Private Equity Findings, Issue 20 | Coller Capital
25 July 2024 Publication
Research & Insights

Private Equity Findings, Issue 20

Topics
Foreword By the numbers
Retrospective: A bigger picture
Overview Understanding LPs performanceThe role of academic research in PEThe most influential pieces of academic researchThe affect of the 2006-07 credit bubble Resilience of the PE industryThe the growth of private debt fundsAreas of current researchAreas of research opportunities?
What’s at stake?
Roundtable: Will AI transform private equity?
Overview PE embracing AI technologiesAI origination for VC investmentsThe limitations of AI & lack of dataAI in decision makingUsing AI to predict future outcomesDo LPs really need AI to process qualitative information?AI techniques to predict company director performanceDo large networks and directorships mean poor performance?What aspects of PE are ripe for AI disruption?AI for PE: hype vs. reality
Time for a new model?
Overview Time for a new model: The research viewpointTime for a new model: The investor viewpoint
The side letter arms race
Do LPs really need AI to process qualitative information?
Foreword By the numbers
Retrospective: A bigger picture
Understanding LPs performance The role of academic research in PE The most influential pieces of academic research The affect of the 2006-07 credit bubble Resilience of the PE industry The the growth of private debt funds Areas of current research Areas of research opportunities?
What’s at stake?
Roundtable: Will AI transform private equity?
PE embracing AI technologies AI origination for VC investments The limitations of AI & lack of data AI in decision making Using AI to predict future outcomes Do LPs really need AI to process qualitative information? AI techniques to predict company director performance Do large networks and directorships mean poor performance? What aspects of PE are ripe for AI disruption? AI for PE: hype vs. reality
Time for a new model?
Time for a new model: The research viewpoint Time for a new model: The investor viewpoint
The side letter arms race
Roundtable: Will AI transform private equity?
Do LPs really need AI to process qualitative information?

 

“AI can be used in conjunction with the LPs’ own assessments to reinforce decision-making around fund selection. It can also be effective as a filter when there are more documents in the pipeline than an LP can comfortably process.”

“Our research is academic. It tests a hypothesis. It is not a commercial product. In plain English, the model is a black box. We don’t know what the magic words or phrases are. So, reverse engineering would be no small task. Of course, a manager could write 10 versions of the text and ask for our programme to rate them all. But that manager would need to have a huge training dataset, which is rare.”
Ludovic Phalippou


 

“Again, it could potentially be used to screen-out rubbish quickly, just like it can be used to filter poor CVs in  recruiting process. But I don’t think it would help with making positive decisions because those are all based round people.”
Anne Glover

 


“I can imagine natural language processing being helpful for investors with small teams. It could help bring an organisation without deep coverage up the knowledge curve more quickly. However, the idea that AI-driven analysis of a private placement memorandum’s (PPM) strategy section would tell us information that we don’t already know doesn’t resonate with me.

“I struggle with the concept that a decision-making process, which incorporates analysis that is inherently subjective, can be disintermediated by AI. There is certainly potential to glean a lot of insight from AI tools and to accumulate and analyse information that is otherwise difficult to access, but I don’t think that this technology is going to put either GPs or LPs out of a job. It is an aid but definitely not a replacement.”
Merrick McKay


 

“My question is whether the predictability will remain if LPs start paying attention and GPs therefore start reverse engineering their prospectuses to mimic those that have been shown to be correlated with success. Once funds start that editing process, the predictability is likely to disappear.”
Léa Stern

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