Search Results (6)

Cryptocurrency markets trade continuously, but their return dynamics need not be uniform over the 24 h cycle. Using hourly Kraken prices from 2016 to 2025, we divide each day into complementary 12 h sessions and evaluate 25 ordered combinations of cash, long, short, momentum, and reversal positions across all 12 non-redundant hourly boundaries. The full-sample selection identifies Reversal/Reversal at an 08:00 UTC daytime start for Bitcoin (BTC) and Long/Reversal at a 05:00 UTC start for Ethereum (ETH). The return mechanisms differ: BTC is associated with conditional reversal in both sessions, whereas ETH combines positive overnight drift with daytime reversal. The selected rules generate higher realized terminal wealth and more favorable drawdown and Sharpe-ratio outcomes than buy-and-hold along the observed full-sample path. These realized differences, however, are not statistically significant in paired bootstrap tests, and Hansen’s Superior Predictive Ability test does not reject the null after accounting for the search over 300 cutoff–strategy combinations. A chronological holdout exercise, in which selection uses only 2016–2020 and evaluation uses 2021–2025, further shows that the ETH rule persists, but the BTC training-selected rule underperforms buy-and-hold. The findings should therefore be interpreted as evidence of asset-specific historical return structure, not as proof of a stable or readily implementable abnormal-profit opportunity. The 0–2 basis-point cost scenarios are illustrative and exclude slippage, market impact, borrowing, and funding costs. Full article

This research explores the impact of ownership structure on the financial performance of Initial Public Offerings (IPOs) in North African markets, a key emerging region that remains insufficiently examined in the academic literature. Drawing on agency theory, signalling theory, and liquidity theory, the study investigates how different shareholder configurations—particularly managerial shareholding, ownership concentration, institutional investor presence, and float—influence both initial underpricing and long-run market performance. Based on a sample of 228 IPO transactions conducted between 2005 and 2023 across six countries (Morocco, Egypt, Tunisia, Algeria, Libya, and Mauritania), the research adopts a quantitative methodology grounded in a hypothetico-deductive approach. The findings support the signalling theory premise that managerial retention constitutes a credible quality signal, showing a strong positive relationship between post-IPO managerial shareholding (MOWN) and long-run performance measured by the 36-month Buy-and-Hold Abnormal Return (BHAR). Ownership concentration (CONC) reduces underpricing while improving long-term performance, reflecting stronger governance discipline. Institutional investor presence (INST) exerts a significant direct effect on both performance dimensions. Conversely, firm size shows no direct significant effect, a result consistent with the institutional specificities of North African markets. These findings underscore the complex mechanisms behind IPO success in this context and offer practical and theoretical implications regarding governance practices and institutional frameworks. The study also outlines avenues for future research, including a deeper examination of regional governance dynamics. Full article

Using a large sample of bank seasoned equity offerings (SEO) from 2002 to 2017, we first documented detailed descriptive statistics, and showed that nonperforming assets ratio, our primary measure of bank asset quality, reached the highest value immediately after the 2008 economic crisis, which also corresponds to a higher number of SEOs around these years because banks needed to recapitalize. The capital ratio, which is required to be at least at a minimum level (relative to risk-weighted assets) for banks by regulation, also increased after the economic crisis, which may be due to a higher requirement for banks as well as banks’ desire to hold more capital. The SEO announcement period abnormal stock-returns reached the lowest number around the economic crisis, as did the longer-run 6-month, post-SEO cumulative abnormal returns and buy-and-hold abnormal returns. Examining the differences between banks, we found, in both univariate and multivariate regression results controlling for other variables, that the bank capital ratio as at the time of the SEO announcement is significantly and positively associated with announcement period abnormal returns, while nonperforming assets ratio of the bank as at the time of SEO announcements is not. However, the nonperforming assets ratio as at the time of the SEO announcements had a significantly negative association with post-SEO, 6-month longer-run abnormal stock returns, while the capital ratio did not have any significant association. The nonperforming assets ratio as at the time of SEO announcement was also significantly and negatively related to bank return on assets 6 months and 12 months after SEO, while the capital ratio was not. Thus, investors appear to perceive a well-ingrained, well-publicized regulatory norm—the capital ratio—as indicative of value creation as at the time of bank SEO announcements, while loan asset quality, which may be relatively more opaque, may determine post-SEO performance. Full article

This study was motivated by the poor performance of the current models used in stock return forecasting and aimed to improve the accuracy of the existing models in forecasting future stock returns. The current literature largely assumes that the residual term used in the existing model is white noise and, as such, has no valuable information. We exploit the valuable information contained in the residuals of the models in the context of cumulative return and construct a new cumulative return gap (CRG) model to overcome the weaknesses of the traditional cumulative abnormal returns (CAR) and buy-and-hold abnormal returns (BHAR) models. To deal with the residual items of the prediction model and improving the prediction accuracy, we also lead the finite difference (FD) method into the autoregressive (AR) model and autoregressive distributed lag (ARDL) model. The empirical results of the study show that the cumulative return (CR) model is better than the simple return model for stock return prediction. We found that the CRG model can improve prediction accuracy, the term of the residuals from the autoregressive analysis is very important in stock return prediction, and the FD model can improve prediction accuracy. Full article

The informed options trading hypothesis posits that option prices lead stock prices. In this paper, we extended the research on this hypothesis to open-market share repurchases. Empirical tests showed that the implied volatility spread was not significantly related to buy-and-hold abnormal stock returns. However, further evidence reveal a significant relationship between implied volatility spread and subsequent stock return volatility around open-market share repurchase events. We concluded that option traders have private information on the volatility of stock returns and superior information processing ability that accounts for prescient pricing behavior in options relative to stocks. Full article

Through the three industrial revolutions, technology has enabled rapid changes in society. In a capitalist society, capital is invested where there is utility, for example, economic benefit. We intend to determine that the stock price of a company that uses a particular technology will change with the life cycle of the technology in question. Specifically, we filtered companies that mainly deal with augmented reality and are listed in Korea’s KOSDAQ market. We grouped these companies based on detailed technologies that constitute augmented reality. We used the event study method to calculate the stock returns against a benchmark. As a result, in the “Peak of Inflated Expectations” stage, the portfolios of all companies using augmented reality generally show higher returns than the benchmark. However, it is difficult to ascertain whether a return generated based on one of the detailed technologies that make up augmented reality is higher or lower than that of the benchmark. During the “Trough of Disillusionment” phase, there was neither a consistent trend of cumulative abnormal returns (CAR) nor buy-and-hold abnormal returns (BHAR). However, during this stage, there was a positive correlation of average BHAR and average abnormal returns between the entire sample’s portfolio and each detailed technology firm’s portfolio. Full article