Market Downturns Research Articles

The Impact of the Covid-19 Pandemic on the Cross-Sectoral Information Flow in the U.S.

In the past decades a connection between the financial services and technology sector has evolved into a dynamic and complex relationship. However, they were not spared from the far-reaching effects that the spread of the Covid-19 pandemic brought, each responding in a distinctive manner. This paper examines the alterations in the bidirectional information flow between the financial services and technology sectors provoked by the virus outbreak by employing the concept of transfer entropy. The results document an asymmetric and modest information flow in distribution tails prior to the pandemic, with the technology sector predominating. However, as we move towards central values, the transfer intensity decreases dramatically, even reaching negative values in the flow from the financial services sector. Following the pandemic declaration, while the technology sector maintains its superior position in information transfer, its strength, shape, and symmetry are notably altered, with transmission doubling from the financial services sector, suggesting cross-sectoral contagion in market downturns and instability of the information transfer.

IPO subscription dynamics: A comprehensive inquiry into the Indian stock market

The Indian IPO market showcased resilience during the global stock market downturn in 2022, emerging as a notable bright spot in regions such as Europe, the Middle East, India, and Africa. As the bullish rally of 2022 persists, Indian stock markets remain enticing for foreign institutional investors in 2023. A resurgence in IPO activity is anticipated, driven by increasing momentum and larger deals that are poised to overcome the constraints of subdued global sentiments and liquidity pressures, addressing the challenges posed by these factors. The study offers insights into factors influencing IPO subscriptions, capitalizing on the context of heightened stock market volatility and optimistic trends in the Indian stock market. A total of 132 IPOs listed on the Indian stock market between April 2019 and March 2023 were analyzed in this study. Multiple Linear Regression was used to assess the strength of the association between several factors outlined in the literature, and the overall subscription. Among the ten variables investigated in the study, it was observed that three variables under the external factors, specifically Grey Market Premium, IPO Rating, and Broker Recommendations, exerted a significant influence on the overall subscription. While other factors such as allocation proportion and issue attributes, were found to have no discernible influence on the overall subscription. The results indicate that the Indian IPO market demonstrates a prevalence of speculative behavior and a stronger reliance on expert recommendations, rather than being primarily driven by IPO characteristics. Acknowledgment Authors acknowledge that the publication fee is funded by Kingdom University, Bahrain.

Enhancing Sustainable Finance through Green Hydrogen Equity Investments: A Multifaceted Risk-Return Analysis

Amidst the global push for decarbonization, green hydrogen has gained recognition as a versatile and clean energy carrier, prompting the financial sector to introduce specialized investment instruments like Green Hydrogen Exchange-Traded Funds (ETFs). Despite the nascent nature of research on green hydrogen portfolio performance, this study examines two key green hydrogen ETFs (i.e., HJEN and HDRO) from April 2021–May 2023, aiming at conducting a multifaceted exploration of their performance, isolating and measuring their sensitivity to the primary market factor, and assessing the capabilities of systematic trading strategies to preserve capital and minimize losses during market downturns. The results spotlight lower returns and higher risks in green hydrogen investments compared to conventional equity (proxied by ETFs offering exposure to developed markets—EFA and emerging markets—EEM) and green energy portfolios (proxied by the ETF ICLN). To comprehensively evaluate performance, an array of risk-adjusted metrics, including Std Sharpe, ES Sharpe, VaR Sharpe, Information ratio, Sortino ratio, Treynor ratio, and various downside risk metrics (historical VaR, modified VaR, Expected Shortfall, loss deviation, downside deviation, and maximum drawdown) are employed, offering a nuanced understanding of the investment landscape. Moreover, single-factor models highlight significant systematic market risk, reflected in notably high beta coefficients, negative alphas, and active premia, underscoring the sensitivity of green hydrogen investments to market fluctuations. Despite these challenges, a silver lining emerges as the study demonstrates the efficacy of implementing straightforward Dual Moving Average Crossover (DMAC) trading strategies. These strategies significantly enhance the risk-return profile of green hydrogen portfolios, offering investors a pathway to align financial and social objectives within their equity portfolios. This research is motivated by the need to provide market players, policymakers, and stakeholders with valuable insights into the benefits and risks associated with green hydrogen investment, considering its potential to reshape the global energy landscape.

Returns behavior of ESG ETFs in the COVID‐19 market crash: Are green funds more resilient?

AbstractThis study explores the importance of environmental, social, and governance (ESG) performance in explaining the returns and volatility of returns of exchange‐traded funds (ETFs) during the market crash induced by the COVID‐19 pandemic. The study uses an unbalanced panel of monthly data during the period 2005–2021 for a diversified sample of 160 iShares Morgan Stanley Capital International conventional and ESG/socially responsible investment (SRI) equity funds. To obtain robust results, a variety of analyses are conducted, including multiple panel regressions based on one‐way clustering, standard error estimates obtained from different covariance matrix estimators that are heteroskedasticity and autocorrelation consistent, simultaneous quantile and inter‐quantile regressions, weighted least square regressions with alternative weights for the regression coefficients, and tests of coefficient equality with bootstrapped standard error of difference in subsample regressions. All the sensitivity analyses give similar results, confirming their validity. The results provide strong evidence for the resilience of sustainable funds during the crash, with ETF returns showing persistence during the market downturn. This study suggests that ESG/SRI investments may help ETFs to outperform their counterparts, indicates that sustainability investments matter in times of crisis, and implies the adoption of strategies that promote sustainable investment development.

The Psychological Impact on Chinese Pilot Students During the Pandemic – The Lesson Learned

This study was intrigued by routine onsite observation among pilot students who were concerned about the market downturn during the pandemic and career uncertainty in the post-pandemic time. The purpose of this study was to discover how COVID-19 affected pilot students and how they reacted and were accustomed to the pandemic regulations. Human Factors, psychological issues, and the status of safety culture were also surveyed. Purposive sampling was used to select two Chinese Civil Aviation Regulation (CCAR) Part 141 flight schools to participate in an online survey. The Cronbach’s alpha, and Spearman’s Correlation Coefficient were calculated. The findings exemplify the emergence of Human Factors and psychological issues, while the flight school’s safety culture remains strong. Simultaneously, the result of this study provides a reference for stakeholders at flight training institutes for coping with future similar crises.

On the prediction of systemic risk tolerance of cryptocurrencies

The role of big data in finance is pivotal, especially in forecasting stock prices, mitigating risk, and assessing market anomalies. With the financial system becoming more interconnected, analytical models using large data are gaining prominence in developing risk spillover models. This study estimates the systemic risk tolerance of twenty-five high-valued cryptocurrencies and finds that Fantom has the highest tolerance, while Bitcoin and Ethereum have a lower tolerance due to their large market share. It also shows that the common trend of cryptocurrencies enhances each other's tolerance and develops a predictive model for systemic risk tolerance. The study can help investors and market participants devise strategies for safe haven investment, hedging, and speculation during a market downturn.

The Analysis of the Impact of Sports Events on Socioeconomic Development

With the sudden rise of various sports events in recent years, the sports industry has gradually come into the limelight, and how to grasp the economic benefits as well as the external impacts brought by sports events will become a problem that must be faced. Based on the impact of large-scale sports events on the local economy and the stock market, this paper studies the various impacts of sports events on society through data collection and comparative analysis and focuses on the negative impacts that are easy to be ignored. Among them, in the stock market, through collecting the stock market fluctuations during the recent Olympic Games, it is estimated that the Olympic Games will disperse the stock market, and further lead to the stock market downturn, and weak rebound. In terms of the local economy, sports events can promote development, whether in terms of social infrastructure construction or nominal GDP. However, the effect of sports on urban economic development is not as great as that of paper data, because the money spent on sports in people's leisure time is nothing more than the replacement of other entertainment, which cannot bring about additional consumption. The conclusion shows that the development of the sports industry is accompanied by certain losses in other aspects, so it is necessary to balance the development plan reasonably in order to maximize the social benefits.

The case for CASE: Estimating heterogeneous systemic effects

The Basel Committee and the Financial Stability Board require a consensus on the identification of characteristics that make a financial institution more prone than others to be severely hit by systemic shocks. This paper introduces a new tool to achieve this goal: a model for the Conditional Average Systemic Effects (CASE). The CASE quantifies the average effect of a system wide shock or market downturn on the profit and loss account of a bank, a firm or on the return of an asset. We propose a linear model for CASE with heterogeneous effects in observable characteristics. These models complement alternative measures of systemic risk and allow researchers to identify the determinants of the vulnerability of a given financial institution. We develop bootstrap inference that accounts for both estimation risk and model misspecification risk, and show the utility of our results in Monte Carlo simulations and an empirical application to 100 large U.S. financial firms.

Judge me on my losers: Do robo‐advisors outperform human investors during the COVID‐19 financial market crash?

As financial institutions increasingly offer robo‐advisors (RAs) as a service, the empirical evidence on their performance, particularly during market downturns, remains limited. We study the impact of using RAs on investment performance during the 2020 financial crisis caused by the COVID‐19 global pandemic. We obtain daily portfolio and transaction data of investors on an online investment platform. Besides making investment decisions by themselves, investors can also leverage an RA system offered by the platform. We match RA users with other investors who did not use the RA with similar investor and portfolio characteristics before the market crash, then compare their portfolio returns after the crash. We find that RA users experienced significantly fewer losses during the market downturn, and this performance advantage came from the RA‐managed assets (rather than assets managed by RA users themselves). We further show that the RA system adjusted its portfolios to hold less risky funds, whereas human investors stayed with their status quo and did not reduce the risk of their portfolios, which partly accounted for the performance discrepancy. Importantly, the RA’s superior performance was not simply a continuation of its performance in a normal market; in fact, we find RA users to have similar portfolio returns as non‐RA investors during normal markets, and the benefits of RA's risk‐reduction trading strategy only manifested during the financial crisis. Our work offers an empirical assessment of RAs’ performance under different market conditions and advances the understanding of algorithmic decision making in financial markets.

Understanding Systemic Risk Dynamics and Economic Growth: Evidence from the Turkish Banking System

The banking crisis experienced at the beginning of 2023 in the aftermath of the global 2008 crisis served as a stark reminder of the importance of systemic risk once again across the world. This study examines the dynamics of systemic risk in the Turkish banking system and its impact on sustainable economic growth between the period of 2007 and 2022. Through the Component Expected Shortfall (CES) method and quantile spillover analysis, private banks, such as Garanti Bank (GARAN), Akbank (AKBNK), İş Bank (ISCTR), and Yapı ve Kredi Bank (YKBNK), are identified as major sources of systemic risk. The analysis reveals a high level of interconnectedness among the banks during market downturns, with TSKB, Vakıfbank (VAKBNK), İş Bank (ISCTR), Halk Bank (HALKB), Akbank (AKBNK), Yapı ve Kredi Bank (YKBNK), and Garanti Bank (GARAN) serving as net risk transmitters, while QNB Finansbank (QNBFB), ICBC Turkey Bank (ICBCT), Şekerbank (SKBNK), GSD Holding (GSD), and Albaraka Türk (ALBRK) act as net risk receivers. Employing the Markov switching VAR (MS-VAR) model, the study finds that increased systemic risk significantly reduces economic growth during heightened financial periods. These findings underscore the importance of monitoring systemic risks and implementing proactive measures in the banking sector. The policy implications highlight the requirement for regulators and policymakers to prioritize systemic risk management. Close monitoring helps detect weaknesses and imbalances that could put financial stability at risk. Timely implementation of policies and rules is crucial in the prevention of the accumulation of systemic risks and in dealing with the existing hazards. Such measures protect the stability of the banking sector and mitigate potential negative effects on the broader economy.

Diversification measures: Mutual fund family case

This study introduces a portfolio theory motivated approach to measuring mutual fund family-level risk diversification. The average cross-fund correlation in idiosyncratic returns measures diversification of idiosyncratic risk, while the correlation of predicted returns from the multifactor model measures factor risk diversification within a fund family. We document a substantial cross-sectional variation in family-level diversification measured with our new proxies independent of the number of funds and objectives in the family. While number of objectives is a popular yet possibly misleading measure of diversification, there is a link between portfolio theory-based diversification measures and the number of objectives in a family. On average, fund families with more investment objectives are more diversified in terms of both idiosyncratic and factor risks; yet, in large families, more investment objectives are associated with increased diversification of idiosyncratic risk but decreased diversification of factor risk. Fund family diversification improves family performance during market downturns and in large families. However, fund family diversification does not significantly affect absolute or relative family size, except for the number of investment objectives, which has a negative relation to family size.

Eigenvalue Tests for the Number of Latent Factors in Short Panels

Abstract This article studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance–covariance matrices of (possibly weighted) asset returns and rely on either an assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to U.S. equity data. Evidence for a different number of latent factors according to market downturns and market upturns is statistically ambiguous in the considered subperiods. In particular, our results contradict the common wisdom of a single-factor model in bear markets.

Land Finance, Real Estate Market, and Local Government Debt Risk: Evidence from China

The financing function of land in China has been extensively documented, but little research has explored the role of land finance in the real estate market affecting local government debt. In light of the recent simultaneous increase in China’s real estate market risk and local government debt risk over the past two years, a pressing need to reevaluate the significance of land finance has arisen. Thus, this study explores the role of land finance in the relationship between the real estate market and local debt risk. The data were collected from 224 Chinese cities from 2010 to 2019. The study found that real estate market prosperity significantly promotes the expansion of local government debt. Land finance acts as a mediator and a moderator in this relationship. The study also found that during periods of real estate market booms, local governments tend to sell more land, which reinforces their debt-raising behavior and exacerbates the impact of the real estate market on local government debt; during periods of real estate market downturns, local governments tend to decrease debt, but the government debt repayment risk increases significantly. Furthermore, the amplifying effect of land finance is more pronounced in economically developed regions and cities with higher public budget revenues.

State-dependent effects of the unconventional monetary policy in stock markets

This study analyzes the state-dependent effect of the Bank of Japan (BoJ)’s intervention in stock markets from 2013 to 2017. A causal inference on such intervention is difficult because of the self-selective behavior of central banks. To address this problem, I apply the propensity score method in a time series context, exploiting stock price information of a single day. The key finding is that the effects are state-dependent and stronger during market downturns.

Portfolio Selection, Periodic Evaluations and Risk Taking

Portfolio risk taking when periodic performance matters Incentives of portfolio managers in real life are typically tied to their periodic performance—a criterion that has received very little attention to date. How does the repeated nature of the rewards influence the portfolio managers’ trading strategies? Tse and Zheng address this question via a dynamic model of investment in “Portfolio selection, periodic evaluations, and risk taking.” The return benchmark of the periodic evaluations plays a crucial role. A reasonable benchmark in conjunction with periodic payouts can mitigate excessive risk taking during market downturns. However, an overly aggressive benchmark could result in limited portfolio growth as well as ruin risk over the long run. The theoretical findings yield policy implications over incentives management and remuneration structure design to better align the short-term periodic interest of the portfolio managers and the long-term performance goal of the stakeholders.

Dynamics of the return and volatility connectedness among green finance markets during the COVID-19 pandemic

This paper examines the return and volatility connectedness among green finance markets including the green bonds, clean energy and socially responsible stocks, especially focusing on the connectedness changes induced by the COVID-19 pandemic. Specially, by employing the TVP-VAR extended joint connectedness approach, five representative time series, i.e., S&P Green Bond index (GBI), S&P Global Clean Energy index (GCEI), S&P Global 1200 ESG index (ESGI), S&P Global 1200 Carbon Efficient index (CEI) and S&P Global 1200 Fossil Fuel Free index (FFFI), are used to measure the connectedness among the markets over the sample period spanning from 1 January 2014 to 31 December 2021. The empirical results show that GBI is the main net receiver of return spillovers, followed by ESGI, CEI and FFFI, whereas GCEI is the main net transmitter of return spillovers during the full sample period. The dynamic total connectedness of five indices' returns is strongly fluctuating and especially exhibits a significant spike after the outbreak of COVID-19 pandemic. Interestingly, the findings of volatility connectedness indicate that ESGI, CEI, and FFFI are net transmitters of volatility spillovers during the full, pre-COVID-19 and post-COVID-19 periods. In addition, the network connectedness analysis highlights the role of GCEI in facilitating the transmission of financial contagion during the full and pre-COVID-19 periods, while that during the post-COVID-19 period shows that GBI receives more spillovers from others, indicating that the connections of the five indices tend to be stronger during market downturn periods caused by extreme events. Our results provide implications for investors and policymakers.

Market States and Lottery Preference: Evidence from Chinese Open‐End Funds*

AbstractRecent studies find that investors prefer funds with lottery‐like payoffs. Using a sample of Chinese open‐end funds, we show that investors' preference for funds' extreme positive payoffs (MAXs) depend on the state of the market: it is significant for MAXs in an unfavorable market but weak or reversed for those in a favorable market. Such state‐dependent preference is irrational because, inconsistent with the flow–MAX relationship, higher MAXs under market downturns are associated with worse performance. We further document support for the salience‐theory‐based explanation for investors' preference and provide counter‐evidence for alternative mechanisms based on rational choice or changes in aggregate flows.

A lognormal/normal regime-switching commodity pricing model

Inspired by the negative price of WTI crude oil observed during the COVID-19 pandemic, we develop a new model for commodity pricing which allows structural change between price normality and lognormality under a Markov regime-switching (RS) framework. We augment the Extended Kalman Filter to calibrate the structural changing model. The model performance in calibration is compared to that of the common RS model with historical WTI spots, various futures and hypothetical scenarios. We conclude that our model is superior in capturing price dynamics especially in the oil market downturns. Encouragingly, the regime probabilities estimated with the new model indicate that during severe events including the 2008–2010 financial crisis, 2014–2016 oil crash and the outbreak of COVID-19 in 2020, WTI spot itself follows normal rather than the widely assumed lognormal process. This finding is consistent with our empirical studies. In addition, we assess the probability density of spot prices with the new model. Finally, we present the PDE finite difference and Monte Carlo approaches to price commodity options under the new model.

Does the Shield Effect of CSR Work in Crises? Evidence in Korea

This paper investigates the impact of corporate social responsibility (CSR) on shareholders’ wealth during market downturn, focusing on the market crash caused by the COVID-19 pandemic and its aftermaths. We evaluate the relationship between firms’ CSR and stock returns using a sample of 803 firms listed on the Korean stock market. The results of our study reveal that firms’ pre-crisis CSR activities do not protect shareholders’ wealth during the crisis; in fact, they negatively affected stock returns during the COVID-19 crisis. This finding is consistent across several robustness tests and challenges the prevailing notion that CSR is solely a philanthropic endeavor. This study suggests that firms need to reconsider their CSR approach in order to better align it with shareholders’ interest.

Stress testing bank insolvency risk by systemic equity market shock: An expected shortfall approach

This paper proposes a simple intuitive approach for assessing and stress testing the insolvency risk of global systemically important banks (G-SIBs) by shocking systemic risk in the equity market. In particular, the paper introduces two metrics to measure the relative adequacy of total loss absorbing capacity (TLAC) upon resolution of a bank under both real market conditions and stress test settings. The metrics may also be calculated on a regular basis for monitoring bank insolvency risk. Three global systemically important banks, including Credit Suisse, are presented as examples for the analysis. To capture the heavy tail feature of equity market downturns and ensure conservatism for stress testing, an analytical framework is adopted based on extreme value theory with expected shortfall, power law distributions and copula method. The analysis concludes that G-SIBs may have a high probability of loss exceeding TLAC under systemic market stress.