Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by...

Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by...

This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...

Companies are increasingly adopting Artificial Intelligence (AI) today. Recently however debates started over the risk of human cognitive biases being replicated (and scaled) by AI. Research on biases in AI predicting consumer choice is incipient and focuses on observable biases. We provide a...

We examine several measures of uncertainty to make five points. First, equity market traders and executives at nonfinancial firms have shared similar assessments about one-year-ahead uncertainty since the pandemic struck. Both the one-year VIX and our survey-based measure of firm-level...

The 2015 workshop on “Recent evolutions of oil and commodity prices”, organized by FEEM, focused on the sharp decline in the oil price in 2014. High crude oil production and slower demand growth explain a large fraction of the current low level of prices, but a complex set of factors is...

Based on data until the mid 2000s, oil price changes were shown to predict international equity index returns with a negative predictive slope. Extending the sample to 2015, we document that this relationship has been reversed over the last ten years and therefore has not been stable over time....

We provide evidence that equity investors with limited attention are slow to incorporate how current oil price changes affect future earnings announcements. A cross-sectional equity trading strategy that exploits this inefficiency yields an annualized Sharpe Ratio of 0.57. Stock prices respond...

Information on economic policy uncertainty (EPU) does matter in predicting oil returns especially when accounting for omitted nonlinearities in the relationship between these two variables via a time-varying coefficient approach. In this work, we compare the forecastability of standard, Bayesian...

This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been...