An Event Study on Stock Market Reactions to Media Disclosures of FinTech Investments: The Case of Korea

In financial markets, firms and investors typically possess asymmetric information regarding technological capabilities, operational efficiency, and future profitability. Classic information economics (Akerlof, 1970) shows that such asymmetry can distort valuation. Signaling theory (Spence, 1973; Connelly et al., 2011) suggests that firms disclose information to reduce informational asymmetry by conveying credible signals about intrinsic value or future prospects.

In corporate finance, signals often arise from dividend announcements (Bhattacharya, 1979; John & Williams, 1985), stock repurchases (Grullon et al., 2002; Yoon, 2015), mergers and acquisitions (He et al., 2020), or voluntary disclosures of major investments (De Jong et al., 1992). Market reactions to such disclosures reflect investors’ updated beliefs incorporating the new information. However, signals may also generate negative or mixed reactions depending on perceived risk, capital expenditure burdens, or concerns regarding management credibility.

FinTech investment disclosures are quintessential signals: they convey technological capabilities and innovation posture; they indicate future strategic direction (digitalization, automation, AI integration); and they potentially reveal cost burdens, organizational disruption, and regulatory risk. Thus, fintech announcements may produce positive reactions (innovation, efficiency gains) or negative reactions (uncertainty, cost overruns, cybersecurity concerns). The empirical sign is therefore ambiguous and warrants systematic testing.

The event-study method evaluates whether an event (such as a disclosure) induces abnormal stock returns, reflecting unanticipated information incorporated into prices. Seminal works (Fama, Fisher, Jensen & Roll, 1969; Brown & Warner, 1985) provide foundational methodology for isolating abnormal return (AR) relative to an expected market model return. Key assumptions include (Fama, 1970): (i) markets incorporate new information rapidly; (ii) events are sufficiently discrete and identifiable; (iii) estimation windows capture normal performance without contamination; and (iv) abnormal returns represent market reactions net of systematic factors.

Event studies have been widely used to study earnings announcements, policy changes, corporate restructuring, sustainability/ESG (environmental, social, and governance) disclosures, and technology adoption announcements (Cao et al., 2020). Recent research applies event studies to fintech contexts, yet studies often treat fintech homogeneously, ignoring technological heterogeneity. This is a major limitation that our study addresses by incorporating technology-specific and industry-specific dimensions.

FinTech is not a single technology. Rather, it is an ecosystem of diverse innovations. Each technology incorporates unique operational characteristics, risk profiles, and adoption patterns. Understanding these differences is essential for analyzing market reactions.

Artificial Intelligence (AI): AI is central to automation, risk scoring, fraud detection, algorithmic trading, underwriting, and personalized financial products. AI signals high innovation potential, scalability, and cost efficiency (Bose et al., 2021; Kou et al., 2021). Empirically, investors tend to interpret AI announcements positively due to high-growth narratives.

Blockchain (BC): Blockchain, a shared digital ledger, offers transparency, immutability, and disintermediation, potentially lowering settlement and reconciliation costs (Catalini & Gans, 2016). However, regulatory uncertainty, governance fragmentation, and scalability issues create downside risks. Markets may react cautiously to BC disclosures depending on context.

Big Data (BD): BD enables sophisticated analytics, customer segmentation, credit scoring, operational risk monitoring, and investment strategies (Chen, Chiang & Storey, 2012). BD is widely adopted across industries but carries privacy, compliance, and data governance concerns. Market reactions may be nonlinear—initial enthusiasm followed by correction.

Internet of Things (IoT): IoT collects real-time data via hardware devices and sensors, enabling telematics insurance, ATM optimization, and IoT-based risk monitoring (Atzori et al., 2010). However, IoT adoption incurs hardware installation and maintenance costs, integration challenges, cybersecurity and privacy liabilities, and exhibits lower technological maturity in financial contexts. Empirical evidence suggests that IoT announcements may lead to muted or negative market reactions due to heightened operational risk perception.

The value chain is a framework for analyzing the activities through which a firm creates value across the entire process of producing and delivering products or services. The efficiency and effectiveness of each activity influence firm performance and, ultimately, are closely linked to the firm’s stock price. In other words, financial outcomes—and thus market valuation—depend on how efficiently each component of the value chain operates and whether it confers competitive advantage. By improving core value-chain activities, a firm can enhance profitability and receive a more favorable market assessment (Porter, 1985). Porter (1985) further argues that firms can secure competitive advantage at each stage of the value chain through cost leadership or differentiation, which in turn can translate into higher firm value and stock prices.

For financial institutions—such as insurers, securities firms, and banks—the structure of the value chain and its stage-specific processes present multiple points at which fintech technologies can contribute to managerial efficiency and performance. The potential contributions by technology and by stage are outlined in Appendix A.

Across the financial industry, the adoption of fintech technologies functions as a core means of enhancing managerial efficiency at multiple stages of the value chain. Although banks, insurers, and securities firms share broadly similar value-chain structures, the ways and purposes for applying fintech differ by sectoral characteristics and strategy. In the product and service development stage, for example, banks leverage artificial intelligence (AI) to analyze customers’ financial histories and behavioral data in order to design customized products (Bose et al., 2021). Insurers employ the Internet of Things (IoT) to build products that incorporate real-time sensor data—such as usage-based insurance reflecting driving behavior (Atzori et al., 2010). Securities firms, by contrast, emphasize big-data analytics (BD) to identify market trends and investor demand and to assemble sophisticated product portfolios, including derivatives and ESG-linked instruments (Sironi, 2016). Table 1 below describes fintech applications at each stage of the value chain for financial firms by sector – insurance, securities, and banking.

A careful examination is needed as to whether the adoption of fintech technologies brings only positive effects to the financial industry. Scholars differ on whether such adoption yields beneficial or adverse consequences. Artificial intelligence (AI) serves as a foundational technology for workflow automation and personalized services across all financial sectors. In banking, AI-based credit-scoring models and personalized product-recommendation systems have delivered strong performance in practice (Kou et al., 2021). Insurers apply AI to fraud detection, new-business underwriting, and automated claims review, improving both efficiency and accuracy (Davenport & Ronanki, 2018). However, AI also raises ethical and institutional challenges including a lack of explainability (Binns, 2018), algorithmic biases that may erode customer trust (Pasquale, 2015), the potential for market manipulation (Biais et al., 2010), and labor-market concerns stemming from reduced employment opportunities. In particular, financial consumer protection and fairness require sustained, in-depth responses. Table 2 summarizes potential positive and negative impacts of each fintech application by financial industry sector.

We define the event date (t = 0) as the earliest public news report of a fintech investment by a publicly listed financial firm. To estimate expected returns, we use an estimation window of t = −130 to t = −6 days around the event date. This excludes the pre-disclosure region where information leakage may occur.

Expected returns are estimated with the market model: (1) Rit=αi+βiRmt+εit {R_{it}} = {\alpha _i} + {\beta _i}{R_{mt}} + {\varepsilon _{it}}

Where R_it is the stock return of firm i at date t, R_mt is the KOSPI market return at date t, α_i is a firm-specific intercept and β_i measures market sensitivity of the returns.

The abnormal return (AR) is the daily (actual) stock return minus the expected return from the market model, AR measures the immediate market reaction to the fintech investment.

(2) ARit=Rit−αi+βiRmt A{R_{it}} = {R_{it}} - \left( {{\alpha _i} + {\beta _i}{R_{mt}}} \right)

Summing AR over an event window captures persistence or reversal of stock price reactions, i.e. cumulative abnormal returns (CAR). We define the event window for CAR to be ±3-days. This window balances two objectives -- avoiding noise from long windows and capturing potential anticipation or delayed market reaction.

(3) CAR−3,+3=∑ ARit {\rm{CAR}}\left[ { - 3, + 3} \right] = \sum {{\rm{A}}{{\rm{R}}_{{\rm{it}}}}}

We conduct two-way ANOVA with event-day AR or event-day CAR as the dependent variable and industry and technology type as the independent factors. The results allow determination of industry and fintech effects on AR to determine if differs across industries and whether there are statistically significant industry × fintech interaction effects. Scheffé post-hoc comparisons are used to identify which groups differ when AR or CAR differs significantly by industry or fintech technology. While ANOVA inherently compares group means without explicit controls, the event-time estimation uses the market model which implicitly controls for overall market return, firm-specific systematic risk (β), and non-event-period performance trends (via the estimation window).

We examine fintech investments over the period January 2016 to December 2020, aligning with the acceleration of digital transformation in Korea’s financial sector. Stock prices are obtained from Korea Exchange (KRX) daily stock prices and KOSPI index returns. To identify fintech investment disclosures, we collected fintech-related news articles for Korean listed financial institutions from Korea Economic Daily, Chosun Biz, Maeil Business Newspaper, Yonhap News, Financial News, and additional verified news portals. In cases where identical news content was distributed across multiple media channels, duplicates were removed and the Yonhap News Agency article was retained as the canonical source for event identification and counting.

A news article qualified as an event if it (i) describes a financial firm’s adoption, investment, or deployment of AI, BC, BD, or IoT; (ii) is publicly disclosed in a reputable media outlet; and (iii) can be linked to a listed firm with available stock-price data. Specific criteria for inclusion included: the disclosure must reference a specific fintech technology (AI, BD, BC, IoT); the firm must be listed on KOSPI/KOSDAQ; daily price and market data must be available for estimation windows; and the event must involve strategic investment, system adoption, product introduction, or technology partnership. News articles announcing pure marketing campaigns without technological content or general digital transformation narratives without specific technology were excluded. Events overlapping with earnings announcements, M&A, or regulatory sanctions within ±3 days were also excluded. Finally, duplicate news within a 15-day window were combined into a single event.

After screening 603 raw articles, 451 unique fintech events remained. Among these events, 277 were reported for securities firms, 101 for banking firms, and 73 for insurance firms. We classify each event into one of four fintech technological domains, using a structured rule-based coding protocol and NLP keyword validation. In total there were 265 AI events, 94 BD events, 46 BC events and 46 IoT events. Table 3 displays the classification of events by financial sector and technology.

Event-study models were estimated to test for abnormal returns associated with fintech adoption disclosures. Models were estimated for the financial industry as a whole and for each financial sector (insurance, securities, banking) separately. Within each sector, models were estimated separately for each technology (AI, BC, BD, IoT).

To ensure the validity of the market-model estimation, we examined model diagnostics including the distribution of estimated beta coefficients and goodness-of-fit measures. The diagnostics indicate stable market sensitivity and satisfactory model fit across firms, suggesting that the reported abnormal returns are not driven by model misspecification. Detailed diagnostic results are reported in supplementary material.

Overall, a total of 20 models (4 industry measures x 5 technology measures) for abnormal returns (AR) and 20 models for cumulative abnormal returns (CAR) were estimated, and estimation results for each model include coefficient estimates for each event period in the event window [−3, +3]. To simplify results reporting, table 4 combines presents coefficients and t-statistics for the event day estimates of (mean) AR and (mean) CAR for each industry and technology. However, estimation results for the entire event window are discussed below, and the full estimation results are available for viewing in the supplementary material.

For the full financial-industry sample, the event-study analysis confirms that fintech disclosures are, on average, value-relevant. On the event day, we observe a significantly positive AR, with the corresponding event day CAR also positive and statistically significant (t = 3.164, p < 0.01). These findings indicate that the market, in aggregate, tends to interpret fintech investment announcements as favorable innovation signals. However, there is a subsequent negative AR on day +1, followed by partial recovery, suggesting that investors quickly reassess the announcement once initial enthusiasm or overreaction is corrected.

A closer look at the technology-specific panels reveals substantial heterogeneity. AI and big data generate largely positive event-day responses and positive short-horizon CAR, while blockchain yields mixed patterns and IoT tends to produce negative or weakly negative abnormal returns, especially in the insurance and securities sectors. This strongly supports the notion that “fintech” is not a monolithic category in the eyes of investors: capital markets evaluate each technology through the lens of perceived implementation feasibility, risk, and value-chain alignment.

For the insurance sector as a whole, fintech disclosure effects are statistically significant on the event day (t = 1.973, p < 0.05) and on day +3 (t = 2.914, p < 0.01). The positive reaction observed on the announcement date appears to persist through +3 days after the disclosure. Mean AR and CAR are positive, suggesting a favorable evaluation of fintech disclosures. Standard deviations indicate substantial cross-event variation—consistent with heterogeneous technologies and industries.

For the securities sector as a whole, fintech disclosure effects exhibit both positive and negative intervals relative to the announcement: t = 2.250 (p < 0.05) at −3 days and t = −2.743 (p < 0.01) at +2 days. The positive pre-event response suggests that the market may have anticipated or pre-priced the information (consistent with potential information leakage or predictability), whereas the sharp decline two days after the disclosure is consistent with disappointment selling following over-optimism or with reduced credibility of the announcement.

For the banking sector as a whole, the disclosure effect is positive on the event day (t = 4.873, p < 0.01), reverses to a negative effect on day +1 (t = −5.012, p < 0.05), and then switches back to a positive effect on day +2 (t = 1.190, p < 0.10). In other words, while the announcement is initially evaluated favorably, a sharp reversal occurs the next day—consistent with post-announcement re-interpretation of information or profit-taking after an initial overreaction—followed by a modest recovery two days later as the market reassesses the news more soberly.

AI-related disclosures generate the most pronounced and immediate market reactions across all financial sectors. In particular, securities firms and banks exhibit statistically significant pre-event price movements, suggesting either anticipatory trading or early information diffusion prior to public disclosure. While announcement-day abnormal returns are generally positive, these gains are often followed by rapid negative corrections, indicating short-term overreaction and subsequent reassessment by investors. This pattern is consistent with the view that AI investments are perceived as strategically important yet complex, with high expectations tempered by uncertainty regarding implementation costs, organizational disruption, and regulatory scrutiny.

Blockchain-related disclosures are characterized by sharp sign reversals around the announcement window. Across industries—and especially in the securities sector—negative abnormal returns observed prior to or on the announcement day are followed by significant positive rebounds shortly thereafter. This mean-reverting pattern suggests that initial market skepticism or uncertainty is quickly corrected as investors reassess the long-term implications of distributed ledger technologies. The results are consistent with blockchain being viewed as a disruptive but increasingly familiar technology, where initial caution gives way to more balanced valuation once information is absorbed.

Big data disclosures exhibit nonlinear and sometimes delayed market responses across all sectors. Significant abnormal returns tend to emerge on or after the announcement day, rather than being concentrated precisely at the event date. In the banking sector, in particular, abnormal returns are observed over an extended portion of the event window, reflecting the pervasive role of data analytics in core banking functions such as credit assessment, customer segmentation, and risk management. These findings suggest that investors process big data investments as gradual efficiency-enhancing initiatives rather than discrete announcement shocks.

IoT-related disclosures differ markedly from other fintech categories in that significant price movements often occur outside the announcement day itself. Except in the banking sector, abnormal returns are more frequently observed in pre- or post-event windows, implying possible information leakage or heightened uncertainty prior to disclosure. Moreover, IoT investments are associated with persistently weak or negative cumulative abnormal returns, reflecting investor concerns about high infrastructure costs, data privacy risks, and regulatory uncertainty. These results indicate that, unlike AI or big data, IoT investments are perceived as higher-risk and less immediately value-enhancing within the financial industry.

Table 5 reports a two-way ANOVA with event-day AR as the dependent variable and industry and fintech type as the independent factors. The results show that industry and fintech each have a significant effect on AR at the 1% level. In other words, AR differs across industries. The industry × fintech interaction term is not significant.

Because AR differs by industry at the 10% level, we conducted Scheffé post-hoc comparisons to identify which groups differ. Results by industry show that insurance vs. securities differs at the 10% level, and securities vs. banking differs at the 1% level. Results by fintech type indicate that the statistically significant difference across fintech types is primarily driven by the contrast between AI-related and blockchain-related disclosures, with AI eliciting stronger immediate market reactions.

Table 6 presents a two-way ANOVA with event-day CAR as the dependent variable and industry and fintech type as the independent factors. The results indicate that industry is significant at the 1% level. The industry × fintech interaction is also significant at the 1% level, implying that industry-level CAR varies by technology type.

Because CAR differs by industry at the 1% level, we again conducted Scheffé post-hoc comparisons to identify group differences. Results by industry show that insurance vs. securities differs at the 10% level, and securities vs. banking differs at the 1% level. Post-hoc comparisons further reveal persistent differences across fintech technologies, particularly between AI and blockchain as well as between big data and IoT disclosures, indicating that technology-related valuation differences extend beyond the announcement day.

Overall, the two-way ANOVA results show that both industry and fintech type significantly affect event-day AR and CAR, and that their interaction effect on CAR is also statistically significant. From a theoretical standpoint, these findings support an extended signaling perspective: fintech disclosures function as multi-dimensional signals whose strength, clarity, and credibility depend jointly on (i) the technology category and (ii) the institutional context of the adopting industry. Industry × technology interactions are statistically significant.

To further examine the joint behavior of abnormal returns (AR) and cumulative abnormal returns (CAR), we conduct a multivariate analysis of variance (MANOVA). The full MANOVA test statistics are reported in Appendix B. Results indicate that differences across industries and fintech technologies are driven primarily by variation in cumulative abnormal returns rather than by short-lived abnormal returns. In particular, the interaction effects observed in the multivariate framework reflect different post-announcement adjustment dynamics for each industry-technology combination, not immediate announcement-day shocks.

These findings suggest that heterogeneity in market responses arises mainly from how investors reassess information over time, rather than from instantaneous reactions to disclosure events. Overall, the MANOVA results provide a complementary but substantively meaningful perspective on the heterogeneous signaling effects documented in the univariate analyses.

This section synthesizes the empirical findings by interpreting how capital markets respond to different fintech technologies across industries. Noting that fintech is not a homogeneous category, the discussion focuses on technology-specific signaling patterns for artificial intelligence (AI), big data, blockchain, and the Internet of Things (IoT) and explains how differences in maturity, implementation risk, and alignment with industry value chains shape the heterogeneous abnormal-return dynamics across technologies. These results are interpreted in light of signaling theory and industry-specific institutional contexts.

Among the fintech technologies examined, AI-related disclosures show the most consistently positive and significant AR and CAR patterns across industries, particularly in securities and banking. Investors likely regard AI as a relatively mature and immediately applicable technology for credit scoring, portfolio management, robo-advisory, underwriting, and fraud detection. Because AI adoption is directly linked to efficiency gains and data-driven decision-making, its announcement carries strong signaling power about managerial competence, cost-efficiency improvements, and future cash-flow growth. These findings are consistent with signaling theory: AI acts as a high-credibility innovation signal.

Big-data disclosures also deliver positive initial reactions, though the magnitude and persistence of CAR are somewhat smaller and more volatile than for AI. Big data is viewed as a foundational capability that enhances customer segmentation, early risk detection, and regulatory reporting; however, privacy concerns, data-governance risks, and compliance costs may dampen investor enthusiasm. The oscillating pattern of positive pre-event and event-day AR followed by negative post-event AR (particularly for banking) suggests that markets may incorporate big-data expectations gradually and then reassess them once more concrete cost and regulatory implications become salient.

Blockchain-related announcements produce negative or insignificant AR in the pre-event and event-day windows for many sub-samples, followed by a notable positive rebound on day +2, especially in securities and insurance. This “V-shaped” pattern is consistent with investors’ mixed views: blockchain is recognized as an important infrastructure technology for clearing, settlement, and smart contracts, but its regulatory environment remains uncertain, and integration with existing legacy systems can be complex and costly. Accordingly, some investors may initially discount blockchain-related announcements, only to partially revise their expectations upward after additional information or investor discussion clarifies the specific use case.

IoT disclosures are characterized by negative AR in the pre-event or event-day windows in multiple sectors, and CAR remains negative or weakly negative across the event window. This is particularly pronounced for IoT-related announcements in insurance and securities. Several factors may explain this pattern. First, IoT projects often require substantial hardware deployment, sensor maintenance, and integration with physical infrastructure, which raises cost and operational-risk concerns. Second, IoT heavily relies on continuous collection of sensitive personal data (e.g., telematics, health sensors), intensifying privacy, cybersecurity, and liability risks. Third, in the Korean financial context, IoT-based business models (such as usage-based insurance) are still relatively nascent compared to AI and big-data analytics. Taken together, these considerations support the view that investors rationally perceive IoT as a higher-risk, more uncertain fintech investment, resulting in negative or muted price responses—rather than contradicting the general efficiency potential of fintech technologies.

The insurance sector shows significant positive event-day and short-horizon CAR for the pooled fintech sample, indicating that, on average, technology investments are seen as valuable. However, sub-sample results by technology exhibit oscillating patterns: AI and big data often produce short-run positive reactions that quickly correct, while blockchain and IoT are associated with higher volatility and mixed signs. This reflects insurance-specific institutional features: strong regulatory constraints, heavy reliance on legacy systems, and complex underwriting and claims processes. Investors may perceive successful technology implementation in insurance as especially challenging, leading to greater uncertainty and post-announcement volatility.

The securities industry demonstrates the strongest and most sensitive reactions to fintech disclosures. AI and big-data announcements, in particular, elicit pronounced positive AR prior to and on the event day, followed by sharp corrections as shown in Table 4-4. This pattern is consistent with the nature of securities firms, whose core value creation depends on trading analytics, market-making, and algorithmic decision-making. When such firms announce AI- or big-data-based initiatives, the market interprets these disclosures as highly relevant to their core business, producing large and rapid price adjustments. The corrections observed on day +2 likely reflect profit-taking and the resolution of initial over-optimism once more detail about the initiative becomes available.

In banking, event-day AR is strongly positive for the pooled fintech sample and for AI and big-data sub-samples, but negative AR on day +1 and correction on day +2 indicate that announcements are initially welcomed and then re-evaluated. Bank investors appear to interpret AI-based credit scoring, fraud detection, and RegTech solutions as strategically important for risk management and regulatory compliance, but they may also recognize implementation costs, model-risk issues, and supervisory scrutiny. Blockchain and IoT disclosures, by contrast, show more muted and statistically insignificant reactions in banking, suggesting that investors consider these technologies less central to banks’ current value drivers or more constrained by regulatory factors.

The evidence from our analysis aligns closely with the value-chain–based identification of each industry’s core fintech technologies. Technologies that are closely aligned with the sector’s value chain (e.g., AI and big data in securities and banking) generate strong, positive signals, while those with higher implementation and regulatory risk (e.g., IoT in insurance) generate weaker or negative signals. Specifically, insurers are most closely associated with IoT and big data; securities firms with AI and big data; and banks with AI as the pivotal technology across value-chain stages. Securities sector shows the strongest positive reactions → highly aligned with AI/BD-driven value chains. Banking shows moderate positive reactions. Insurance shows weak or negative reactions—consistent with heavier regulatory constraints, longer product cycles, and opaque cost structures. These findings support H3.

This section presents robustness checks conducted to verify that the main empirical findings reported in the previous sections are not driven by model specification sample composition, or event clustering. All economic interpretations of the results are provided in the main Results section.

We first explored alternative estimation approaches to check that our results are not driven by a particular choice of return model. First, we implemented a portfolio-time-series approach and cluster-robust standard errors (by date, industry, and technology); results show that key findings—positive AI effects, negative IoT effects, and significant industry × technology interactions—remain statistically significant. Second, we conducted block bootstrap resampling with 1,000 replications, which again confirmed that the main patterns persist under alternative inference methods. Third, we re-estimated AR and CAR using alternative expected-return models (market-adjusted, CAPM, and Fama–French three-factor models). The direction and significance of AI, IoT, and interaction effects were robust to these specifications, indicating that our results are not driven by a particular choice of return model.

We also recognized that the uneven distribution of events across technologies (AI accounts for 58.8% of announcements whereas blockchain and IoT each account for only 10.2%) inevitably reduces statistical power for detecting effects in the smaller technology groups and may partially contribute to statistically insignificant results in certain industry–technology subsamples. To address this limitation, we assessed the sensitivity of all key findings to alternative sampling strategies. Specifically, we constructed balanced subsamples using (i) downsampling of the AI group, (ii) oversampled datasets for blockchain and IoT, and (iii) SMOTE-like synthetic oversampling to approximate equal event counts across technologies. The balanced-sample tests (46 events per technology group) replicate the core patterns observed in the full sample, indicating that AI’s strong positive effects and IoT’s systematically negative effects do not arise simply because the AI group dominates the dataset numerically. Likewise, the SMOTE-like synthetic oversampling and block-bootstrapped t-statistics reveal no systematic inflation or deflation of effect sizes. Across all procedures, the direction and significance of the core results—positive AI effects, negative IoT effects, and strong industry × technology interactions—remain qualitatively unchanged. This indicates that the main conclusions are not artifacts of sample-size imbalance.

Finally, we explored the robustness of results to excluding events or event periods. We excluded events occurring during the COVID-19 period and found no evidence of systemic break patterns during that time. We removed event clusters (dates with many simultaneous disclosures), events occurring within ±3 days of quarterly earnings announcements, and events coinciding with major industry conferences and found the main AR/CAR patterns unchanged.