Bitcoin pioneer Charlie Shrem peels back the layers on the lives and backgrounds of the world's most impactful innovators. Centering around intimate narratives, Shrem uncovers a detailed, previously unspoken story of the genesis and evolution of bitcoin, cryptocurrency, artificial intelligence, and the web3 movements. Join Shrem as he journeys through the uncharted territories of tech revolutions, revealing the human side of the stories that shaped the digital world we live in today.
…
continue reading
Content provided by Village Global. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Village Global or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!
Go offline with the Player FM app!
Data-Driven Insights on Venture Capital with Steve Kim
MP3•Episode home
Manage episode 342353406 series 2832826
Content provided by Village Global. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Village Global or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Steve Kim (https://www.linkedin.com/in/stevenrkim/), Partner and head of Investment Strategy at Verdis, a 9-generation single family office, joins Olga Serhiyevich (@olgaserhi), Head of Investor Relations at Village Global, on this episode.
Takeaways:
Early stage venture is a power law asset class where the returns of the asset class are driven by outliers. The best way to increase probability of getting asset class rate of return is by increasing variance in the portfolio through diversification. Pattern matching tends to reduce variance and contrary to industry’s beliefs, is undesirable from the systematic approach perspective.
There is no limit to diversification beyond practical limitations of being able to see and invest in all the relevant deals for GPs. 20-30 portfolio companies is a typical level of diversification in other asset classes including growth equity and buyout where returns are normally distributed. In early stage venture (pre-seed to Series A) this level of diversification is less likely to produce industry average returns on a consistent basis.
The average rate of unicorn production is 1-2% in the industry but it varies across sectors, vintage years and geographies. So, Verdis chooses to maximize diversity across the number of companies, sectors and vintages because there is no clear indication in data that subsets of those are more likely to produce outliers but invest with a bias towards key geographies due to higher concentration of unicorns there.
Most of the outliers in the US of the last decade came from two geographies - California and New York. The magnitude of these outliers was also significantly greater than unicorn companies built elsewhere. For example, on average it takes 4 non-California outliers to equal the magnitude of outcome of a California unicorn.
Startup exit data from other geographies looks a lot more normally distributed which calls for a different approach.
Data-driven investment strategy’s main drawback is the backward-looking nature of the approach. But it’s useful in that it provides a systematic approach to guide portfolio construction.
If managers believe that the part of the VC asset class they focus on follows power law distribution, then they would want to have the most diversified portfolio as possible with a lot more than traditional 10-20 companies. In the power law world, losses don’t matter.
One of the key insights from investing in venture for almost two decades is that most managers are going to stage-drift. Allocating to emerging managers who often focus on early stage due to smaller fund sizes and comfort with first check investing is one way for LPs to hedge against stage-drift.
In Verdis’s view, low reserves and quick capital deployment cycle is advantageous to LPs focused on multiples not IRRs.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
…
continue reading
Takeaways:
Early stage venture is a power law asset class where the returns of the asset class are driven by outliers. The best way to increase probability of getting asset class rate of return is by increasing variance in the portfolio through diversification. Pattern matching tends to reduce variance and contrary to industry’s beliefs, is undesirable from the systematic approach perspective.
There is no limit to diversification beyond practical limitations of being able to see and invest in all the relevant deals for GPs. 20-30 portfolio companies is a typical level of diversification in other asset classes including growth equity and buyout where returns are normally distributed. In early stage venture (pre-seed to Series A) this level of diversification is less likely to produce industry average returns on a consistent basis.
The average rate of unicorn production is 1-2% in the industry but it varies across sectors, vintage years and geographies. So, Verdis chooses to maximize diversity across the number of companies, sectors and vintages because there is no clear indication in data that subsets of those are more likely to produce outliers but invest with a bias towards key geographies due to higher concentration of unicorns there.
Most of the outliers in the US of the last decade came from two geographies - California and New York. The magnitude of these outliers was also significantly greater than unicorn companies built elsewhere. For example, on average it takes 4 non-California outliers to equal the magnitude of outcome of a California unicorn.
Startup exit data from other geographies looks a lot more normally distributed which calls for a different approach.
Data-driven investment strategy’s main drawback is the backward-looking nature of the approach. But it’s useful in that it provides a systematic approach to guide portfolio construction.
If managers believe that the part of the VC asset class they focus on follows power law distribution, then they would want to have the most diversified portfolio as possible with a lot more than traditional 10-20 companies. In the power law world, losses don’t matter.
One of the key insights from investing in venture for almost two decades is that most managers are going to stage-drift. Allocating to emerging managers who often focus on early stage due to smaller fund sizes and comfort with first check investing is one way for LPs to hedge against stage-drift.
In Verdis’s view, low reserves and quick capital deployment cycle is advantageous to LPs focused on multiples not IRRs.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
662 episodes
MP3•Episode home
Manage episode 342353406 series 2832826
Content provided by Village Global. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Village Global or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Steve Kim (https://www.linkedin.com/in/stevenrkim/), Partner and head of Investment Strategy at Verdis, a 9-generation single family office, joins Olga Serhiyevich (@olgaserhi), Head of Investor Relations at Village Global, on this episode.
Takeaways:
Early stage venture is a power law asset class where the returns of the asset class are driven by outliers. The best way to increase probability of getting asset class rate of return is by increasing variance in the portfolio through diversification. Pattern matching tends to reduce variance and contrary to industry’s beliefs, is undesirable from the systematic approach perspective.
There is no limit to diversification beyond practical limitations of being able to see and invest in all the relevant deals for GPs. 20-30 portfolio companies is a typical level of diversification in other asset classes including growth equity and buyout where returns are normally distributed. In early stage venture (pre-seed to Series A) this level of diversification is less likely to produce industry average returns on a consistent basis.
The average rate of unicorn production is 1-2% in the industry but it varies across sectors, vintage years and geographies. So, Verdis chooses to maximize diversity across the number of companies, sectors and vintages because there is no clear indication in data that subsets of those are more likely to produce outliers but invest with a bias towards key geographies due to higher concentration of unicorns there.
Most of the outliers in the US of the last decade came from two geographies - California and New York. The magnitude of these outliers was also significantly greater than unicorn companies built elsewhere. For example, on average it takes 4 non-California outliers to equal the magnitude of outcome of a California unicorn.
Startup exit data from other geographies looks a lot more normally distributed which calls for a different approach.
Data-driven investment strategy’s main drawback is the backward-looking nature of the approach. But it’s useful in that it provides a systematic approach to guide portfolio construction.
If managers believe that the part of the VC asset class they focus on follows power law distribution, then they would want to have the most diversified portfolio as possible with a lot more than traditional 10-20 companies. In the power law world, losses don’t matter.
One of the key insights from investing in venture for almost two decades is that most managers are going to stage-drift. Allocating to emerging managers who often focus on early stage due to smaller fund sizes and comfort with first check investing is one way for LPs to hedge against stage-drift.
In Verdis’s view, low reserves and quick capital deployment cycle is advantageous to LPs focused on multiples not IRRs.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
…
continue reading
Takeaways:
Early stage venture is a power law asset class where the returns of the asset class are driven by outliers. The best way to increase probability of getting asset class rate of return is by increasing variance in the portfolio through diversification. Pattern matching tends to reduce variance and contrary to industry’s beliefs, is undesirable from the systematic approach perspective.
There is no limit to diversification beyond practical limitations of being able to see and invest in all the relevant deals for GPs. 20-30 portfolio companies is a typical level of diversification in other asset classes including growth equity and buyout where returns are normally distributed. In early stage venture (pre-seed to Series A) this level of diversification is less likely to produce industry average returns on a consistent basis.
The average rate of unicorn production is 1-2% in the industry but it varies across sectors, vintage years and geographies. So, Verdis chooses to maximize diversity across the number of companies, sectors and vintages because there is no clear indication in data that subsets of those are more likely to produce outliers but invest with a bias towards key geographies due to higher concentration of unicorns there.
Most of the outliers in the US of the last decade came from two geographies - California and New York. The magnitude of these outliers was also significantly greater than unicorn companies built elsewhere. For example, on average it takes 4 non-California outliers to equal the magnitude of outcome of a California unicorn.
Startup exit data from other geographies looks a lot more normally distributed which calls for a different approach.
Data-driven investment strategy’s main drawback is the backward-looking nature of the approach. But it’s useful in that it provides a systematic approach to guide portfolio construction.
If managers believe that the part of the VC asset class they focus on follows power law distribution, then they would want to have the most diversified portfolio as possible with a lot more than traditional 10-20 companies. In the power law world, losses don’t matter.
One of the key insights from investing in venture for almost two decades is that most managers are going to stage-drift. Allocating to emerging managers who often focus on early stage due to smaller fund sizes and comfort with first check investing is one way for LPs to hedge against stage-drift.
In Verdis’s view, low reserves and quick capital deployment cycle is advantageous to LPs focused on multiples not IRRs.
Thanks for listening — if you like what you hear, please review us on your favorite podcast platform.
Check us out on the web at www.villageglobal.vc or get in touch with us on Twitter @villageglobal.
Want to get updates from us? Subscribe to get a peek inside the Village. We’ll send you reading recommendations, exclusive event invites, and commentary on the latest happenings in Silicon Valley. www.villageglobal.vc/signup
662 episodes
Todos os episódios
×Welcome to Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.