In this paper, we examined how macroeconomic cycles shape the financial outcomes of advertising firms in Spain. Using population-level firm data for all Spanish provinces over 2000-2024, we combined province-level Toda-Yamamoto Granger causality and Hatemi-J asymmetric causality tests with panel-level evidence based on the heterogeneous panel causality framework of Emirmahmutoglu and Kose (2011). All causality specifications incorporated controls for the scale of the local advertising market, including firm-size proxies and aggregate advertising activity. The results revealed strong business-cycle asymmetries and substantial geographical heterogeneity. However, the uneven distribution of statistically significant provincial effects was not random. Ridge-penalized logistic models showed that the probability of observing significant first-stage effects is systematically related to provincial macroeconomic scale and financial structure, and that these determinants differ sharply across business-cycle regimes. During expansions, significant effects concentrate primarily on balance-sheet outcomes (solvency and equity), whereas during recessions, significance shifts to operating-performance outcomes (revenues and EBITDA). Overall, the findings refine the procyclicality debate in advertising by linking cyclical sensitivity to regional macro-financial structures and state-dependent mechanisms governing operating outcomes and balance-sheet resilience.
In this paper, we examined the impact of derivatives on the default risk, financial performance, and firm value of banks operating in the Europe, Middle East, and Africa (EMEA) region. The analysis was based on a balanced panel dataset comprising 189 banks (94 from Europe and 95 from the Middle East and Africa) covering the period 2011-2016. Our major findings revealed that the total notional derivatives position has a positive effect on risk mitigation, which is more pronounced in the case of hedging activities than in trading activities. Conversely, the overall use of derivatives does not exhibit a statistically significant relationship with financial performance as measured by accounting-based indicators. However, the market appears to assign a valuation premium to banks that use derivatives for hedging purposes, while banks with larger trading exposures experience a negative valuation effect. Finally, we found that the influence of total derivatives on performance and risk reduction varies significantly between European banks and those in the Middle East and Africa.
In this paper, we developed an adaptive copula-based pairs trading framework for cryptocurrency markets and examined the performance of a market overlay variant. Pair selection was conducted using cointegration techniques based on the Augmented Dickey-Fuller (ADF) and Kapetanios-Shin-Snell (KSS) tests. Dependence between paired assets was then modeled using copulas, enabling flexible and nonlinear joint behavior. From the estimated copula models, we constructed a Copula Mispricing Index (CMI) trading signal, with closed-form conditional distributions derived under Gaussian, Student-t, Clayton, Gumbel, and Frank copulas. Copula family selection was guided by the Akaike Information Criterion (AIC) and further validated through goodness-of-fit diagnostics based on the Rosenblatt transform, employing Kolmogorov-Smirnov and Cramer-von Mises uniformity tests. The proposed framework was evaluated using an extensive backtesting exercise on 26,257 hourly observations of ten Binance USDT perpetual futures contracts, spanning January 2021 to December 2023. Empirical results indicated that, under baseline transaction costs of 0.08% per round trip, market-neutral copula-based strategies exhibited relatively low risk, with maximum drawdowns remaining below 20%. However, these strategies generated negative net returns after accounting for trading costs. In contrast, the Alpha Overlay variant, which relaxed strict market neutrality by incorporating directional exposure, closely tracked the buy-and-hold benchmark over the sample period. Overall, the findings highlight the robustness and limitations of copula-based pairs trading in highly volatile cryptocurrency markets, underscoring the importance of transaction costs and strategy design in determining net performance.
In this study, we examined whether organizational maturity influences the adoption of modern performance measurement tools and digital performance management systems, as well as the perceived effectiveness of traditional financial ratios, in the shipping industry. Grounded in the Technology-Organization-Environment (TOE) framework and complemented by institutional and contingency perspectives, we assessed whether structural longevity drives modernization in performance infrastructures. Using survey-based evidence from 164 Greek shipping management firms, we estimated logistic models for adoption outcomes and OLS and ordered logit models for evaluative perceptions, supported by robustness checks and diagnostics. The findings indicated that organizational maturity, operationalized as years of operation, is negatively associated with the adoption of modern performance tools and digital systems, but shows no statistically significant relationship with the perceived effectiveness of traditional financial ratios. These results challenge the assumption that experience and historical presence inherently foster technological advancement. Instead, maturity appears to reflect institutional embeddedness and structural stability rather than readiness for innovation. By distinguishing structural longevity from adaptive capacity, we refine the organizational dimension of the TOE framework. Moreover, our work contributes to a more nuanced understanding of modernization trajectories in capital-intensive, institutionally embedded industries. The results further suggest that adoption behavior and evaluative practices may evolve asymmetrically, with implications for theory development and managerial strategies to upgrade performance management infrastructures.
In this paper, we present a simplified mathematical model using a system of ordinary differential equations to describe the relationships between money flow, staff size, and the number of enterprises managed by a corporation. Despite ignoring external shocks, our stability analysis reveals inherent instabilities in the system, highlighting significant risks for both venture success and staff employment. This model aims to support risk management by emphasizing the intrinsic sensitivity and potential instability in managing multiple enterprises. The methodology is based on the analysis of a system of differential equations, and the major contributions are a novel mathematical model and an explicit stability analysis with relevance to real-world situations.
This study delved into the determinants of the financial performance of energy firms in both developed and prominent emerging markets, employing a comparative lens. By examining companies within the electric power generation, transmission, and distribution sectors in BRICS and G7 markets throughout the years 2018-2022, including the disruptive COVID-19 period, we leveraged the dual multiple factor analysis (DMFA) technique. Our analysis uncovered four key dimensions-asset-to-debt ratios, operational profitability, liquidity, and the interplay between growth and financial stability-providing clarity on over 65% of the sector's financial dynamics. Our primary findings underscore discernible heterogeneity and emphasize the heightened resilience demonstrated by G7 firms, particularly during the pandemic era. The implications of these disparities carry significant economic and financial ramifications for both groups, influencing their response and recovery mechanisms in the face of disruptive shocks.
The growing focus on corporate sustainability is prompting family firms worldwide to accelerate their adoption of environmental, social, and governance (ESG) practices. In this study, this study explores the influence of ESG performance on corporate financial performance (CFP) in family firms, focusing on how family control, ownership, leadership, and directorship moderate this relationship. Analyzing data from 72 Malaysian family firms between 2018 and 2022, this study extends the socioemotional wealth theory by illustrating how family-specific factors shape the ESG-CFP dynamic, utilizing a hierarchical linear modeling approach. The findings reveal a positive relationship between ESG and CFP, with family control, ownership, leadership, and directorship further enhancing this connection. These results reflect how family firms pursue their socio-emotional wealth objectives through governance mechanisms. This study adds to the literature by offering valuable insights into the sustainability practices of family firms in developing countries.
Volatility indices reflect the risk-neutral expectation of future volatility implied in option prices, differing from the volatility predicted by historical volatility forecasting frameworks. Prior studies have overlooked the influence of low-frequency macroeconomic factors on the volatility of the derivatives market. This study applied the GARCH-MIDAS model to forecast the VIX (equities) and GVZ (gold) volatility indices, highlighting the synergistic effects of mixed-frequency modeling and economic policy uncertainty (EPU) indices. After risk neutralization, we accounted for the forward-looking nature of volatility indices by incorporating cross-month adjustments to long-term variances under a risk-neutral framework. Empirical results show that incorporating mixed-frequency components improves forecasting accuracy. The results of the model confidence set (MCS) test verify statistical robustness, whereas the evaluation of economic significance highlights practical relevance. Overall, the integration of EPU into risk-neutral GARCH-MIDAS frameworks provides superior predictive performance compared to other approaches and reveals the critical role of macroeconomic uncertainty in volatility forecasting.
In this paper, we investigate the dynamic relationship between economic uncertainty in China and share prices in the Chinese airline industry, focusing on periods of market crisis. Using daily data from January 2015 to June 2022 and covering major crisis events, including the China stock market crash, US-China trade war, COVID-19 pandemic, and Russia-Ukraine war coinciding with Shanghai's lockdown, we employed Time-Varying Parameter Vector Autoregression (TVP-VAR) connectedness and Quantile-on-Quantile regression methodologies to examine dynamic spillover effects between Chinese economic policy uncertainty (EPU) and individual airline stocks across market conditions. We found that policy uncertainty transforms from a passive recipient to the dominant spillover transmitter only during the most severe crisis (2022 Russia-Ukraine war/Shanghai lockdown), with total connectedness reaching 82.82% compared to 60% in normal periods, and that state-owned airlines exhibit systematically higher sensitivity to policy uncertainty during stable market conditions, while private airlines show greater resilience. These findings challenge the conventional view that policy uncertainty uniformly affects all firms within a sector, revealing that crisis severity and firm characteristics jointly determine vulnerability patterns, with critical implications for portfolio diversification strategies and policy coordination mechanisms in emerging market economies where government intervention plays a central role in economic stability.
Green finance is a key policy tool for sustainable development. The establishment of Green Finance Reform and Innovation Pilot Zones (GFRIPZs) has become an important driver of green growth. In this study, we used data on A-share listed firms from 2012 through 2023 and treated the 2017 launch of the first GFRIPZs as a quasi-natural experiment. We estimated a difference-in-differences (DID) model to measure the effect of green finance on firm-level pollution emissions. The results showed that GFRIPZs significantly reduce pollution emissions among listed firms. These findings hold after several robustness checks. Further analysis showed that the reduction mainly comes from green innovation, especially end-of-pipe treatment technologies. In contrast, source-control technologies show no clear effect. Heterogeneity tests show stronger effects for state-owned firms, large firms, financially constrained firms, firms facing stricter environmental regulation, and firms with greater government attention. Overall, the evidence indicated that green finance reduces pollution and offers policy guidance for China's green transition.
This paper examines the extent to which standardized financial statement disclosures can anticipate both overall environmental, social, and governance (ESG) ratings and their evolution over time for listed firms. Utilizing a longitudinal dataset of 851 companies spanning 11 years, we systematically extract 279 International Financial Reporting Standards (IFRS)-compliant accounts and generate an extensive set of financial ratios to capture the static and dynamic features of corporate performance. Through an empirical comparison of traditional machine learning, gradient boosting (LightGBM, XGBoost), and neural network methods (multilayer perceptrons, convolutional neural networks, and TabNet), the analysis finds that boosting algorithms deliver consistently superior accuracy in ESG prediction tasks involving high-dimensional tabular data. Notably, the overall ESG composite score exhibits the highest level of predictability from financial information, while environmental ratings remain more elusive. Further investigation reveals that balance sheet variables most strongly explain absolute ESG levels, whereas cash flow metrics are pivotal in predicting annual changes in the scores. These findings indicate that both corporate financial structures and resource flows encode substantial amounts of information that is relevant to sustainability evaluations. By linking financial accounting theory with ESG analytics, this study provides a rigorous, data-driven framework that offers practical insights for researchers, policy-makers, and market participants seeking to enhance the reliability and timeliness of ESG evaluations using objective financial data.
This paper aims to analyze the monetary authority's decision to intervene in the foreign exchange market in the inflation targeting regime. Different from previous studies, the present study expands the intervention not only in the currency but also in the security markets. Taking the case of Indonesia over the period from 2005 (7) to 2023 (12), the two-stage least squares and generalized method of moment estimations found that exchange rate fluctuations dominantly affect the monetary authority intervention in both markets. Exchange rate movements are associated with a 1.35% increase in currency market intervention, consistent with precautionary motives. Meanwhile, the impact of financial stability depends on the methods used and episodes of economic uncertainty, particularly in relation to capital outflows. However, inflation pressure from the target has little to no effect on the intervention. Those findings suggest that the trilemma impossibility among credible monetary policy, exchange rate, and capital mobility holds. Accordingly, a discretionary intervention strategy could save foreign reserves as well as avoid confusion between exchange rate and inflation stability goals.
Non-linearities in the inflation-output relationship at an aggregate or a sectoral level seems to be a particularly convincing explanation for the observed inflation dynamics in the post-pandemic period. Therefore, in this work, we tested the validity of cross-equation restrictions implied from an aggregate Phillips curve under linear and non-linear stochastic bivariate representations for core inflation and the output gap, using quarterly data from the euro area over the period 2000-2024. A novel feature of our approach was that we did not assume an expectations formation mechanism, but we restricted expectations to be consistent with the aggregate supply and a large information set that included the history of core inflation and the tightness of economic activity. The results provided strong support for the non-linearities argument in explaining the recent quick rise and the subsequent immaculate and rapid fall in inflation. An important policy implication is that the European Central Bank has rightfully not insisted on interest rate hikes, as the inflation episode appears to be mostly a supply side issue.
This study examines the impact of chief executive officers' (CEOs)' power on banks' risk-taking for publicly listed commercial banks in Vietnam from 2011 to 2021. Using generalized least square (GLS) random effect (RE) estimation, this study finds that the presence of powerful CEOs, with a large share of ownership and a role as the chairperson of the bank boards, reduce banks' risk-taking. Regarding other bank governance factors, a larger bank board results in lower bank risk-taking, while board independence, in contrast, is positively associated with bank risk. These results are robust to different proxies for banks' risk-taking and different estimation techniques.
It is sometimes acknowledged that (sell-side) equity analysts' recommendations influence investors and therefore market prices. In particular, the S&P 500 is expected to decline (or rise) when analysts revise their targets downward (upward, respectively). Our findings indicate not only that the analysts' consensus exert no influence on market prices, but also that, conversely, analysts appear to set their target prices based on markets prices. Employing a kinetic theory framework, we model the dynamics of the analysts' opinions, by taking both the mutual influences shaping price consensus and the dynamics of the actual S&P 500 index level into account. The model is calibrated on a training subset of data and tested on an independent set to assess its predictive power. Our tests show that just three free parameters are enough to accurately predict the one-year average price forecasts of analysts.
