This study investigates how green finance responds to extreme geopolitical turbulence, revealing when sustainable investments collapse or unexpectedly flourish amid global instability. Specifically, we examine the time-varying causal relationship between green finance and geopolitical risk, together with eight other financial market indices: carbon allowances, bitcoin electricity consumption, clean energy, renewable energy, world equity, climate change, volatility, and oil price. Our dataset spans 12 years of weekly data from January 2012 to December 2024. Using a recursive evolving time-varying Granger causality estimation, we find that green finance and geopolitical risk strongly influenced the world equity market index in 2015 and 2024, respectively. Geopolitical risk exerted a strong causal influence on the volatility index in 2024. We document moderate bidirectional causality between green finance and clean energy, between green finance and renewable energy, and between geopolitical risk and clean energy. Interestingly, the Bitcoin Electricity Consumption index strongly influenced both green finance and geopolitical risk from 2020 to 2022. This finding reveals that the energy consumption of bitcoin mining carries significant environmental and geopolitical consequences, with direct effects on green finance and geopolitical risk.
This paper examines how firms' payout and investment policies respond to exogenous cash windfalls from litigation settlements, focusing on the moderating role of corporate governance. We compare the behaviour of windfall firms - those receiving large litigation settlements - to matched control firms, accounting for cross-sectional heterogeneity in board independence, CEO duality, CEO equity ownership, and blockholder ownership. Our findings indicate that windfall firms with strong governance are more likely to increase shareholder distributions and research and development (R&D) investments. In contrast, firms with weaker governance exhibit signs of the free cash flow problem, allocating windfalls to potentially inefficient capital investments. Market valuation analyses reveal that increases in payouts and R&D by windfall firms enhance future shareholder value, while increases in capital expenditures are penalized by the market. This study provides new evidence on the real effects of legal outcomes on corporate policies, highlighting the role of corporate governance in shaping post-litigation corporate behaviour and ensuring that windfalls are used to enhance shareholder value.
This study examines the impact of the June 2025 Israel-Iran conflict on global stock markets. Country-wise results indicate the vulnerability of several Eurozone nations to this event, attributable to their heavy reliance on oil imports. Panel market results show the severity of this geopolitical shock for all the panels examined, underscoring the critical role of geopolitical events in shaping investor sentiment and stock market performance. Cross-sectional analysis reveals that stock markets in happier nations and in countries that have progressed toward achieving the 17 Sustainable Development Goals have greater resilience to the negative impact of conflict events.
This paper explores the predictability of monthly US stock returns using adaptive LASSO on firm-specific characteristics from June 1990 to December 2022. By efficiently selecting relevant features, such as lagged returns, mean log-volumes, market values, dividend yields, and R&D expenses, the study develops threshold-based portfolios incorporating transaction costs and no-trade zones. Empirical results show that portfolios based on expected Sharpe ratios outperform benchmarks such as the S&P 500, with the adaptive LASSO portfolio achieving a 337.20% cumulative return and a 76.74% annualised Sharpe ratio. Compared with machine learning methods such as random forests and XGBoost, adaptive LASSO offers superior interpretability and robustness, highlighting its effectiveness for dynamic, cost-aware portfolio management while mitigating overfitting.
This study proposes a hybrid financial forecasting framework, GA-Contemplation (GAC), which integrates Large Language Models (LLMs) with Genetic Algorithms (GAs) to analyse short-horizon stock price movements in the Taiwan equity market. Using structured prompt-engineering techniques - Skeleton-of-Thought, Take-a-Step-Back, and Chain-of-Thought - LLMs extract semantically informed predictive factors from financial news through structured and stepwise prompt-guided reasoning, while GAs optimise factor selection and decision thresholds. Empirical results indicate favourable relative performance of GAC compared with a buy-and-hold benchmark. Additional analyses indicate that structured prompting is more beneficial in low-dimensional and interpretable factor configurations, with diminishing marginal effects as factor dimensionality increases. Overall, the findings provide methodological insights and exploratory empirical evidence on combining LLM-based reasoning with evolutionary optimisation for short-horizon financial analysis.
This study investigates the volatility spillover between tourism tokens and travel and tourism (T&T) subsector indices, using Diebold and Yilmaz (2012) approach in both the static and dynamic time domains. The study's sample period is the daily data from December 2021 to September 2024 for three major tourism tokens and six T&T subsector indices. The empirical findings suggest a weak and time-varying interdependency between tourism tokens and T&T subsectors. The results also reveal that tourism tokens offer portfolio diversification and enhance hedging performance. This study thus provides useful insights for individual investors, portfolio managers and policymakers.
In dynamic markets, strategic aggressiveness affects firm survival. Reverse mixed-ownership reform helps private firms adjust governance and build advantages. Based on this premise, this study utilises a sample of Chinese A-share non-financial private listed firms from 2007 to 2024 to empirically examine the relationship between reverse mixed-ownership reform and strategic aggressiveness in private enterprises. The results indicate that state-owned capital equity participation significantly reduces the strategic aggressiveness of private firms. Channel tests reveal that the governance effect lowers corporate risk-taking, curbs managerial overconfidence, and decreases the frequency of strategic committee meetings; the resource effect alleviates corporate financing constraints and reduces inefficient investment. Further analysis shows that this impact is moderated by the heterogeneity of state-owned capital, the level of regional private economic development, and industry competition intensity. This study offers new insights for optimising strategic decisions and fresh evidence on how reverse mixed-ownership reform promotes private sector development.
This study uses daily data from February 1, 2020, to July 6, 2024, to analyse the dynamic connectedness of equities, bonds, energy, precious and base metals, cryptocurrencies, and agricultural and food commodities. We use the time-varying parameter vector autoregression (TVP-VAR) framework to assess aggregate and bilateral connectedness measures (TCI, NET, NPDC, PCI) and apply these findings to portfolio allocation using minimum variance (MVP), minimum correlation (MCP), and minimum connectedness (MCoP) techniques. The results show an average Total Connectedness Index of 42.54%, with sharp spikes during the COVID-19 crisis and the Russia-Ukraine war, underscoring the sensitivity of cross-asset spillovers to systemic shocks. Equities, notably the S&P 500 and QGREEN, perpetually act as net transmitters, whereas gold, BTC, and agricultural commodities predominantly behave as receivers. PCI analysis reveals stable clusters of strongly connected pairs - equities (S&P500-QGREEN), bonds (S500B-S500GB), and industrial commodities (copper-oil) - while agricultural assets (LC and SB) remain weakly connected in daily frequency, they become more integrated weekly, implying weaker diversification at medium horizons. Portfolio analysis demonstrates that MCP delivers the highest Sharpe ratio, with MCoP close behind, while MVP underperforms in periods of high equity-bond co-movement. Bilateral hedge ratios confirm that bonds are the most effective variance absorbers, but risk-adjusted outcomes improve when allocations also minimise connectedness.
We examine the momentum effect in the Canadian residential property market across 11 metropolitan areas from 1990 to 2019. Consistent with prior research on the US housing market, we find strong evidence that Canadian metropolitan areas tend to continue their historical trajectories in housing market performance, demonstrating a strong momentum effect. Using zero-cost long-short portfolios formed based on lagged metropolitan-level housing market performance, we document average returns of up to 0.45% per month, which annualizes to approximately 5.54% per year using monthly compounding. These returns are both statistically and economically significant and remain robust across various formation and holding periods. The momentum effect is most pronounced during the 2000s and becomes stronger during housing market booms. Finally, we analyse the drivers of residential property appreciation in each metropolitan area, providing robust evidence on potential channels through which the momentum effect manifests in Canadian real estate markets.
This study evaluates machine learning models for forecasting daily Bitcoin returns using on-chain, macroeconomic, and market variables from January 2017 to December 2023. We implement a rolling-window framework with window lengths ranging from 365 to 730 days and compare several machine learning models against an autoregressive benchmark. Random Forest and Support Vector Machine achieve the lowest forecasting errors consistently across volatility regimes. Feature importance analysis using permutation importance and SHAP decomposition reveals that on-chain variables account for approximately 50 per cent of total forecasting contribution, with transaction fees and mining-related metrics ranking among the top important variables. Traditional market indicators such as VIX show limited relevance for Bitcoin return forecasting. These findings highlight the distinct informational value of blockchain-native variables for cryptocurrency forecasting.
This study uses the Diebold-Yilmaz (2012) and Barun & iacute;k-K & rcaron;ehl & iacute;k (2018) frameworks to examine time-varying volatility spillovers among five key rare earth minerals, cryptocurrencies, and macroeconomic uncertainty indexes. Our results reveal considerable cross-market spillovers (31.75% of total variance), which are short-term (29.97%, 1-4 days) in nature and over 50% during the COVID-19 pandemic. Ethereum (70.99%) and bitcoin (66.58%) emerge as predominant short-term transmitters, whereas dysprosium (31.45%) has a more long-lasting, cross-horizon effect. Macroeconomic uncertainty indices act as net recipients. This increased short-run spillover requires forward-looking macroeconomic policy and integrated risk management directed at cryptocurrency and strategic rare earths for financial stability.
This study examines whether investor sentiment expressed on X (formerly Twitter) can be used to build outperforming stock portfolios in Brazil. Using over 1,500 daily return observations from 394 companies and a robust sentiment index built with dynamic Portuguese-language dictionaries and machine learning, we test hypotheses via VAR and multifactor OLS regressions. Results show that while sentiment does not Granger-cause returns, its momentum is positively associated with performance. The new sentiment risk factor is statistically significant in some models, especially with high-liquidity stocks. These findings offer practical implications for asset pricing and portfolio construction in Latin America's largest emerging capital market.
This paper examines the effect of lead underwriter ranking on the offer price revision of IPO (initial public offering) firms from the perspective of underwriter quality. Using the offer price revision from the SDC (Securities Data Company) in the US and a sample of 2,188 IPOs from 1990 to 2019, we find that the offer price revision of IPO is positively associated with high-ranked lead underwriters, while negatively related to low-ranked lead underwriters. Additional analysis suggests that IPO underpricing has a positive relation with high-ranked lead underwriters but no relation with low-ranked lead underwriters. Moreover, our study reveals that IPO underwritten by low-ranked underwriters raise significantly lower amounts in IPO and have poorer long-run performance.
This study examines the influence of Environmental, Social, and Governance (ESG) performance on firm value, with a specific focus on the moderating effects of ESG controversies and gender diversity on corporate boards. Drawing on stakeholder and agency theories, we analyse how corporate governance mechanisms impact the effectiveness of ESG strategies in enhancing firm performance. Using panel data from 400 publicly listed firms in Western Europe between 2001 and 2021, we apply two-stage least squares (2SLS) regression to address potential endogeneity. The findings indicate that ESG performance has a positive impact on firm value; however, this relationship is negatively moderated by ESG-related controversies. In contrast, gender diversity at the board level strengthens the ESG-firm value link, suggesting the importance of inclusive governance in advancing sustainability agendas. This study contributes to the corporate governance literature by highlighting the dual role of risk (through controversies) and inclusivity (through board diversity) in shaping the financial relevance of ESG performance. Policy implications are provided for firms seeking to integrate ESG principles while maintaining effective governance accountability.
This study investigates REIT volatility under macroeconomic and geopolitical uncertainty using an extended GARCH-MIDAS framework. It incorporates Global Economic Policy Uncertainty (GEPU) and Global Economic Conditions (GECON) as low-frequency predictors, examining their individual and interactive effects across developed and emerging markets. To capture structural shifts and nonlinear dynamics, the model is enhanced with regime-switching variants: MS-GARCH-MIDAS and the flexible FTP-MS-GARCH-MIDAS. Results show that GEPU significantly increases REIT volatility, especially in emerging markets. While GECON's direct effects are mixed, its interaction with GEPU reveals that volatility responses are conditional on macroeconomic strength supporting theories of real options and ambiguity aversion. Out-of-sample forecasts using the Diebold-Mariano test confirm that interaction-enriched models, particularly those with FTP-based regime switching, outperform simpler benchmarks. A robustness test replacing GEPU with Geopolitical Risk (GPR) yields similar findings. The FTP-MS-GARCH-MIDAS model using GPR*GECON offers the strongest predictive performance. These results highlight the importance of accounting for regime shifts and uncertainty-macroeconomy interactions in REIT volatility modelling. For investors and policymakers, the study provides valuable insights into the time-varying and conditional nature of volatility under global uncertainty. It contributes to the literature by validating the superior forecasting power of regime-aware, interaction-based GARCH-MIDAS models.
This paper demonstrates that overlaying a combination of trend-following and tail risk hedging strategies onto a global equity portfolio significantly enhances performance. These strategies are complementary. Tail risk hedging mitigates equity risk effectively during sudden market crashes, while trend-following supports equity during slower bear markets. By employing a portable alpha framework, the performance of a 100% global equity portfolio is compared with a Portable Alpha portfolio that retains full equity exposure (beta) while layering on trend-following and tail risk hedging strategies (alpha). The resulting portfolio returns remain largely driven by global equity but exhibit a large, positive, and statistically significant alpha of 0.25% per month after controlling for traditional equity factors and other asset class excess returns. Outperformance in absolute terms was strongest during periods of market turmoil, while the improvement in risk-adjusted performance was evident across the entire period.
This paper analyses the impact of monetary policy on corporate investment efficiency using data from 624 listed firms in Vietnam between 2007 and 2023, covering both the global financial crisis and the COVID-19 pandemic. We show that a relaxed monetary policy is associated with higher investment efficiency. Our additional analysis indicates that the effects of monetary easing are most evident among underinvesting firms, consistent with the view that lower interest rates alleviate financing constraints and facilitate access to external funding, allowing firms to pursue potential investment projects. However, the effects of monetary policy differ significantly during economic shocks. The impact is amplified during the financial crisis, but it weakens and even reverses during the COVID-19 pandemic. By highlighting the distinct effects of these two shocks, our paper contributes to the literature on macroeconomic policy transmission during crises.
Accurate estimation of the covariance matrix is essential for mean-variance portfolio optimisation, yet the sample covariance matrix is a notoriously noisy estimate, especially in high dimensions. Contemporary shrinkage methods attempt to mitigate this noise but often retain significant estimation error in higher-dimensional settings or become computationally impractical in these scenarios.In this paper, we present a novel non-linear shrinkage method, Adaptive Beta Shrinkage. We also investigate an existing method, CorShrink, which has yet to be applied in a financial context. In empirical studies, Adaptive Beta Shrinkage outperforms all surveyed contemporary methods in terms of realised risk and risk-adjusted returns for large asset universes. For smaller asset universes, the best method is Munro's (2010) equally-weighted blend estimator.
Increasing income inequality in recent decades raises concern about how it impacts our social and economic development. We study how income inequality affects the municipal bond market in the United States. While greater inequality may increase productivity and demand for tax-exempt bonds, there is also evidence to suggest that it negatively impacts a region's economy by lowering overall consumption, growth, and social cohesion. Our results indicate that the bond yields of U.S. counties and states with high income inequality exhibit significantly higher bond yields. A one-standard-deviation increase in income inequality increases bond yields by 2.36 basis points for county-level bonds and 4.58 basis points for state-level bonds. This negative effect of inequality on bond borrowing costs is more pronounced in counties which rely more on high income households for tax revenue. We also find that the bonds issued by counties with high income inequality are more likely to have insurance.
This study estimates a three-factor affine term structure model using Korean government and corporate bond yields to examine how Bank of Korea policy rate changes affect the yield curve. By decomposing 3-year bond yields into expected short-rate and term premium components, we conduct event studies around fifteen major policy announcements. The results show that most yield movements are driven by changes in expected short rates, highlighting the role of forward guidance in the Korean context. HighlightsMonetary policy effects in Korea are mainly driven by expected short-term rates.Term premiums primarily reflect compensation for interest rate and inflation risks.Monetary policy announcement effects depend on macro conditions.
