There are benchmarks here, http://ann-benchmarks.com/ , but LSH underperforms the state of the art ANN algorithms like HNSW on recall/throughput.
LSH I believe was state of the art 10ish years ago, but has since been surpassed. Although the caching aspect is really nice.
This approach seems feasible tbh. For example, a stock's historical bids/asks probably don't deviate greatly from month to month. That said, the generation of a good hash is dependent on the stock ticker, and a human doesn't have the time to find a good one for every stock at scale.
HNSW index is slow to construct, so it is best suited for search or recommendation engines where you build the index and serve. For workloads where you continuously mutate the index, like streaming clustering/deduplication LSH outperforms HNSW.
It might not be trendy, but it doesn't mean it can't work as good or better than HNSW. It all depends on the hashing function you come up with.
see ullman's text: mining massive datasets. it's free on the web.
i don't even think they called it ANN. it was high performance, scalable deduplication. (which is, in fact, just fast/scalable lossy clustering)
collaborative filtering was kind of a cute joke at the time. meanwhile they had lsh, in production, actually deduplicating the internet.