EVOVEST

Our history

Established in 2017, Evovest has grown and strived to innovate investment management. The company was built with the goal to automate investment decisions with a scientific approach. The development of an investment process that uses a combination of machine learning in conjunction with the best practices in portfolio management. Discover the evolution of the company and the investment process down below.

Corporation

2021

    September
    • Team expansion and new advisory board functions
    June
    • Evovest reached 40M USD in AUM
    March
    • Launch of the strategy with the QEMP

2020

    September
    • First institutional mandate with the selection of our global equity strategy for the Quebec Emerging Manager Program (QEMP)
    January
    • Establishment of an independent chairman of the board with Michel Tremblay

2019

    September
    • Start of our first institutional due diligence process with an established service provider
    January
    • Launch of the Evovest Global Equity Fund in partnership with Majestic Asset Management

2018

    December
    • Evovest team grows to 4
    June
    • Entrepreneurship grant from Fondation Montreal Inc
    April
    • Seed capital for R&D
    January
    • Regulatory approval to operate as a portfolio manager

2017

    May
    • Evovest foundation

Investment process

2021

    May
    • Deployment of a proprietary neural decisions trees algorithm
    March
    • Risk modelisation updated by using a new distance based-measure
    January
    • ESG integration to our investment process via Truvalue labs

2020

    June
    • Migration to daily data with weekly rebalancing
    January
    • Risk modelisation based on risk factor

2019

    March
    • Initial development of Evotrees, an open-sourced and Julia based gradient boosted trees library
    January
    • Basic risk control addition for sector and beta control

2018

    October
    • Database reassessment and selection of S&P Capital data
    May
    • Design of our Evolutive Learning Test (ELT) that overcome most biases in quantitative finance
    January
    • R&D process with monthly data and machine learning algorithms