On the topic of financial markets and aggregation of signals, there is actually a lot of ML-based signal aggregation going on within trading companies. A common structure is having specialized individuals or teams develop predictive signals (for publicly traded securities) and then have them aggregated into a single meta-prediction (typically a “microprice”, or theoretical price) which is then used for order placement.
A couple anecdotes:
There is some publicly available story about a high frequency KOSPI options desk that made a substantial improvement in P/L after adding a Random Forest in their signal aggregation logic. Eventually, this became the difference between making and losing money
Accurate up until about a year or two ago, I believe one of the top 10 worldwide trading firms was using a simple average as their aggregation mechanism for most of their trading. This would include averaging effectively redundant signals which could be easily identified as such.
On the topic of financial markets and aggregation of signals, there is actually a lot of ML-based signal aggregation going on within trading companies. A common structure is having specialized individuals or teams develop predictive signals (for publicly traded securities) and then have them aggregated into a single meta-prediction (typically a “microprice”, or theoretical price) which is then used for order placement.
A couple anecdotes:
There is some publicly available story about a high frequency KOSPI options desk that made a substantial improvement in P/L after adding a Random Forest in their signal aggregation logic. Eventually, this became the difference between making and losing money
Accurate up until about a year or two ago, I believe one of the top 10 worldwide trading firms was using a simple average as their aggregation mechanism for most of their trading. This would include averaging effectively redundant signals which could be easily identified as such.