When I was doing the first project, one topic caught my attention: Nowcasting for financial markets.
- Traditionally, quant strategies have focused on forecasting prices, using structured data to make long-range predictions. However, these forecasting models do not adjust quickly to changing market conditions. This disadvantage becomes particularly serious during the epidemics.
- In contrast, nowcasting models use unstructured datasets to make direct measurements and short-range predictions, which are more reliable and make full use of millions of recent observations.
Then I have questions like what machine learning models are suitable for making nowcasting? Or what changes can we make to the forecasting model to realize nowcasting?