The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the...

The transfer of information by telecommunication channels is accompanied by message hashing to control the integrity of the data and confirm the authenticity of the data. When using a reliable hash function, it is computationally difficult to create a fake message with a pre-existing hash code,...

Over recent years, companies have been increasingly adopting algorithmic decision systems (ADS) to replace humans. In this paper, we focus on how ADS facilitates human managers’ decision making rather than replacing it. Our pilot field experiment shows that ADS generally improves the...

We contribute to the burgeoning literature on macroeconomic forecasting by exploring the benefits of machine learning methods. The performance of machine learning algorithms, factor models and MIDAS in forecasting China’s real GDP growth rate is evaluated in an extensive pseudo out-of-sample...

Despite the widespread adoption of recommender systems by online investment platforms, empirical research into their impact on online investors’ behaviors is scarce. Using data from a global e-commerce platform, the authors of this study adopt a regression discontinuity design and causally...

This paper uses a machine-learning-based framework to analyze lending decisions. We develop an adversarial learning model that learns the mortgage lending rules for non-minority borrowers and decompose loan decisions into model explained, favorable disagreement, and unfavorable disagreement...

We let "Algorithmic Market-Makers" (AMMs), using Q-learning algorithms, choose prices for a risky asset when their clients are privately informed about the asset payoff. We find that AMMs learn to cope with adverse selection and to update their prices after observing trades, as predicted by...