Purpose: This study aims to investigate the occurrence of pre-merger earnings management for a sample of 197 stock- and cash-financed UK acquirers between 1990 and 2009. It also examines the earnings management behaviour around the change in the Corporate Governance Code in 2003 based on the Higgs recommendations. Design/methodology/approach: Mean and median accrual- and real-based manipulation are examined in the period before the announcement of a merger and acquisition. These are compared across stock and cash acquirers as well as before and after the implementation of the Higgs recommendations. Logistic regressions are also run to examine accrual- and real-based manipulation across stock and cash acquirers after controlling for variables that may affect the acquisition type. Findings: The study found some evidence of upward pre-merger accrual-based earnings management by stock-financed acquirers, which is in line with the findings of Botsari and Meeks (2008). Furthermore, no significant changes were found in the post-Higgs period, which indicates that the recommendations put forth by Higgs may not have been successful in mitigating earnings management. The evidence also shows that cash bidders engage in pre-merger real earnings manipulation through lower discretionary expenses, possibly to enhance cash availability for the bid. Practical implications: The findings in this study confirm earnings management exists around mergers and acquisitions and provide some evidence that the recommendations set out in the Higgs Report do not appear to have mitigated earnings management activities. This is of interest to regulators as well as investors and academicians. Originality/value: This provides the first analysis in the UK examining the use of real-based earnings management activities by UK acquirers. It also extends prior research around corporate governance changes that occurred in the UK.

This study examines the association between derivatives trading and the spot return volatility of cryptocurrencies, focusing specifically on Bitcoin (BTC) and Ethereum (ETH). It analyzes market connectedness and volatility spillover dynamics, assessing how spot and futures markets co-evolve from the introduction of BTC futures in 2017 through 2022, while reviewing the period overlapping with COVID-19 for BTC. Daily data are used to capture medium-term volatility transmission while avoiding microstructure noise common in high-frequency series. The empirical framework integrates ARIMA models for return dynamics, GARCH models for conditional volatility estimation, and a Time-Varying Parameter Vector Autoregression (TVP-VAR) to capture evolving spillover patterns between spot and futures markets. A Dynamic Conditional Correlation (DCC) extension is applied to trace shifts in the strength of co-movement over time. Together, these models allow for the examination of volatility clustering and dynamic interconnectedness that static models may overlook. The results indicate pronounced, time-varying spillover associations between spot and futures markets. Bitcoin exhibits higher overall connectedness (43%) and stronger conditional correlations (DCC = 0.93) than Ethereum (29% connectedness; DCC = 0.65), reflecting differences in market maturity, liquidity, and sample period. Both BTC and ETH show bidirectional volatility transmission between spot and futures markets, suggesting that derivatives trading is closely intertwined with spot market behavior for these two major cryptocurrencies. By jointly examining BTC and ETH within a unified modeling framework, this study provides updated evidence on volatility transmission in cryptocurrency markets and highlights how spillover dynamics vary across assets with different structural characteristics. The findings offer practical insight for portfolio management, hedging design, and the monitoring of interconnected risk, particularly in settings where spot and futures markets influence one another over time. For investors, the strong spot-futures co-movement implies limited within-asset diversification but supports the use of futures for hedging spot exposure; for risk managers and regulators, the time-varying connectedness signals when cross-market risk monitoring is most warranted.

This study examines the impact of U.S. and EU Fintech mergers and acquisitions (M&As) on acquirers’ market performance between 2012 and 2022, with a focus on acquisition type and structure. Using event study methodology, we measure short-term cumulative abnormal returns (CARs) across six event windows, comparing performance before and during the COVID-19 pandemic. Results show that Fintech M&A announcements positively influence acquirers’ market performance, with stronger effects during the pandemic. Domestic and cash-financed deals outperform cross-border and stock-financed ones in both periods. The results offer compelling insights for investors and analysts regarding the effectiveness of Fintech M&As in enhancing the acquirers’ market performance. They can prioritize firms engaged in domestic and cash-financed Fintech M&As for potential short-term gains. Additionally, the findings present signals for shareholders, aiding in more efficient capital allocation strategies in the stock market. In addition to its robust method of collecting data and selecting Fintech Acquirers, the originality of this research lies in addressing the gap regarding the recent impact of Fintech M&As on acquirers’ market performance in both US and EU settings. By extending the analysis beyond 2019, this study aims to explore the impact of Fintech M&As on acquirers’ stock returns (cross-border vs domestic and cash vs stock) during the COVID-19 pandemic.