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Summary: We propose a multivariate regime switching model based on a Student‐ t copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two‐step procedure carried out through the Expectation–Maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student‐ t copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log‐returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets. [ABSTRACT FROM AUTHOR]

This study explores whether frequent trading is profitable to investors in an emerging stock index futures market. Our analyses, based on long‐term data from 2010 to 2023, indicate that the effect of trading frequency differs across investor types and market conditions. Only some domestic institutions gain additional profits from more frequent trading, and such a tendency is apparent when the futures price falls and when the futures market volatility is low. Foreign investors experience losses as they trade more when the market is bearish and are frequently net long. The performance of domestic individuals does not depend on their trading frequency in general; however, they lose more from trading when the market is bearish and when the market is less volatile. [ABSTRACT FROM AUTHOR]

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]

Purpose: The purpose of this paper is to scrutinize the safe haven benefits of 13 individual commodities for the USA and Chinese equity sectors during the financial turmoil period. Therefore, sectoral investors in the USA and China could invest in those specific commodities that provide stable returns during the health crisis and financial turmoil periods. Design/methodology/approach: The daily data spans from February 1, 2015, to July 28, 2022. The present study applies several different approaches to analyzing the data set. The author apply the cross-quantilogram (C.Q) methodology to capture the lead-lag bivariate quantile interdependence between two stationary time series variables during the bearish, bullish and normal periods. Then the study used the hedging effectiveness (HE) and conditional diversification benefits (CDB) approaches to capture the hedging and diversification benefits of commodity classes and individual commodities. Findings: The noteworthy findings of the quantilogram methodology reveal that livestock and agriculture commodities serve as better refuges as compared to the precious metals and energy index in both countries. On average, precious metals failed to serve as safe haven investments for the USA and Chinese equity market sectors. All energy commodities except soybean oil had strong comovements with China and the US equity sectors during bearish, bullish and normal periods. Lean hogs, fiddler cattle and live cattle are perfect hedging assets for both countries due to the presence of blue color at normal and bullish periods in all C.Q heat-maps. The HE table depicts that commodity indices and individual commodities failed to serve as hedging assets for the Chinese equity sectors. But commodities are semistrong hedging assets for the US equity sectors and the S&P 500 due to the average HE values being 0.7 and above. The CDB values depict that precious metals provide diversification benefits in both equity markets. Practical implications: The present study results have important implications for equity sector investors of the USA and China in suggesting particular commodity during the financial turmoil period. During the bearish market condition, risk averse equity sector investors can invest in livestock commodities and agriculture commodities, due to their relatively stable returns. In addition, policymakers can use the analysis insights to formulate policy tools and monitoring mechanisms, effectively mitigating the unfavorable effects arising from asymmetric dependence between commodities and equity sectors during the upper tail, middle and lower tail. Policymakers can suggest equity investors to invest in which commodity during extreme conditions. Originality/value: The current study has the following points of originality. First, to the best of the author's knowledge, this is the first study to investigate the individual commodities' roles as safe havens taken from all four major commodity classes. More importantly, it is also noticeable that the safe haven abilities of commodities are usually tested for the stock market, but the equity sectors are ignored. Therefore, the present study used both stock market and sectoral indices data. [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]

In this paper, we attempt to explore the effects of various uncertainty measures – namely, implied volatility (VIX), tail risk (SKEW), economic policy uncertainty (EPU) and partisan conflict (PCI) indices-, on U.S. REITs returns at sector level, using the non-linear Markov regime-switching model. Our empirical results reveal that uncertainty measures have regime-dependent impacts and do not affect the return dynamics of REIT sectors in a uniform way. Office and hotel & lodging REITs exhibit the strongest sensitivity to VIX and EPU, respectively, during bearish market periods. While residential REITs are the most resilient to uncertainties, healthcare REIT returns are negatively affected from all the uncertainty factors only in the low variance regime. Hence, our findings show evidence of asymmetric, non-linear and sector-dependent linkages between REITs and uncertainties. These results provide valuable insights and important implications for REIT investors. [ABSTRACT FROM AUTHOR]

Sustainability concerns are increasingly shaping global investment decisions, prioritising sustainable impacts over mere profit-making. This study examines the performance of Environmental, Social, and Governance (ESG) funds in emerging markets, focusing on bull and bear market scenarios. A Markov Regime Switching model was employed to analyse daily price data from BRICS (Brazil, Russia, India, China, and South Africa) ESG market indices spanning from 31 January 2011 to 2 January 2023. The analysis highlights the variability of ESG returns performance across different market conditions, emphasising the need for tailored investment strategies that account for market dynamics. Additionally, patterns in ESG returns and volatility are identified, along with pairwise relationships among ESG markets within the emerging markets bloc. The study also examines the influence of investor sentiments on ESG performance and controlled for the influence of macroeconomic factors on ESG performance amid changing market conditions. Finally, insights into transition probabilities between bullish and bearish market conditions offer guidance for more resilient investment portfolio management. This research contributes to advancing empirical knowledge on ESG investing in emerging markets, emphasising the significance of investor sentiments and market conditions for sustainable investment practices and long-term financial success. [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]

This article investigates a regime-switching investment strategy aimed at mitigating downside risk by reducing market exposure during anticipated unfavorable market regimes. We highlight the statistical jump model (JM) for market regime identification, a recently developed robust model that distinguishes itself from traditional Markov-switching models by enhancing regime persistence through a jump penalty applied at each state transition. Our JM utilizes a feature set comprising risk and return measures derived solely from the return series, with the optimal jump penalty selected through a time series cross-validation method that directly optimizes strategy performance. Our empirical analysis evaluates the realistic out-of-sample performance of various strategies on major equity indices from the US, Germany, and Japan from 1990 to 2023, in the presence of transaction costs and trading delays. The results demonstrate the consistent outperformance of the JM-guided strategy in reducing risk metrics such as volatility and maximum drawdown, and enhancing risk-adjusted returns like the Sharpe ratio, when compared to both hidden Markov model-guided strategy and the buy-and-hold strategy. These findings underline the enhanced persistence, practicality, and versatility of strategies utilizing JMs for regime-switching signals. [ABSTRACT FROM AUTHOR]

Summary: Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes that all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross‐section of state‐specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared with several benchmark models including univariate Markov switching models. [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]

Purpose: This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread. Design/methodology/approach: The authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%. Findings: The study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold. Originality/value: This study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns. [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]

Copyright of Contabilidad y Dirección is the property of ACCID and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors' investigation. Design/methodology/approach: The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT). Findings: At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant. Originality/value: The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors' research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions. [ABSTRACT FROM AUTHOR]

Academic research on trend-following investing has almost exclusively focused on testing various trading rules' profitability. However, all existing trend-following rules are essentially ad hoc, lacking a solid theoretical justification for their optimality. This paper aims to address this gap in the literature. Specifically, we examine the optimal trend-following when the returns follow a two-state process, randomly switching between bull and bear markets. We show that if a Markov model governs the return process, it is optimal to follow the trend using the Exponential Moving Average rule. However, the Markov model is unrealistic because it does not represent the bull and bear market duration times correctly. It is more sensible to model the return process by a semi-Markov model where the state termination probability increases with age. Under this framework, the optimal trend-following rule resembles the Moving Average Convergence/Divergence rule. We confirm the validity of the semi-Markov model with an empirical study demonstrating that the theoretically optimal trading rule outperforms the popular 10-month Simple Moving Average and 12-month Momentum rules across a universe of international markets. [ABSTRACT FROM AUTHOR]

The paper examines how central bank communication affects the macro information in analyst forecasts. Using quarterly data of Chinese‐listed firms from 2007 to 2018, we find that richer and more frequent central bank communication increases the macro information contained in analyst forecasts. This effect is realized through the employment of in‐house economists by security firms. In addition, we document that the effect of central bank communication on macroeconomic information in analyst forecasts is more salient under a contractionary monetary policy regime, during a bear market, or when the economic policy is more uncertain. We also show that analyst forecasts are more sensitive to central bank communication when firms that they follow are state‐owned enterprises, have larger leverage ratios, or are located in more developed regions. In addition, analyst forecasts are more susceptible to central bank communication when the communication is in an informal oral format, when the public has more trust in the credibility of the central bank communication, and when the central bank pays more attention to expectation management after 2010. Finally, we show that richer and more frequent central bank communication also improves the accuracy of analyst forecasts. [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]

House valuation involves forecasting household demand for a neighborhood, whereas commercial property valuation involves forecasting area economic activity. Yet, we show optimistic expectations for both. We investigate further possible disparities due to different cultures of home ownership, short, medium, and long-term investment horizons, and bullish or bearish trends. We use an experimental framework to demonstrate expectations are optimistic under all conditions. Buyers use trends to surmise future prices in bull markets, expect price increases in oscillating markets, and price reversals in bear markets, failing to anticipate price busts. Policy implications are important in preventing property boom-induced financial crises. [ABSTRACT FROM AUTHOR]

We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets. We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets, focusing on the role of stablecoin issuances. We study the stock, bond, cryptocurrency, and stablecoin markets and find very high connectedness, which varies over time in response to up/down trends in financial markets. The results show that during financial turmoil, cryptocurrencies amplify downside risks rather than serve as diversifiers. In addition to risky assets from conventional financial markets, cryptocurrencies champion the transmission of spillovers to digital and conventional markets. In contrast, changes in stablecoin issuances produce few shocks because of their pegged prices, but they facilitate investors' switch from volatile cryptos to more stable digital instruments; that is, we observe a phenomenon designated by us as the "flight-to-cryptosafety." We draw insightful conclusions, provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance. Highlights: Connectedness among stocks, the US Treasuries, cryptos, and stablecoins is analyzed High connectedness exists due to stablecoin issuances Cryptos amplify downside risks and are not diversifiers in extreme market trends Stablecoins facilitate the flight-to-safety and flight-to-cryptosafety phenomena Stablecoin issuances are a potential hedge signal for large scale and institutional investors [ABSTRACT FROM AUTHOR]

Cryptocurrencies are in the center of attention of investors, public authorities and researchers, but the interest has shifted from purely financial aspects regarding the way of trading, lack of regulation and supervision of transactions, volatility, correlation with other assets to aspects related to sustainability taking in account the high energy consumption generated by the mining process and the impact on environmental pollution. Bitcoin was chosen for the research considering the dominance that this financial asset has on the cryptocurrency market and its position as alpha currency.The article focuses on the relationship between Bitcoin transactions and energy consumption, for period 1st January 2019—31st of May 2022, this interval having significant price movements. The authors made a prediction of the Bitcoin price using a complex meta-model and SQL analytical functions. The analysis is based on 15 fundamental variables in order to forecast the price: Bitcoin data (prices and volume), electricity price and traded quantity on day-ahead market (DAM), gas price and traded quantity on DAM, inflation in EU, EU-ETS emissions certificates and oil prices. The study reveals the importance of the relationship Bitcoin—energy—carbon emissions, elements that capture the impact of the mining process on the environment from the perspective of energy consumption. Investors on the Bitcoin market must be aware not only of the importance of financial aspects on the price of cryptocurrencies (inflation, demand, offer), but also of other elements related to the evolution of energy prices (electricity, oil, gas, renewable energy) and the evolution of emissions certificates prices. Considering the promotion of the principles of sustainable development on the capital market, portfolio investors have become increasingly attentive to the social and environmental performance of financial assets. This study aims to make financial market players aware of the non-financial implications of their transactions. In addition, the energy transition and the reconfiguration of the energy mix are elements of impact on the cryptocurrency market through the technical levers involved in the mining process. [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]

We develop a foreign exchange market model in which a market maker adjusts the exchange rate with respect to the trading behavior of chartists, fundamentalists and a central bank. While chartists bet on the persistence of bull and bear markets, fundamentalists speculate on mean reversion. The central bank seeks to stabilize the foreign exchange market by placing buy (sell) orders when the undervaluation (overvaluation) of the exchange rate exceeds a certain threshold. Since a one-dimensional piecewise-linear discontinuous map with three branches determines the evolution of the exchange rate, we use a combination of analytical and numerical tools to explore the extent to which the central bank is able to tame the behavior of the foreign exchange market. [ABSTRACT FROM AUTHOR]

To contribute to the inconclusive existing literature about presence of herd-investing behavior in Pakistani stock market, this study utilizes non-linear regression approach introduced by Chang et al. (2000) to analyze investors' herding behavior in Pakistani stock investors and whether it varies during extreme market conditions. The study finds a symmetric and significant correlated trading behavior, both during bullish and bearish market conditions, among Pakistani stock investors, though the static herding behavior is absent at the full sample level. In addition, the herding behavior persists not only during the global financial crisis but also during COVID-19 pandemic, albeit more prominently during the former. The analysis of study also reveals a symmetric and significant correlated trading behavior during both lockdown and post-lockdown periods of COVID-19. These findings imply that Pakistani stock investors comprehend herding patterns in crafting efficient and proactive investment strategies to maximize portfolio diversification advantages, particularly during extreme market conditions. [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]

The article discusses the challenges faced by researchers in the process of publishing their scientific papers. It highlights the difficulties of getting papers accepted by journals, the overwhelming workload of reviewers, and the decreasing readership of published papers. The author suggests that the current system of publishing needs to be reformed and proposes the idea of providing salary support for reviewers to ensure timely and fair reviews. The article concludes by emphasizing the need for a discussion on improving the review and publication process. [Extracted from the article]

Services and economic growth are inseparably connected in developed economies. However, digitally enabled transactions continue to transform how services are conceptualized, produced, and delivered around the world. These changes blur the lines between goods and services by creating new combinations of products that call into question fundamental generalizations of the traditional services advertising paradigm. To redress this flaw and regenerate interest in an underexploited research domain, this article (1) introduces a literature-based generalized framework for services advertising and (2) provides a future research agenda for reengaging academicians and practitioners. Although the theoretical basis for services advertising received attention soon after the concept of services marketing was born, it peaked between 1996 and 2005, before waning over the past 15 years. The diminution of empirical attention to this important topic was due, in part, to an overreliance on the traditional goods/services distinction, the lack of an agreed-upon definition, and the theory–practice gap in service-based marketing. Resurrection of this critically needed research will allow new advertising strategies to be developed that will enhance both theory testing and practitioner needs. [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]

This article analyzes the returns of equity factors and provides insights into their long-term performance, risk characteristics, and behavior. The study reveals that most factors have a positive premium and a negative market beta over the long run. Factors with positive betas do not generate alpha, while factors with negative betas perform well in bear markets. The article also discusses the cyclicality of factor performance and identifies low-beta, seasonal, and momentum effects across factors. These findings can be used to design more effective factor-based investment strategies and contribute to future research in this area. The study is based on data from Jensen, Kelly, and Pedersen (2021) and offers valuable insights into the behavior of equity factor returns. It shows that most factors in the sample have a positive premium and a negative long-term market beta. Factors tend to perform better in bear markets, and the decline in factor performance after 2004 can be attributed to the lower frequency of bear markets during that period. The study also explores the quant cycle, low-beta effect, and seasonal and momentum effects in factor returns. These findings enhance our understanding of factor behavior and can guide the development of more robust factor-based investment strategies. [Extracted from the article]

This article combines the traditional definition of portfolio risk with minimum-spanning-tree-based "interconnectedness risk" to improve equal risk contribution portfolio performance. We use betweenness centrality to measure an asset's importance in a market graph (network). After filtering the complete correlation network to a minimum spanning tree, we calculate the centrality score and convert it to a centrality heuristic. We develop an adjusted variance–covariance matrix using the centrality heuristic to bias the model to assign peripheral assets in the minimum spanning tree higher weights. We test this methodology using the constituents of the S&P 100 index. The results show that the centrality equal risk portfolio can improve upon the base equal risk portfolio returns, with a similar level of risk. We observe that during bear markets, the centrality-based portfolio can surpass the base equal risk portfolio risk. [ABSTRACT FROM AUTHOR]

Copyright of Zoophilologica: Polish Journal of Animal Studies is the property of Wydawnictwo Uniwersytetu Slaskiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Against the background of the global active pursuit of carbon neutrality, this paper uses the DY spillover index method to analyze the spillover network effects between carbon, fossil energy and financial markets. The research results show that the spillover effects between these three markets change over time, with an average spillover index of 25.30%, showing a significant mutual influence. Further analysis found that the EU carbon market plays an important role in spillover effects. Especially under the influence of extreme events, the spillover effects reach their peak. At this time, the degree of mutual influence between markets is as high as 60.01%. In addition, during the COVID-19 epidemic, the spillover effect of the EU carbon market on other markets also reached its maximum, indicating that the epidemic increased the contagion of cross-market risks and caused the carbon market to bear greater risks. The research results of this article have important guiding significance for environmental protection investment and emphasize the importance of formulating differentiated environmental protection policies in different time frames. Facing the dual challenges of global climate change and promoting the goal of carbon neutrality, governments and relevant institutions should pay close attention to changes in spillover effects between markets and timely adjust environmental protection policies to achieve maximum results. [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]

We examine the dynamics of tail dependence across returns of 105 global energy firms from 26 countries covering the regions of America, Asia Pacific and Europe. A partial correlation-based approach is used to quantify the dependence structure and level of systemic risk under relatively stable and extremely bearish and bullish market conditions. The dependence network of energy stock returns is constructed based on the novel triangulated maximally filtered graph (TMFG). The results reveal a high degree of tail dependence and role played by geographical proximity. The strongest links are found under extreme bearish market conditions. American and European energy firms are more interconnected and contribute more to systemic risk than Asian-Pacific companies. The dependence intensifies during periods of market turmoil, especially during the COVID-19 pandemic. A higher instability in the dependence structure is observed during extremely bearish market circumstances. A simple portfolio trading strategy based on the dependence ranking of energy firms outperforms a naive equally-weighted buy-and-hold portfolio strategy. [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]

This paper provides new evidence on the dynamic dependence and connectedness between investments in Financial Technology (FinTech) and green assets across different market conditions and investment horizons. The paper uses daily data and relies on wavelets coherency and quantile-based connectedness methods. First, our results indicate that the co-movement between FinTech and green bonds and clean energy stocks is mostly positive and strongest in the long-term but weak in the short-term, indicating a high probability of large joint losses for long-term investors and hedging opportunities of FinTech stocks for short-term investors in green financial assets. Second, the level of connectedness is stronger at both tails of the return distribution and similar in the short- and long-term while in the medium-term, normal market period total connectedness became stronger than the bearish market period connectedness during the COVID-19 pandemic. Lastly, results also indicate that across all market conditions and time scales, FinTech stocks dominate most of the green financial assets as demonstrated by the net pairwise directional risk spillover. This suggests that FinTech stocks may not offer good hedging opportunities for green financial indexes. The paper provides some crucial implications based on these findings. [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]

We study the 'loss harvest profile'--the total cumulative losses and the contour of these cumulative losses--of direct indexing (DI) investment over time, focusing on two key drivers: volatility environment and cash contributions by the investor. Whether the DI investor overlaps with a bear market (with high volatility) initially is a critical factor that determines the total loss harvest potential for a lump-sum investor. Initial volatility environment is also a major determinant of the loss harvest contour the investor experiences several years into tax-loss harvesting. Additional cash contributions generally lessen the importance of the initial volatility environment in shaping the loss harvest profile. [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]