Disclaimer Robeco Switzerland Ltd.
The information contained on these pages is solely for marketing purposes.
Access to the funds is restricted to (i) Qualified Investors within the meaning of art. 10 para. 3 et sequ. of the Swiss Federal Act on Collective Investment Schemes (“CISA”), (ii) Institutional Investors within the meaning of art. 4 para. 3 and 4 of the Financial Services Act (“FinSA”) domiciled Switzerland and (iii) Professional Clients in accordance with Annex II of the Markets in Financial Instruments Directive II (“MiFID II”) domiciled in the European Union und European Economic Area with a license to distribute / promote financial instruments in such capacity or herewith requesting respective information on products and services in their capacity as Professional Clients.
The Funds are domiciled in Luxembourg and The Netherlands. ACOLIN Fund Services AG, postal address: Leutschenbachstrasse 50, CH-8050 Zürich, acts as the Swiss representative of the Fund(s). UBS Switzerland AG, Bahnhofstrasse 45, 8001 Zurich, postal address: Europastrasse 2, P.O. Box, CH-8152 Opfikon, acts as the Swiss paying agent.
The prospectus, the Key Investor Information Documents (KIIDs), the articles of association, the annual and semi-annual reports of the Fund(s) may be obtained, on simple request and free of charge, at the office of the Swiss representative ACOLIN Fund Services AG. The prospectuses are also available via the website https://www.robeco.com/ch.
Some funds about which information is shown on these pages may fall outside the scope of CISA and therefore do not (need to) have a license from or registration with the Swiss Financial Market Supervisory Authority (FINMA).
Some funds about which information is shown on this website may not be available in your domicile country. Please check the registration status in your respective domicile country. To view the Robeco Switzerland Ltd. products that are registered/available in your country, please go to the respective Fund Selector, which can be found on this website and select your country of domicile.
Neither information nor any opinion expressed on this website constitutes a solicitation, an offer or a recommendation to buy, sell or dispose of any investment, to engage in any other transaction or to provide any investment advice or service. An investment in a Robeco Switzerland Ltd. product should only be made after reading the related legal documents such as prospectuses, annual and semi-annual reports.
By clicking “I agree” you confirm that you/the company you represent falls under one of the above-mentioned categories of addressees and that you have read, understood and accept the terms of use for this website.
Super Quant internship theme
Machine Learning
Can machines learn Finance? This is the title of a recent paper by Israel, Kelly, Moskowitz (2020) where they discuss the premises of machine learning (ML) techniques for predicting asset returns. Gu, Kelly, Xiu, (2020) provide empirical evidence for tackling this question. They apply a set of different machine learning methods for predicting stock returns and they find that they are not only successful but they also outperform standard linear methods used in the financial literature.
Machine learning methods allow for more flexibility in capturing the relation between predictors and expected returns. Contrary to linear methods, ML can easily leverage on the non-linearities and interactions between the predictors. On the other side, ML models, because of the many degrees of freedom, are more prone to fit noise, especially in a low-signal to noise environment which characterizes financial markets.
During these challenging projects, you will apply advanced statistical techniques to large datasets containing for example individual company characteristics or macro-economic series. A good understanding of statistics and machine learning, a practical mindset and the ability to work with large amounts of data are crucial for this project. On top of that, you have strong programming skills, preferably in Python.
The goal of the project is to generate insights that will help Robeco to better translate our alpha predictions into solutions accustomed perfectly to clients’ risk profiles.
Examples of previous internship projects
Statistical clustering techniques in stock and bond selection
Predicting company earnings using Machine Learning
