Models deliver Beta, Humans deliver Alpha
Plus: Howard Marks, vibecoding pixel trackers, monitoring foreign language media and Claude Cowork
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Models deliver Beta, Humans deliver Alpha
Harvard study shows AI can predict 71% of mutual fund trading decisions, but the remaining 29% of trades generate the most alpha (Matt Levine via Justina Lee)
Howard Marks describes the human x-factor (Oaktree LP Letter)
Great investors…have to be strong exactly where Claude admits AI might be weakest: in dealing with novel developments where there’s not enough prior experience for dependable patterns to have been compiled (and learned by AI during its training). They also have to make subjective decisions regarding qualitative factors and exercise taste and discernment. For instance, choosing the right counterparties has played an important part in Oaktree’s success. How will AI make judgments of that sort? And there’s something else: AI doesn’t have skin in the game. It doesn’t feel the weight of concentrated positions or the fear of capital loss. Its willingness to take risk might not be constrained by humans’ normal risk aversion. The best investors sense potential risk intuitively, and this contributes greatly to their success.
Especially when investors are dealing with new and untried products, CEOs, or industries, there can be few facts or analogous experiences, meaning we have to rely on “opinion or speculation.” Given the limitations discussed above on AI’s ability to tackle brand new situations, will its speculation about new things – as opposed to extrapolating historic patterns – be consistently superior to that of all humans? I believe there will continue to be human investors who are superior to AI, since I don’t think AI will be able to do an unbeatable job of these things.
My take: LLMs level the playing field in processing public information and increase the reward for proprietary research, personal relationships and experience
Creative LLM uses case
Hayden Capital vibecoded a pixel tracker for Applovin (APP) (LP Letter)
For example, I recently “vibe-coded” our own Applovin Axon Pixel Tracker, to track Applovin’s new ecommerce push (LINK). The program scans the top 100,000 ecommerce websites, and whether they’ve adopted Applovin’s ecommerce tools – useful for us tracking adoption in real time. I did this all with Claude Code, in just a couple hours over a weekend, and runs on Amazon’s AWS.
Norges Bank uses Claude to monitor foreign language media for ESG issues in their portfolio (CNBC)
“Often, this information has not been captured in international media coverage or data vendor alerts…In multiple instances, we identified and sold these investments before the broader market reacted to the risks, avoiding potential losses.” NBIM said using AI this way had been particularly valuable for researching smaller companies in emerging markets, where news about the firm may be limited to small media outlets in local languages.
VC shares which of his workflows are mostly code vs mostly LLM driven (@ttungaz)
BlackRock and Schonfeld are hiring AI engineers for post-trade operations (BlackRock, Schonfeld)
AWS Bedrock post on using their graphRAG workflow to analyze 10-K filings and identify shared risk relationships across the S&P 100 (AWS)
Rutgers finance professor shares 8 tips for using OpenAI Batch API (50% cheaper) for large scale transcript analysis (LinkedIn)
Interesting paper
Compares LLM use in stock pitches on Seeking Alpha vs r/WallStreetBets. AI drives better returns in the former, more professional community, while on r/WallStreetBets AI drives abnormal trading and lottery outcomes. Interesting take on how LLMs may impact retail and institutional trading (NBER)
New tools
Review of Claude Cowork, a non-technical desktop version of Claude Code (Buyside AI Reviews). When asked to turn a lender presentation into a leveraged loan screening model, the tool made several key errors:
I do think people underestimate (i) the amount of time it takes to check/correct output and (ii) the willingness of senior folks to actually do the checking. And given the black box nature of LLM reasoning, the checking needs may not scale down as fast as AI capabilities scale up.
More: Claude launches Cowork and plugins for finance (announcement)
Checkmate, another AI expert call service, launches (CheckmateResearch.ai). Other services where LLMs source and conduct interviews include: AlphaSense, Guidepoint, NewtonX, Qualitate, Ribbon, Synquery. Interesting reddit thread where experts debate the future of this format: “Please boycott ai mod calls”
FirstDraftResearch, which looks like a “cursor for public market investors,” announces private beta (@atelicinvest)
Bloomberg launches <ASKB> - conversational AI interface (Bloomberg)
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