At Evovest, our objective is to help our clients grows their investment compared to the market by using technologies that enable cost efficiency and the objectivity of the machine.
At its core, we are looking to choose between different investment opportunities to find the one maximizing our client’s investment. It is a difficult task and history has proven that few are able to do it consistently.
As an example, is Apple Inc. (AAPL) or Microsoft Inc. (MSFT) the best investment for the next months or years? To answer this question, we need to look at how investment managers try to answer it. We invite you to read our discovery series that goes in more details.
At Evovest, we try to answer this question for all possible stocks in the developed countries around the globe.
After answering this question, we build investment portfolios for our clients.
We combine data, machine learning and the human experience to solve this problem.
Financial data is vast, but our investment process will look at various sources of data that includes, but not limited to:
Financial Statements
Financial Ratios
Commodities
Macroeconomic Indicators
Technical Indicators
ESG scores
The human experience is used to build different investment factors and design the problem architecture. This is where the investment domain knowledge is critical. We need to carefully design every step of the investment process to eliminate possible behavioural or construction biases.
The last step is to use machine learning to model the future excess returns of the individual securities. We need powerful computers to efficiently run the different R&D processes.
Once we have the machine prediction, we can carefully build investment solutions that encompasses investors preferences for risk or ESG constraints.
We invite you to visit the team page, but everyone works closely to bring new ideas to the investment process. Team efforts are key to our success.