AI in Fundamental L/S Equity
Curious what the use of AI/ML is for these types of funds. Is it most useful as a form of "research assistant", helping synthesize large amounts of data? Is it used as a more effective screener, analyzing information/trends to determine companies/industries that could be worth a look?
Would love some background on this as well. I think there's definitely ways for AI to be implemented, but I believe the human element is always going to be necessary in terms of stock selection.
There is lots of applications of NLP for processing news, SEC filings, earnings calls and synthesizing that data or creating alerts. so the ML would be built into the NLP part, as opposed to using ML to value the companies themselves
Are there any firms that specialize in doing this analysis? I.e. data vendors that aggregate the info. Or is much of the work brought in house to the fund itself?
There are a few firms that are vendors of data/visualization/signals to hedge funds and AMs. A lot of these are on the smaller side or startups. For example: https://www.marketwatch.com/story/ge-earnings-can-ceo-culp-finally-set-…
Anonymized credit card transactions are sold to funds who use those to predict earnings reports. Right now some funds use analysts to go through the credit card data to predict earnings. More sophisticated investors use algorithms to automatically trade on this data.
no one does this in the way you are talking about at traditional l/s funds. the "data scientists" that work at traditional l/s funds scrape data in very basic ways and output it. that's it. definitely no ML.
if you are a junior person that knows how to do this good luck trying to convince anyone that's been doing this for 10 years to listen to you or to give you the resources / time to work on this. it just won't happen. generally speaking you will look like a complete idiot if you bring this up in a mixed setting.
i am not saying i think it's not a good idea. it might be if done very well. i am just saying there is a very real institutional bias against doing this in my experience.
What about in event-driven scenarios? For example, using ML to predict likely M&A candidates, or to derive likelihood of a positive/negative event in the short-term.
Yeah that's a retarded question. As someone who does ML/AI, there is simply no way to usefully apply it here. When you say ML....you really mean to say statistics?
This is wrong. Dont speak on things you dont understand
mike: feel free to mention at least 2 marquee / well-known TRADITIONAL LONG SHORT funds that people have heard of and that do this in a meaningful way. To be specific, point us to 2 articles that verify your claim. If you can't then i will rest my case.
Traditional long short funds that arent adapting to advanced data analytics are getting their faces ripped off, its a dying hedge fund model. Take your pic of a high profile failure. Like Balyasny who fired their whole equities division. Once one of the best in the industry.
Also TBH, ive got better things to do than look up articles. I browse this website sometimes, the amount of misinformation about the industry is appalling. Just because funds arent talking about what techniques theyre using, doesnt mean it isnt happening. Hottest job on wall street is a data scientist, not a yale history degree pouring over 10ks.
The team at Baly that was completely cut was the Synthesis team, which used exactly these types of quant techniques to inform discretionary trades...
They did get rid of a lot of fundamental equities PMs too but definitely not everyone.
yeah i agree with you. i was saying the traditional long short IS NOT adapting for better or for worse. think you misread what i said lol
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AI/alt-data is definitely used for suggesting opportunities or screening the universe of stocks for a fundamental PM. I think many L/S funds are doing it now. However, unlike quant funds most of these firms don't treat data science as a front-line activity or pay the people doing it well. They treat data science like risk or technology, just due to how the firms are managed and organized.
To be fair, quant funds also don't treat data scientists as front line. 2sig has data scientists but are not renumerated as well as quants. Even there they are thought of as auxiliary support that source,clean, and do some prelim analysis of datasets.
The term "data scientist" has also been heavily diluted over time, and sometimes refers to more of a data engineer or business analyst today, especially in a technology-focused firm. The original data scientists from 5-10 years ago are often called something else now, such as quant researcher (who were doing this type of work before the term data science came up), research scientist, applied scientist, etc.
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