Shipfix Data in Trading Forward Freight Agreements (FFAs)
Forward Freight Agreements (FFAs) are a freight derivative used both by shipowners and charterers, to manage their exposure to spot market volatility.
Increasingly these derivatives are utilised by non-traditional shipping market speculators from the financial industry, trading on the future direction of freight markets.
FFAs have gained in popularity over the last few years because of:
- the unprecedented volatility of freight rates since the onset of the pandemic as spot freight rates surged in excess of 150%.
- the technical advancements of screen-based pricing bring extra transparency to market participants
- diminishing asymmetric information, traders can now leverage datasets such as Shipfix's aggregated orders and tonnage data, to perform fundamental and technical analysis.
The recent extreme volatility of shipping markets means these instruments are expected to continue to grow in popularity and liquidity, as more people learn about this expanding marketplace.
Shipfix, AI-empowered collaborative shipping platform, processes and extracts the key shipping data points from hundreds of thousands of emails per day - giving our users close to 95% coverage of the spot shipping market (aggregated and anonymised).
Shipfix data gives traders the unique ability to map spot cargo orders to ship supply within different basins, thereby users can monitor daily evolving demand and supply dynamics of the different shipping segments. Shipfix data has a crucial time advantage over traditional shipping data sources such as satellite (AIS) data, giving traders signals before ships even get loaded.
Shipfix’s dataset is already successfully leveraged by shipowners and FFA traders, creating unique trading signals on the future direction of freight markets.
Join our latest Shipfix webinar to get powerful insights into FFA trading, as well as giving you an insight into how you trade them.
A guide to Forward Freight Agreement trading and how to leverage Shipfix data.
Register Here