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Collaboration between universities and industry is increasingly perceived as a vehicle to enhance innovation. Educational institutions are encouraged to build partnerships and multidisciplinary projects based around real-world open problems. Projects need to benefit student learning, not only the organisations looking for innovations. The context of this study is a multidisciplinary innovation project, as experienced by the students of an University of Applied Sciences in Finland. The purpose of this paper is to unfold students’ conceptions of the learning experience, to help teachers and curriculum designers to organise optimal conditions and processes, and support competence development. The research question was: How do students in higher professional education experience their learning in a multidisciplinary innovation project?

The study took a phenomenographic approach. The data were collected in the form of weekly diaries, maintained by the cultural management and social services students (n=74) in a mandatory multidisciplinary innovation project in professional higher education in Finland. The diary data were analysed using thematic inductive analysis.

The results of the study revealed that students’ understood the learning experience in relation to solvable conflicts and unusual situations they experienced during the project, while becoming aware of and claiming their collaborative agency and internalising phases of an innovation process. The competences as learning outcomes that students could name as developed related to content knowledge, different personal characteristics, social skills, emerging leadership skills, creativity, future orientation, social skills, technical, crafting and testing skills and innovation implementation-related skills, such as marketing, sales and entrepreneurship planning skills. However, future orientation and implementation planning skills showed more weakly than other variables in the data.

The findings suggest that curriculum design should enable networked, student-led and teacher supported pedagogical innovation processes that involve a whole path from future thinking and idea development through prototyping to implementation planning of the novel solution. Teachers promote deep comprehension of the innovation process, monitor and ease the pain of conflict if it threatens motivation, offer assessment tools and help in recognising gaps in individual competences and development needs, promote more future-oriented, concrete and implementable outcomes, and facilitate in bridging from innovation towards entrepreneurship planning.

The multidisciplinary innovation project described in this study provides a pedagogical way to connect higher education to the practises of society. These results provide encouraging findings for organising multidisciplinary project activities between education and working life. The paper, therefore, has significant value for teachers and entrepreneurship educators in designing curriculum and facilitating projects. The study promotes the dissemination of innovation development programmes in between education and work organisations also in other than technical and commercial fields.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

This study aims to use gray models to predict abnormal stock returns.

Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model.

Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models.

The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.