This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We...

We investigate factors influencing country-level renewable energy growth by applying FEVD and PCSE estimation methods in a unique sample analysis. With a longer time series (1990–2010) and a broader sample size of countries (including Brazil, Russia, India, China and South Africa) than...

We model 73.62 million London Stock Exchange (LSE) trades and show that the LSE's high rate of failure to open at the opening auction only relates to low volume stocks. Low volume stock traders avoid trading until the open; this seems connected to their evading the informed trading-dominated...

<title>Abstract</title> <italic>We investigate the impact of after-hours trading on magnitude and timing of price discovery over the close-to-close period on the world's largest carbon trading platform, the European Climate Exchange (ECX). Low-volume trading in carbon financial instruments can lead to relatively high...</italic>