Well, even somebody who knows the bare minimum about finances understands that currently, the market is struggling. The first response to the pandemic outbreak happening last March was losses and panic. The S&P 500 index fell by over 30% during the course of one month, and Dow Jones dropped by 3,000 in a single day. The government’s bonds also plummeted, and the whole world was like a child in the fog. In the volatile times, Nowcasting and Alternative Data suddenly became needed. But why?
The dramatic turn caused big trouble for central banks, which had to adjust their policies to the unpredictable and lightning-fast-changing conditions. There was no data to back them up, as the tools used by the committees to analyze their further movement are all backwards-looking. These are consumer sentiment indexes or GDP (gross domestic product) figures, for instance.
So how were the central banks possibly able to make any predictions and adjust their actions? Where did the necessary data come from in the first place if the market was so volatile?
More and more banks have actually decided to go with ‘nowcasting’ – which uses the news data in order to rectify the forecasts and reach an immediate insight (or at least more immediate) insight into the things that are currently changing and shaping the market.
In order to really make sure the topic of nowcasting will be covered with proper accuracy, we decided to follow a great Disruption Banking piece, where Harry Clynch conducted a fascinating interview with Dow Jones’ Market Specialist Director, Simon Rodda. If you want to read the quoted Rodda’s words in the full version of the interview, feel free to use the following link, which will take you to the original material: https://disruptionbanking.com/2021/03/18/alternative-data-and-nowcasting-are-trending-amongst-central-banks-dow-jones-explains-why/.
Rodda began the discussion with the topic of Norges’ (the Norwegian Central Bank) initial foray into nowcasting. The bank had a project which was in cooperation with the Center of Applied Macroeconomics and Commodity Prices at the Norwegian Business School. The initial project was based on the information of the local newspapers. The academics would be looking at the possible way to read the consumer sentiment from the information in the articles. The language used there played a huge role in the whole process as well. After the research, it was all matched with the official data, and it actually appeared that their findings could be found not in the latest data, but the one issued in the next quarter. That means their findings were front-running, and it gave some very encouraging signals.
The academics working on the project approached Dow Jones and started a cooperation with Simon’s team. Their goal was to create a model similar to the US economy. It has been actually going on for up to five years before the pandemic started but after Covid-19 hit, it became apparent that their work suddenly became of a really high significance.
As Rodda stresses, the pandemic outlook brought the biggest changes for the economy in at least the last few centuries. Both the volatility and the crisis were off any scale. And all of a sudden, the usual central bank’s decision-making processes became practically useless. There was no time to be wasted, as the three-month cycle was simply ineffective. This was the ‘perfect’ time for the nowcasting to be fully implemented and proven in the combat conditions.
The nowcasting’s ability to perform in real-time and deliver predictions based not on the past, but the presence, really facilitated the process of decision-making. The fast pace of the changes forced the banks to do anything possible to adjust to the situation within hours.
Simon Rodda highlights one more aspect of the nowcasting – the sentiment present in the news. He stresses how important the choice of topic is, and the language used to deliver the information. To get to know why he thinks the textual analysis is so important, and to find more of his thoughts, visit the mentioned Disruption Banking piece.