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Predictive Analytics in Venture Investing


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Notes from 4-17-2013 presentation by Correlation Ventures

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Predictive Analytics in Venture Investing

  1. 1. Predictive Analytics in Venture Investing Hosted by Correlation Ventures 4-17-2013Industry Returns 25 year VC returns: 16.5% mean, 3% median Are there “just 15 deals” that matter each year? The data shows that there are 400-600 5x returns per vintage (3 yrs) so more like 200/year Low (45%) repeat rate of being top quartile in fund returns from one fund to the next 35% of capital deployed created 45% of winners (>5X returns) One third of rounds that were undersubscribed led to one third of the winners Winners broadly dispersed by sector stage and manager Geographic proximity to portfolio companies does not improve returns, actually the inverse was true (surprising! – or maybe not…)History of Correlation Inventor of Falcon (fraud detection backed by Greylock and battery; acquired by Fair Isaac) recruited to run analytics, got together with longtime life sciences and tech investors (David Coats, Trevor Kienzle) 30 VCs personally invested including former head of NVCA Recruited Steve Caplan and Matt Rosencroft as advisers 30 funds have provided peer data for exchange for fundraising analysis data o Want to be the industry flag bearers to help people raise capital o New research emails come out once per quarter. Have records for 60,000 financings since 87 (90% of financings and 2/3 of returns)Investment Process/Parameters Decisions in two weeks or less Dont repeat diligence that the lead has done; overlay a fast analytical approach - no fly outs reqd from mgmt team or customer calls Need five documents - 5-10 min process from entrepreneurs Vote with lead; tells team what the reserve is Can go up to $2M on first check, $4M total (can go as small as $50k) o Pay-to-plays not an issue, plans reserves appropriately to follow on but would get converted to common if no other choice (doesn’t expect that to happen in too many deals and does reserve a slush fund to prevent good deals from running away) o Thinks that there is no difference between how much they own and how those investments perform o Making smaller investments decreases portfolio risk by enabling diversification opportunitiesData Processing Filtered out: insider rounds, strategic led, non-VC deal, inexperienced VC led (lower quartile by activity over five year period) Factor model: financing, syndicate, lead (firm and partner) Thirty factor empirical weighting; 100 PT scale
  2. 2. o Need a score of 70 to consider; about 15% of these make it o Then call with lead VC and team o Do background checks and legal work at own expense  Looking to ensure unusual factors arent popping up (ie: criminal; lead VC is mgmt and investor – surprised ALDEA made it through the process!)  48 investments as of Friday (pace about 3 per month); 40% of dollars are in life sciences (recent exit: RQx w/ Avalon Ventures, sold to Genentech)  Can follow performance of “anti-portfolio” – the deals they screen but pass on (Correlation’s LP’s will be interested in this data)Good News Number of financings has increased (near peak since 2001 bubble) Can consider 1650 deals each year - wants to see as many as possible Active VC firms bottomed out in 2010 Capital raised by US VC funds: $21B in 2012 Returns have improved dramatically 10% (those at 90th percentile) did 5.5x or better last year o Mean was 3.1x last year, up from 0.4x post-bubble o Driven by lower pre money valuations, higher exit values, more capital efficiency  Pre money valuations: 25% reduction since 2004  Exit values: 90th percentile exit in 2001 was $50M, in 2021 it was $300M  Capital efficiency: $21M in 2004; $18M today (explanation for tech co’s: push off server expenses to amazon)