a data-driven approach to equity investment
Identifying and selecting outstanding investment proposals is an experience-based task as well as an extremely time-consuming process. The variables to be evaluated are many, extremely complex, and change over time. However, the large amount of data available today can be used in a systematic way to provide insights that would otherwise be difficult to identify in a traditional way. Since data mining has already radically changed many areas of the financial sector, it is foreseeable that in the near future it will have a game-changing impact on the traditional VC industry as well. To date about 80 investment firms around the world (mainly based in California, as you can see from the figure) have claimed to use data-driven or quantitative methods for the assessment of prospective investments. Within this innovation process, we can work together to embed some custom analytics tools within your deal flow. Combining your expertise with data-driven analytics tools can give you a competitive advantage in the investment landscape and decrease investment risk through a quantitative and explainable approach.
Scroll down to see how we can work together.
how I can support your deal flow
At the state of the art, data mining techniques can provide significant value to your decision-making process mainly in the early stages of deal flow: from trend analysis, to scouting of potential new projects to screening and evaluation of individual proposals. My data science activity focusses specifically on these phases.
– developing data driven tools –
implementing models using
python
scikit learn
keras
tensor flow
Making the evaluation process of a startup automatic is something futuristic, but probably still a bit far from our time. However, imagine having a suite of very focused software tools at your disposal that can quantitatively support you in some specific steps of your decision-making process. As a researcher, I’m used to developing code by myself to find a scientific answer to specific research questions. In particular, I work in the area of machine learning, using the python language and several data science libraries. We can then develop side by side the tools you find most useful to make your investment processes more efficient and effective.
– custom data analysis –
Would you like to analyze a data set for business intelligence purposes? Would you like to learn more about the potential of a specific industry or emerging technology? Would you like to have an interpretation of data related to a specific business phenomenon? Well, we can do that together. With my hybrid background between management engineering and data science, I can apply complex data analysis techniques and make data meaningful for business. Considering my activity as a researcher, each analysis is carried out with the utmost scientific accuracy, paying particular attention to the methodological aspect.
– benchmarking –
10,211
companies in 64 industries
19,398
funding rounds
6,044
patents
> $70B
funding amount
When evaluating a potential investment, it can be helpful to benchmark the quality of the proposal against industry averages or other successful startups. The same logic can be used to also compare the performance of your own portfolio with that of other leading investors. In the last years I’ve had access to some of the major databases that track startups, investors, and entrepreneurs. By carrying out a large-scale comparison I can support you in defining your results quantitatively, so that you can better identify potential areas for improvement.
– deal scouting –
The next big thing can be anywhere. Finding the right projects to invest in is a challenging and complex activity. If you’d like to have an additional source, I can come alongside your scouting process. In fact, as an academic researcher I’m always in contact with innovative projects, resulting both from a variety of research groups and from university students. In addition, to expand the pool of proposals, I can also apply data mining techniques to automatically identify emerging startups from online sources.
