Quick thought:
The use of Alternative Data has exploded in use over the last decade. With many investment firms relying on the data to make countless investment decisions, opportunities may arise to mislead underlying algorithms and models who rely on such data as a primary input.
As a simple example, if a model has identified a publicly traded retailer with sequential order ids, a bad actor may put through thousands of fake, low value ($0.01 tickets) or subsequently canceled orders to have the model/algo think underlying fundamentals are improving in real-time.
It's important to think of the underlying data source. Is it web scraped from a publicly modifiable property (sentiment, inventory, listings)? Is it network data that can be faked or spoofed (traffic, dns records)? I suspect this is an area ripe for manipulation and the SEC (Securities and Exchange Commission) may find it tough to uncover and subsequently enforce.
In conclusion, one needs to understand fully what the source data is and what it's telling you makes sense. Always confirm with other metrics or channel checks.
