We directly optimize the objectives of portfolio management via reinforcement learning---an alternative to conventional supervised-learning-based paradigms that entail first-step estimations of return distributions, pricing kernels, or risk premia. Building upon breakthroughs in AI, we develop...

Insufficient Social Cost of Carbon (SCC) estimation methods and short-term decision-making horizons have hindered the ability of carbon emitters to properly correct for the negative externalities of climate change, as well as the capacity of nations to balance economic and climate policy. To...

We present and contrast a one-agent versus a two-agent theoretical model for residential real estate brokerage consideration in China's housing market. We then perform a series of simulations using values present in the Chinese marketplace to identify which system should prevail in terms of...

This paper investigates the capacity decisions of complementary suppliers who produce different components of a product. The suppliers solicit private forecast information from a retailer who has more precise information as compared to the suppliers regarding the market. In this context, the...

We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a...

"This book addresses key issues relating IoT service designs: how to converge and share heterogenous sensors; how to design real-time society-scale IoT services by flexibility and scalability; how to deeply mine events from physical entities to improve the QoS of IoT services; and how to...