Last year Accenture invited us to spend autumn in Hong Kong with the innovation teams at BNP Paribas, Bank of America Merrill Lynch, Commonwealth Bank of Australia, Credit Suisse, Goldman Sachs, HSBC, J.P. Morgan, Macquarie Group, Morgan Stanley, Nomura and Sun Life Financial. The focus was on defining the market fit of our "distributed ledger of probabilities" forecasting technology within the Financial Services & Insurance Sectors.
We decided to utilise our time there to build a Robo-VC model.
In our first post we demonstrated how VC investment is a random walk.
This model explores the idea that Venture Capital portfolio optimisation is a function of the market... Or, more accurately, the portfolio manager's relationship with the market. Rather than the individual portfolio manager's ability to consistantly pick winners.
The rules behind this model are very simple.
VC relationships are represented as a matrix. The portfolios can be read from top to bottom or left to right. The red hot spots represent high growth investments. VC's in axis x can only invest jointly with VC in axis y. The trigger - as previously stated - is a successful partnership (Think: Red Hot Spot)
It assumes if two VC's have partnered on a successful investment(>5x) they will invite each other to participate in other >5x investments within their collective portfolios.
By doing this the Portfolio Managers are leveraging the latent network effects in the marketplace.
This graph maps the net ROI multiples across each of the 19 VC Portfolios.
As you can see this networked model mirror the VC market.
The performance of the 'networked' managers is significantly improved by leveraging the ability of the rest of the herd to identify and profit from the outliers in the market.
Meanwhile the isolated VC's perform well below market expectations.
This suggests you still have to 'pick a winner' to buy yourself 'a seat at the table'. But successful portfolio management is primarily a function of managing your position in the marketplace. (i.e. Relationships matter). Suggesting the fundamental objective of any participant in the venture capital market isn't to be an outlier but to mirror the behaviour of other VCs. (i.e. Pick investments VC's would choose - or, at least aspire - to invest in) & repeatedly signal that behaviour to the market.
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Questions of accuracy
Tymbals is an experiment in machine learning and statistical modelling of small data pools. Tymbals is still under development. It is still learning. Tymbals is Beta - i.e. Pre-release.
The probabilities and outputs (e.g. calculators, charts, visualisations) will evolve and change as the system ingests more data.
Tymbals is a probability model. The results generated by Tymbals are market estimates based on the cummulative value of the data within the distributed data pools.
All data inputs are automatically added to the learning pool from which Tymbals models are generated.
If you do not want your data added the data pool do not use Tymbals
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