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This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

This study has conducted exploratory research to understand who should comprise the members of a resilient supply chain for promoting an entrepreneurial ecosystem of a startup project and to determine the mechanisms for the balanced coexistence of all stakeholders. This is necessary to ensure mutual benefits for all stakeholders, each of whom has multidimensional interests. Additionally, this supply chain must be able to withstand any potential disruption risks.

This research has employed a mixed-design approach. In this context, the study conducted an extensive qualitative and quantitative investigation, including 30 interviews and a survey involving 180 potential stakeholders in this supply network, respectively in the capital city of Bangladesh, Dhaka. The analysis of the interviews utilized principles of matrix thinking, while structural equation modeling (SEM) through LISREL was employed to understand cause-and-effect relationships.

Network, platform and governance—these three independent constructs have the potential to contribute to the dependent construct, a resilient supply chain, aimed at promoting an entrepreneurial ecosystem for startup projects. It has been revealed that the management of such projects depends on the rules and regulations within the ecosystem. An excellent governance mechanism is essential for this purpose. To facilitate coexistence, the establishment of a platform is crucial, where cooperation among all members is mandatory.

For practitioners, three distinctive but closely interdependent issues are explored and resolved in this philanthropic study. It has unfolded the elements of any startup project with essential settings.

The identification of the structural dynamics of potential stakeholders within the entrepreneurial ecosystem of startups is largely absent in existing literature. Therefore, there is a need to comprehensively investigate the entire network, including their roles, responsibilities and associations. This study makes a significant and novel contribution to the existing literature. Academics and practitioners alike have ample opportunities to learn from this new aspect of relationships across three distinct areas: the entrepreneurial ecosystem, startup projects and the development of a resilient supply chain.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.