We use deep neural networks to estimate an asset pricing model for individual stock returns
that takes advantage of the vast amount of conditioning information, keeps a fully flexible …

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We directly optimize the objectives of portfolio management via deep reinforcement learning-
--an alternative to conventional supervised-learning paradigms that routinely entail first-step …

Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior
results in many sequence modeling tasks. The ability of Transformer to capture long-range …

Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We
propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique …

This paper examines which safe haven assets should be used when improving out-of-
sample portfolio performance. We define a market state with recurrent neural network (RNN) …

We directly optimize the objectives of portfolio management via reinforcement learning—an
alternative to conventional supervised-learning-based paradigms that entail first-step …

The COVID-19 epidemic has had a profound effect on almost every aspect of daily life,
including the financial sector, education, transportation, health care, and so on. Among …

Weather risk affects the economy, agricultural production in particular. Index insurance is a
promising tool to hedge against weather risk, but current piecewise-linear index insurance …

We measure the nature and severity of a variety of belief distortions in market reactions to
hundreds of economic news events using a new methodology that synthesizes estimation of …