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Academia and financial practitioners have mixed opinions about whether artificial intelligence (AI) can beat the stock market. The purpose of this paper is to investigate theoretically what would happen if AI has further evolved into a superior ability to predict the future more accurately than average investors.

A theoretical model in an endowment economy with two types of representative investors (traditional investors and AI investors) is proposed, and based on the model, a long-run survival analysis for both types of investors is implemented.

The model presented in this paper indicates that being equipped with a superior ability to predict the future more accurately than traditional investors cannot guarantee AI investors to always beat the stock market in the long run. Those investors may be extinct, all depending on the structure/parameters of the stock market.

To the best of the author’s knowledge, they are the first to set up a representative agent equilibrium model to explore the above question seriously.

This paper aims to explore the allocation of the exit value of a start-up company in market equilibrium between an angel investor and an entrepreneur in the very early-stage financing market.

The theoretical model is established based on the two-sided random search theory and the model’s ability to match the empirical data is evaluated via simulation.

The model indicates that the allocation of the final investment outcome is not proportional to the initial investments by the angel investor and the entrepreneur. The simulation results show that the continued investment by the entrepreneur and the private benefit acquired by the angel investor have a more profoundly negative influence on the angel investor’s share of the exit value of the start-up company. Moreover, the market search structure represented by the matching probability of an angel investor to an entrepreneur has a more significant impact on the angel investor’s share than the other model parameters.

The importance of market search friction in the very early-stage financing market is emphasized. The concepts of continued investments and private benefits are introduced and quantified for the first time under the framework of angel investment. The impacts of such model parameters as the matching probability of an angel investor to an entrepreneur, the success rate of a start-up company, the bargaining power of an angel investor and the discount rate on the allocation of the exit value of the start-up company are investigated as well.

This paper aims to study the concept, characteristics and factors of the resilient supply chain (RSC) and develop a hierarchical structural model and classify the factors based on their interrelationships.

This paper has used a mixed-approach of literature review and expert opinion to identify the factors of RSC. For the development of the structural model and clustering of the factors, this paper has used the total interpretive structural modeling approach with Matrice d’Impacts Croises-Multiplication Applique and Classment and decision-making trial and evaluation laboratory analysis.

In total, this study has identified 17 factors that enable the 3R capability of the RSC. The result shows that the factors have a close dependence relationship with supply chain (SC) risk management culture as the most influencing factor. Further, this study classifies the factors into enablers and strategies.

This research work is the theoretical contribution to the RSC concept and helps the experts to develop and improve the resilient ability in the SC. This research is based entirely on subjective expert feedback; thus, the results are sensitive to the expert’s judgment.

This research will help the decision-makers in allocating the resources and policies to develop or improve the SC capabilities.

This research work is the first kind of research in the field of the RSC that considers the 3R concept to identify and model the resilient factors of the SC. Along with the theoretical concept, this research provides empirical evidence for the importance ranking of the factors.