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This paper aims to verify how the intellectual property (IP) box affects firms’ effective tax rate, growth and innovation activity outcomes related to intellectual property rights.

Implementing the innovation box regimes into the tax system intends to encourage firms to engage in more innovative activities. In UK, Italy and Poland, the IP box tax relief was introduced in 2013, 2015 and 2019, respectively. In return, companies may reduce their tax rate to increase their investment and innovativeness. With a panel model approach – system GMM and DiD with multiple time periods – it analyses data from the Orbis database for 2011–2019 of 673 firms from the gaming industry in 11 countries and hand-collected data on intellectual property rights protection. The authors study public and private companies from the gaming sector in leading European markets and all three countries that protect intellectual property rights of software (Japan, South Korea, the USA).

Recent reforms enable gaming companies to use preferential tax treatment for IP-related income and significantly impact a firm’s revenue growth.

Nevertheless, European gaming firms require time to leap the gap to the growth and innovativeness of countries that protect software.

The authors show that the IP box stimulates gaming firms to protect IP via wordmarks, figurative marks, trademarks and software patents that bring effects in five years. Despite the critics against IP box, the authors prove its lagged efficiency, especially in profitable and larger firms.

The accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.

This paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.

The results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.

This paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.