event is pronounced more in the group of firms about which the information asymmetry is high between investors and firms.Research limitations/implicationsPortfolio investors can adjust their portfolios accordingly by selling stocks of the matching rival firms during a crash period. Policymakers may develop policies so as to protect the interests of small investors in the events of crashes in the markets. They can reduce the information asymmetry between the firms and the investors by making information about the firms more transparent, so as to reduce the contagion in case of crash event.Practical implicationsThis study has important implications for portfolio investment managers and policymakers.Originality/valueTo the best of authors' knowledge, this is the first study that combines the jump and crash events and attempts to assess their spillover effects on other firms in Chinese stock market.
{"title":"Spillover effects of crash and jump events: evidence from Chinese market","authors":"M. Usman, W. Akhter, A. Haque","doi":"10.1108/cfri-07-2022-0126","DOIUrl":"https://doi.org/10.1108/cfri-07-2022-0126","url":null,"abstract":"PurposeThis paper aims to investigate the spillover effects of jump and crash events among Chinese nonfinancial firms.Design/methodology/approachThis sample consists of more than 1.5 million weekly observations of over 3,000 Chinese listed firms over the period 1991–2015. The authors utilize univariate tests to compare the post-event performance of matched peer and non-peer control firms and cross-sectional regressions of their abnormal returns/cumulative abnormal returns (ARs/CARs) and returns on assets (ROAs).FindingsThe authors find that extreme risk-adjusted abnormal stock returns (stock price crashes and jumps) generate statistically significant ARs/CARs in the same directions in industry, size, leverage, and geographical location matched peer firms in Chinese stock market. Further tests reveal that peer firms' response to the crash event is pronounced more in the group of firms about which the information asymmetry is high between investors and firms.Research limitations/implicationsPortfolio investors can adjust their portfolios accordingly by selling stocks of the matching rival firms during a crash period. Policymakers may develop policies so as to protect the interests of small investors in the events of crashes in the markets. They can reduce the information asymmetry between the firms and the investors by making information about the firms more transparent, so as to reduce the contagion in case of crash event.Practical implicationsThis study has important implications for portfolio investment managers and policymakers.Originality/valueTo the best of authors' knowledge, this is the first study that combines the jump and crash events and attempts to assess their spillover effects on other firms in Chinese stock market.","PeriodicalId":44440,"journal":{"name":"China Finance Review International","volume":" ","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43965128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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