The usefulness of alternative data such as sentiment comes from its ability to instantly capture new price-moving information, providing an information edge to asset managers that use such data. Other alternative data sources can provide information that was recently unavailable. But how to assign the monetary value to an alternative dataset?
This question is interesting for data users as well as data providers, such as our partner Refinitiv. Information about how valuable a particular dataset is for investors, allows data providers to optimize their pricing schemes and increase revenues. On the other hand, the value of alternative data for data consumers (such as asset managers) lies in its monetizing potential and in increasing their returns.
The value of alternative data depends on the investor’s profile and the fund’s size. For example, signals extracted from some alternative datasets require immediate action, such as social media sentiment. So the value of such a dataset could be greater for investors who rebalance their portfolios more frequently. The size of a fund also plays an important role: a seemingly expensive dataset might generate many multiples of its cost in additional profits for a fund large enough – but possibly not for a smaller fund.
Another issue is that the value of alternative data depends on how it is going to be used. In other words: what are the signals extracted from these data and what is the envisioned investment strategy? Finally, the value of alternative data is not an absolute number, but it is always relative to a certain benchmark – which can be passive, such an index, or active, such as an alternative strategy (for instance, the one currently employed by the fund), which does not use such data.