In this talk, we cover various aspects of forecasting methods used in finance. A smart consensus of various economists, brokers, analysts and forecasters is a good way to think about forecasts and we illustrate the methodologies needed to score, rank and aggregate forecasts from multiple sources. Another important area for consensus forecasting is error & regime change detection. We also talk about the challenges of calibrating machine learning models to partial ground truth data on errors and how an ensemble methodology is needed to precisely identify different classes of errors accurately to allow for automation of the entire process from error detection & removal to scoring/ranking of forecasters to the final smart consensus forecast.

 

We will demonstrate use cases in company financials estimates, economic indicator and FX/Commodity spot rates forecasting.