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This research investigates the relationship between clean energy stock and oil market returns utilizing Granger predictability in distribution and quantile impulse response analysis. We find that clean energy stock returns Granger predict oil price returns during "normal times" based on the distribution's center, but not vice versa. During bullish market episodes, there is bidirectional Granger predictability between the returns of clean energy stocks and oil market returns. Nonetheless, we find that clean energy stock returns Granger predict oil returns in bearish markets without any evidence of the contrary. This indicates that oil returns cannot be used to hedge the downside risk associated with renewable energy company purchases. Quantile impulse responses for the relationship between clean energy stocks and the crude oil market reveal bidirectional and significant responses, where a negative shock during an extremely down market reveals a negative response in the other market and a positive shock during an extremely up market reveals a significant positive response. This shows that neither market can be utilized to offset risks in the other market. [ABSTRACT FROM AUTHOR]

This study investigates the impact of climate policy uncertainty (CPU) on energy and metal commodity futures markets by employing quantile regression, which accounts for various (bearish, normal, and bullish) markets. Our results reveal that the impact of CPU shocks is heterogeneous and market condition‐specific. Particularly, CPU exerts a significantly negative effect on all commodities, except natural gas, in a bearish market. Under a normal market, the impact of CPU on energy returns varies across commodities whereas for a bullish market, the CPU effect is mixed. The results also reveal natural gas to be a good hedge instrument for climate policy risk. We further conducted channel analysis using the theory of storage and hedging pressure hypothesis. The key finding reveals inventory level as the transmission channel of climate policy risk. Our findings have implications for the inventory management strategies of producers and suggest that regulators should consider market‐based policies in their decarbonization efforts. [ABSTRACT FROM AUTHOR]

This study investigated the extent to which the low-risk anomaly is present in a pooled sample of nine African frontier equity markets from March 2004 to July 2023. Portfolio-level analysis was used to investigate the total stock returns of equally weighted quintile portfolios sorted on historical volatility. The presence of a monotonic relationship between historical volatility and subsequent returns was evaluated using monotonicity tests, controlling for possible mediating variables. The results revealed evidence of a statistically significant low-volatility premium, except during bull markets. These findings imply that investors can expect to earn higher returns by allocating to stocks with lower historical volatility, but only during bear markets. These results hold for realistic portfolio construction constraints, such as (il)liquidity and high transaction costs. [ABSTRACT FROM AUTHOR]

The variance risk premium is a critical predictor of expected returns. However, numerous studies indicate that expected returns depend strongly on the state of the economy. Herein, we examine the effect of the variance risk premium in different market states by using cross – sectional regression and predictability of returns. Our empirical results show that the variance risk premium is a significantly priced factor in bull markets. Additionally, predicted return horizons are shorter in bear markets than in bull markets. Compared with that in bull markets, the predictive ability of the variance risk premium diminishes more rapidly in bear markets when the horizon period is lengthened. [ABSTRACT FROM AUTHOR]

The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences. [ABSTRACT FROM AUTHOR]

The paper explores the concept of cash refund capital reductions (CRCR) in the context of firms listed on the Taiwan Stock Exchange Corporation (TSEC) and GreTai Securities Market (GTSM). CRCR is a distinct and increasingly vital financial tool used by these firms. Taiwan stands alone in adopting CRCR, making it a challenge to establish both a theoretical framework and gather empirical evidence on this subject. In this study, we examined 205 CRCR announcements made between 2002 and 2022, delving into their impact on both bull and bear markets. Our investigation reveals that the market responses to CRCR announcements were more significant during bull markets compared to bear markets. Additionally, we delve into the crucial factors influencing market responses in these varying market conditions. The findings underscore the importance for firms announcing CRCR to carefully consider the prevailing market conditions. [ABSTRACT FROM AUTHOR]

In this study, we decompose momentum indicators for the Japanese stock market into two components, high-to-price and price-to-high. High-to-price has a lower downside risk and higher Sharpe ratio than price-to-high. We find that a conventional momentum strategy combines the characteristics of high-to-price in a bull market and those of price-to-high in a bear market. In particular, the large drawdowns of momentum strategies reported in previous studies seem to be largely owed to those of price-to-high in bear markets. It is possible that the mechanism generating factor returns differs among the three strategies. [ABSTRACT FROM AUTHOR]

An asset pricing model with two types of chartists and fundamentalists and trend followers is considered, it is driven by a two-dimensional piecewise linear discontinuous map with three subsystems. There are great differences in the dynamic behaviour between expected offset (the expectations of trend followers offset the difference between the expectations of Type 1 traders in bull and bear markets) and expected non-offset. It is proven that there is no chaos in the dynamic of system with expected offset. However, chaos may exist in the dynamic of system with expected non-offset. We present a systematic approach to the problem of analysing the bifurcation phenomena associated with the appearance/disappearance of cycles, which may be related to several bifurcations. The multi-stability regions in parameter plane and related basins of multi-attractors in phase space are investigated. This paper aims to uncover the endogenous law of the unpredictability and excessive volatility in financial markets. [ABSTRACT FROM AUTHOR]

A previous discussion with Dr. Amy Hubble exploring asset allocation decisions in different historical economic and interest-rate environments generated quite a few comments and questions. This column is a follow-up discussion with Dr. Hubble on how discipline can serve as a financial service professional's value proposition during highly volatile periods. In this interview, Dr. Hubble illustrates the importance of maintaining discipline in bull and bear markets. [ABSTRACT FROM AUTHOR]

We build an active asset management model to study the interplay between the career concerns of a manager and prevailing market conditions. We show that fund managers overinvest in market-neutral strategies, as these have a reputational benefit. This benefit is smaller in bull markets, when investors expect more managers to use high-beta strategies, making their performance less informative about their ability than in bear markets. Consequently, fund flows that follow high-beta strategies are less responsive to the fund's performance, and flow-performance sensitivity is higher in bear markets than in bull markets. [ABSTRACT FROM AUTHOR]

Purpose: The priority of this study is to contribute to the literature by examining herding behavior at different periods of the COVID-19 pandemic. Furthermore, this study aims to investigate the herding behavior conditioned on market liquidity and information demand. Design/methodology/approach: This study investigates herding behavior in Vietnam's stock exchanges (Ha Noi Stock Exchange and Ho Chi Minh Stock Exchange) on a sample of daily stock closing prices of 425 firms from 2018 to the first half of 2022. Findings: The research confirms the existence of herding behavior not only for the whole but also during and post-COVID periods. These results are robust in both bull and bear markets, further confirming the influence of COVID-19 on herding in Vietnamese background. Moreover, when the authors condition exogenous factors for each period, the herding tendency is more evident at the medium market liquidity level than at high and low levels. Besides, the pandemic causes herding behavior of investors with low and medium information demand. Research limitations/implications: These findings imply some recommendations that facilitate investors, policymakers and researchers in the context of the COVID-19 crisis. Originality/value: The study contributes to the herding literature by examining herd behavior during the post-COVID period, suggesting the long-term impact of the health crisis. Furthermore, the research provides new evidence of herding behavior conditioned on market liquidity and information demand during different COVID sub-periods. [ABSTRACT FROM AUTHOR]

The purpose of the study is to compare the effect of country-based and global financial factors on Islamic stock market returns of BRIC (developing) and G7 (developed) country groups covering data from March 26, 2015, to March 31, 2021, for BRIC countries and July 27, 2015, to March 31, 2021, for G7 countries. A newly developed method of moment quantile regression (MMQR) is used to detect the asymmetric effects on Islamic stock market return across bullish, bearish, and normal market conditions, which is essential to better analyzing the stock market return. The outcome demonstrates that exchange rates have a significantly positive effect on stock market returns while CDS has a significant negative impact on the stock market return in G7 countries in both bearish and normal markets. On the contrary, we observe that the link between CDS and Islamic stock returns is negative but statistically insignificant in the case of BRIC. Additionally, we consider the Morgan Stanley Capital International (MSCI) world index and the CBOE Volatility Index (VIX) as the global financial factors. We find that the estimated coefficients of the MSCI world index have significant and positive effects on the stock market return in the case of both BRIC and G7 countries at all quantile levels. VIX has a significant negative impact on the Islamic stock return of the G7 across all quantiles. On the other hand, it has significant negative impacts only in transition (Q6) and a bullish period in the case of BRIC. The outcomes of the research will assist portfolio risk managers, international investors, and policymakers in building diversified portfolios and making investment decisions throughout the market’s busts and booms. [ABSTRACT FROM AUTHOR]

Analyzing the interdependencies among financial institutions is critical for designing systemic risk monitoring mechanisms; however, most existing research focuses on the first moment of the return distribution, which falls into the conventional models of choice under risk. Previous literature has observed the scarcity of investors' attention and processing power, which makes the traditional theory of choice under risk more vulnerable and brings the salience theory that accommodates investors' cognitive limitations to our attention. Motivated by evidence of salience theory value (STV) containing unique information not captured by traditional higher-order moments, we employ a quantile connectedness approach to examine the STV interconnectedness of China's systemically important banks (C-SIBs). The quantile approach allows us to uncover the dynamic STV interconnectedness of C-SIBs under normal, bearish, and bullish market conditions and is well-suited to extreme risk problems. Our results show that the C-SIBs system is asymmetrically interconnected across quantiles and at higher levels under bullish than bearish market conditions. Principally, a bank's performance in the C-SIBs system depends on its systemic importance and market conditions. Furthermore, the comparative analysis indicates that STV could provide more information than higher-order moments in capturing the dynamic change in the C-SIBs system and detecting some market events more precisely. These results have important implications for policymakers and market participants to formulate regulatory policy and design risk management strategies. [ABSTRACT FROM AUTHOR]

In decentralized finance (Defi), market participants are allowed to have the right to manage their own funds as opposed to centralized finance (Cefi) with a central custodian, centralized exchanges (CEX). Most Defi projects provide their own service and simultaneously issue a unique token that can be traded in decentralized exchanges (DEX). However, the values of these tokens have rarely been studied. We confirm that the prices of tokens in the Defi market have a persistent tendency to move together. We also demonstrate that the correlation gradually increased from the Defi market's inception and the price co-movement increased in a bear market, and conversely, decreased in a bull market. Specifically, we find a notable difference in the level of price co-movement between CEX cryptocurrencies and DEX tokens. [ABSTRACT FROM AUTHOR]

In this study, we used data on China's initial public offerings (IPOs), market volatility and macro environment before and after two stock crashes during 2006–2016 to investigate how different investor sentiment affects IPO first-day flipping. The empirical results show that the expected returns of allocated investors are affected by sentiment, with allocated investors having higher psychological expectations of future returns during an optimistic bull market and their optimism discouraging first-day flipping, while higher risk-free interest rate levels and rising broad market indices also discourage first-day flipping and tend to sell in the future. The pessimistic bear market during which allocated investors have lower psychological expectations of future returns, their pessimism will promote first-day flipping, and the increase in the risk-free rate level will also promote first-day flipping, which is the opposite of the optimistic bull market, indicating that their risk aversion has increased and they tend to sell on the same day. We also found an anomaly that the greater the decline in the broad market index during a pessimistic bear market, the more inclined the allocated investors are to sell in the future when the broad market index rises in an attempt to gain higher returns. These findings help explain and understand the impact of market and macro index fluctuations on investor behavior under different investor sentiments. [ABSTRACT FROM AUTHOR]

Entropy serves as a measure of chaos in systems by representing the average rate of information loss about a phase point's position on the attractor. When dealing with a multifractal system, a single exponent cannot fully describe its dynamics, necessitating a continuous spectrum of exponents, known as the singularity spectrum. From an investor's point of view, a rise in entropy is a signal of abnormal and possibly negative returns. This means he has to expect the unexpected and prepare for it. To explore this, we analyse the New York Stock Exchange (NYSE) U.S. Index as well as its constituents. Through this examination, we assess their multifractal characteristics and identify market conditions (bearish/bullish markets) using entropy, an effective method for recognizing fluctuating fractal markets. Our findings challenge conventional beliefs by demonstrating that price declines lead to increased entropy, contrary to some studies in the literature that suggest that reduced entropy in market crises implies more determinism. Instead, we propose that bear markets are likely to exhibit higher entropy, indicating a greater chance of unexpected extreme events. Moreover, our study reveals a power-law behaviour and indicates the absence of variance. [ABSTRACT FROM AUTHOR]

Motivated by the relationship between trading intensity and volatility and the attractiveness of duration-based volatility estimators, this paper investigates the ability of price duration to forecast realized volatility of Bitcoin. Using high-frequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique duration-augmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events. [ABSTRACT FROM AUTHOR]

Research has shown that tail risk hedging using explicit option purchases and trend-following effectively mitigate equity tail risk. This paper demonstrates that these defensive qualities can be leveraged into offence: overlaying a two-pronged tail risk management strategy that employs both option-based tail risk hedging and trend-following has empirically enhanced absolute and risk-adjusted returns relative to a buy-and-hold equity portfolio. Option-based tail risk hedging has empirically mitigated equity risk best during market crashes, while trend-following has notably supported equity during slower bear markets. In combination, and when overlayed onto equity, the resulting portfolio reliably outperformed equity during the most significant drawdown episodes suffered over the period under investigation and broadly maintained this advantage during equity bull markets. [ABSTRACT FROM AUTHOR]

This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling. [ABSTRACT FROM AUTHOR]

This paper investigates a non-zero-sum stochastic differential game between two competitive CARA insurers, where we adopt the different classes of premium principles (including the expected value premium principle, the variance premium principle and the exponential premium principle) and each insurer aims to maximize the expected exponential utility of his terminal wealth relative to that of his competitor. Moreover, both insurers are allowed to purchase reinsurance treaty to mitigate individual claim risks and can invest in a financial market consisting of a risk-free asset, a risky asset where the instantaneous rate of investment return follows an Ornstein-Uhlenbeck process which can reflect the changes of bull market and bear market. The optimal reinsurance strategy has a non-trivial structure which is distinguished from the conventional proportional and excess-of-loss reinsurance strategies. Furthermore, we derive the optimal reinsurance and investment strategies under the variance premium principle and expected value principle. In addition, we give another model which considers the correlation between risk model and financial market under the expected value principle. Finally, numerical analyses are provided to analyze the effects of model parameters on the optimal strategies under different cases. [ABSTRACT FROM AUTHOR]

This paper aims to study the potential effects of changes in international risk factors on leading European construction companies' returns. The study is conducted on a sample period between January 2000 and December 2019 and applies an extension of the Fama and French five‐factor model (2015) using the quantile regression methodology. Specifically, this research extends the Fama and French (2015) factor model by adding risk factors such as nominal interest rates, momentum and momentum reversal factors (Carhart, 1997) and the traded liquidity factor (Pastor and Stambaugh, 2003). For robustness, this study splits the entire sample period into three sub‐periods: pre‐crisis, crisis, and post‐crisis. As expected, the highest R2 coefficients are observed in the extreme quantiles, that is, in the bullish and bearish market states, revealing a U‐shaped relationship in the explanatory power of this factor model. [ABSTRACT FROM AUTHOR]

This study examines the spillovers and connectedness between oil and the African stock markets under bearish, normal, and bullish market conditions. Using the quantile connectedness method, we find higher spillovers under bearish market conditions than in both tranquil and bullish market conditions. Oil is a net transmitter of spillovers in the African markets. Furthermore, Ghana, Kenya, Nigeria, and South Africa are net receivers of spillovers, and Tunisia, Egypt, Morocco, and Mauritius are net transmitters of spillovers in the lower quantile. In the median quantile, Ghana shifts to being a net transmitter of spillovers, whereas Egypt becomes a net receiver of spillovers. In the upper quantiles, all markets are net transmitters of spillovers, except for Mauritius and Egypt. We find a strong connectedness between oil and the Nigerian market during bearish and tranquil market conditions which alleviates the bullish market scenario. Moreover, spillovers reached the maximum level in early 2020, corresponding to the first wave of the COVID-19 pandemic. The portfolio analysis shows that an optimally weighted portfolio offers the best downside risk for all markets. The hedged portfolio offers the best risk reduction for all economies. [ABSTRACT FROM AUTHOR]

This study investigates the effects of the COVID-19 pandemic on herding behaviour among investors in two well-developed markets. Utilizing daily prices of stock indexes from the period of December 5, 2017 to February 28, 2022 for USA and January 9, 2018 to February 28, 2022 for UK, we test for herding behaviour using the quantile regression approach in addition to the OLS model. We found no evidence of herding before the COVID-19 pandemic in both bullish and bearish markets for USA and UK. However, herding incidence was discovered in the USA and UK bullish market during the COVID-19 period. In the bearish market, herding behaviour was only found during the COVID-19 period in USA. The study provides policymakers and investors with information to draw significant measures in their investment portfolio management during crises and pandemics. [ABSTRACT FROM AUTHOR]

Stock markets are flooded with various uncertainties such as stock market scenarios, especially the input parameters of stock portfolio models. It is hard for stock market investments to jointly deal with them. Although robust portfolio is the prevailing portfolio policy handling with uncertainties, it always focuses on the worst-case scenario of all the possible realizations of uncertain input parameters, resulting in the optimal portfolios considerably conservative as well as the portfolio performances very inferior. To this end, based on Markowitz's mean–variance (MV) model, this paper builds the new robust portfolio model (RMV-best) from the innovative perspective of best-case scenario, exactly contrary to the model based on the worst-case scenario (RMV-worst). Furthermore, stock market scenarios which always shift uncertainly show the periodic characteristics, and can be divided into three movement statuses: the bull, the bear and the steady. To verify the consistency of RMV-best, RMV-worst and MV portfolio policies over the different motion periods and identify the differences of these portfolio policies at various movement statuses, the mix data including two motion periods and three industry sectors is employed. Eventually, empirical results indicate that RMV-best is always the most favorable portfolio policy at the bull and the bear market, while MV can produce more profitable portfolios at the steady market over two motion periods. However, the drawbacks of RMV-worst are also confirmed in this study. [ABSTRACT FROM AUTHOR]

This paper examines the simultaneous effects of oil shocks on stock markets under different regimes in Norway (as an oil-exporting economy) and Japan (as an oil-importing economy). We use a Structural Threshold VAR model and rely on the monetary policy's effectiveness. Our results show that oil shocks, mainly oil demand shocks, are significantly associated with the oil structure of the country (oil-exporting or oil-importing), channels of the effectiveness of the monetary policy, and the stock market regimes. Furthermore, the channel through which the money supply growth affects Norway's economic growth depends on the stock market regimes. Moreover, the results persist for the money growth-inflation nexus. Similarly, the impact of oil demand shocks depends on the Japanese stock market's regime and the monetary policy's effectiveness. Finally, we find that positive output shocks positively affect the stock markets of both countries in the long run regardless of the stock market conditions. In the short run, a positive inflation shock has a negative (positive) impact on Norway (Japan) during bullish and bearish market conditions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explore the relationship between Dow Jones Islamic Market World Index, Islamic gold-backed cryptocurrencies and halal chain in the presence of state (regime) dynamics. Design/methodology/approach: The authors have used the Markov-switching model to identify bull and bear market regimes. Moreover, the dynamic conditional correlation, the Baba, Engle, Kraft and Kroner- generalized autoregressive conditional heteroskedasticity and the wavelet coherence models are applied to detect the presence of spillover and contagion effects. Findings: The findings indicate various patterns of spillover between halal chain, Dow Jones Islamic Market World Index and Islamic gold-backed cryptocurrencies in high and low volatility regimes, especially during the COVID-19 pandemic. Indeed, the contagion dynamics depend on the bull or bear periods of markets. Practical implications: These present empirical findings are important for current and potential traders in gold-backed cryptocurrencies in that they facilitate a better understanding of this new type of assets. Indeed, halal chain is a safe haven asset that should be combined with Islamic gold-backed cryptocurrencies for better performance in portfolio optimization and hedging, mainly during the COVID-19 period. Originality/value: To the best of the authors' knowledge, this paper is the first research on the impact of the halal chain on the Dow Jones Islamic Market World Index return, Islamic gold-backed cryptocurrencies returns in the bear and bull markets around the global crisis caused by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This study examines the investment strategies of fund managers without business or IT background. The non-professional managers are inclined to pursue higher returns but with greater volatility. They are aggressive in the bull market while cautious in the bear market. With their participation, the funds could gain significantly higher abnormal returns even in economic downturns. The superior performance can be attributed to their management style. The non-professional managers would avoid being multi-tasked unless the financial market is thriving, while the professional ones would simultaneously operate several funds in general. [ABSTRACT FROM AUTHOR]

Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish to bearish markets and vice versa. Popular tools for modelling financial time series are hidden Markov models, where a latent state process is used to explicitly model switches among different market regimes. In their basic form, however, hidden Markov models are not capable of capturing both short- and long-term trends, which can lead to a misinterpretation of short-term price fluctuations as changes in the long-term trend. In this article, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of market behaviour, which can contribute to the development of more sophisticated trading strategies. The feasibility of the suggested approach is illustrated in two real-data applications, where we model data from the Deutscher Aktienindex and the Deutsche Bank stock. The proposed methodology is implemented in the R package fHMM, which is available on CRAN. [ABSTRACT FROM AUTHOR]

The rapid and far-reaching spread of COVID-19 related news can alter investors' perceptions and investment behaviour and affect security returns. We investigate this hypothesis by studying the impact of COVID-19 related news (panic news, media-hype new, fake news, infodemic, and Stringency measures) and alternative measures of COVID-19 developed by Narayan et al. (2021) on Sukuk returns during different market conditions. Empirical results show asymmetric comovement between global Sukuk returns, panic news, travel bans, and the percentage of information about the novel COVID-19 pandemic. Moreover, the COVID-19 news affects the Sukuk returns only when the sukuk markets are bearish. The Sukuk returns are not affected by medical and vaccine information, and the aggregate COVID-19 index that captures the pandemic sentiment. [ABSTRACT FROM AUTHOR]

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements. Hence, the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system (CETS). Through the staggered difference-in-difference (DID) model and the propensity score matching-DID model, the impact of CETS on reducing the risk of stock price crashes is examined using data from China's A-share heavily polluting listed companies from 2007 to 2019. The results of this study are as follows: (1) CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas. Specifically, CETS reduces the skewness (negative conditional skewness) and down-to-up volatility of the firm-specific weekly returns by 8.7% and 7.6%, respectively. (2) Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition, short-sighted management, and intensive air pollution. (3) Mechanism tests show that CETS can reduce analysts' coverage of heavy polluters, reducing the risk of stock price crashes. This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk. [ABSTRACT FROM AUTHOR]

We study at-the-market (ATM) equity offerings, which are direct share issuances sold in the secondary market that forgo underwriters and "dribble-out" shares over time rather than raising them all at once. Enabled in 2008, their use has increased dramatically, and in 2016, their incidence and total proceeds were, respectively, 63% and 26% of those for seasoned equity offerings (SEOs). Determinants of firms' choice between ATMs and SEOs are consistent with the costly certification hypothesis of Chemmanur and Fulghieri (1994). We also find that 65% of ATM proceeds are used to stockpile cash compared to 84% of SEO proceeds. [ABSTRACT FROM AUTHOR]

Carry, value, and momentum factors are said to be everywhere according to a growing body of research. As such they may be the most robust styles across asset classes and history. In this article, the authors look to clear up the following questions: How do multi-asset styles perform across time and across different market regimes? How should multi-asset styles be expected to behave during alternative phases of the stock market cycle? Are cross-asset styles sensitive to volatility conditions? Are there different responses to changes in bond yields? Is any style more likely to be structurally more cyclical or defensive? To do so, the authors describe how single asset class factors behave and then look into to the current debate opposing style rotation to diversification to answer the question: Is there a case for more time-varying and concentrated multi-asset style portfolio constructions? [ABSTRACT FROM AUTHOR]

There is a big controversy among both investment professionals and academics regarding how the termination probability of a market state depends on its age. Using more than two centuries of data on the broad US stock market index, we revisit the duration dependence in bull and bear markets. Our results suggest that the duration dependence for both bull and bear markets is a nonlinear function of the state age. It appears that the duration dependence in bear markets is strictly positive. For 93% of the bull markets, the duration dependence is also positive. Only about 7% of the bull markets, those with the longest durations, do not exhibit positive duration dependence. We also compare a few selected theoretical distributions on their ability to describe the duration dependence in bull and bear markets. Our results advocate that the gamma distribution most often provides the best fit for both the survivor and hazard functions of bull and bear markets. However, our results reveal that none of the selected distributions accurately describes the right tail of the hazard functions. [ABSTRACT FROM AUTHOR]

This article examines the asymmetric volatility spillover effects between Bitcoin and alternative coin markets at the disaggregate level. We apply a frequency connectedness approach to the daily data of 11 major cryptocurrencies for the period from 1 September 2017 to 2 March 2022. We try to uncover the existence of the "fear of missing out" psychological effect and "pump-and-dump schemes" in the crypto markets. To do that, we estimate the volatility spillovers from Bitcoin to altcoin and the cryptos' own risk spillovers during bull and bear markets. The spillover results from Bitcoin to altcoin provide mixed results regarding the presence of this theory for major cryptocurrencies. However, the empirical findings carried out by the cryptos' own spillover effects fully confirm the existence of a fear-of-missing-out effect and pump-and-dump schemes in all cryptocurrencies except for USDT. [ABSTRACT FROM AUTHOR]

The quest for parsimonious models has been a key objective in asset pricing. However, there appears to be no consensus on the most successful asset pricing strategy in the literature, especially for the South African Market. Using financial statements from January 2000 to December 2015, this article explores how market anomalies affect the performance of securities in the Johannesburg Stock Exchange's (JSE's) resources, industrial, and finance sectors. We investigated the efficacy of several asset pricing models and their capacity to account for market anomalies in the JSE's resources, industrial, and financial sectors, as well as the applicability of the Fama and French five-factor model. The study used multiple regression techniques and applied stationarity and cointegration methods to ensure robust results. Results also suggest that when the FF5FM is implemented, there is statistical significance at the 10% level for the CMA in the resources sector as the value factor disappears. The FF5FM results in the industrial sector show a significance level of 5% in the SMB. The financial sector seems to have the majority of the style-based risk factors as the SMB is positively significant at a 5% level, the HML is significant at a 1% level, and the CMA is negatively significant at a 10% level of significance. The results suggest that the Carhart Four Factor model is the best to use in all market conditions. Results also show that value becomes redundant in a bullish market, but the opposite holds in a bearish market for a model with operating profitably and investing factors. These findings highlight the necessity for investors to determine which investment risk elements produce abnormal returns in both bearish and bullish market circumstances before investing. [ABSTRACT FROM AUTHOR]

This paper investigates the hedge and safe-haven properties of green bonds (GBs) performing as a safeguard against oil price shocks and uncertainty, in comparison to the corresponding roles of gold, the 3-month European government bills, and the U.S. 3-Month T. bills. Oil price shocks are disentangled into oil supply, oil demand, and oil risk shocks based on the recent methodology of Ready (2018), generated in a framework that includes gold, oil shocks, and oil uncertainty (OVX). The ability of GBs to protect against oil price shocks and oil uncertainty is examined using the GARCH and quantile regression (QR) models. The results show that GBs are more appropriate than gold and conventional bonds (CBs) as hedging and safe haven tools against oil price shocks and oil uncertainty. Besides, the ability of GBs to protect against oil price shocks and uncertainty depends on whether the oil shocks are supply, demand, or risk shocks. Furthermore, we find that GBs serve as a strong hedge and/or a safe haven against structural oil shocks generally under bearish GBs market conditions. The European government bills and the US T. bills play the role of weak hedge and a safe haven asset against oil uncertainty and oil shocks. This role varies across quantiles (bear and bull market conditions). Relevant risk management implications are discussed. [ABSTRACT FROM AUTHOR]

The existence of behavioural bias such as positive feedback trading (PFT) and herding is well researched for advanced economies. This paper explores whether foreign institutional investors (FIIs), a prominent investor group, pursue these strategies in India. The contribution lies in the application of multiple measures of herding. Results indicate that FIIs exhibit PFT behaviour in equity and to some extent, in debt. At the individual stock level, FIIs herd during non-extreme market conditions, but market-level measures indicate absence of herding in extreme market conditions. In the bullish market, they behave more rationally compared to bearish or normal market conditions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the dependence structure between the US conventional stock market and each Islamic and conventional stock market provided by the Dow Jones index, namely, for the UK, Canada, Europe, the emerging countries and Asia-Pacific. This paper considers both the bearish and bullish market phases of the 2008 global financial crisis to analyze the financial contagion. Design/methodology/approach: The authors implement the copula framework-based GJR-GARCH-t model for the period from December 31, 2004 to September 30, 2016. Findings: The marginal models suggest a strong persistence of volatility in all stock markets. The dependence structure for stock market pairs under-consideration is not all strictly symmetrical. Moreover, the Islamic stock markets witness the same behavior as their conventional counterparts. Finally, the resilience and the decoupling hypotheses are not all around upheld by the empirical proof. Originality/value: The findings of this paper are very important for global investors in their risk management during extreme market events. As the Sukuk is considered as a safe haven during crisis episodes, the investors are invited to take it into account for further portfolio diversification. [ABSTRACT FROM AUTHOR]

The Theory demonstrates that oil price and oil volatility (OVX) are significant determinants of economic activity; however, studies seldom consider both variables in the oil–exchange rate nexus and ignore the distributional heterogeneity of the exchange rate. We investigate their joint effect and employ both the quantile regression and Markov switching models to address this. We differentiate between positive/negative shocks and control for the effect of the global financial crisis in 2008 and the COVID-19 pandemic in 2020. We observe that OVX shocks significantly impact the exchange rate for all countries whereas, oil price shocks only affect the exchange rate of oil importing countries. Rising (falling) OVX causes the local currency to depreciate (appreciate). The impact of rising or falling OVX is the same for oil importing and oil exporting countries whereas the impact of rising and falling oil price varies. The impact of oil price and OVX on exchange rate is affected by market conditions. The exchange rate responds to oil price and OVX mostly at lower quantiles (bearish markets) for all countries, which reveals investors sensitivity. In contrast, a weak to no significant response is observed at the higher quantiles (bullish market). Our results are robust in model selection (Markov switching models). [ABSTRACT FROM AUTHOR]

We examine whether and how investor sentiment affects stock liquidity in the Chinese stock market. Utilizing big data mining techniques, we excavate posted texts of a large stock forum and devise daily investor sentiment indicators. While examining the relationship between investor sentiment and stock liquidity, we also test whether order flow imbalance, attributed to investor sentiment, is the channel where investor sentiment delivers its impact on stock liquidity. We find that investor sentiment correlates positively with stock liquidity, and order flow imbalance is proven as an intermediary in this process. This positive correlation is stronger in a bear market than in a bull one. Additionally, liquidity sensitivity is higher for firms with higher book‐to‐market ratio, larger size and lower risk. When the market is less regulated, such as relaxed short‐selling restrictions, liquidity sensitivity also rises. [ABSTRACT FROM AUTHOR]

We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation. [ABSTRACT FROM AUTHOR]

This research explores the sensitivity of the returns of some selected European companies to changes in the explanatory factors proposed during the sample period between January 2000 and December 2019. We focus on listed companies in the tourism and services sector and estimate an extension of the Fama and French five-factor model (2015) by applying the quantile regression approach. Specifically, this study starts from the Fama and French risk factors and adds the nominal interest rates, a momentum and momentum reversal factors and a traded liquidity factor. For robustness, this research divides the whole sample period into three sub-periods: pre-crisis, crisis and post-crisis. In line with the previous literature, the explanatory power of this factor model shows a U-shape, which is compatible with the highest R 2 coefficients in the extreme quantiles, as well as in extreme stages of the economy, that is, in the bullish and bearish market states. [ABSTRACT FROM AUTHOR]

We study how market regimes can influence investors' behaviour under the framework of prospect theory. Two market regimes (bull and bear) are considered, which affect our investors' reference points and their loss-averse behaviours. Using both theory and empirical evidence, the paper shows the disposition effect in a bull market is stronger than that in a bear market. Investors learn from both domains (gain and loss) in a bear market and more from the gain domain in a bull market. These results also provide an explanation for asymmetric learning regarding the disposition effect. [ABSTRACT FROM AUTHOR]

The novel quantile connectedness network method is used to investigate the vulnerability of emerging stock markets to global shocks in the normal, bear and bull markets. The size of the system-wide shock for an emerging market is doubled, while its own shock is halved in the bear and bull markets relative to the normal market and vice versa. As the size of the systemic shock increases in the bear and bull markets, which leads to an increase in the bilateral shock for emerging markets. Although the dollar index emerged as a risk factor only in the normal market, oil is not a risk factor for the emerging market bloc, irrespective of the state of the market. However, the US stock market is a major risk factor for emerging markets in all kinds of market conditions, although the degree of the shock spillover is more pronounced in the normal market than in the bear and bull markets. The robustness of the vulnerability is verified in a time-varying framework. Policy implications are also discussed. [ABSTRACT FROM AUTHOR]

This paper examines the quantile spillovers and interconnectedness between green bonds, renewable energy, and the G7 sustainability markets under extreme market conditions. It reaches beyond the widely employed studies based in the mean values of variables by resorting to the advanced quantile vector auto-regression methodology. The results show that the overall connectedness is stronger during extreme, either bearish or bullish market situations, than during normal market conditions. Furthermore, we show that all sustainability markets, except Japan, are the net transmitters of shocks, while all the green bonds are the net receivers. Moreover, we document that spillovers vary significantly over time and that extreme market conditions reduce the net spillover influences. The relative tail dependence analysis shows that the connectedness at the lower quantile relatively dominates the connectedness at the upper quantile. Finally, the total spillovers are higher at the extremes than at the median quantile, whereas the periods of major crises are associated with unusually high connectedness, as consistent with the literature on contagion effects. [ABSTRACT FROM AUTHOR]

This study examines the nonlinear dependence between carbon market and stock market in China under normal and extreme market conditions by employing two novel nonlinear approaches, namely, quantile coherency and causality-in-quantiles methods. Given our results on the overall and sector level of stock market, we find that there is a negative dependence between the two markets under bearish and normal market states in the short- and medium-term respectively, while the dependence becomes positive under bearish and bullish market states in the long-term. Furthermore, we also prove that the Granger causality from carbon market to stock market exists. However, no evident impacts from stock market to carbon market have been found. Additionally, at sector stock market, we discover heterogeneity across market conditions. And emission-intensive sector stock indices are more affected by carbon prices. [ABSTRACT FROM AUTHOR]