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The purpose of this paper is to propose a method for the transfer of knowledge and skills in project management (PM) based on techniques in knowledge management (KM).

The literature contains studies on methods to extract experiential knowledge in PM, but few studies exist that focus on methods to convert extracted knowledge into practical knowledge and transfer it to learners. This research proposes a model of PM skills transfer management, which consists of a PM knowledge extraction phase, PM knowledge recognition phase, practical knowledge transfer phase, and practical knowledge evaluation phase, and examines the model's effectiveness.

Through multi‐agent simulation (MAS), expert communities for knowledge extraction can be vitalized. A PM skills transfer management maturity model (PMST3M) was proposed that is capable of evaluating PM skills transfer management.

The present work could have considered KSM in‐depth with a view to adding value to the virtualization of community of PM experts.

The paper presents a detailed critique of a knowledge‐management‐based process of transferring PM skills.

The purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.

Six liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.

The result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.

In the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.