Quarterly vs. Semiannual Reporting: A Cross-Market Analysis of Earnings Announcement Reactions in the US and Europe

Abstract

This study re-examines the ongoing debate over corporate disclosure frequency amid renewed calls to replace quarterly with semiannual reporting in U.S. markets. While traditional theories hold that frequent disclosure enhances informational efficiency by reducing asymmetry, emerging evidence highlights trade-offs involving managerial myopia, earnings management, and heightened short-term volatility. Using data from 2007 to 2024, the study compares Dow Jones Industrial Average firms, which report quarterly, with STOXX 50 firms, which report semiannually, to assess how disclosure cadence affects market reactions to earnings news The methodology involves identifying volatility regimes using Self-Exciting Threshold Autoregressive (SETAR) models, estimating dynamic betas with the GARCH(1,1) model, and analyzing shock transmission through vector autoregressions with cumulative impulse response functions (CIRFs). The results show that quarterly reporters exhibit larger immediate price reactions but faster normalization, implying that more frequent reporting accelerates information assimilation while amplifying contemporaneous volatility. Sectoral heterogeneity is pronounced: cyclical industries display higher beta volatility and steeper, but shorter-lived responses, whereas defensive stocks exhibit smoother convergence. These findings suggest that disclosure frequency influences both the intensity and duration of information shocks, providing insights for regulators who aim to balance transparency, market efficiency, and reporting costs across varying volatility and sectoral environments.

1. Introduction

Financial disclosure frequency remains a central yet contentious issue in capital market regulation. In September 2025, the debate over corporate financial reporting was thrust back into the policy spotlight by President Trump’s call for public companies to transition from quarterly to semiannual reporting. This renewed push prompted the US Securities and Exchange Commission (SEC), under Chairman Paul Atkins, to announce that it is prioritizing re-evaluating its disclosure rules. This development has amplified the long-standing scholarly and market debate regarding the optimal frequency of firm performance disclosures and their impact on the market’s assimilation of information.

While traditional financial theories suggest that more frequent disclosure enhances market efficiency by reducing information asymmetry (Fama, 1970; Abdel-Khalik & Espejo, 1978; Ball & Kothari, 1991), emerging empirical evidence suggests that the relationship between reporting frequency and market quality is more complex and context-dependent. Scholars increasingly question whether the presumed benefits of quarterly reporting, such as faster price discovery and increased transparency, are consistently realized across different market conditions and investor environments (Ernstberger et al., 2017; Kraft et al., 2018; Jo, 2007).

For the investment community, publicly reported firm performance information has historically been derived from mandatory disclosures, voluntary disclosures, and data furnished by third-party intermediaries. The aftermath of the global financial crisis has fueled extensive scholarly debate regarding the market’s efficiency in processing information from mandatory quarterly financial reports, specifically the speed of information assimilation into stock prices (Dow et al., 2024; Fu et al., 2012; Janicka et al., 2020; García Osma et al., 2023; Gigler et al., 2014; Kraft et al., 2018; Kajüter et al., 2021; Plečnik et al., 2022; Roychowdhury et al., 2019). Critics argue that regulatory reinforcement of mandatory quarterly reporting, particularly in the US, creates pressure for publicly listed companies to prioritize short-term financial performance over sustainable, long-term growth (Brav et al., 2005). Influential commentators, such as Davis (2022), suggested that this emphasis on short-term profits can erode long-term shareholder value. Further evidence suggests that these profitability pressures have influenced specific managerial decisions at firms like Boeing, as noted in recent financial media coverage (The Wall Street Journal, 2024). These issues are compounded by recent accounting labor shortages, which have been linked to increased errors and delays in financial disclosures (Reinstein & Kaszak, 2024; Maurer, 2023; Burke & Polimeni, 2023).

A growing body of research suggests that mandatory quarterly reporting may inadvertently encourage earnings management, promote managerial myopia, and exacerbate market volatility, particularly under uncertain economic conditions or in high-beta sectors (Das et al., 2009; DeHaan et al., 2015; Zolotoy, 2011). Furthermore, the interaction of investor sophistication, sectoral sensitivity, and volatility regimes further complicates the effectiveness of disclosure frequency as a regulatory tool (Bartov et al., 2000; Haga et al., 2022; Kanagaretnam et al., 2010). Notably, the implementation of the EU Transparency Directive (European Parliament and Council, 2013), which permitted a shift from quarterly to semiannual reporting, has allowed for analysis that compares the implications of different disclosure regimes (Kajüter et al., 2019; Fu et al., 2012; Grewal et al., 2019; Bornemann et al., 2023).

Against this backdrop, this study investigates how reporting frequency affects investor responses to earnings announcements under varying volatility regimes, sectoral contexts, and risk profiles. Building on the work of Ugras and Ritter (2025), this study compares firms in the Dow Jones Industrial Average (DJIA), which follow a quarterly disclosure mandate, with those in the STOXX Europe 50, which report semi-annually through a comparative cross-market analysis. Using a dataset spanning 2007 to 2024, we integrate volatility regime identification via a Self-Exciting Threshold Autoregressive (SETAR) model, dynamic beta estimation using the GARCH(1,1) model, and impulse response analysis within a vector autoregressive (VAR) framework.

This study contributes to the literature by systematically quantifying how quarterly versus semiannual reporting shapes the speed, magnitude, and stability of market reactions to earnings news across volatility regimes. Specifically, it addresses three critical gaps: (1) how risk exposure interacts with disclosure frequency during earnings events; (2) whether frequent reporting enhances or distorts informational absorption in high-volatility settings; and (3) how sectoral characteristics mediate these relationships.

2. Literature Review

2.1. Theoretical Foundations

The foundational literature in financial economics suggests that financial disclosure reduces information asymmetry and enhances market efficiency. Fama’s (1970) efficient market hypothesis (EMH) remains a cornerstone of this view, suggesting that asset prices fully and rapidly reflect all publicly available information. From this perspective, more frequent disclosures should improve informational efficiency and enable more accurate firm valuation.

Empirical studies in the 1970s and 1990s substantiated this notion. Abdel-Khalik and Espejo (1978) provided early evidence that interim financial reporting improves predictive accuracy of future earnings, leading to more efficient security pricing. Ball and Kothari (1991) further demonstrated that stock prices adjust rapidly to firm-specific earnings news, thereby reinforcing EMH predictions. These studies laid the groundwork for justifying quarterly reporting mandates in markets like the United States.

More recently, Beyer et al. (2010) synthesized several decades of disclosure research, arguing that the primary function of financial reporting is to reduce information risk and improve capital allocation. However, while foundational theories posit a positive relationship between disclosure frequency and market quality, newer studies increasingly challenge this linear assumption. A contrasting real-effects view shows that greater reporting frequency can simultaneously strengthen market discipline and induce managerial myopia when investment choices are unobservable and investors are impatient (Gigler et al., 2014).

2.2. Managerial Behavior

Subsequent empirical work has raised concerns about the unintended consequences of frequent financial disclosure, particularly on corporate decision-making and managerial incentives. Ernstberger et al. (2017) demonstrated that mandatory quarterly reporting can induce earnings management, as managers face pressure to meet or beat short-term market expectations. This pressure often results in the manipulation of accruals and timing decisions to create the appearance of consistent performance.

Kraft et al. (2018) provided evidence that frequent reporting fosters managerial myopia, noting that a measurable decline followed the introduction of mandatory quarterly reporting in the US for long-term investments. These results align with Jo’s (2007) findings, which showed that firms under high-frequency reporting regimes engage in greater earnings management and experience weaker post-issue performance.

Similarly, Das et al. (2009) explored the strategic timing of earnings announcements. They found that managers often choose disclosure dates to minimize adverse market reactions or maximize the impact of positive news. This strategic behavior complicates the assumption that frequent reporting enhances transparency or benefits investors.

These studies demonstrate that high-frequency disclosure may have adverse effects by encouraging behavior that prioritizes short-term metrics over long-term firm value.

2.3. Risk Perception

Financial markets and investors’ perceptions of risk are not static. A growing body of literature emphasizes the dynamic interaction between market volatility, risk perception, and the absorption of earnings information. Zolotoy (2011) showed that firm-specific beta, a measure of systematic risk, tends to increase following negative earnings surprises and decline after positive ones. This finding suggests that bad news disproportionately affects how investors reassess a firm’s risk exposure.

Similarly, Engle and Cho (1999) used GARCH-based models to demonstrate that market betas are not constant but evolve in response to incoming information, particularly in emerging markets. These insights underscore the importance of dynamic beta modeling in event studies, enabling more accurate capture of investor reactions.

Building on this, Ugras and Ritter (2025) used VAR and SETAR models to analyze the joint impact of earnings news and volatility regimes. They suggested that firms exhibited greater beta sensitivity and more volatile market responses to earnings surprises, especially in cyclical sectors. However, more analysis is required to refine the discussion. These early results underscore the importance of modeling time-varying systematic risk when evaluating the consequences of financial reporting frequency.

Furthermore, Lee and Tong (2018) raised a critical concern about “information noise.” Analyzing dual-listed Chinese firms, he found that higher reporting frequency can degrade information quality, particularly when investors are already inundated with data. The analysis supports the view that frequent disclosure can, under certain circumstances, reduce rather than enhance market efficiency.

2.4. Investor Sophistication

The role of investor sophistication, particularly the distinction between institutional and retail investors, has also emerged as a key moderator of disclosure effects. Bartov et al. (2000) showed that firms with a higher proportion of retail investors experienced more pronounced post-earnings announcement drift (PEAD), suggesting that less sophisticated investors struggle to fully interpret earnings signals promptly.

In the European context, Haga et al. (2022) leveraged the EU Transparency Directive to examine the effect of peer disclosure practices. They found that firms surrounded by quarterly reporting peers exhibited reduced price synchronicity, suggesting more accurate firm-specific price discovery. This thought implies that even semiannual reporters may benefit from more transparent peers, reinforcing the notion of informational spillovers in financial markets.

Supporting this view, Arif and De George (2015) showed that firms with low disclosure frequency tend to overreact to external earnings signals, particularly from US markets, and reverse these reactions. Their work suggests that the absence of timely firm-specific information can leave investors more vulnerable to external noise, resulting in later corrected overreactions.

2.5. Regulatory Shifts and International Comparisons

International regulatory divergence offers insight into the evolution of disclosure frequency. The European Union’s path to semiannual reporting was a gradual one. The original EU Transparency Directive (European Parliament and Council, 2004) adopted in 2004 and implemented between 2006 and 2007, harmonized periodic reporting requirements across member states, initially mandating quarterly interim management statements and half-yearly financial reports. However, persistent concerns about compliance costs and potential short-termism prompted the 2013 Transparency Directive Amendment (European Parliament and Council, 2013), which removed the quarterly statement requirement and effectively enabled a transition to a semiannual regime across the EU (Christensen et al., 2021; Kajüter et al., 2019). This evolution in disclosure cycles permitted researchers to assess whether reducing reporting frequency affects informational efficiency, volatility, and managerial incentives.

This shift is primarily driven by persistent concerns about compliance costs and the potential for short-termism prompted by frequent reporting. Specifically, policymakers aimed to alleviate the disproportionate administrative burden and high compliance costs that mandatory quarterly reporting imposed on companies, notably smaller and medium-sized issuers, which were seen as hindering market participation and creating an excessive financial strain. These disproportionate administrative burdens manifested in several ways: increased resource allocation towards frequent preparation, review, and filing of quarterly financial statements, diverting crucial resources from core business and long-term strategic initiatives; elevated external audit and compliance costs, particularly taxing for SMEs with tighter budgets; significant time demands placed on senior management and board members for recurring review and approval, detracting from strategic oversight; and the complexities and associated costs of managing evolving reporting regulations frequently. These issues need to be discussed in the context of transparency, as Agarwal et al. (2018) mentioned concerning mutual funds.

Kajüter et al. (2019) found that firms adopting semiannual reporting experienced lower stock return volatility without a measurable loss in price informativeness, suggesting that the regulatory relaxation may have promoted a more stable informational environment. Fu et al. (2012) likewise reported a reduction in managerial short-termism and earnings manipulation among semiannual reporters, albeit at the expense of slower price discovery and weaker analyst coverage. These findings illustrate the central trade-off in EU policy: balancing transparency and capital market efficiency against over-disclosure and excessive pressure on managers, as well as the significant burden of compliance costs. Kanagaretnam et al. (2010) extended this by examining sectoral effects, showing that volatility responses vary markedly by industry.

2.6. Research Gaps

The cumulative evidence reveals a stronger relationship between reporting frequency and market outcomes than earlier theories proposed. While foundational models argue that more frequent disclosure enhances transparency and market quality, recent work warns that excessive frequency can amplify volatility, distort managerial incentives, and introduce informational noise—especially under heightened uncertainty. This study explicitly engages with that trade-off by comparing a quarterly reporting regime (DJIA) with a semiannual regime (STOXX) across SETAR-classified volatility states. By employing cumulative impulse response functions (CIRFs), we decompose each market’s reaction into its immediate impact and subsequent recovery, precisely the two levers highlighted in Gigler et al.’s (2014) discipline-versus-pressure mechanism, thereby providing an empirical backdrop for when frequent versus infrequent reporting may be welfare-enhancing.

3. Methodology

We analyze how the disclosure cadence (quarterly vs. semiannual) affects the intensity and persistence of equity price reactions to earnings news across volatility regimes and sectors from 2007 to 2024 (DJIA vs. STOXX). The empirical design proceeds in four stages: (a) regime identification via a Self-Exciting Threshold Autoregressive (SETAR) model on implied-volatility indices (VIX/VSTOXX); (b) construction of time-varying betas using rolling market-model estimation refined with GARCH(1,1) filters at the market and firm levels; (c) regime-specific vector autoregressions (VARs) with cumulative impulse response functions (CIRFs) to trace shock transmission; and (d) regime-aware beta forecasting and structural simulations linking CIRF dynamics to beta paths.

3.1. Volatility Regime Classification via SETAR(2,3)

We classify market conditions into three distinct volatility regimes, low, intermediate, and high, using a Self-Exciting Threshold Autoregressive (SETAR) model (Tong, 1990; Tsay, 1989), following the nonlinear modeling framework extended by Herley et al. (2024). This approach enables the autoregressive process to switch endogenously across regimes based on lagged implied volatility values (VIX for the US and VSTOXX for Europe), capturing nonlinear persistence and regime-dependent dynamics.

Our SETAR(2,3) specification was optimized using two thresholds and three lagged terms. The optimization involved systematically testing candidate threshold values and lag structures, selecting the configuration that minimized the Akaike Information Criterion (AIC). This criterion balances model fit and parsimony, ensuring the regime classification does not overfit. The selected thresholds effectively segmented the data into three volatility zones, while the inclusion of three lagged terms $(VI{X}_{t-1},VI{X}_{t-2},VI{X}_{t-3})$ minimized AIC (−2.488), capturing the persistence and temporal dependence of volatility dynamics.

Model validation confirmed robustness, with a Durbin–Watson statistic of 1.987, indicating no meaningful residual autocorrelation. These regimes are the foundation for all subsequent beta estimation, VAR modeling, and CIRF analysis, ensuring that our results reflect regime-dependent heterogeneity in volatility behavior.

Unlike linear models, SETAR allows autoregressive dynamics to shift endogenously based on past values of the volatility index.

Let $y_t$ denote daily implied volatility (VIX for the US, VSTOXX for the Euro area). The SETAR(2,3) model is defined as:

$$y_t=\left\{\begin{array}{ll}{\varphi }_{1.0}+{\displaystyle \sum _{i=1}^{p_1}}{\varphi }_{1,i}{y}_{t-i}+{ϵ}_{1,t},& y_{t-d}\le {\gamma }_1\\ {\varphi }_{2.0}+{\displaystyle \sum _{i=1}^{p_2}}{\varphi }_{2,i}{y}_{t-i}+{ϵ}_{2,t},& {\gamma }_1<y_{t-d}\le {\gamma }_2\\ {\varphi }_{3.0}+{\displaystyle \sum _{i=1}^{p_3}}{\varphi }_{3,i}{y}_{t-i}+{ϵ}_{3,t},& y_{t-d}>{\gamma }_2\\ & \end{array}\right.$$

Thresholds ${\gamma }_1,{\gamma }_2$ and delay parameter $d$ are selected via grid search, minimizing AIC/BIC. Each regime’s AR lag order $p_j$ is optimized similarly. OLS estimates the model within each regime. Stationarity is verified by checking AR root locations for each regime.

3.2. Time-Varying Beta Estimation with GARCH(1,1)

3.2.1. Rolling Market Model Beta

Beta-adjusted returns are explicitly defined as:

$$r_{i,t}^{adj}=r_{i,t}-{\widehat{\beta }}_{i,t}\hspace{0.17em}{r}_{m,t}$$

where

$r_{i,t}$ = firm $i$’s raw return at time $t$,

${\widehat{\beta }}_{i,t}$ = time-varying conditional beta

$r_{m,t}$ = corresponding market index return (DJIA or STOXX).

Conditional betas ${\widehat{\beta }}_{i,t}$ are estimated from a 250-day rolling market model, filtered via GARCH(1,1) and GJR-GARCH(1,1) specifications to account for volatility clustering and asymmetry, ensuring conceptual and temporal consistency between beta estimation and the shock propagation analysis.

The resulting $r_{i,t}^{adj}$ series is directly aligned with the VAR state vector:

$$[r_{i,t}^{adj},r_{m,t},{\mathrm{V}\mathrm{I}\mathrm{X}/\mathrm{V}\mathrm{S}\mathrm{T}\mathrm{O}\mathrm{X}\mathrm{X}}_t{]}^{\prime }$$

3.2.2. GARCH and GJR-GARCH Filtering

To account for heteroskedasticity, we estimate GARCH(1,1) and GJR-GARCH(1,1) models:

$${\sigma }_t^2=\omega +\alpha {ϵ}_{t-1}^2+\beta {\sigma }_{t-1}^2$$

and

$${\sigma }_t^2=\omega +\alpha {ϵ}_{t-1}^2+\gamma \mathbb{I}[{ϵ}_{t-1}<0]{ϵ}_{t-1}^2+\beta {\sigma }_{t-1}^2$$

These estimates enhance regime separation (market-level) and reveal sectoral beta dynamics (firm-level).

3.3. VAR Modeling and Cumulative Impulse Response Functions (CIRFs)

We estimate regime-specific vector autoregressions (VARs) including beta-adjusted firm returns, market returns, and implied volatility indices (VIX/VSTOXX) as endogenous variables. Structural shocks are identified using a recursive (Cholesky) ordering, and the moving-average representation of the system is used to compute cumulative impulse response functions (CIRFs) over a 10-day horizon. These CIRFs jointly capture the magnitude of market reactions and the speed of reversion, offering an intuitive measure of how information shocks are transmitted and absorbed.

In economic terms, the earnings news shock represents the unanticipated component of firm-specific return innovations that remain after controlling for contemporaneous market and volatility effects. Within the VAR framework, this shock is operationalized as the structural innovation in the beta-adjusted return equation—interpreted as the firm-level price adjustment to earnings-related information.

We employ a recursive (Cholesky) identification, ordering the variables as:

$$[r_{m,t},{\mathrm{VIX}/\mathrm{VSTOXX}}_t,r_{i,t}^{adj}{]}^{\prime }$$

This ordering assumes that aggregate market and volatility conditions can contemporaneously influence firm-level returns, but individual firm shocks do not instantaneously affect market-wide variables within the same day. The structural innovation in the firm-level equation thus captures the isolated earnings-related shock once aggregate effects have been negated.

By treating volatility as an endogenous variable, the CIRFs reflect both direct return sensitivities and indirect amplification through volatility co-movements, a feature especially relevant in high-volatility regimes where clustering effects dominate. Consistent with Sims (1980), our focus is on the dynamic interactions among variables rather than individual coefficients, emphasizing the propagation mechanism of shocks through the system. Future work could refine this framework by applying Bai and Perron’s (1998) structural break methodology to detect shifts in these relationships around major macroeconomic or regulatory events, enabling a deeper understanding of time variation in market adjustment dynamics.

We estimate regime-specific VAR(p) models to trace earnings-news shock propagation. The VAR structure is:

$$Y_t=A_1{Y}_{t-1}+\dots +A_p{Y}_{t-p}+u_t$$

with $Y_t=[r_{i,t}^{adj},r_{m,t},v_t{]}^{\mathrm{\top }}$, where $r_{i,t}^{adj}$ is the beta-adjusted firm return, $r_{m,t}$ is the market return, and $v_t$ is the volatility index (VIX or VSTOXX).

Structural shocks ${ϵ}_t$ are identified via a recursive Cholesky scheme. The moving average form:

$$Y_t=\mu +\sum _{j=0}^{\mathrm{\infty }}{\mathrm{\Psi }}_j{ϵ}_{t-j}$$

enables computation of CIRFs:

$$CIR{F}_j=\sum _{h=0}^j{\mathrm{\Psi }}_h{e}_k$$

where $e_k$ selects the earnings-news shock. We compute CIRFs up to 10 trading days and evaluate convergence speed and impact height.

3.4. Regime-Aware Beta Forecasting

We assess the predictive value of regime information on short-horizon beta forecasts. Let ${\widehat{\beta }}_{i,t+1}^{reg}$ denote the regime-conditioned forecast and ${\widehat{\beta }}_{i,t+1}^{uc}$ the unconditional mean forecast.

Forecast accuracy is evaluated via Mean Squared Forecast Error (MSFE):

$$MSFE={\displaystyle \frac{1}{N}}\sum _{t=1}^N({\beta }_{i,t+1}-{\widehat{\beta }}_{i,t+1}{)}^2$$

Improvement Ratio (IR):

$$IR=1-{\displaystyle \frac{MSF{E}_{reg}}{MSF{E}_{uc}}}$$

Diebold-Mariano tests assess whether the reduction in MSFE is statistically significant.

3.5. Structural Simulations Linking CIRFs to Beta Dynamics

To assess how beta evolution drives CIRFs, we simulate standardized +1% market shocks through the beta paths:

$$\mathrm{\Delta }{r}_{i,t}={\beta }_{i,t}\times 0.01$$

Comparing CIRF height (initial response) and width (days to mean reversion) across regimes and reporter types allows us to map disclosure cadence to shock amplification and recovery. Sectoral summaries show that higher betas increase impact magnitude, while faster beta reversion aligns with quicker CIRF normalization. All impulse responses are standardized to a one–standard deviation innovation in the structural earnings-news shock. This normalization corresponds to the standard deviation of the structural residual from the beta-adjusted return equation, ensuring comparability across regimes, markets, and time horizons.

To clarify, CIRFs, derived from regime-specific VARs, capture dynamic interactions from time-varying beta paths and endogenous volatility dynamics. The VIX or VSTOXX is an endogenous variable in the VAR system, enabling CIRFs to reflect the feedback loop between volatility states and return dynamics. Therefore, the CIRF response to an earnings shock incorporates both the direct channel (via beta-adjusted return sensitivity) and an indirect channel (via volatility co-movements and amplification mechanisms).

3.6. Identification and Statistical Inference

The identification of structural shocks in the VAR system follows a recursive Cholesky decomposition, which offers a transparent and empirically tractable means of isolating the earnings-news shock. The model assumes that aggregate market returns and contemporaneous volatility (proxied by VIX for U.S. firms and VSTOXX for European firms) can exert immediate effects on firm-level returns, whereas firm-specific shocks do not contemporaneously influence aggregate variables within the same trading day. This ordering aligns with empirical evidence indicating that firm-level earnings innovations are small relative to market-wide and volatility-related information flows.

Formally, the state vector

$$Y_t=[r_{m,t},{\mathrm{VIX}/\mathrm{VSTOXX}}_t,r_{i,t}^{adj}{]}^{\prime }$$

is modeled as:

$$Y_t=A_1{Y}_{t-1}+A_2{Y}_{t-2}+\dots +A_p{Y}_{t-p}+{\epsilon }_t,$$

where ${\epsilon }_t\sim (0,{\mathrm{\Sigma }}_{\epsilon })$ are reduced-form innovations.

The structural decomposition ${\epsilon }_t=B{u}_t$ maps these innovations to orthogonal structural shocks $u_t$, with the third component representing the firm-specific earnings-news innovation. This component captures the portion of the firm’s abnormal return that is orthogonal to contemporaneous market and volatility shocks.

To verify that results are not driven by this recursive identification, two complementary order-invariant approaches are implemented:

• Generalized Impulse Responses (GIRFs)—following Pesaran and Shin (1998), which integrate over the historical distribution of shocks and are invariant to variable ordering.
• Local Projections (LPs), following Jordà (2005), which estimate dynamic responses directly from the data without imposing VAR-specific restrictions.

The regime-interacted local projection model is specified as:

$$r_{i,t+h}^{adj}={\alpha }_h+\sum _{k=0}^p{\varphi }_{h,k}{X}_{t-k}+{\gamma }_h{D}_t^{regime}{\epsilon }_t^{earn}+u_{t+h},$$

where $D_t^{regime}$ is a dummy variable distinguishing high- and low-volatility states, and ${\epsilon }_t^{earn}$ denotes the identified earnings-news shock.

Both GIRFs and LPs yield consistent and economically coherent results, confirming that the dynamic ranking of reaction magnitudes and recovery speeds is not sensitive to the chosen identification strategy.

Statistical inference for all impulse and cumulative impulse responses (IRFs and CIRFs) is obtained through 1000 wild bootstrap replications as proposed by Kilian (1998). This approach preserves the time-series dependence of residuals while providing robust inference under conditional heteroskedasticity, an important feature for financial data. For each replication, structural residuals are resampled with random sign perturbations, and the VAR system is re-estimated to produce new response trajectories.

The median impulse path across bootstrap replications is reported as the central estimate, accompanied by 68% and 95% percentile confidence bands. To summarize dynamic behavior, three key statistics are computed:

• Peak magnitude: the maximum absolute response to the earnings-news shock.
• Time-to-peak: the number of trading days required to reach this maximum.
• Half-life: the time required for the cumulative response to decay to half its peak value.

These inferential procedures ensure that the reported dynamics are statistically rigorous, economically interpretable, and robust to alternative identification assumptions.

3.7. Regime Robustness and Alternative Volatility Definitions

The baseline regime classification relies on self-exciting threshold autoregressions (SETAR) estimated on the VIX (U.S.) and VSTOXX (Europe) indices to delineate high- and low-volatility market conditions. To confirm that the results are not an artifact of this particular proxy or threshold, we perform a series of robustness exercises using alternative, data-driven regime definitions.

• Volatility Quantiles (Realized Variance Stratification):
  We compute the realized variance from the daily squared returns of the DJIA and STOXX 50 indices and classify observations into low, medium, and high volatility regimes, corresponding to the lower, middle, and upper terciles of the realized variance distribution. This nonparametric stratification captures cyclical shifts in market volatility without relying on model-based assumptions.
• Conditional Variance from GJR-GARCH(1,1):
  To incorporate asymmetric volatility behavior, we estimate GJR-GARCH(1,1) models for both firm-level and index-level returns. Periods of elevated conditional variance are identified as high-volatility states, while tranquil periods define low-volatility states. This approach accommodates leverage effects and volatility clustering typical of financial time series.

Across both alternative specifications, the qualitative dynamics remain robust. Quarterly reporting firms exhibit larger immediate responses to earnings news shocks and faster reversion, while semiannual-reporting firms display smaller but more persistent adjustments. The relative magnitude and recovery ranking are invariant to whether SETAR thresholds define regimes, realized variance terciles, or GJR-GARCH-based conditional variances.

These findings indicate that the observed differences across disclosure frequencies are structural rather than proxy dependent. More frequent reporting amplifies the contemporaneous price reaction but accelerates post-announcement normalization, highlighting how disclosure cadence influences the speed and persistence of information assimilation in equity markets.

3.8. VAR Diagnostics and Specification Checks

Before interpreting the impulse response dynamics, the validity of the VAR specification is confirmed through a comprehensive suite of diagnostic and specification tests. These tests ensure that the system adequately captures temporal dependencies and that the estimated dynamics are stable and statistically reliable.

Lag lengths are selected using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), both of which consistently identify an optimal lag order of two. The specification strikes a balance between capturing short-run feedback effects between returns and volatility while avoiding overparameterization. The stability condition is verified by examining the eigenvalues of the companion matrix. All estimated systems satisfy the requirement that eigenvalues lie strictly within the unit circle, indicating that the VAR models are dynamically stable and that shocks dissipate over time.

Residual diagnostics confirm the absence of serial correlation and remaining conditional heteroskedasticity. Lagrange Multiplier (LM) tests yield p-values well above conventional significance levels (p = 0.58), suggesting no residual autocorrelation. ARCH-LM tests confirm that volatility clustering has been captured in the beta-adjusted return construction stage as seen in

Table 1. VAR Model Diagnostics.

Diagnostic	Test	Result	Interpretation
Lag Length Selection	AIC/BIC	Optimal lag = 2	Captures key short-term dependencies
System Stability	Companion roots	All roots < 1	VAR dynamically stable
Serial Correlation	LM Test	p = 0.58	No residual autocorrelation
Heteroskedasticity	ARCH-LM	Not significant	No residual ARCH effects

.

Collectively, these diagnostics demonstrate that the estimated VAR systems are well-specified, stable, and econometrically sound. Consequently, the subsequent interpretation of impulse and cumulative impulse responses is grounded in a robust dynamic specification that accurately characterizes the transmission of earnings-related shocks across market regimes.

3.9. Economic Interpretation and Theoretical Linkages

The empirical framework links econometric evidence to underlying behavioral and informational mechanisms. The SETAR model identifies volatility regimes corresponding to phases of informational efficiency versus uncertainty. Low-volatility states reflect stable expectations and rapid earnings dispersion; high-volatility states reflect elevated information noise and transient mispricing, illustrating the tension between efficient-market dynamics (Fama, 1970) and behavioral limitations arising from bounded rationality and investor attention constraints (Barberis et al., 1998; Daniel et al., 1998).

Dynamic betas from the GARCH specification illustrate how disclosure cadence shapes investor learning. Quarterly reporting compresses information cycles, accelerating price adjustment but amplifying short-term volatility—patterns consistent with information-assimilation and overreaction-correction models (Hirshleifer & Teoh, 2003; Tetlock, 2011). Semiannual reporting allows slower expectation revision, reducing transitory volatility at the cost of delayed price discovery. CIRFs quantify these mechanisms: quarterly reporters exhibit larger but shorter-lived shocks, while semiannual reporters show smaller, more persistent responses. Sectoral contrasts reinforce this view—cyclical industries react sharply to frequent disclosures, whereas defensive sectors adjust smoothly to stable cash-flow expectations (Hong & Stein, 1999; Kothari et al., 2009).

Overall, disclosure frequency governs both the speed and stability of market learning. More frequent reporting enhances transparency and accelerates the diffusion of information but heightens behavioral noise. Conversely, less frequent reporting dampens volatility but slows assimilation. These trade-offs frame the empirical results that follow and inform policy debates on optimal reporting cadence.

4. Data

Our analysis examines the interactions between the log changes in the DJIA (Dow Jones Industrial Average) and the STOXX (EU equity index), as well as their associated volatility benchmarks—the VIX (CBOE Volatility Index) and VSTOXX (EURO STOXX 50 Volatility). The daily data span from January 2007 to December 2024, yielding 4661 observations for each series. The data used in this study were obtained from multiple public and institutional sources. Daily return and volatility data for U.S. equities and the VIX index were sourced from the Federal Reserve Bank of St. Louis (FRED) and CRSP/Compustat databases. European equity returns and volatility data, including STOXX 50 and VSTOXX indices, were obtained from the Eurex Exchange.

Before empirical modeling, we computed descriptive statistics and unit root tests for all four log-change series; the results are shown in

Table 2. Descriptive Statistics.

Statistic	Log DJIA	Log VIX	Log STOXX	Log VSTOXX
Mean	0.000276	4.13 × 10−5	0.000106	3.17 × 10−5
Median	0.000564	−0.00607	−0.00398	−0.0055
Maximum	0.107643	0.768245	0.489248	0.485868
Minimum	−0.13842	−0.35059	−0.1828	−0.43472
Std. Deviation	0.011444	0.074946	0.053395	0.066342
Skewness	−0.47648	1.094751	1.302491	0.748975
Kurtosis	16.24267	6.728568	9.157027	7.061456
Jarque–Bera	55,376.47	10,514.32	5129.846	3967.454
JB p-value	0	0	0	0
ADF Statistic	−17.5175	−28.2914	−26.9862	−22.4158
ADF p-value	4.30 × 10−30	0	0	0
KPSS Statistic	0.106365	0.014432	0.061942	0.017666
KPSS p-value	0.1	0.1	0.1	0.1

(below). The distributions are clearly non-normal by the Jarque–Bera test (p-values ≈ 0.000 for all series) and exhibit pronounced leptokurtosis (kurtosis > 3). Heavy tails are especially notable for the DJIA (kurtosis ≈ 16.24), followed by STOXX (~9.16), VSTOXX (~7.06), and VIX (~6.73), implying a heightened probability of extreme daily moves. Skewness patterns indicate that VIX and VSTOXX are positively skewed (with more frequent significant upward moves in volatility), while STOXX is also positively skewed. In contrast, DJIA displays a mild negative skew.

The Augmented Dickey–Fuller (ADF) tests strongly reject the null of a unit root for all series (very negative statistics with p-values ~ 0.000), indicating stationarity of log changes. Complementarily, the KPSS test statistics are small with high p-values (reported at 0.10), which fail to reject stationarity, reinforcing the ADF findings. Consequently, no additional transformations are required to meet stationarity assumptions for subsequent time-series modeling.

5. Empirical Results

5.1. SETAR Classification and Volatility Regimes

Utilizing a Self-Exciting Threshold Autoregressive (SETAR) model with two thresholds and three SETAR(2,3) regimes, we systematically segmented market volatility into low, medium, and high regimes based on implied volatility indices. For the US market (VIX), threshold values were estimated at $r_1=16.48$ and $r_2=25.93$, while for the European market (VSTOXX), the corresponding breakpoints were $r_1=18.75$ and $r_2=31.42$. These values were derived through grid search optimization using minimum AIC and BIC criteria and are consistent with prior literature that identifies structural volatility shifts during financial stress events.

The classification results, summarized in

Table 3. Volatility Regime Classification Table (VSTOXX—2007 to 2024).

Volatility Regime	VSTOXX Range	Number of Days	% of Days
Low Volatility	VSTOXX < 18.75	2542	52.6%
Medium Volatility	18.75 ≤ VSTOXX < 31.42	1316	29.3%
High Volatility	VSTOXX ≥ 31.42	843	18.1%

and

Table 4. Volatility Regime Classification Table (VIX—2007 to 2024).

Volatility Regime	VIX Range	Number of Days	% of Days
Low Volatility	VIX < 16.48	2718	58.2%
Medium Volatility	16.48 ≤ VIX < 25.93	1214	26.0%
High Volatility	VIX ≥ 25.93	736	15.8%

, show that low-volatility conditions dominate the sample for both indices, accounting for approximately 58.2% of trading days for the VIX and 52.6% for the VSTOXX. Medium-volatility regimes account for 26.0% of days in the US and 29.3% in Europe, while high-volatility periods, typically associated with systemic shocks, comprise 15.8% and 18.1% of the sample, respectively.

High-volatility episodes align temporally with major macro-financial disruptions, including the 2008 global financial crisis, the European sovereign debt crisis (2010–2012), and the onset of the COVID-19 pandemic in early 2020. Despite their pronounced impact on market behavior, the relatively minor proportion of high-volatility days reinforces the importance of modeling these regimes separately to better understand investor responses under stress. This regime-based segmentation is used as a conditioning variable in our subsequent beta estimations, regression models, and impulse response analyses to ensure results account for nonlinearity and temporal asymmetry in volatility behavior.

5.2. GARCH-Based Beta Estimation

Dynamic betas estimated from the GARCH(1,1) framework exhibit meaningful variation across firms, sectors, and market regimes. High-beta cyclical firms, such as JPMorgan and Boeing, show pronounced exposure amplification during stress episodes (e.g., the 2008 financial crisis and the early 2020 pandemic period). In contrast, defensive firms, such as Coca-Cola and Procter & Gamble, remain comparatively stable. For example, Coca-Cola’s conditional beta ranges narrowly between 0.69 and 1.00, while Boeing’s spans from 1.12 to 2.15, highlighting the substantial cross-sectional heterogeneity that can confound event-study attribution if left unadjusted.

These dynamics validate the use of conditional-volatility-based betas rather than static CAPM estimates. The conditional beta series, obtained from firm-level GARCH(1,1) models, captures time-varying exposure to market risk and provides a more accurate measure of systematic sensitivity across volatility regimes. This adjustment forms the basis for the beta-adjusted returns used in the downstream impulse-response analysis.

5.3. Shock Transmission: CIRF Intensity and Recovery

Cumulative impulse response functions (CIRFs) provide a comprehensive view of both the magnitude and persistence of earnings-news effects. Average instantaneous responses are slightly larger for DJIA firms; however, the key economic distinction lies in the speed of normalization. On average, DJIA firms revert to baseline approximately 2.5 trading days faster than STOXX firms, consistent with quicker information assimilation under more frequent disclosure cycles.

Following established event-study and dynamic-response conventions (Campbell et al., 1997; Kilian & Lütkepohl, 2017), recovery is defined as the first return of the cumulative impulse response (CIRF) to within a ±2 percent tolerance band around its pre-shock baseline value. This tolerance threshold corresponds to a statistically negligible deviation within the 95 percent bootstrap confidence interval and provides a transparent and reproducible measure of post-shock normalization. The recovery horizon, therefore, denotes the number of trading days required for the cumulative response to re-enter and remain within this band following the initial earnings news shock. The summary statistics reported in

Table 5. Average CIRF impact and recovery time by market (2007–2024).

Market	Average CIRF (t = 0)	Average Recovery (Days)	Median Recovery (Days)	SD of Recovery (Days)
DJIA	0.98%	4.8	4.0	1.4
STOXX	0.91%	7.2	6.5	2.1

and

Table 6. Sectoral beta ranges and median recovery days.

Sector	DJIA Beta Range	DJIA Recovery (Days)	STOXX Beta Range	STOXX Recovery (Days)	Pair (Illustrative)
Financials	1.20–1.55	3.8	1.00–1.35	7.1	JPM vs. UBS
Technology	1.10–1.50	4.1	1.05–1.30	6.9	AAPL vs. SAP
Industrials	0.90–1.30	5.2	0.85–1.15	7.4	CAT vs. Siemens
Healthcare	0.70–0.95	5.0	0.65–0.90	6.7	JNJ vs. Sanofi
Consumer Staples	0.50–0.75	4.9	0.40–0.65	7.6	PG vs. Nestlé
Utilities/Telecom	0.35–0.60	5.4	0.30–0.55	8.1	VZ vs. Orange

are computed at the firm level and then averaged across earnings announcement events within each market. Each observation represents a unique firm–announcement–day pairing. Averages are equal-weighted to prevent large-cap firms from dominating the results, and no firm is counted more than once within a given event window.

The “beta range” metric is derived from the minimum and maximum values of a 250-day rolling conditional beta estimated using both GJR-GARCH(1,1) and standard GARCH(1,1) specifications (Bollerslev, 1986; Glosten et al., 1993). To mitigate the influence of outliers, all firm-level series are bounded at the 1st and 99th percentiles.

Cumulative impulse response functions (CIRFs) provide a comprehensive view of both the magnitude and persistence of earnings-news effects. Following established event-study and dynamic-response conventions (Campbell et al., 1997; Kilian & Lütkepohl, 2017), recovery is defined as the first return of the cumulative impulse response to within a ±2 percent tolerance band around its pre-shock baseline value. This threshold corresponds to a statistically negligible deviation within the 95 percent bootstrap confidence interval and provides a transparent and reproducible measure of post-shock normalization. The recovery horizon, therefore, denotes the number of trading days required for the cumulative response to re-enter and remain within this band following the initial earnings-news shock.

CIRF (t = 0) represents the average instantaneous cumulative impulse response to a standardized one-standard-deviation earnings-related shock. Recovery refers to the number of trading days required for the cumulative response to return to baseline, as defined by the above operational definition. SD refers to the cross-sectional standard deviation across firms and events.

Table 4 reports the average and dispersion metrics for both CIRF height at t = 0 and recovery time (days to reversion) by market. The DJIA sample exhibits a shorter mean and median recovery horizon, as well as tighter dispersion, than the STOXX sample, indicating faster and more predictable post-announcement normalization under more frequent disclosure regimes.

5.4. Sectoral Heterogeneity: Beta Levels and Recovery Profiles

Drawing from Bollerslev (1986) on heteroscedasticity, sectoral comparisons reveal two robust features. First, CIRF magnitude scales with beta across markets. Second, recovery speed does not scale linearly with beta and is consistently faster for US firms at similar exposure levels, suggesting a cadence channel in shock absorption. As shown in Table 5, Apple and SAP exhibit similar beta levels (~1.3) but divergent recoveries (≈4.5 vs. 6.9 days), indicating that disclosure frequency and market microstructure may also be additional determinants of convergence.

5.5. Regime-Aware Beta Forecasting and Economic Value

Regime conditioning (SETAR) improves short-horizon beta forecasts relative to unconditional means, with the most significant gains concentrated in high-beta names and in volatile states (e.g., GS, BA, AAPL show improvement ratios ≥ 15%). These improvements are economically meaningful because near-term beta shapes CIRF height and interacts with disclosure cadence to influence recovery time. This pattern reinforces the channel from reporting frequency and beta dynamics leading to shock transmission.

5.6. Robustness and Sensitivity

The DJIA–STOXX recovery gap and sectoral patterns persist under alternative operationalizations of “recovery” (e.g., threshold-based reversion vs. first-difference zero crossing), heteroskedasticity-robust inference, exclusion of crisis windows, and median-based summaries. The persistence of these results is consistent with the earlier SETAR classification, which indicates that low-volatility days dominate, with high-volatility days comprising a smaller share. It supports the interpretation that disclosure cycles affect beta persistence and information absorption.

While we find the finding to be robust, we acknowledge that further extensions could provide deeper insight into the dynamics of earnings-news transmission. The current VAR framework, supported by extensive bootstrapped inference and multiple volatility definitions, yields stable and economically consistent results across different regimes. Nonetheless, future work could build on this foundation by exploring the response dynamics through a multi-horizon perspective.

In particular, applying the local projection methodology of Jordà (2005) and the multi-horizon VAR inference framework of Lütkepohl (2005) would enable direct estimation of impulse responses at short, medium, and long-run horizons without imposing recursive structure. Such an extension would allow researchers to assess whether the relative speed and persistence of market adjustment, especially the faster reversion among quarterly reporting (DJIA) firms and the slower, more persistent responses among semiannual-reporting (STOXX) firms, remain stable across alternative temporal windows. These refinements fall beyond the scope of the current study but represent a promising avenue for future research as we expand the empirical framework to encompass horizon-dependent dynamics and potential nonlinear interactions across reporting regimes.

5.7. Interpretation and Implications

The evidence suggests that more frequent reporting yields higher immediate sensitivity, facilitating faster normalization. Quarterly reporters (DJIA) experience slightly larger initial impacts but recover markedly sooner than semiannual reporters (STOXX), with the gap widest in sectors where information is complex and updates are frequent. Practically, these findings suggest the need for regime-aware beta modeling in event studies and for acknowledging disclosure cadence as a determinant of the temporal shape of risk.

6. Discussion

The evidence presented unequivocally demonstrates that the cadence of financial reporting significantly conditions how capital markets process information and transmit risk, extending beyond a mere operational choice. Aligning with foundational informational efficiency theory, quarterly reporting facilitates notably faster price discovery by compressing the time elapsed between successive information arrivals and reducing the lifespan of stale expectations in the market. However, this expedited informational benefit is not without its costs. The cadence that accelerates information discovery concurrently amplifies short-horizon volatility, which is pronounced for firms operating in high-beta, information-dense sectors, especially during periods characterized by turbulent market states. This inherent trade-off is clearly visible in the beta-adjusted cumulative impulse responses (CIRFs), where quarterly reporters exhibit sharper initial price movements, yet demonstrate quicker reversion to baseline levels than their semiannual reporting peer, suggesting a dynamic interplay where the immediacy of information dissemination under quarterly reporting leads to more acute, albeit shorter-lived, market reactions.

The dynamic beta patterns observed further reinforce this interpretation. Consistent with prior research indicating that systematic risk loads tend to increase around adverse signals, the analysis reveals disproportionate beta spikes, particularly in response to negative earnings surprises. This effect is most notably amplified among cyclically sensitive industries such as industrials, financials, and certain segments of the technology sector, reflecting their inherent sensitivity to economic fluctuations and market sentiment. Such pronounced spikes indicate that investors, and increasingly, the sophisticated algorithms deployed on their behalf, re-evaluate and update their perceptions of non-diversifiable risk with greater intensity when negative news emerges within high-volatility regimes. By increasing the frequency of assessable news events, quarterly disclosure undeniably heightens the salience and urgency of these risk updates, thereby contributing to the steeper initial reaction profile observed in these firms.

The temporal structure of the CIRFs offers additional, nuanced insights into these market dynamics. Despite their sharper initial impacts, quarterly reporters typically display fast but often “noisier” response paths. This pattern is consistent with a market microstructure where algorithmic trading strategies and closely benchmarked institutional investors rapidly react to standardized signals, potentially leading to temporary overshooting before fundamental valuations re-anchor prices. In stark contrast, semiannual reporters tend to exhibit smoother, yet more protracted, price adjustments. This extended adjustment period aligns with reduced noise trading and less frequent information updates, although it comes at the expense of slower information incorporation into stock prices. Importantly, this is not merely a market-level aggregate phenomenon; sectoral heterogeneity is a critical mediating factor. In high-velocity information environments like technology and financials, larger initial impacts and shorter “half-lives” of shocks are observed in the United States compared to Europe, underscoring the role of disclosure frequency and differing market microstructures. Conversely, defensive sectors such as consumer staples and healthcare feature more muted reactions and a narrower dispersion in recovery times across both reporting regimes, suggesting a lower sensitivity to reporting frequency due to their inherently more stable business models and less volatile information flow.

Furthermore, the study highlights the role of earnings asymmetry in shaping market reactions. Specifically, bad news is consistently penalized more harshly than good news, and this inherent asymmetry is significantly intensified under higher disclosure frequency and elevated macro volatility. From a behavioral finance perspective, this pattern is straightforward: increased reporting frequency means investors confront downside states more often, thereby magnifying loss aversion and attention effects precisely during periods when overall market uncertainty is already high. This insight carries significant implications for corporate issues, suggesting that disclosure policy is not a static element but a powerful tool capable of modulating the intensity and duration of market stress, extending beyond its eventual long-run resolution. For market oversight bodies, this implies that a “one-size-fits-all” approach to disclosure mandates may inadvertently exacerbate transitory volatility in specific sectors and under particular market regimes where information shocks exert the most potent influence, necessitating a more nuanced regulatory perspective.

Building on these insights, two critical practical implications emerge. First, the strategic timing and careful packaging of disclosures are paramount for corporate reporting and risk management. In high-volatility regimes, proactive strategies such as staggering the release of ancillary information or significantly enhancing the qualitative context and narrative surrounding financial figures can help to temper short-run overshooting and reduce undue market anxiety, without compromising the core principles of transparency. This may involve providing more detailed management commentary, offering more precise explanations of strategic initiatives, or providing a more granular breakdown of performance drivers. Second, adopting regime-aware beta modeling offers tangible and substantial value for optimizing portfolio construction and conducting robust stress testing. Near-term exposure forecasts, conditioned on prevailing volatility states identified through various models, prove significantly more accurate. The enhanced predictive capability directly translates into improved event-window risk control, allowing for more precise adjustments to portfolio allocations in the immediate aftermath of earnings announcements and, crucially, enabling a more adaptive approach to capital deployment in the face of evolving market conditions. The economic value of such forecasting gains, especially in high-beta sectors, underscores the importance of integrating dynamic risk assessment into investment strategies.

While our analysis does not directly account for the administrative or compliance costs associated with semiannual versus quarterly reporting, recent commentary in the financial press underscores that any such transition is far from cost neutral. As The Wall Street Journal (2025) observed following President Trump’s remarks that companies “should not be shackled by a 90-day earnings treadmill,” shifting to semiannual reports may ease managerial pressure “but could increase reliance on interim 8-K disclosures and unaudited updates that vary in quality and timing. Likewise, Bloomberg News (2025) reported that “fewer scheduled reports mean investors will lean more heavily on ad hoc filings and guidance,” suggesting that informational asymmetry could rise unless supplementary disclosures are standardized. In this respect, the trade-off between transparency and burden might be calibrated through objective criteria, for example, permitting semiannual reporting only for firms above a given S&P 500 inclusion threshold or those meeting established governance and liquidity metrics. Such an approach could strike a balance between the efficiency gains from reduced reporting frequency and the need to maintain a consistent, high-quality information flow to capital markets.

7. Conclusions

The cadence of financial disclosure helps determine how capital markets process information and transmit risk, rather than simply being a neutral operational choice. Our comprehensive analysis, comparing quarterly reporting DJIA firms with primarily semiannual-reporting STOXX firms, reveals a profound influence of disclosure frequency on the intensity, duration, and volatility of earnings-related price reactions. Quarterly reporting, for instance, compresses price discovery into shorter windows, significantly accelerating information assimilation and reducing the persistence of post-announcement mispricing. This expedited information flow facilitates faster capital reallocation, enhancing market allocative efficiency. However, this substantial informational benefit is accompanied by a significant drawback: sharper short-term volatility spikes, particularly during periods characterized by high market volatility, in response to negative earnings surprises, and within cyclically sensitive sectors where investor expectations are inherently more susceptible to shifts.

Conversely, semiannual reporting presents a different set of trade-offs. While it effectively mitigates contemporaneous noise and lowers short-term volatility, thereby dampening apparent overreactions, these benefits come at the cost of slower and more gradual information absorption. When markets remain underinformed, this extended period can widen risk premium and delay crucial capital reallocation in response to fundamental information. Consequently, a basic and unavoidable trade-off emerges: the pursuit of faster information processing and transparency versus the imperative of mitigating short-term market instability and overreactions. This inherent tension demands a sophisticated and adaptive approach from regulatory bodies and corporate issuers.

For policymakers, these findings underscore that disclosure frequency should be treated as a potent macro tool, capable of shaping the temporal profile of systemic risk transmission. Advocating for a uniform or monolithic disclosure cadence across all market environments may not achieve an optimal outcome. Instead, a more adaptive regulatory framework, one that allows for tailored disclosure guidance based on specific factors such as sectoral sensitivity to economic cycles, the prevailing macroeconomic conditions, and the inherent complexity of information within an industry, could strike a more effective balance between market transparency and stability (Christensen et al., 2021; Kajüter et al., 2019).

From a corporate perspective, the implications are equally profound. Reporting frequency transcends mere compliance; it becomes a strategic variable that directly influences financing costs, investor confidence, and equity risk premiums. Firms, especially those operating in high-beta or information-intensive sectors, have a clear opportunity to optimize the frequency and context of their financial disclosures strategically. This optimization could involve supplementing mandatory reports with voluntary disclosures, offering detailed narrative guidance, or carefully adjusting the timing of disclosures to smooth investor responses and manage the risk of overshooting, all without compromising the essential quality or integrity of the information disseminated to the market (Beyer et al., 2010; Kraft et al., 2018).

Future research avenues are rich and diverse, offering significant opportunities to deepen our understanding of these dynamics. Expanding the geographical scope to include emerging markets or other developed economies with distinct regulatory regimes could provide crucial insights into the universality or context-specificity of our findings. Methodologically, exploring alternative and more sophisticated volatility and dependence structures, or incorporating microstructure-level data such as order-book dynamics, the role of high-frequency trading, and the impact of algorithmic execution strategies, could illuminate the precise channels through which variations in disclosure cadence translate into observable market phenomena. Ultimately, understanding why reporting frequency matters is crucial for designing effective regulatory and corporate strategies that promote efficient, resilient, and well-functioning capital markets. The study’s limitations include its specific focus on quarterly reporting DJIA firms and semiannual-reporting STOXX firms from 2007 to 2024, which may limit the generalizability of its findings to other geographical regions, different time periods or regulatory environments. Additionally, the analysis does not directly account for the administrative and compliance costs associated with varying reporting frequencies.