Multi Asset Boutique Flexible Allocation

Vontobel Fund - Vescore Artificial Intelligence Multi Asset


Fund strategy

Investment objective

This absolute-return-oriented multi-asset fund aims to participate in rising markets and achieve steady value growth in the long term with a balanced risk profile (usual target volatility: 8%).

Key features

The fund invests worldwide mainly in equities, government bonds, and currencies. Based on quantitative models and artificial intelligence, it systematically adapts its asset allocation to the risks and opportunities offered by the prevailing market conditions.


Vescore's proven investment process amalgamates outstanding proprietary models, cutting-edge technology, and active management. For this fund, the highly experienced investment team uses their artificial-intelligence-driven model, which decides on the optimal asset allocation to various other models (fundamental, risk, trend, business cycle). Further models are applied for the allocation within each asset class. This multi-model strategy makes investment decisions without emotional biases, while ensuring systematic risk control at all times.

Performance YTD
As at Sep 29 2020

Why invest?

  • We apply artificial intelligence to assess the financial markets at great depths, at anytime and at high speed.
  • We are active investors. Our systematic investment processes provide the optimal allocation without behavioral bias.
  • We are a very transparent investor and offer cost-efficient implementation.

“I am Artificial. I am Ami, the CIO of Vescore’s Artificial Intelligence fund”

How AI helps

Our investment process

The investment process consists of three steps: tactical asset allocation, investment management, and risk management. With the use of machine learning algorithms, Ami oversees the asset allocation committee and decides on the optimal asset allocation – the split between equities, fixed income, and commodities.

In the investment management step, AMI consults three experts for the allocation within the asset classes: Eugene (equities), Oldrich (fixed income), and Maynard (commodities). During the final risk-management step, AMI’s risk adviser, Carl, ensures that the portfolio is in line with relevant rules and regulations at all times.

Investment opportunity

  • Investing in global risk premia is the most sustainable source of return, as proven by financial market research. Since risk premia vary over time, dynamic management based on AI adds value.
  • The most prominent risk premia pursued in this fund are equities, fixed income, and commodities.
  • With this fund, you can participate in them in a straightforward fashion in a currency-hedged way.
  • This fund targets an annual volatility of 8% and an excess return of 5% over cash investments.

Investment philosophy

Academic research has proven that economically justified risk premia offer sustainable sources of investment returns. Since risk premia vary over time, active investment adds value. Research upholds the fund’s models and systematic approach, and spurs continual innovation.

Model-based allocation and risk management, precisely implemented, ensure optimal exposure and unbiased portfolio adjustments. The character of the models enables investment transparency for investors. Using liquid instruments enables efficient and cost-effective implementation.

Investment team

  • Heritage over 20 years: Vescore’s proprietary models for asset allocation and risk management grew out of research at the University of St. Gallen in the late 1990s and have worked well through all economic cycles.
  • Commitment to innovation: Our ‘Research & Development’ Team applies strong research backgrounds and continues innovating, dedicated to developing new models and maintaining and adjusting existing ones. The team follows a research strategy, monitors academic research and reviews feedback from the portfolio and clients.
  • Systematic and coherent processes: based on the model design, the Computation Team checks data quality, calculates portfolios daily, and supplies allocation data to portfolio managers as well as for reporting and subsequent analysis. This team also tests and verifies any model adjustments before introducing them into the investment process, with clients notified of material changes. The Portfolio Management Team monitors and implements calculated portfolios daily, transforming allocation adjustments into investments using exchange-traded financial instruments.

Ami Vescore

Chief Investment Officer, Vescore Artificial Intelligence Investment Team

All data is as at Aug 31 2020 unless otherwise indicated.

Daily Performance

Periodic Performance

I EUR 0.4% -1.9% 17.9%

Rolling Performance

Sep 01 2015 - Aug 31 2016 Sep 01 2016 - Aug 31 2017 Sep 01 2017 - Aug 31 2018 Sep 01 2018 - Aug 31 2019 Sep 01 2019 - Aug 31 2020

Annual Performance

Risk Data

Volatility 11.8%
Sharpe ratio negative
Information ratio
[1 year]
Past performance is not a reliable indicator of current or future performance. The return may go down as well as up, e.g. due to changes in rates of exchange between currencies. The value of invested monies can increase or decrease and there is no guarantee that all or part of your invested capital can be redeemed.

All data is as at Sep 29 2020 unless otherwise indicated.

Fund data
Portfolio Manager AMI
Fund Domicile Luxembourg
Fund Currency EUR
Share Class Currency EUR
End of fiscal year 31 August
Share Class Launch date Oct 26 2018
Distribution type Accum
Fund Registrations AT, CH, DE, ES, FR, GB, IT, LU, NL, NO, SE
Share Class Registrations AT, CH, DE, ES, FR, GB, IT, LU, NL, NO, SE
Nav Information
Highest since launch 128.11
Lowest since launch 98.74
Fund volume in mln. EUR 24.96
Share class volume in mln. EUR 16.69
Fees And Expenses
Management fee 0.50%
TER 0.94% (Feb 28 2020)
ISIN LU1879231667
Valor 43789873
Bloomberg VOVAIIE LX
Depository RBC Investor Services Bank S.A.
Management Company Vontobel Asset Management S.A.
Swiss Paying Agent Bank Vontobel AG
Swiss Representative Vontobel Fonds Services AG

Available Share Classes

Share class Currency ISIN Distrib. Type Launch date Management fee TER TER Date
A EUR LU1879231311 Dist Retail Oct 26 2018 1.00% 1.48% Feb 28 2020
B EUR LU1879231402 Accum Retail Oct 26 2018 1.00% 1.48% Feb 28 2020
C EUR LU1879231584 Accum Retail Oct 26 2018 1.50% 1.98% Feb 28 2020
HI (hedged) CHF LU1879232046 Accum Institutional Oct 26 2018 0.50% 1.00% Feb 28 2020
HI (hedged) GBP LU1879232129 Accum Institutional Oct 26 2018 0.50% 1.00% Feb 28 2020
I EUR LU1879231667 Accum Institutional Oct 26 2018 0.50% 0.94% Feb 28 2020
N EUR LU1879231741 Accum Retail Oct 26 2018 0.50% 0.98% Feb 28 2020
Click here to see an overview of our shareclass naming convention.

* TER includes performance fee where applicable

All data is as at Aug 31 2020 unless otherwise indicated.

Country Weighting

Document Date DE EN FR IT
Factsheets & Commentaries
Factsheet Aug 2020
KIID Sep 2020
Legal Documents
AGM EGM invitation Jan 2020
Articles of Association Apr 2016
Notification to Investors Nov 2019
Sales Prospectus Dec 2019
Financial Reports
Annual Report Aug 2019
Dividend Payout Jan 2019
Semi-Annual Report Feb 2020
Dealing Information
Holiday Calendar 2020 Jan 2020
List of Active Retail Share Classes Dec 2018
Sanctioned Countries Sep 2016
Shareclass Naming Convention Nov 2019


  • Limited participation in the potential of single securities

  • Success of single security analysis and active management cannot be guaranteed

  • It cannot be guaranteed that the investor will recover the capital invested

  • Derivatives entail risks relating to liquidity, leverage and credit fluctuations, illiquidity and volatility

Morningstar rating: © 2020 Morningstar, Inc. All rights reserved. The information contained herein: (1) is proprietary to Morningstar and/or its content providers; (2) may not be copied or distributed; and (3) is not warranted to be accurate, complete, or timely. Neither Morningstar nor its content providers are responsible for any damages or losses arising from any use of this information.

The reference of Ami as the portfolio manager and CIO of the sub-fund is linked to the artificial intelligence methodology which is exclusively used for investment decisions. Ami and her team are fictitious individuals – the final execution of Ami’s investment decisions is performed by the official Vescore portfolio management team.