Alternative Data 1Q’17 Winners and Losers
Is alternative data table stakes? Who got burned this quarter? Intra-earnings company performance might very well hold the answers to these questions. We looked at a pool of 17 stocks...
Is alternative data table stakes? Who got burned this quarter? Intra-earnings company performance might very well hold the answers to these questions.We looked at a pool of 17 stocks with broad alternative data coverage from many sources - lets call these 'data-driven' stocks - to analyze their intra-earnings (period between one quarterly announcement and the next) performance vs. their reaction after earnings announcement. We discovered that alternative data was likely very helpful for 9 companies, neutral for 5 and not helpful for 3. Our analysis looked at how many of these data-driven stocks experienced post announcement surprises to understand the success or failure of their respective datasets during that period. Assuming successful datasets should both accurately detect inflection points in company performance, and be fully assimilated by investors intra-earnings, then there should be no surprises following the earnings announcement. For example, BABA, a highly data-driven stock, traded up 16% intra-earnings. The day after earnings, the stock held those gains and traded up just 2%. In other words, alternative data was likely very helpful in accurately estimating company performance during the intra-earnings period. See full results in the table below: To clarify our data quality assessments: 'Helpful' incidents confirmed our assumption, meaning, data-driven stocks where intra-earnings performance was not reversed after 1Q'17 earnings announcement. Conversely, 'Not Helpful' exhibited stock reversals after earnings, in which case the dataset probably failed to estimate performance accurately intra-earnings. 'Neutrals' were directionally correct but inaccurate in magnitude, which could also be seen as a partial-wins. Of course, there are many factors and market influences other than alternative datasets that impact an individual stocks' performance. We intended for this analysis to be looked at in a vacuum from those clear factors, including:
- other intra-quarter news,
- non-data events (e.g. margins, m&a announcements, management change) disclosed during earnings, and
- sell-side analysts revising their estimates.