The article examines the impact of cyberattacks on the market capitalization and short-term returns of leading companies in the finance, telecommunications, and information technology (IT) sectors. Using event analysis, abnormal return (AR), and cumulative abnormal return (CAR) estimation, the market sensitivity to cybersecurity incidents is determined for over 30 events over the period 2018–2024. The results indicate a short-term negative effect of cyberattacks, especially in the financial sector, while technology companies demonstrate a faster recovery of market positions. Differences in investor risk perception are identified depending on the industry, the duration of the attack, the history of previous incidents, and the reputational stability of the company. Cases of repeated attacks on one company are analyzed separately, indicating a change in the intensity of the market reaction over time. The findings complement existing empirical evidence on stock market sensitivity to cybersecurity incident disclosures documented in prior event-based studies (Cavusoglu et al., 2004; Romanosky, 2016) and provide insights for improving cyber risk management strategies, particularly with respect to disclosure practices and reputational shock monitoring systems.

Effects of Monetary Policies on Stock Market Performance in Nigeria: Further Investigation

Structuring corporate actions can be challenging due to the differing expectations and objectives of various stakeholders. In this context, scrip dividends represent an attractive instrument because of the flexibility they offer: shareholders can choose between receiving a cash dividend or additional shares at a previously determined subscription price, which is typically set at a discount to the prevailing market price. For firms considering such programs, the acceptance rate is a key decision metric. Whereas numerous and recent studies exist on other forms of corporate actions, scrip dividends have received comparatively little attention and constitute a largely under-researched field (Dennis & Weston, 2025; Drienko & Khorsand, 2023; Rau et al., 2024). This paper provides the first comprehensive empirical analysis of the factors influencing acceptance rates, based on a novel dataset covering all scrip dividend programs conducted in Germany. Using regression analysis, we find that shareholder concentration, the proportion of domestic investors in the shareholder base, and the discount on newly issued shares have a significant effect on acceptance.

This study examines the pricing efficiency of the Mint Gold Certificate (ALTINS1) traded on Borsa Istanbul and its relationship with the underlying asset (gram gold), focusing on the structural break identified in the data. Analyses conducted using Mann–Kendall trend analysis, the Pettitt structural break test, Rolling Window regression, and the Threshold Error Correction Model (Threshold ECM) reveal that certificate prices have systematically decoupled from the underlying asset, creating a persistent premium exceeding 16%. The findings indicate that the risk structure of the certificate has diverged from the underlying asset, the market has become desensitized to high premium levels (asymmetric threshold effect), and prices move independently of fundamental value through a speculative feedback loop (Granger causality). The study argues that the root cause of this anomaly lies in the “Limits to Arbitrage” problem stemming from supply constraints and short-sale bans, offering new evidence on the pricing efficiency of financial innovations in emerging markets.

The primary focus of this study is to examine how shocks and volatility are transmitted across sector indices in the Indian stock market. This study employs a dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, utilizing daily data from five prominent sectoral indices of the National Stock Exchange (NSE) in India, from 1 January 2016 to 31 May 2024. The study shows that sectoral volatility responds very differently across pre-COVID, COVID, post-COVID and geopolitical-crisis periods, with pro-cyclic sectors displaying sharper shock sensitivity during the pandemic and defensive sectors showing stronger stability under geopolitical stress. Consumer durables and financial services consistently provide the strongest hedging benefits during COVID and post-COVID periods, while IT-linked pairs exhibit weak hedging relationships throughout. The geopolitical shock generates volatility but does not compress sector movements as strongly as the pandemic, resulting in shallower and less persistent spillovers. Dynamic hedge ratios and portfolio weights reveal that IT becomes more relevant only in the post-COVID phase, but it does not function as a strong hedge during crisis-driven volatility. Crisis conditions tend to compress diversification opportunities by increasing correlations and connectedness, requiring more careful portfolio rebalancing. These findings offer new insights into how sector-specific behaviours and hedging performance evolve under varying macro-financial conditions, contributing to adaptive risk-management and portfolio-optimization strategies.

This study investigates volatility spillovers from the stock markets of the United States, Germany, China, and Japan to the UK stock market using daily data from major benchmark indices (FTSE 100, S&P 500, DAX, Shanghai Composite, and Nikkei 225) and Brent crude oil prices. Using a novel two-stage bootstrap framework, we first model time-varying conditional volatilities with GARCH-family models and compare them with long-memory FIGARCH specifications to account for persistent volatility dynamics. These volatilities are then incorporated into a VAR-X model, treating Brent crude oil price volatility as an endogenous or exogenous variable in robustness checks. To overcome limitations of traditional VARs, bootstrap-corrected GIRFs are employed to trace dynamic, order-invariant impacts across key sub-periods: the global financial crisis, Brexit, COVID-19, and the Ukraine war. We also benchmark our results against the Diebold–Yilmaz connectedness index and conduct rigorous out-of-sample forecasting and Value-at-Risk backtesting. Results reveal heterogeneous spillovers: US and German shocks trigger strong, immediate, and persistent UK market volatility, reflecting deep integration; Chinese shocks are delayed and gradual, while Japanese shocks are muted or short-lived. Spillover intensity is time-varying, peaking during global crises. Our model outperforms standard benchmarks in out-of-sample volatility forecasting and risk management applications. The study offers critical insights for investors seeking international diversification and for policymakers aiming to manage systemic risk in an interconnected global financial system.

ABSTRACT This study examines the short-run stock market effects of China’s 2024 vehicle trade-in policies, focusing on listed automobile manufacturers and upstream suppliers. Using an event-study design around three milestones, we find that the initial announcement generates weak and mostly insignificant responses for manufacturers but significantly positive reactions for suppliers. The release of implementation guidelines is followed by significantly negative abnormal returns for manufacturers, while supplier responses are largely insignificant. After the subsidy increase, mixed manufacturers and suppliers earned significantly positive abnormal returns, whereas NEV-focused manufacturers did not. Cross-sectional results further show that market liquidity and investor sentiment amplify positive reactions in demand-supporting rounds.

This study examines whether the outcomes of Twenty20 (T20) international cricket matches played by Pakistan influence stock market performance, focusing on the Karachi Stock Exchange (KSE) 100 Index. Grounded in behavioural finance theory, the research explores the notion that investor sentiment shaped by emotionally charged national sporting events may spill over into financial decision-making. Using secondary data on Pakistan’s T20 matches and daily KSE-100 index returns covering the period from 2006 to 2024, the study applies a dummy regression methodology to capture the effects of match wins and losses on subsequent stock market returns. Match outcomes are represented through binary variables, while stock returns are computed using logarithmic daily index changes. Descriptive statistics and regression results reveal that although Pakistan’s T20 victories and defeats generate observable emotional reactions among the public, their direct impact on stock market returns is statistically insignificant. Both winning and losing coefficients are found to be positive but insignificant, leading to acceptance of the null hypothesis that T20 match results do not materially affect KSE-100 returns. The findings suggest that the short duration of the T20 format, market holidays, timing of matches, and rapid dissipation of investor emotions limit the persistence of sentiment effects on trading behaviour. This study contributes to the behavioural finance literature by extending sports–finance analysis to the T20 cricket format in Pakistan, offering insights for investors, policymakers, and researchers on the limits of non-economic sentiment in influencing emerging stock markets.

Irrational cognition in the stock market

The impact of investor sentiment index on stock market crash risk: An analysis of the mediating effect of stock price synchronization

Disciplining the factor zoo: Identifying pricing factors in the Chinese stock market

Quantile auto-encode narrative asset pricing model in the Chinese stock market

Digital Media Information and Household Stock Market Participation: Evidence from China

Psychological influences on forecast bias: The impact of mood, depression, and trading performance on investor expectations.

Forward-looking signals and the predictability of size effect in the Taiwan stock market

Volatility in China's new energy stock market: The contributions of overnight information and discrete jumps

Beyond connectivity: Stock market participation in a network

The present study examines emerging trends in stock market awareness and participation among Indian college students using exclusively secondary data. Recent years have witnessed a notable expansion in youth commitment with equity markets, supported by increased digital access, widespread adoption of mobile trading platforms, and greater visibility of financial information online (NSE, 2023; BSE, 2022). Monitoring reports suggest that while basic awareness of investment avenues is steadily improving, deeper conceptual understanding of risk, diversification, and regulatory frameworks remains limited among young adults (SEBI, 2020; SEBI, 2023). Findings from previous academic studies further indicate that participation by college students is often influenced by peer groups, social media content, and FinTech interfaces rather than structured financial education (Sharma & Jain, 2021; Verma, 2022). This analysis highlights a growing gap between rising enthusiasm for participation and insufficient financial literacy, a trend also noted in national financial literacy assessments (RBI, 2022). The study concludes that targeted investor learning programmes, curriculum-level interventions, and improved oversight of digital financial communication are necessary to promote informed and sustainable investment behaviour among India’s youth.

ABSTRACT In this study, we explore the impact of investor sentiment on stock market dynamics through stock closing price predictions. We propose a Multi-Head Attention Long Short-Term Memory Network (MA-LSTM) designed to predict stock closing prices by integrating both stock features and investor sentiment. By merging the capabilities of Long Short-Term Memory (LSTM) and Multi-Head Attention mechanisms, MA-LSTM adeptly captures the temporal dependencies concealed within stock market data and investor sentiment features. Investor sentiment is estimated using a Bidirectional Encoder Representations from Transformers (BERT) model, based on investor messages collected from social media platforms. To enhance sentiment estimation accuracy, we conduct further pre-training of the BERT model in the stock market domain. We combine investor sentiment with stock price data and feed it into the MA-LSTM model for predicting the closing prices of prominent stocks such as Apple and the SPDR S&P 500 ETF. The experimental results demonstrate the superiority of the proposed method over the traditional LSTM model, regardless of the inclusion of sentiment features. Particularly the MA-LSTM model with sentiment features has good effectiveness. It’s evident that incorporating sentiment features enhances the forecasting performance of stock closing prices.

This article aims to analyze the Compensation Mechanism for Damages (MRP), an investor protection instrument in the Brazilian capital market (stock market), from the perspective of Law and Economics (LE). The objective is to verify the economic incentives generated by the subjective liability rule governing the MRP and to what extent agents act under these effects; that is, the efficient level of liability. To this end, through empirical research, the criteria from jurisprudence and MRP decisions regarding the liability rule (objective or subjective) applied to intermediaries and participants in the stock exchange for illicit practices were extracted. Finally, with the support of LE, the economic incentives and social costs involved in encouraging agents to adopt mitigating measures to avoid harm to investors are identified, considering the assumption that investors may seek compensation from the MRP.