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Editor’s introduction

The 40th issue of Financial Innovation (FIN), Volume 9, No.4 (2023) presents 18 papers contributed by authors and co-authors from Twenty-five countries and areas: Brazil, Bulgaria, China, Croatia, Czech Republic, Denmark, Hungary, India, Iran, Japan, Lebanon, Nigeria, Palestine, Poland, Portugal, Romania, Serbia, Spain, Sweden, Switzerland, Taiwan, Tanzania, Türkiye, UK and USA. These papers can mainly be categorized into three sub themes.


Ho (2023) probes the influence of innovation spillovers in the artificial intelligence (AI) and financial technology (Fin-tech) industries on the value of the internet of things (IoT) companies. It indicates that the more firms accept innovation spillovers from other industries, the better the excess return; however, it differs because of industrial attributes and the environmental changes induced by COVID-19. Toma et al. (2023) concludes that eye tracking is a versatile tool that can facilitate the inference of cognitive states to better understand financial decision-making with the potential to improve trading performance if leveraged in appropriate experimental frameworks. Irimia-Diéguez et al. (2023) aims to identify the factors influencing companies’ intentions toward adopting Fintech services. Its findings support the TRA and TPB models and confirm their robustness in predicting companies’ intention and use of Fintech services. Rjoub et al. (2023) investigates the financial technology (FinTech) factors influencing Chinese banking performance. An adaptive neuro-fuzzy-based K-nearest neighbors’ algorithm was proposed and findings showed that it achieved 91% accuracy, 90% privacy, 96% robustness, and 25% cyber-risk performance.

Behavioral Finance

Biswas & Pamucar (2023) addresses a real-life problem of mobile wallet selection in the Indian context and finds that user friendliness, features, and awareness are some of the factors that influence the final ranking. Jou & Lee (2023) investigates the design of the royalty rate in a first-price auction across three types of investments and implies that auctioneers may charge differential royalty rates for different types of investments. Martinez-Blasco et al. (2023) generates seven MS Excel files and six R files to develop the analyses required for the event study methodologies using both tools. The research outcome will benefit financial students, researchers, and professionals. Erer et al. (2023) examines the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices during the COVID-19 outbreak and the global financial crisis (GFC) within the framework of the adaptive market hypothesis. The findings reveal that convenient portfolio allocation, risk diversification, and hedging strategies may provide higher economic development. Wu et al. (2023) proposes a social-financial approach (SFA) to fill the methodological research gap in strategic policy design for managing financial transitions during social changes and argues that market efficiency evolves in stages with the financial transition. Batrancea et al. (2022) confirms the slippery slope framework’s assumptions regarding the attitudes of the self-employed taxpayers; trust and power fostered intended tax compliance and diminished tax evasion, trust boosted voluntary tax compliance, whereas power increased enforced tax compliance.

Financial risk management and analysis

Emtehani et al. (2023) aims to solve supply chain inefficiencies caused by some members' financial problems. Its results indicate that the worse the financial status of the manufacturer, the more bargaining power suppliers have in determining the contract parameters for more profit. Amirteimoori et al. (2023) introduces a stochastic cost-efficiency model to develop a value-based measure of the scale elasticity of firms facing data uncertainty. An illustrative empirical application to the Indian banking industry was made to compare inferences about their efficiency and scale properties. Gržeta et al. (2023) examines the impact of the introduction of two major regulatory changes (Basel II and Basel III) on bank performance. It suggests that a distinction between small and large banks when introducing new regulatory frameworks should be made if a reasonable level of competition is to be preserved. Xia et al. (2023) evaluates users’ willingness to use China’s digital currency electronic payment (DCEP) system and its influencing factors. It contributes to a better understanding of the adoption context of China’s digital currency electronic payment and provides relevant policy guidance. Vanini et al. (2023) defines three models: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud. The models were tested and their risk framework is viable from a business and risk perspective. Xu & Ma (2023) develops an intelligent option portfolio model that allows selling options contracts to earn option fees and considers the high leverage of options in the market. It is demonstrated that the model obtains a satisfactory out-of-sample return, which is significantly positively correlated with the volatility of the underlying asset and negatively correlated with the forecast error of the forward price. Saliba et al. (2023) provides strong empirical results for modeling the impact of country risk, especially financial, economic, and political risks, on the banking sector’s credit risk of BRICS countries. Shi et al. (2023) investigates whether corporate sustainability practice can reduce the financial distress risk of air carriers. The findings support the claim that implementing environmental actions may increase financial distress risk, and by improving social and governance activities, airlines can mitigate the risk of financial distress.

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Correspondence to Gang Kou.

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Kou, G. Editor’s introduction. Financ Innov 9, 118 (2023).

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