Despite concerns that alternative data could spell the demise of those funds unable to keep up with the diverse skills required to interpret it, new research suggests the opposite scenario is more likely, with funds and alternative data sellers growing together.
Using a theoretical model of the interaction between data sellers, buyers and the skills to process the data, a new paper from the University of Toronto’s Rotman School of Management, The University of Hong Kong, and Hong Kong University of Science and Technology, shows that alternative data encourages firms to bring in more highly skilled people to extract unique insights from the data, leading to stronger performance for the fund and for the market.
That can be expensive in the short-term, but the model shows that data sellers respond by providing bigger data samples. Larger samples allow buyers to draw more accurate information, which in turn improves how informative or predictive a stock’s price is of a company’s future earnings, which benefits everyone participating in the market.
Once funds have made the initial costly investments into skills acquisition, they have ready capacity to process more alternative data more cheaply over time, leading them to seek out more alternative data. Eventually, what was once considered “alternative” could become more of a standard source of information.