Search Results (238)

This study investigates the short-term and long-term impacts of gross domestic product (GDP), inflation, foreign capital flows, trade balance and interest rate on stock market performance in Saudi Arabia for the period 1990–2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) are employed to empirically examine the short-run and long-run relationships. The ARDL-ECM technique is effective for analyzing cointegration and assessing adjustment processes. Additionally, impulse response function (IRF) analysis based on the vector autoregression (VAR) model, estimated using these macroeconomic indicators, is applied in this paper. This study provides novel insights and addresses emerging gaps in the literature concerning Saudi Arabia as a developing economy. The long-term relationship in the bounds test results confirms its existence. In the long run, inflation and interest rate exert a statistically significant negative effect on stock market performance, while the trade balance has a significant positive impact. GDP and foreign capital inflows do not exhibit statistically significant long-run effects. Short-run dynamics indicate persistence in stock market performance along with significant effects from inflation and interest rate changes, while GDP and foreign capital inflows remain statistically insignificant in the long-run scenario. Forecast error variance decomposition (FEVD) results show that approximately 68.5% of the variation in market performance is explained by its own shocks, followed by foreign capital flows (16.3%) and inflation (8.4%). While foreign capital flow does not exhibit statistical significance in the ARDL long-run estimates, its contribution in variance decomposition highlights its role as an important source of external shocks. These findings are relevant to various stakeholders, including investors and policymakers. Additionally, policy emphasis should be placed on controlling inflation and maintaining stable interest rates while improving trade balance conditions. Although foreign capital flow does not show a direct long-run effect, its role in influencing market variability suggests the need for a stable and well-regulated investment environment. Full article

Despite the rapid institutionalization of ESG reporting mandates worldwide, the empirical question of whether ESG disclosure constitutes a structural, long-run determinant of corporate financial performance—rather than a cyclical or spurious co-trending artifact—remains unresolved. The prior literature predominantly employs short-panel estimators that assume stationarity and conflate long-run equilibrium effects with transitory associations. This study addresses that gap by applying a five-step non-stationary panel econometric framework to a sample of 479 S&P 500 firms across eleven GICS sectors over 2010–2022 (5084 firm-year observations), a period chosen to capture the full institutionalization of Bloomberg ESG reporting standards and to encompass two major macroeconomic stress episodes (the 2015–2016 commodity downturn and the COVID-19 shock). Im–Pesaran–Shin panel unit root tests confirm that ESG disclosure scores and financial performance measures are both integrated of order one. Pedroni residual-based panel cointegration tests decisively reject the null of no long-run relationship (Z = −62.38 for the ROA equation), establishing a stable cointegrating equilibrium. Fully Modified OLS and Dynamic OLS group-mean estimators yield bias-corrected long-run coefficients, and a panel error correction model quantifies short-run adjustment dynamics. The key finding is that a ten-point improvement in ESG disclosure is associated with a permanent nine-to-ten percentage-point gain in return on equity (FMOLS β = +1.023, p < 0.01; DOLS β = +0.914, p < 0.01), while the effect on return on assets is positive but more modest and sensitive to estimator choice. Complementary fixed-effects regressions reveal an asymmetric moderating role of macroeconomic uncertainty: equity market volatility (VIX) amplifies the ESG performance premium, whereas acute credit market stress (TED spread) attenuates it. Board governance variables are statistically insignificant across all five specifications, indicating that H3 (board governance) is not supported; this outcome is attributed to limited within-firm governance variation in the large-cap S&P 500 universe rather than a genuine absence of governance effects. The results are robust to lagged ESG measurement, winsorization, and alternative interaction specifications. The findings provide strong econometric evidence for the structural, permanent nature of the ESG–financial performance link in large-cap U.S. equities, with direct implications for mandatory disclosure policy and ESG-integrated investment strategies. Full article

Standard asset pricing models treat price dynamics as a stochastic process driven by undifferentiated random noise, rendering them agnostic about the primary engine of price discovery: the arrival of economically significant information. This paper introduces ORAKULUM, a structured Information-Impact Asset Pricing Model that reconceptualises the log-price as a signed information ledger. Each market-relevant event appends a weighted entry that either permanently revises the market consensus or temporarily disturbs it before decaying exponentially toward the new equilibrium. Mathematically, ORAKULUM is a jump-diffusion process combining a Wiener component for continuous micro-uncertainty with a Poisson-driven jump component for discrete macroeconomic and geopolitical shocks. The log-price identity $x\left(t\right)=x\left(0\right)+\mu ·t+\sum A_i+\sum B_i·e^{(-\gamma \left(t-t_i\right))}+\sigma ·W\left(t\right)$ decomposes price dynamics into permanent and transient information impact, admits a natural event catalogue calibration, and supports Monte Carlo scenario simulation. We present the complete theoretical foundations, a closed-form expected path solution, a gradient-descent calibration procedure, and a fully documented Python3 reference implementation. An empirical illustration applies the model to XAU/USD and EUR/USD market data downloaded from Yahoo Finance, demonstrating ORAKULUM’s capacity to generate economically interpretable, real-time prediction clouds in response to central bank communications, inflation releases, and geopolitical shocks. Full article

This paper examines the interaction between fiscal and monetary policies in Myanmar under ongoing political and economic uncertainty. We estimate a small open-economy New Keynesian DSGE model using Bayesian methods, combining the Kalman filter with Markov Chain Monte Carlo sampling on quarterly data from 2013Q1 to 2022Q1. The results show a persistent regime of monetary and fiscal policy conflict. While the central bank follows an active anti-inflationary interest rate rule that satisfies the Taylor principle, fiscal policy shows weak responsiveness to public debt, providing limited fiscal backing for monetary stabilization. As a result, monetary tightening aimed at controlling inflation exacerbates fiscal stress through the debt-service channel, undermining the overall effectiveness of macroeconomic stabilization. Political instability emerges as a key structural driver of macroeconomic fragility. Political shocks are highly persistent and are transmitted primarily through increases in the country risk premium, accounting for more than 50% of real exchange rate volatility and generating exchange rate depreciation, higher inflation, and output contraction. Overall, the findings indicate that monetary tightening alone is insufficient to restore macroeconomic stability in fragile and conflict-affected economies. Credible fiscal adjustment and improvements in political stability are necessary to contain external vulnerabilities and restore the effectiveness of monetary policy. Full article

This study examines the nonlinear, threshold-dependent relationship between financial leverage and firm performance in publicly traded tourism firms in Turkey, and investigates how this relationship has evolved under post-COVID-19 multi-shock conditions. The main aim of the research is to identify the thresholds at which borrowing becomes a source of financial vulnerability and to analyse how this process deepens under macroeconomic shocks. For this purpose, quarterly panel data covering the period from Q1 2012 to Q1 2025 for 22 tourism firms listed on Borsa Istanbul were used. Firm performance was measured through accounting-based indicators Return on Asset(ROA) and Return on Equity(ROE), and a market-based indicator (stock returns). In the empirical analysis, both the random-effects panel regression model and the endogenous-threshold panel regression methods were applied. The findings indicate that the relationship between financial leverage and performance is nonlinear, and a significant regime change occurs when the leverage ratio exceeds approximately 60–70%. In the post-COVID-19 period, both accounting-based and market-based performance indicators under high-leverage regimes became more sensitive to financial vulnerability. Additionally, the effects of the real effective exchange rate and the service sector price index on firm performance have strengthened in the post-crisis period. The study reveals that financial fragility in the tourism sector is a structural feature sensitive to thresholds and crisis regimes rather than temporary shocks. In this regard, the research highlights the limits of debt-based growth strategies and contributes to early warning mechanisms for policymakers, investors, and firm managers. Full article

This study investigates the key determinants of firm profitability in the global automotive sector, examining whether superior returns on assets (ROA) stem from operational efficiency, strategic leverage, or innovation intensity, and highlighting the potential trade-off between efficiency and investment in capital-intensive industries. Analysing a global panel dataset of 192 automotive firms from 38 countries/regions over 2010–2024, a fixed effects regression model with Driscoll–Kraay standard errors was applied to control for unobserved heterogeneity, heteroskedasticity, and cross-sectional dependence across 11 financial and strategic variables. The findings reveal that firm size and inventory turnover are significant positive drivers of profitability, while research and development (R&D) intensity exerts a strong negative impact. The positive association with the effective tax rate reflects reverse causality, where more profitable firms incur higher tax burdens, rather than a causal effect of taxation on performance. Notably, working capital management, leverage, sales growth, and capital expenditure showed no statistically significant effects after controlling for firm and time effects. Temporal fluctuations, including a marked profitability decline in 2024, underscore the sector’s sensitivity to macroeconomic shocks. This study contributes robust, large-scale empirical evidence on the short-term profitability trade-off associated with R&D intensity in a globally integrated industry, addressing cross-sectional dependence through its methodological approach. Full article

The interaction between crude oil prices and exchange rates is central to understanding global financial stability and macro-economic balances. Contrary to traditional static analyses, the heterogeneous market hypothesis argues that market participants have different time horizons and that multi-scale analysis is necessary to capture dynamic changes in crisis periods. This study examines volatility spillovers between WTI crude oil and the Russian ruble using wavelet coherence, phase difference, and predictive information flow analysis in a time–frequency framework. The analysis separates short-term [2–32 days] transient shocks from long-term [32–256 days] structural changes. Findings show that a negative spillover, initially led by WTI, with evidence of dynamic, frequency-dependent leadership shifts during the 2020 shock, was interpreted as a result of the overnight price gap and a failure of microstructural synchronisation. With the outbreak of the 2022 Russia–Ukraine war, the relationship shifted to a strong, positive, and high-intensity risk transfer, consistent with contagion theory. Crucially, by 2024, a structural decoupling emerged due to geoeconomic fragmentation, signalling that the ruble no longer exhibits traditional petro-currency behaviour. These results offer critical signals for policymakers regarding reserve management and for market participants regarding new liquidity risks. Full article

Coffee-price volatility is a recurrent external shock for Peru’s small open economy, with potentially uneven consequences across sectors. This study evaluates whether global coffee prices and domestic macro-agricultural indicators share stable long-run equilibria and quantifies the transmission of coffee-price shocks to the terms of trade, nominal exchange rate, consumer prices, agricultural GDP, and total GDP. Using a multivariate vector error-correction model identified via Johansen cointegration, and controlling for major global disruptions and ENSO-related seasonality, we trace dynamic effects through impulse-response analysis. The results indicate economically meaningful cointegration, implying that external prices and domestic aggregates are linked by long-run restrictions. A positive coffee-price shock produces heterogeneous real effects: the response of aggregate GDP is modest and short-lived, while agricultural GDP reacts more strongly and persistently. The shock propagates mainly through external and nominal channels—especially the exchange rate and terms of trade—whereas consumer-price pass-through is present but comparatively moderate. These findings contribute to the commodity-shock literature by providing sector-sensitive evidence for an agricultural export shock and by clarifying the mechanisms through which coffee-price movements propagate to domestic activity and prices in a small open agricultural economy. Full article

This study examines the relationship between manufacturing-specific foreign direct investment (FDI) and manufacturing output in ASEAN-6 countries over the period 2012–2022. While existing empirical studies largely rely on aggregate FDI measures, such evidence may obscure sector-specific mechanisms through which foreign investment affects production capacity and industrial performance. Focusing on manufacturing-oriented FDI allows for a more direct assessment of how sector-targeted investment is associated with industrial resilience and value-added stability, which represent the economic dimension of sustainability considered in this study. Sustained industrial output performance is proxied by manufacturing value added (GDPm) and interpreted as the manufacturing sector’s ability to maintain and expand value added over time amid macroeconomic volatility and external shocks. Using a balanced panel dataset of six ASEAN economies (ASEAN-6) with 66 country-year observations and a fixed-effects specification selected through standard model-selection tests, the results indicate that manufacturing-specific FDI is positively and statistically significantly associated with manufacturing output at the panel-average level. Manufacturing contribution to GDP also exhibits a strong positive association, while exchange rate movements are negatively associated with manufacturing output. Inflation is positively associated with output during the study period and is interpreted as a context-specific co-movement rather than a normative implication for long-run sustainability. To provide additional insight into shock-period dynamics, the analysis compares pre-COVID (2012–2019) and COVID/post-COVID (2020–2022) sub-period estimates. The positive association between manufacturing-oriented FDI and output is more pronounced before the pandemic. It weakens during the pandemic and early recovery years, consistent with supply-chain disruptions and temporarily reduced absorption capacity. The findings highlight the importance of sector-specific FDI, industrial structure, and macroeconomic stability in supporting manufacturing resilience in ASEAN-6 economies. Full article

This study examines the relationship between firms’ financial adaptability and performance during periods of macroeconomic stress. Using panel data on companies listed on the Mongolian Stock Exchange from 2015 to 2024, the analysis measures financial adaptability through a Firm Adaptability Index (FAI) constructed from observable indicators of liquidity, coverage capacity, and asset-use efficiency. The index is constructed using principal component analysis (PCA) to avoid arbitrary equal-weighting assumptions, and the debt ratio is deliberately excluded to prevent multicollinearity with the leverage control variable used in the regression models. The empirical framework primarily relies on panel regression models with interaction terms, supplemented by a DID-style comparison and an event-study-based diagnostic. The validity of the quasi-experimental design is confirmed by a formal parallel-trend test and placebo checks using artificial shock dates. The findings do not support the view that financial adaptability exerts a uniformly strong and stable direct effect on firm performance across all conditions. Instead, its empirical relevance becomes more visible when macroeconomic conditions worsen. In particular, the interaction result related to interest rates suggests that firms with higher levels of financial adaptability tend to exhibit less pronounced profitability sensitivity to financing cost pressure. Additional analyses point to short-term liquidity buffers as a plausible channel and show that the strength of this relationship varies by firm size and sectoral characteristics. This study contributes to the literature by bringing together the related concepts of financial flexibility, organizational resilience, dynamic capabilities, and strategic adaptability within a firm-level empirical setting. It also proposes a practical way to measure financial adaptability not through a single proxy, but through a composite index that integrates several observable financial dimensions. Overall, the evidence suggests that financial adaptability is better understood not as a constant determinant of profitability, but as an internal capability whose relevance becomes more apparent under conditions of heightened uncertainty. Full article

This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period of heightened political uncertainty, namely the cancellation and re-run of the 2019 Istanbul local elections. This setting provides a unique opportunity to analyze how political events and news-driven information flows influence financial market dynamics. The empirical analysis is based on a comprehensive dataset that includes daily price indicators (opening, closing, high, and low values), technical indicators, selected macroeconomic variables, and Turkish-language news headlines. Textual data are processed using topic modeling techniques to extract latent information embedded in financial news, allowing for the incorporation of qualitative signals into the forecasting framework. From a methodological perspective, this study employs a feedforward deep neural network model designed to capture nonlinear relationships across heterogeneous and contemporaneous features. Feature selection is conducted using the Boruta algorithm, while hyperparameters are optimized via grid search. The model structure reflects a deliberate design choice aimed at capturing short-term, news-driven shocks and cross-feature interactions, which are particularly relevant during periods of political uncertainty. The results indicate that incorporating textual information significantly improves forecasting performance and that news topics related to political decisions, central bank policies, and geopolitical developments have a measurable impact on the XBANK index. Furthermore, the findings suggest that the political uncertainty surrounding the 2019 local elections led to increased market sensitivity and volatility, highlighting the role of information shocks in emerging financial markets. Overall, this study contributes to the literature by combining financial and textual data in an emerging market context, utilizing Turkish-language news sources, and providing empirical evidence on the impact of political uncertainty on the BIST bank index. Full article

This study examines whether fiscal decentralization operates as a strategic macroeconomic risk-management instrument and whether its effectiveness depends on institutional quality. Using a balanced panel of 27 European Union member states over 2008–2023, a composite fiscal decentralization index combining expenditure and revenue autonomy is constructed, and a dynamic specification is estimated using a two-step System-GMM estimator. Output volatility is measured as a five-year rolling standard deviation of real GDP growth. The results indicate that fiscal decentralization exhibits a statistically significant effect on volatility whose direction depends on governance quality. Institutional quality directly reduces volatility, and the interaction between decentralization and institutional quality is negative and highly significant. A critical institutional threshold of 1.865 (WGI estimate scale), above which decentralization reduces output volatility, is identified. These findings indicate that decentralization functions as a conditional risk-management mechanism embedded within institutional capacity. The results provide policy-relevant insights into EU fiscal architecture design in an era of recurrent macroeconomic shocks. Full article

This study examines the dynamic interlinkages among energy, food, and metal commodity markets under geopolitical tensions using daily data from January 2022 to July 2025. The empirical framework integrates correlation analysis, Granger causality tests, and a Vector Error Correction Model (VECM) to capture both short- and long-run transmission mechanisms, with robustness assessed through impulse response functions, forecast error variance decomposition, and a Diebold–Yilmaz connectedness analysis across three structurally distinct geopolitical event windows. The results reveal asymmetric and sector-specific transmission patterns in which geopolitical risk significantly influences key commodity prices—particularly WTI crude oil, wheat, copper, and aluminium—confirming its role as a primary external shock driver. WTI emerges as the dominant transmitter of shocks, while industrial metals exhibit strong internal connectedness. Critically, gold’s role proves to be conditional and context-dependent: within an integrated energy–food–metal network under geopolitical stress, it functions primarily as a net receiver and passive absorber of macroeconomic uncertainty rather than as a systemic transmitter, a finding that complements, rather than contradicts, its established safe-haven role in financial asset pricing frameworks. These findings are subject to limitations, including reliance on futures price data and a linear VECM framework that may not fully capture nonlinear or regime-dependent dynamics. Full article

This study examines how the COVID-19 period is associated with changes in digital payment usage, rather than simply whether adoption increased, in Saudi Arabia using monthly data from January 2019 to July 2025. An Interrupted Time Series (ITS) approach is employed to assess both the immediate and long-term effects associated with the pandemic on a digital payment Intensity (DPI) index constructed from national point-of-sale (POS) transaction data to capture aggregate electronic payment usage relative to cash withdrawals. The results show that the onset of the COVID-19 period is associated with a sharp and statistically significant one-time increase of approximately 7 to 13% in digital payment intensity, followed by stabilization at a higher level rather than sustained acceleration. This finding challenges the common view that digital payment adoption followed a continuously accelerating path, instead showing that the pandemic induced a discrete upward shift without altering the underlying growth trajectory. The estimated effects remain robust across multiple model specifications, including dynamic ITS models, seasonal adjustments, alternative break dates, exclusion of overlapping usage variables, and parsimonious infrastructure-only models. Inflation and ATM usage consistently show negative associations with digital payment intensity, highlighting the role of macroeconomic stability and cash substitution in shaping payment behavior. The study therefore offers a more nuanced interpretation of post-pandemic digital adoption by showing that the main effect of COVID-19 was a one-time level shift rather than a lasting change in growth dynamics. Focusing on aggregate usage intensity rather than access or account ownership, it provides a system-level perspective on digital payment behavior in response to large-scale shocks. Overall, the evidence suggests that the pandemic period coincided with a discrete upward realignment in digital payment usage in Saudi Arabia, reflecting the interaction between crisis-driven behavioral change and strong pre-existing digital infrastructure under Vision 2030. Full article

Population aging in middle-income economies produces macroeconomic and distributional consequences that aggregate frameworks cannot detect. This paper develops a multi-household CGE model calibrated to a 26-sector Social Accounting Matrix for Thailand (2024) and traces the labor, saving, and fiscal channels of aging across eleven counterfactual scenarios. Three findings emerge. First, aging’s primary macroeconomic cost operates through capital accumulation, not output contraction: investment falls seven times faster than the GDP under a savings-driven closure, because middle-aged households—the economy’s dominant net savers—compress lifecycle saving in response to aging. The saving channel alone amplifies the labor supply shock four-fold (range: 3.5–4.5). Second, aging can raise elderly welfare. When elderly households retain labor market attachment, wage gains from tighter factor markets outweigh declining capital returns—a welfare reversal invisible to representative agent and OLG frameworks by construction. The critical labor income threshold is ${\alpha }_L^{\ast }=35.5\%$ (range: 34.8–36.2%), confirmed across all participation increments tested (elderly welfare gain: THB 341–521 million). Third, no single instrument satisfies efficiency and equity simultaneously. Pension transfers crowd out investment nonlinearly above 12 percent of tax revenue (range: 10–14%); health demand expansion is the decisive complement that converts redistribution into a near-Pareto improvement. Policy complementarity is an empirical necessity, not a theoretical refinement. Collectively, these results reframe demographic aging as a factor price redistribution mechanism whose welfare incidence is determined by the cohort-level income composition—with direct implications for aging policy in middle-income economies facing rapid demographic transitions under tighter fiscal constraints than for advanced economies encountered at equivalent demographic stages. Full article