Housing prices are known to respond slowly and heterogeneously to macroeconomic variations from the demand and supply sides of housing. This offers a possibility to model the long memory and varied persistence of housing price adjustments, through which a refined characterisation of the macroeconomic-housing price interaction in equilibrium can be developed. Our article advances a theoretical argument, supported by empirical findings in the United States, that macroeconomic variations trigger varied reactions on housing demand and supply sides. This leads to distinct trajectories of equilibrium housing price formations governed by differential price adjustments on the two sides of housing. An established longer memory on the supply side of housing demonstrates its higher persistence of disequilibrium deviations than on the demand side. In the equilibrium, certain macroeconomic factors are found to exert dual but heterogeneous roles in housing demand- and supply-side dynamics. The net role of each such factor is negative led by its even stronger negative role on the demand side compared against a smaller positive one on the supply side. Our findings contribute to deeper reflections on the likely ineffectiveness of macroeconomic interventions in housing price dynamics.

Housing prices are known to respond slowly and heterogeneously to macroeconomic variations from the demand and supply sides of housing. This offers a possibility to model the long memory and varied persistence of housing price adjustments, through which a refined characterisation of the macroeconomic–housing price interaction in equilibrium can be developed. Our article advances a theoretical argument, supported by empirical findings in the United States, that macroeconomic variations trigger varied reactions on housing demand and supply sides. This leads to distinct trajectories of equilibrium housing price formations governed by differential price adjustments on the two sides of housing. An established longer memory on the supply side of housing demonstrates its higher persistence of disequilibrium deviations than on the demand side. In the equilibrium, certain macroeconomic factors are found to exert dual but heterogeneous roles in housing demand- and supply-side dynamics. The net role of each such factor is negative led by its even stronger negative role on the demand side compared against a smaller positive one on the supply side. Our findings contribute to deeper reflections on the likely ineffectiveness of macroeconomic interventions in housing price dynamics.

Being able to predict changes in Bitcoin prices is purportedly a boon for risky investors, more so, if the forecasts are largely unconditional and can only be explained by the series' own historical trajectories. Although memory dynamics have been exploited in forecasting changes in prices, Bitcoin markets pose additional challenges, because the lack of proper financial theoretic model limits the development of adequate theory-driven empirical construct. In this paper, we propose a class of autoregressive fractionally integrated moving average (ARFIMA) model with asymmetric exponential generalized autoregressive score (AEGAS) or exponential GAS with leverage effect (EGAS-L) errors to accommodate a complex interplay of 'memory' to drive predictive performance (an out-of-sample forecasting). Our conditional variance includes leverage effect, jumps and fat tail-skewness distribution, each of which affects magnitude of memory the Bitcoin price system would possess. This enables us to build a true forecastfunction. We estimate several models using the Skewed Student-t maximum likelihood and find that the informational shocks, in general, have permanent effects on Bitcoin price changes. We show that this model has better predictive performance over competing models. The prediction from this model beats comfortably the random walk model. Accordingly, we find that the weak efficiency assumption of cryptocurrency markets stands violated over a long period.

We study the impact of Brexit uncertainty on one of the most important forms of corporate investment: mergers and acquisitions (M&As). Brexit provides us with an ideal natural experiment to explore the real effects of economic uncertainty and understand the underlying transmission mechanism. We document a significant decline in the number of M&A deals for UK firms after Brexit compared to EU firms. This inhibiting effect is amplified by the channels of real options, foreign trade, and financial constraints. Overall, our results provide for deeper understanding of this unprecedented uncertainty in Brexit policy on local M&A activity. Policy makers are urged to respond.

This contribution revisits the origins of the concept of creative destruction by returning to Werner Sombart’s Krieg und Kapitalismus (1913). Although Sombart never used the expression schöpferische Zerstörung, a systematic page-by-page reading shows that he articulated a destruction–creation mechanism that anticipates the structural logic later formalized by Joseph Schumpeter. Sombart presents war as simultaneously destructive and generative, arguing that fiscal, institutional, and ecological ruptures are not peripheral to capitalism but central to its emergence. His analysis of deforestation, scarcity, and technological substitution (especially the transition from wood to coal and coke) offers an early model of how material crises can trigger innovations that reshape production systems and energy regimes. The article also examines the subsequent marginalization of Sombart’s contribution, situating it within the broader methodological and political transformations of twentieth-century economics. It contrasts Sombart’s historically grounded mechanism with the evolution of Schumpeter’s thinking from 1911 to 1942, when the canonical formulation of creative destruction finally emerged. More broadly, the paper reflects on the historical evolution of economic concepts, illustrating how scientific ideas often achieve recognition not through priority of insight alone, but through subsequent reinterpretation, analytical elaboration, and institutionalization. Reassessing Sombart thus enriches the genealogy of creative destruction and sheds new light on contemporary debates surrounding innovation, energy transitions, geopolitical conflict, and artificial intelligence. Building on recent work by Acemoglu, Mokyr, and others, the article argues that Sombart emerges not as the inventor of the term but as the first thinker to articulate its structural logic. More fundamentally, it suggests that the history of creative destruction itself illustrates a broader movement from historical interpretation (Verstehen) toward analytical explanation (Erklären), reminding us that concepts, no less than data, have histories of their own.

We examine the relationship between political partisanship and commercial bank efficiency in the United States from 1972 to 2020, assessing the persistent influence of political affiliations at the state and District of Columbia levels. Bank efficiency scores are estimated using a double-bootstrap approach, and the analysis is conducted within a Spatial Dynamic panel Tobit framework that controls for a broad set of banking and macroeconomic factors. The results reveal a significant effect of US state and national elections on interdependent bank efficiency scores, providing robust support for the partisan theory in the context of US banking over five decades. We find compelling evidence that Democratic victories at both the state and national levels are associated with higher state-level bank efficiency, even after accounting for bank-specific characteristics. Additionally, changing the political party in power every four years could enhance the efficiency of the US banking system. These findings suggest that political change, rather than being purely disruptive, can act as a catalyst for efficiency improvements in the US banking system. The results remain consistent across multiple robustness tests.

Rationale: The decision to end one's life arises from a complex interaction of psychological, social, and economic forces, making the identification of its key determinants both theoretically and empirically challenging. For these individuals, the perception of the present value of continued life is effectively zero. The governing theory to model suicide is based on the expected utility framework, which predicts that the risk of suicide rises with age and falls with income. However, a growing body of cross-national empirical evidence challenges these predictions, suggesting the need for strong microlevel insights from a behavioural theoretic model. Objective and methodology: This paper develops such a framework by modelling suicidal decisions within learning-driven cumulative prospect theory. The setting allows individuals to evaluate choices by evaluating gains and losses relative to dynamically shifting reference points. We then empirically estimate the parameters of the prospect function using household-level panel data. Results and conclusions: We show that individuals update beliefs through experience and error so that new information affects decisions through its interaction with accumulated negative shocks. Consequently, perceived prospects depend not only on current conditions but also on the relative weight assigned to past losses and potential gains. We observe that individuals evaluate their circumstances relative to a personal reference point rather than according to absolute living conditions. Our empirical exercise yields testable implications that suicide rates do not uniformly increase with age or decline with income.

Accurately identifying the determinants of oil spot prices remains a persistent challenge. This paper proposes a spillover-based approach to information-set selection, in which candidate system specifications are ranked based on their internal interconnectedness rather than via individual variable screening. Conceiving markets as dynamically evolving information networks, we implement the architecture of Total Spillover Index (TSI) to quantify the transmission of shocks across variables within candidate systems. We then construct, within a cointegration framework, alternative models representing Brent and WTI markets from both isolated and globally integrated perspectives. Spillover analysis shows that systems that incorporate global market indicators exhibit very strong interconnectedness and respond more sensitively to macroeconomic shocks, as reflected in their co-movement with the Global Economic Policy Uncertainty Index. Out-of-sample forecasts using both Fractional Cointegration Vector Autoregressive (FCVAR) models and Long Short-Term Memory networks show that a hybrid global specification consistently outperforms models that are restricted to isolated markets, particularly at medium and longer horizons. These results suggest that information coherence, capturing persistent cross-variable transmission within the system, provides a useful criterion for identifying forecasting-relevant information-sets in complex market environments such as global oil markets.

Empirical cryptocurrency researchers frequently use concepts of mean reversion, trend and cointegration to characterise price dynamics and tests of market inefficiency. This paper introduces a broader memory-driven framework to examine how the concept of market efficiency has evolved over time, especially by characterising varied mean-reversion strategies with slow-paced error corrections to identify a semi-strong market efficiency in crypto markets. We find strong evidence that market efficiency in Bitcoin (BTC) and Ethereum (ETH) is heterogeneous and time-varying across all frequencies. Event shocks and structural breaks exert substantial influence on abrupt changes in efficiency, while results at the 240-min sampling interval show robustness. The findings suggest that policymakers should time interventions to mitigate lag effects, release policy during low-leverage periods and closely monitor the two distinct waves of efficiency breaks as well as cross-frequency risk exposure. For practitioners, linking efficiency dynamics to trading strategies can enhance risk management and statistical arbitrage opportunities, while scholars can build advanced regime-switching models informed by these empirical patterns. This paper provides new insight into price patterns and market efficiency dynamics, contributing to the understanding of the complex interplay between event shocks, structural breaks and regime switching of market efficiency in cryptocurrency markets.

PurposeThe purpose of this research is to understand the effect of innovation and networks in mitigating the effect of COVID-19 on small and medium-sized enterprises' (SMEs’) performance. Moreover, we aim to explore how different regions withstand the effects of the COVID-19 recession. To do this, we carried out panel data analysis on firm-level data and generated several interesting results. We explore how networking and innovation can help mitigate the effects of extreme shocks on SMEs’ performance. Design/methodology/approachWe use a rich and detailed longitudinal dataset and apply panel data econometric methods over the period 2015–2021. We also conduct several robustness tests to address endogeneity and examine the regional disparities between core and peripheral regions. FindingsOur study uniquely focuses on the effects of COVID-19, innovation and external advice (as a proxy for networks) on the performance of small and medium-sized enterprises (SMEs). Importantly, we also explore the interaction effects of innovation and external advice with COVID-19. First, we find that both external advice and financial obstacles are associated with firm performance. Second, we find the interaction effect between innovation and the COVID-19 recession dummy to be positive and statistically significant. This suggests that innovation can be an important resilience strategy for SME performance during periods of economic downturn. Third, we find significant regional differences between the SMEs that operate in peripheral regions and those operating in core regions. Our findings are generally robust to potential endogeneity concerns. Originality/valueOverall, the paper contributes to the theoretical and empirical literature on pandemic-driven and/or financial crises and the resilience of SMEs.

The investor psyche that finds expression in sentiments has been centralized given its crucial spillovers with risk perceptions, eliciting clear interpretation of the spillover impact and its dynamics under the ongoing climate vulnerability. This paper analyzes the time-varying industrial spillover between sentiments and risks, and its asymmetric evolutions when facing climate policy uncertainty (CPU). Our results, drawn based on a historical dataset in the U.S., show that the risk of technology sector and the sentiment of financial sector are the two largest information providers. Sectors related to the real economy are found to be the greatest information receivers. Moreover, sectoral spillovers are less affected by CPU in normal market conditions but are influenced with a larger magnitude during extreme periods. Dirty and clean sectors exhibit similar roles in forming the spillover, while their roles show distinct responses when facing shocks to CPU. Our findings for the dynamic spillovers of sentiments and risks should be of interest to various stakeholders toward financial stability and green transition.

Thanks to the pressing environmental concerns, lately a substantive body of research have attempted to assess the magnitude of volatility spillover from traditional to the sustainable asset markets. Yet, we have insufficient understanding on varied diversification advantages of sustainable assets such as the blue and green ETFs against price movements in other asset markets. This is important because corporations are now legally bound to ensure a structural shift of production externalities as they steadfastly adopt sustainable practices across production lines. However, persistent market uncertainties can confuse investors of the potential diversification benefits as they do not stick to a strong sensemaking of the future return value of blue and green ETFs. In this circumstance, one would expect potential heterogeneity in the strength of dynamic interconnectedness among both classes of assets as economies move steadily from low to high uncertainty episodes. This paper analyzes the impact of the Covid-19 pandemic and Russia-Ukraine war on spillover dynamics and demonstrate by using a Quantile VAR framework. We study how the desired narrative of ‘shock absorption’ and ‘shock dumping’ characteristics of assets change during turbulent times. We surmise that uncertain times triggers highetend information asymmetric for a prolonged period and investors normally become ‘short-term’ gain-centric because they do not yet have a clear vision for long-term growth returns from traditional assets. A trade-off between traditional and sustainable assets acrue but there is a bias towards sustainable assets as stringent environmental laws pave the way for a secured diversification benefits from the latter class of assets.

Climate change is emerging as a significant threat to sustainable human development in the coming decades, and the thirteenth of the United Nations' Sustainable Development Goals (SDGs) aims precisely to mitigate this threat through practical action. We employ a composite system synergy model to measure the synergy between China's industrial and innovation chains and explore pathways for listed enterprises to achieve carbon neutrality in pursuit of SDGs. The findings reveal an upward trend in the synergistic degree of China's industrial and innovation chains, with a greater degree observed in the eastern regions. The combined development of the two chains demonstrates an ability to mitigate the intensity of carbon emissions across enterprises through digital advancements and advancements in green technology. Additionally, disparities in geographical location, the degree of marketisation, the nature of enterprise equity, and pollution levels of enterprises are found to exert asymmetric effects on carbon emissions. Moreover, green finance emerges as a significant mediator in enhancing the inhibitory effect of dual-chain synergy on enterprise carbon emission intensity. Our results trigger important policy narratives: a coupling of interaction between the government and the market can deliver carbon neutrality and facilitate a steadfast transition to a low-carbon economy for businesses.

This paper undertakes a horse races style comparison of the efficacy of a range of multifactor asset pricing models in explaining the cross section of stock returns in African securities markets. Valuation factors used include size, book-to-market value, momentum, operating profit, asset growth or investment, liquidity and investor protection. Using monthly returns of 375 blue chip firms from 8 African equity markets over 23 years, we undertake a horse-race style comparison of various classes of augmented CAPM models. We show that both the Fama & French (2015) five factor and Fama & French (2018) six factor framework yield the highest explanatory power. Analysis of costs of equity and optimized portfolio opportunity set simulations reveal substantial differences arising and borne by practitioners from the contrasting application of different asset pricing models underscoring the timely importance of our study.

This paper investigates the stability and co-movement of cryptocurrency assets in Decentralized Finance (DeFi), with a focus on the Speed of Adjustment (SA), the rate at which shocks dissipate, and prices revert to long-run equilibrium. SA provides a critical measure of market efficiency and portfolio allocation in a highly volatile DeFi environment. We extend conventional cointegration analysis by applying a Fractionally Cointegrated Vector Autoregressive framework, which captures slow error corrections. Rolling estimations generate a time-varying series of SA, allowing examination of its evolution and cross-asset spillovers. The results reveal multiple cointegrating relationships, heterogeneous adjustment speeds, and strong contagion effects among DeFi assets. For instance, RPL exhibits rapid yet volatile adjustment, while LDO, BAL, and SNX revert more slowly, reflecting distinct risk-return trade-offs. Spillover analysis highlights high systemic interconnectedness, underscoring challenges for diversification and contagion management. Overall, dynamic SA emerges as a valuable forward-looking indicator of stability in digital asset markets.

This paper examines the impact of the Sustainable Finance Disclosure Regulation (SFDR) on greenwashing by equity mutual funds in the EU. We propose a unique measure called the Greenwashing Index, based on a fund's decarbonisation effort relative to its flows, to quantify the level of greenwashing. Using a difference-in-differences analysis, we find that following the enactment of the SFDR, Article 9 funds experience a lower level in their greenwashing index relative to a control group of funds. However, for Article 8 funds we do not observe any significant reduction in the level of their greenwashing index relative to the same control group. We also use a regression discontinuity design (RDD) and find that the decline in the greenwashing index is more concentrated in Article 9 than in Article 8 funds which indicates a different effect of the SFDR on greenwashing behaviour between those funds. Our findings also show that Article 9 funds decarbonise their portfolios by primarily following a portfolio tilting strategy to overweight low carbon-intensive holdings following the introduction of the SFDR.

Institutional investors have been shown to impact firm performance. We extend on the literature by documenting how the impact varies with a firm's life cycle. Utilizing a large sample of U.S. corporations, from 1990 to 2020, empirical findings suggest a positive association between institutional ownership and firm performance for firms in the introduction and decline phases of the life cycle. Firms in the intro or decline phases with high institutional ownership exhibit a positive association between asset turnover and operating performance and an inverse association between operating expenses and firm performance. In these phases the drivers of firm performance function better in the presence of institutional ownership. We conclude that institutional investors' ability to positively impact performance is most significant at firms that require their involvement the most. Our results are robust to alternative firm life-cycle measures, model specification, and potential endogeneity concerns.

Debt mitigates agency problems between managers and stockholders by reducing free cashflows; yet, why managers voluntarily adopt debt discipline remains unclear. This paper examines how chief executive officers' (CEOs') managerial traits, shaped by national culture, influence leverage decisions. Analysing 3338 CEOs from 41 nationalities in 2280 US firms in Bloomberg 3000 index (from 2007 to 2024), we find that cultural values impact CEOs' perceptions of debt's costs/benefits. High-mastery CEOs reduce debt regardless of current leverage, while highly embedded CEOs inadvertently pursue target capital structures. A non-US CEO sample shows that cultural values are portable. Our findings are robust to sensitivity and endogeneity tests.

As one of the most prominent cryptocurrencies, Bitcoin has been at the forefront of a major revolution in the financial and technological sectors. This study utilizes data from social media to extract the emotional tendencies of investors in the Bitcoin market and analyze differences in investor behavior under various emotional features. We find that when investors exhibit reluctance (such as Sadness and Fear) to buy Bitcoin, it is the opportune moment to invest and achieve returns higher than expected. Conversely, when the emotional tone of investors becomes positive (such as Joy and Love), indicating a tendency to invest, we choose to avoid investing. Our research has also revealed that such emotional cues can assist in better predicting returns in the Bitcoin market. Analyzing market emotions contributes to a deeper understanding of market fluctuations and investor behavior. Our findings help stakeholders recognize the role of subjective emotions in the market and provide them with prudent investment advice: avoid relying excessively on the feelings of others, as this may trigger investment losses