Risk Sentiment Research Articles

Imported financial risk in global stock markets: Evidence from the interconnected network

This paper proposes an interconnected network, including the volatility layer and sentiment layer, to examine imported financial risk in global stock markets. We compare and explore the topology structures of global volatility risk spillovers and sentiment risk spillovers based on the static sample and dynamic sample. Our results show that sentiment risk spillovers across global stock markets are stronger than volatility risk spillovers. Meanwhile, we observe that volatility risk spillovers and sentiment risk spillovers among global stock markets are heterogeneous during periods of financial system stress. The market-level analysis suggests that developed economies, such as the US, UK, and France, are dominant players in global volatility risk and sentiment risk. Finally, we observe that Asian stock markets suffered from substantial imported volatility risks during the crisis, but imported sentiment risks in Asian stock markets are weak.

Assessment of text-generated supply chain risks considering news and social media during disruptive events

Information flow is an important task in a supply chain network. Disruptive events often impede this flow due to confounding factors, which may not be identified immediately. The objective of this study is to assess supply chain risks by detecting significant risks, examining risk variations across different time phases and establishing risk sentiment relationships utilizing textual data. We examined two disruptive events—coronavirus disease 2019 (Omicron phase) and the Ukraine–Russia war—between November 2021 and April 2022. Data sources included news media and Twitter. The Latent Dirichlet Allocation algorithm was applied to the textual data to extract potential text-generated risks in the form of “topics.” A proportion of these risks were analyzed to assess their time-varying nature. Natural language processing-based sentiment analysis was applied to these risks to infer the sentiment coming from the media using the ordered probit model. The results identify various unnoticed risks, for example: logistics tension, supply chain resiliency, ripple effect, regional supply chain, etc. that may adversely affect supply chain operations if not considered. The outcomes also indicate that textual data sources are capable of capturing risks before the events actually occur. The outcomes further suggest that text data could be valuable for strategic decision making and improving supply chain visibility.

International capital flow pressures and global factors

The risk sensitivity of international capital flow pressures is explored using a new Exchange Market Pressure index that combines pressures observed in exchange rate adjustments with model-based estimates of incipient pressures that are masked by foreign exchange interventions and policy rate adjustments. The sensitivity of capital flow pressures to risk sentiment evolves over time, varies significantly across countries, and differs between normal times and extreme stress events. Across countries, risk sensitivities and safe haven status are associated with self-fulfilling exchange rate expectations and carry trade funding currencies. In contrast, current indicators of a safe macroeconomic profile are only weakly associated with safe haven status.

Factors impacting the price of the gold: an empirical study of EGARCH model

Purpose- The gold as a fundamental asset has been displaying a high and sharp price volatility in international financial markets. The price dynamics of gold are believed to be influenced by non-linear dependencies with stock market indices, exchange rates, and commodity prices. Therefore, it is rational to examine the factors contributing to the non linear inferences. As a financial asset for the portfolio investment and hedging, gold prices display multilateral and dynamic data patterns this study aims to focus the analysis of price volatility which will contribute to the volatility structure, persistence, and correlated behaviors of gold prices. Methodology- To display and analyze the persistence level of volatility in the financial markets, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specification is employed. GARCH class models are further applied to determine the relevance of the leverage effect of market news, the progress of spillover pattern, and the effects on the risk-premium. Findings- Volatility in gold price has become an important feature of the financial markets in terms of volatility trading in adjusting the international portfolio investments. The correlated structure of the financial markets and the contagion effects of the news have displayed the asymmetric and complex but time-fragmented portfolio returns in terms of short and long term volatility formation and forecasting. Conclusion- Since gold’s safe haven status have played a major stake in the determination of the gold price, it is undeniable that the speculators’ rising power, the artificial intelligence utilisation in robo-trading, global and regional geo-political tensions, the recent developments in the risk sentiment in investment portfolios have an increasing amount of influence on gold prices. Keywords: Gold returns, US Dollar Index, exponential generalized autoregressive conditional heteroskedasticity, volatility persistence. JEL Codes: C58, G12, C22, G17

Construction of Korean Shipping Industry Risk Sentiment Index Using News Articles

Construction of Korean Shipping Industry Risk Sentiment Index Using News Articles

Cryptocurrency and Innovation: The Relationship with Technology in Light of Risk Aversion and Market Sentiment

The paper performs a (Panel) Moderated Regression, Causality and Cointegration (complete version) test on the relationship between the exchange rate of major selected cryptocurrencies vs. major fiat currencies, and selected technology indices of various geographic areas and economic groups in the world, representative of different countries or areas in the world (Europe) and for different levels of market capitalization. The dataset allows to focus on the comparison pre- and post-Covid19 (complete version), testing the relationship in those two different time frames. The novelty of the study is the use of risk aversion and VIX volatility index (market sentiment) as control variables. Various indices of risk aversion have been developed, presenting different levels of trade-off in terms of availability (ease of calculation) and effectiveness. A good candidate seems to be the Global Risk Appetite Index (GRAI) by Kumar and Persaud (2002). As for the VIX index, it has been widely used as a proxy of market sentiment, but rarely applied to the understanding of currency trends (especially cryptocurrency). Our expectation is to observe some significant moderation effect for some of the indicators considered, in general and when observing the data for different sub-periods. with investors probably trying to hedge equity investments and diversify their portfolios differently, with risk aversion and VIX index to be a driving factor of those choices. We also expect results to be confirmed by the Causality and Cointegration (complete version) test. The paper aims to contribute to the Fintech literature and shed light on the possibilities of diversification offered by tech-related portfolios. As a corollary, it aims to determine how the effect of risk aversion and general market risk sentiment drives those choices. Keywords: Cryptocurrency; Technology Index; Risk Appetite; Market Sentiment; Causality; Cointegration; Panel Regression.

Construction of Korean Shipping Industry Risk Sentiment Index using news articles

Construction of Korean Shipping Industry Risk Sentiment Index using news articles

Global Spillovers of a Chinese Growth Slowdown

This paper analyzes the potential spillovers of a slowdown in Chinese growth to the United States and the rest of the world. Through a combination of structural VAR and DSGE analyses, we find that (1) spillovers from China to the rest of the world have grown significantly in the past decade; (2) the negative growth spillovers to the United States are more modest than to emerging market economies—particularly for commodity exporters—or other advanced economies, primarily because the latter group has larger direct exposure in trade to China; and (3) although the United States has limited direct financial exposure to China, the negative spillovers to the U.S. economy are amplified significantly if the negative Chinese growth shock leads to adverse global risk sentiment and monetary policy in the United States is constrained in its reaction.

Sentiment or habits: Why not both?

Habits and sentiment are important psychological behaviors in asset pricing. In this article I nest consumer sentiment as a risk factor into the Campbell–Cochrane (CC) habit model and examine its impact on asset prices. The model provides an economic mechanism for the pricing of sentiment risk through its impact on habit sensitivity and equilibrium habit levels but finds its market price of risk much lower than fundamentals. The additional sentiment factor does not improve the CC model, with both models returning a matched moments error of 12 % $12 \% $ from 1980Q1 to 2021Q4. The sentiment factor, however, subsumes risk aversion with a lower resulting risk coefficient than the CC model without sentiment. Furthermore, the model shows that during the COVID period, the risk premium was driven more by consumption growth than sentiment.

What Happens in China Does Not Stay in China

Spillovers from China to global financial markets have been found to be small owing to China's limited integration in the global financial system. In this paper, however, we provide evidence that China constitutes an important driver of the global financial cycle. We argue that because of China's importance for global consumption, stronger Chinese growth raises global growth prospects, inducing an increase in global risk sentiment and an expansion in global asset prices and global credit. Two contributions are key to this finding: (1) We construct a measure of China's credit impulse to identify Chinese policy-induced demand shocks. Our approach takes advantage of the fact that a primary tool of China's stabilization policy-encompassing monetary, fiscal, and regulatory policies-is controlling the amount of credit in the economy. Without China's credit impulse, it is difficult to discern global financial spillovers; (2) We estimate an alternative measure of Chinese GDP growth that captures its business cycle given data concerns about the smoothness of official GDP data. Without China's alternative GDP measure, it is difficult to attribute any global cycle movements to economic developments in China.

Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach

The COVID-19 pandemic has led to extensive news coverage, causing investor sentiment to swing, which has further increased financial market price volatility. There is an increasing need to find a hedge against sentiment risk. This paper examines the hedge capabilities of gold and Bitcoin against COVID-19-related news sentiment (CNS) risk under a nonlinear autoregressive distributed lag (NARDL) model. Our empirical results reveal that there is an obvious asymmetric effect from the CNS on gold prices in the short run and that the decrease in the COVID-19-related news index would have a greater impact on gold prices than when it increases. The impact of CNS on Bitcoin prices is asymmetric in the long and short term, especially in the long term. In addition, we conclude that gold is a hedge against CNS risk in the long term, and the hedging effect of Bitcoin is mainly reflected in the short-term.

The determinants of cross-border bond risk premia

Although cross-border bond issuance by emerging market economies (EMEs) has surged and dominated financing since the financial crisis of 2008, the sources of variation in cross-border bond risk premia have been ignored. This study fills this gap in the literature by examining the dynamic impact of interest rate differentials, exchange rates, and the broad dollar index on cross-border bond risk premia by applying a time-varying parameter vector autoregression (TVP-VAR) model with stochastic volatility. Generally, the risk premia responses are time-varying, lagged, and heterogeneous, with a reduction as the lag time horizon expands and the most significant in major global events. The empirical findings highlight that the broad dollar index dominates in explaining the variation of cross-border bond risk premia rather than the other two potential factors; we call this the “cross-border bond risk premia puzzle.” Furthermore, we find that the broad dollar index positively impacts cross-border bond risk premia via two transmission channels: 1) exchange rate expectations, which are significantly negatively driven by the broad dollar index, and 2) risk sentiment, which is negatively reflected by the index itself; both effects are negative. Moreover, the effect of the broad dollar index on risk premia is more pronounced for high-yield bonds than for investment-grade bonds. High-yield bond risk premia are negatively driven by exchange rate expectations, while investment-grade bond risk premia are positively driven by the local currency spread. Finally, these findings provide fresh insights into the aforementioned relationships; thus, we outline the implications.

The effect of customer concentration on stock sentiment risk

The effect of customer concentration on stock sentiment risk

CEO political connection and stock sentiment beta: Evidence from China

This study examines the impact of CEO political connection on stock sentiment beta. We theorize that CEO political connection contributes to elevated stock sentiment sensitivity because investors overvalue executives' political connection in a transitioning economy. Panel regression results with data of Chinese listed companies from 2009 to 2019 support our hypotheses. The impact of CEO political connection on stock sentiment risk is more pronounced during high sentiment periods and among non-state-owned enterprises. Investors often ignore politically connected CEOs' deficiency in internal management reflected by low quality internal information, ineffective supervision and control, and high management risk. These deficiencies contribute to higher stock sentiment beta. Noise trading uplifts stock sentiment beta, especially for companies with politically connected CEO. Encouragingly, the national anti-corruption campaign has curbed investors' adulation of political connections. This study warns investors and corporate boards against CEO political connection and calls for a more prudent treatment.

COVID-19 related media sentiment and the yield curve of G-7 economies

We explore the connectedness of the components of the sovereign yield curve (slope, level and curvature) across G-7 countries and media sentiment about COVID-19. The recent pandemic is a unique opportunity to identifying the transmitters and receivers of risk. Our results indicate that media sentiment along with the US yield curve components are main transmitter of spillovers, whereas Japan is the leading recipient of spillover. Among the European countries, we notice France as a major transmit, whereas Germany and UK switch role as transmitter and receiver alternatively. The results are important for mapping the interconnectedness between countries. In addition, policy makers can use them when devising disaster plans to prepare for future market crises.

Dependence Structures between Sovereign Credit Default Swaps and Global Risk Factors in BRICS Countries

This study investigates the tail dependence structures of sovereign credit default swaps (CDSs) and three global risk factors in BRICS countries using a copula approach, which is popular for capturing the “true” tail dependence based on the “distribution-adjusted” joint marginals. The empirical results show that global market risk sentiment comoves with sovereign CDS spreads across BRICS countries under extreme market events such as the pandemic-induced crash of 2020, with Brazil reporting the highest bilateral convergence followed by China, Russia, and South Africa. Furthermore, oil price volatility is the second biggest risk factor correlated with CDS spreads for Brazil and South Africa, while exchange rate risk exhibits very low co-dependence with CDS spreads during extreme market downturns. On the contrary, exchange rate risk is the second largest risk factor co-moving with China and Russia’s CDS spreads, while oil price volatility exhibits the lowest co-dependence with CDS in these countries. Between oil price and currency risk, evidence of single risk factor dominance is found for Russia, where exchange rate risk is largely dominant, and policymakers could promulgate financial sector regulations that mitigate spill-over risks such as targeted capital controls when markets are distressed.

Global risk and portfolio flows to emerging markets: Evidence from irish-resident investment funds

In this paper we analyse the behaviour of investment fund flows to emerging markets after the Global Financial Crisis (GFC). Our data point towards a structural growth in investment funds that purchase debt rather than equity securities. Empirically, we find that debt flows have higher sensitivity to global risk than equity flows, suggesting an increasing policy focus primarily on debt funds. We also show that Irish-resident funds’ sensitivity to changes in global risk sentiment co-varies negatively with a measure of sovereign credit rating of the receiving emerging market economy, a finding consistent with a notion that good fundamentals in host countries can mitigate outflows arising from swings in external funding conditions. Finally, we show that funds whose equity structure is tilted towards investors residing further away tend to invest less in countries than funds whose investors are closer to the same countries. This is noteworthy, given these results refer to Irish-resident investment funds that intermediate global financial flows.

Macro Sentiment Risk and Hedge Fund Returns

Macro Sentiment Risk and Hedge Fund Returns

Global risk sentiment and the Swiss franc: A time-varying daily factor decomposition model

This paper develops an FX factor model to decompose short-term bilateral exchange rate dynamics into different global factors and local uniqueness. We apply the model to the Swiss franc exchange rates against the US dollar (USDCHF) and the euro (EURCHF) between 2006 and 2018 and decompose daily dynamics into three global factors: risk, US dollar, and euro. The model captures daily dynamics well, explaining approximately 73% of the variation in USDCHF and 37% of the variation in EURCHF, reflecting the importance of global factors for Swiss franc exchanges rates. The risk factor contributes the most to Swiss franc dynamics, especially in times of a worsening risk environment, highlighting the role of the Swiss franc as a safe-haven currency. Global factors had been almost completely reflected in USDCHF dynamics before the euro area debt crisis, but once that crisis began, they also became important for EURCHF. In a case study of the introduction of the EURCHF minimum exchange rate we showcase the importance of momentum in daily Swiss franc returns.

Analysis and impact of COVID-19 disclosures: is IT-services different from others?

PurposeThere is ambiguity regarding whether coronavirus disease 2019 (COVID-19) is a boon or bane for the IT services industry. On the one hand, it has created opportunities, especially with the growth of collaborative technologies. On the other hand, many firms have reduced their IT budgets owing to the ongoing recession. This study explores how IT firms have assessed the risk of the pandemic in the early days and informed capital market participants. In addition, it examines the impact of such online disclosures on information asymmetry.Design/methodology/approachThe authors analysed annual reports of publicly listed firms in the USA filed on the Securities and Exchange Commission website in 2020 and examined whether the disclosure scenario of technology firms was different from that of the other industries. Moreover, the risk sentiment of COVID-19-related disclosures was assessed by employing text analytics. Information asymmetry was measured using the bid–ask spread.FindingsOverall, it was found that IT services firms were less likely to discuss the COVID-19 pandemic in their annual reports. Interestingly, it was observed that technology firms that chose to communicate about the pandemic had a lower incidence of words related to risks. Furthermore, communicating about COVID-19 in annual reports calms investors and improves the information asymmetry situation about the firm. Variation in the severity of the pandemic and the responses of state governments was controlled for by employing state-fixed effects in the empirical models.Originality/valueThe authors inform the literature on corporate disclosures and technology and highlight the importance of effectively communicating about the pandemic.